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Big Data for Social Transportation
... With Big data, we can think of various innovative services by mining it. There are studies which shows that Big data can be helpful in finding better routes, estimation of travel delays, identification of poor road infrastructure [19]. However, the four characteristics, i.e., volume, velocity, value, and variety of Big data, become a bottleneck in integrating it with transportation. ...
... Supervised learning, unsupervised learning, ontology-based, and reinforcement learning methods help mine and analyze the data. AI-empowered Big data analytics assists in developing ITS services like traffic flow prediction, route planning, asset maintenance, traffic anomaly detection, and signal control [15], [19], [44], [45]. ...
Advances in the connected vehicle and cloud computing technologies, Big data, and artificial intelligence techniques have opened new research opportunities. We can integrate them to work out the issues originating from transportation complexities and offer improved services. In this work, we present a seamless multi-module multi-layer vehicular cloud computing system developed using resources of parked vehicles, cloud computing facilities, and vehicular networking technologies. It can offer transportation-specific AI and Big data-empowered services to on-road vehicles. As use cases, we present two innovative and improved services, vehicular Big data mining and vehicular route optimization. A physical testbed is formed to show the feasibility of this work. Results analysis shows that the systems perform better than the standalone systems and servers under different scenarios. Relevant fundamental challenges and future outlooks are also highlighted in this work.
... Finally, (X. Zheng et al., 2016) have thoroughly reviewed the significance of Big Data in social transportation by presenting various data sources, techniques, and implementation procedures to tackle traffic issues in urban areas for a better understanding and development of next-generation ITS. The framework on Big Data implementation in ITS is shown in Figure 3. ...
... Passenger information, which represents all the transport facilities and service information provided to the road and pedestrian users. Social Media is one of the latest technological advances that provide its user information for the development of transport facilities as demonstrated in (Kaplan & Haenlein, 2010;Zheng et al., 2016). In their work, they elaborated on implementation of various techniques like crowd sourcing, visualisation, and problem tackling services, thereby proving the significance of Big Data in social transportation. ...
The application of big data in urban transportation and development of smart cities has been attracting global interest. The overburdened transport infrastructure due to rapid urbanisation should be integrated with innovative technologies and brand-new ideas such as smart city in order to enhance its performance. Big data is now the emerging exemplar in intelligent transportation systems for effective management of all data for implementing safer, cleaner, and well-planned transport services, as well as providing personalised transport experience for road users. In this chapter, the authors lay forward the current research endeavours on big data for urban transportation infrastructure, its implementation, baseline framework, and usage on fields such as planning, routing, network configuration, and infrastructure maintenance. This chapter evaluates the contributions of big data on urban transport modelling techniques, tools, and mobility. Finally, the present trends and future challenges of big data are summarised for helping researchers to facilitate the development of smart cities.
... Besides the traditional way via infrastructure-supported sensor networks, "social sensors" was proposed to collect traffic-related information via social network and social media from a humanized perspective [3]. To conclude the emerging area in detecting traffic information from social networks, Prof. Wang introduced "social transportation" as a new direction for computational transportation study [4], [5]. ...
... But these devices are expensive to deploy and maintain, and their functions are often limited due to their fixed locations. The emergence of "social sensors" [3] and "social transportation" [5] has greatly expanded the scope of collecting traffic data, besides from physical sensor networks, the applications of the two concepts can sense traffic context via social sensor networks. Social media and social networking platforms such as Weibo and Wechat provide ubiquitous chances for people to share ideas and information publicly about traffic. ...
In traditional practices of transportation system's constructions, traffic-related information is collected based on dedicated sensor networks, which are not only coverage-limited but also cost-consuming. With the enrichment of the concepts concerning 'social sensors' and 'social transportation', Sparse Mobile Crowdsensing (MCS) is proposed to collect data from only a few subareas by recruiting participants with portable devices and to infer the data in unsensed subareas with acceptable errors. However, in real-world sensing campaigns, the Sparse MCS systems often fail to collect data from any subareas of interest since the assumption about sufficient participants is not always realistic. To be specific, the recruitment of participants is often limited by interest deficiency, privacy awareness, and distribution biases. To handle this problem, we introduce the dedicated sensing vehicles (DSVs) into traditional Sparse MCS to improve subarea coverage and inference performance. To achieve effective collaboration among DSVs and mobile users, we first design a crowd-aided vehicular hybrid sensing framework, which defines the order of task assignment for different participants as well as the budget allocation. In terms of DSVs route planning, we propose a three-step strategy, including optimal route searching, fused route selection, and final route determination. Moreover, mobile users are selected based on a novel selection strategy. Experimental findings on two real-world datasets validate the effectiveness (with less inference error) of the hybrid sensing framework, in comparison with the user-only/DSV-only framework and five baselines. Results reveal important implications of applying the hybrid sensing paradigm in intelligent transportation systems to enhance data collection
... Particularly, the smart city data collected from the IoT-based infrastructure and analyzed by dissimilar techniques are able to mainly develop the number of monitoring and rejoin services and tasks (i.e., [11,44]). Smart city data effect on various services in a variety of interdisciplinary fields are resource efficiency, smart transportation, and mob resource-based services [11,60]. For instance, the transport management system (TMS) procedure is derived from the utilization of actual data (like social media data on behalf of the detection of road accidents and traffic congestions) and innovative technologies (like smartphones and smart cars) that intend to put aside time and inhabitants' road-based protection [11]. ...
... The consequence of mob-sensing and big data go over the data resources, analytical advances, and application-related methods with the preface to the deployment and development of intelligent transportation system (ITS) services [53,60]. Cisco declared that cities leveraging the data can achieve nearby 30% growing energy efficiency. ...
Fog computing offers an integrated key in support of communications, data gathering, device management, services capabilities, storage, and analysis at the edge of the network. This allows the deployment of centrally managed infrastructure in an extremely distributed environment. The present work discusses the most significant applications of fog computing for smart city infrastructure. In the smart city environment running a lot of IoT-based services, computing infrastructure becomes the most important concern. Thousands of smart objects, vehicles, mobiles, and people interact with each other to provide innovative services; here the fog computing infrastructure can be very useful from the perspective of data and communication. The chapter focuses on three main aspects: (a) deployment of data and software in fog nodes (b) fog-based data management and analytics, and (c) 5G communication using the fog infrastructure. Working models in all the perspectives have been presented to illustrate these fog computing applications. Use-cases have been added from the successful implementations of smart city infrastructure. Further, the challenges and opportunities have been presented from the perspective of growing interest in smart cities.
... In the transportation context, social networks are generally accessed through mobile personal devices which, in conjunction with data entered by the users, provide spatial, temporal and emotional information about users and their environment [254]. From this information, useful models for ITS applications can be retrieved, such as models for user emotional behavior, mobility pattern, and traffic-related events (e.g., accident, street blocked, scheduled maintenance in traffic equipment) [255]. In social transportation, the user acts as a social sensor, perceiving the environment with a perspective different from that provided by hardware sensors. ...
... In social transportation, the user acts as a social sensor, perceiving the environment with a perspective different from that provided by hardware sensors. Despite being able to improve ML tasks performance, new types of data sources need to be fused with the data already in place, an endeavor that is still in early stage of development for both scientific and engineering fields [255]. Despite of this, the social approach for transportation data is being recognized as a field with potential for future researches with a growing number of related works [256]. ...
Intelligent Transportation Systems, or ITS for
short, includes a variety of services and applications such
as road traffic management, traveler information systems,
public transit system management, and autonomous vehicles, to name a few. ITS are expected to be an integral part
of urban planning and future smart cities, contributing
to improved road and traffic safety, transportation and
transit efficiency, as well as to increased energy efficiency
and reduced environmental pollution. On the other hand,
ITS pose a variety of challenges due to its scalability and
diverse quality-of-service needs, as well as the massive
amounts of data it will generate. In this survey, we explore
the use of Machine Learning (ML), which has recently
gained significant traction, to enable ITS. We provide a
thorough survey of the current state-of-the-art of how
ML technology has been applied to a broad range of ITS
applications and services, such as cooperative driving and
road hazard warning, and identify future directions for
how ITS can further use and benefit from ML technology.
... In recent times, research in several domains is heavily focusing on large-scale data analysis utilizing sophisticated computing capabilities and machine learning (Golbeck et al. 2011;Zheng et al. 2015;Erickson et al. 2017;Nguyen et al. 2018;Chakraborty et al. 2020Chakraborty et al. , 2021aChakraborty et al. , 2022. However, causal analysis of data for prediction purposes has received limited attention in the literature. ...
Since the increasing outspread of COVID-19 in the U.S., with the highest number of confirmed cases and deaths in the world as of September 2020, most states in the country have enforced travel restrictions resulting in sharp reductions in mobility.
However, the overall impact and long-term implications of this crisis to travel and mobility remain uncertain. To this end, this study presents an analytical framework that determines and analyzes the most dominant factors impacting human mobility
and travel in the U.S. during this pandemic. In particular, the study uses Granger causality to determine the important predictors influencing daily vehicle miles traveled and utilize linear regularization algorithms, including Ridge and LASSO
techniques, to model and predict mobility. State-level time-series data were obtained from various open-access sources for the period starting from March 1, 2020, through June 13, 2020, and the entire data set was divided into two parts for training and testing purposes. The variables selected by Granger causality were used to train the three different reduced order models by ordinary least square regression, Ridge regression, and LASSO regression algorithms. Finally, the prediction accuracy of the developed models was examined on the test data. The results indicate that the factors including the number of new COVID cases, social distancing index, population staying at home, percent of out of county trips, trips to different destinations, socioeconomic status, percent of people working from home, and statewide closure, among others, were the most important factors influencing daily VMT. Also, among all the modeling techniques, Ridge regression provides the most superior performance with the least error, while LASSO regression also performed better than the ordinary least square model.
... Businesses may more effectively personalize their services and solutions to each client profile and increase return on investment by analyzing this data. (Zheng et al., 2015) Fraud Detection: Big data has revolutionized the methods and technologies used to detect fraud. In the past, fraud detection methods have been based on linear analytics that use simple algorithms to identify potential fraud. ...
Innovative and sophisticated technologies have been rapidly developing in recent years. These cutting-edge advancements encompass a wide spectrum of devices like mobile phones, PCs and social media trackers. As a consequence of their widespread usage, these technologies have engendered the generation of vast volumes of unstructured data in diverse formats, spanning terabytes (TB) to petabytes (PB).This vast and varied data is called big data. It holds great promise for both public and private industries. Many organizations utilize big data to uncover useful insights, whether for marketing choices, monitoring specific actions, or identifying potential threats.This kind of data processing is made possible using different methods known as Big Data Analytics. It allows you to gain significant advantages by handling large amounts of unorganized, organized, and partially organized information quickly, which would be impossible with traditional database techniques. While big data presents considerable While it offers benefits for businesses and decision-makers, it also puts consumers at risk. This risk results from the use of analytics technologies, which need the preservation, administration, and thorough analysis of enormous volumes of data gathered from many sources. Consequently, individuals face the risk of their personal information being compromised as a result of the collection and revelation of behavioral data. Put simply, the excessive accumulation of data may lead to multiple breaches in security and privacy. Nevertheless, the realm of big data does indeed raise concerns pertaining to security and privacy. Scholars from various disciplines are actively engaged in addressing these concerns. The study will concentrate on large data applications, substantial security hurdles, and privacy concerns. We'll talk about potential methods for enhancing confidentiality and safety in problematic big data scenarios, and we'll also analyze present security practices.
... tandard text by correcting spelling, grammar, and other errors (Neto et al., 2020;Dirkson et al., 2019). The results of text normalization can help improve the accuracy of collecting correct and consistent words for further analysis. The data obtained from social media was still unstructured which still needed to be improved (H. Zheng et al., 2020;X. Zheng et al., 2015). Several studies had been carried out on social media data in many languages such as Indian (Tanna et al., 2020;Roshini et al., 2019;Kumar et al., 2021) Chinese (Xuanyuan et al., 2021;Liu & Chen, 2019). The conducted research focused on improving the technique of the preprocessing process and the completion of nonstandard and unstructur ...
is one of the most important data sources in social data analysis. However, the text contained on Twitter is often unstructured, resulting in difficulties in collecting standard words. Therefore, in this research, we analyze Twitter data and normalize text to produce standard words that can be used in social data analysis. The purpose of this research is to improve the quality of data collection on standard words on social media from Twitter and facilitate the analysis of social data that is more accurate and valid. The method used is natural language processing techniques using classification algorithms and text normalization techniques. The result of this study is a set of standard words that can be used for social data analysis with a total of 11430 words, then 4075 words with structural or formal words and 7355 informal words. Informal words are corrected by trusted sources to create a corpus of formal and informal words obtained from social media tweet data @fullSenyum. The contribution to this research is that the method developed can improve the quality of social data collection from Twitter by ensuring the words used are standard and accurate and the text normalization method used in this study can be used as a reference for text normalization in other social data, thus facilitating collection. and better-quality social data analysis. This research can assist researchers or practitioners in understanding natural language processing techniques and their application in social data analysis. This research is expected to assist in collecting social data more effectively and efficiently.
... It is worth noting that it is common to find research similar to Zheng et al. [2016], where the usage of Big Data on Social Transportation is reviewed, mentioning several topics clearly related to context-awareness, but without even mentioning the word "context". A careful researcher in this area must be aware of this fact because most of the projects related to ITS have some use of computational context. ...
Design and development of context-aware Intelligent Transportation Systems (ITS) are not trivial due to the large number of possible context elements that may be relevant to the application and the lack of structured information to guide system designers in this task. This paper proposes that context elements with common characteristics can be grouped into categories, and these categories can be organized in a taxonomy. This taxonomy could help system designers with the task of modeling and developing new context-aware ITS. We performed a literature review of 68 articles describing 70 ITS applications with context-aware features to identify context elements used in this type of application. Furthermore, we also analyzed three commercial ITS applications. We used data collected from the analysis of these 73 projects to define the categories and identify their relationships. We propose a taxonomy with 79 categories, with 57 leaf categories (a category without children subcategories). We also performed two experiments to validate whether the exposure to this taxonomy could improve the quality of an ITS application during its design, with favorable results showing a 2.7 times increase in the average amount of relevant context elements used in the application. Finally, we compiled a knowledge base of which context element categories are used in the 73 analyzed projects. It is another companion information that can be used to help system designers. The proposed taxonomy of context element categories organizes the information of the context-aware ITS domain in a way that can ease the task of designing such systems and improve the usage of context-aware features. The overall methodology used in this work to create the taxonomy for the ITS domain could be applied to other popular domains of context-aware applications.
... One of the most traditional problems in transportation systems is the management of traffic flow. In [31], the authors apply the concept of Cyber-Physical-Social Systems (CPSS) to achieve signals from both the physical and social spaces. The proposed CPSS-based Transportation System (CTS) is a software-defined transportation system that creates an environment where human factors, transportation systems, and computing technologies are integrated and interact to provide intelligent responses that affect the real world (Fig. 3). ...
The big data concept has been gaining strength over the last few years. With the arise and dissemination of social media and high access easiness to information through applications, there is a necessity for all kinds of service providers to collect and analyze data, improving the quality of their services and products. In this regard, the relevance and coverage of this niche of study are notorious. It is not a coincidence that governments, supported by companies and startups, are investing in platforms to collect and analyze data, aiming at the better efficiency of the services provided to the citizens. Considering the aforementioned aspects, this work makes contextualization of the Big Data and ITS (Intelligent Transportation System) concepts by gathering recently published articles, from 2017 to 2021, considering a survey and case studies to demonstrate the importance of those themes in current days. Within the scope of big data applied to ITS, this study proposes a database for public transportation in the city of Campinas (Brazil), enabling its improvement according to the population demands. Finally, this study tries to present clearly and objectively the methodology employed with the maximum number of characteristics, applying statistical analyses (box-and-whisker diagrams and Pearson correlation), highlighting the limitations, and expanding the studied concepts to describe the application of an Advanced Traveler Information System (ATIS), a branch of Intelligent Transportation System (ITS), in a real situation. Therefore, besides the survey of the applied concepts, this work develops a specific case study, highlighting the identified deficiencies and proposing solutions. Future works are also contemplated to expand this study and improve the accuracy of the achieved results.
... Existing work leverages social media data to analyze travel behaviors, including activity pattern classification [11], location inference [12], travel activity estimation [13], and longitudinal travel behavior inference [14]. A forecasting model is proposed to predict mode choices according to the check-in information of individual tweets [15]. ...
This paper aims to leverage Twitter data to understand travel mode choices during the pandemic. Tweets related to different travel modes in New York City (NYC) are fetched from Twitter in the two most recent years (January 2020-January 2022). Building on these data, we develop travel mode classifiers, adapted from natural language processing (NLP) models, to determine whether individual tweets are related to some travel mode (subway, bus, bike, taxi/Uber, and private vehicle). Sentiment analysis is performed to understand people's attitudinal changes about mode choices during the pandemic. Results show that a majority of people had a positive attitude toward buses, bikes, and private vehicles, which is consistent with the phenomenon of many commuters shifting away from subways to buses, bikes and private vehicles during the pandemic. We analyze negative tweets related to travel modes and find that people were worried about those who did not wear masks on subways and buses. Based on users' demographic information, we conduct regression analysis to analyze what factors affected people's attitude toward public transit. We find that the attitude of users in the service industry was more easily affected by MTA subway service during the pandemic.
... Or finally, groups of users can be exploited for targeted advertising: advertising companies create targeted ad groups, based on interests, lifestyles, demographics, geolocation or mobility patterns. Similar examples can be found in mobile alerting systems or social transportation systems [9] where mobility groups are utilized for ride-sharing or to react to disruptions of transportation services in real-time. ...
The proliferation of smartphone devices has led to the emergence of powerful user services from enabling interactions with friends and business associates to mapping, finding nearby businesses and alerting users in real-time. Moreover, users do not realize that continuously sharing their trajectory data with online systems may end up revealing a great amount of information in terms of their behavior, mobility patterns and social relationships. Thus, addressing these privacy risks is a fundamental challenge. In this work, we present $TP^3$, a Privacy Protection system for Trajectory analytics. Our contributions are the following: (1) we model a new type of attack, namely 'social link exploitation attack', (2) we utilize the coresets theory, a fast and accurate technique which approximates well the original data using a small data set, and running queries on the coreset produces similar results to the original data, and (3) we employ the Serverless computing paradigm to accommodate a set of privacy operations for achieving high system performance with minimized provisioning costs, while preserving the users' privacy. We have developed these techniques in our $TP^3$ system that works with state-of-the-art trajectory analytics apps and applies different types of privacy operations. Our detailed experimental evaluation illustrates that our approach is both efficient and practical.
... Big data is a general term that refers to the technologies and techniques for processing and analysing large amounts of data, whether structured, semi-structured, or unstructured [26]. Big data is commonly used in all fields, including transportation [27,28]. The ability to process this data is used for decision making, from big data to analysis and prediction. ...
The need to solve public transport planning challenges using 5G is demanding. In 2019, the world started using 5G technology. Unfortunately, many countries have no equipment that is compatible with 5G infrastructures. There are two main deployment options for countries willing to accept 5G. They can directly venture to install relatively expensive infrastructure, called 5G SA (standalone access). However, more countries use the 5G NSA (non-standalone access) alternative, a 5G network supported by existing 4G infrastructure. One of the considerations for choosing NSA 5G is that it still performs 4G equalisation in its area. The data throughput is faster but still uses the leading 4G network. Interestingly, there are three types of 5G: low-band (sub-6), middle-band (sub-6), and high-band (millimetre-wave (mmWave)). The problem is determining the kind of 5G needed for public transport planning. Meanwhile, mobile network big data (MNBD) requires robust and stable internet access, with broad coverage in real time. MNBD movement includes the movement of people and vehicles, as well as logistics. GPS and internet connections track the activity of private vehicles and public transportation. The difference between mmWave and sub-6 5G can complement transportation planning needs. The density and height of buildings in urban areas and the affordability of the range of the connections determine 5G. This study examines the literature on 5G and then, using the bibliographic method, matches the network coverage obtained in Indonesia using nPerf data services. According to the data, urban areas are becoming more densely populated. Thus, this could show the differences in the data quality outside of metropolitan areas. This study also discusses the current conditions in terms of market potential and the development of smart cities and provides an overview of how real-time mobile data can support public transport planning. This article provides beneficial insight into the stability and adjustment of 5G, where the connectivity can be adequately maintained so that the MNBD can deliver representative data for analysis.
... The effect of online facilities on transport devices is examined in who signified the possibility of GPS furnished phones as widely obtainable web traffic congestion observing methods to offer dependable visitors information instantly (Keskar et al., 2021). Yang et al. (2017) suggested a visitor observing technique contingent on GPS enabled cell devices, applying the comprehensive scope offered by the connected network in numerous cities. Zheng et al. (2015) unveiled a visitor observing technique dependent on virtual transport channels and executed it with a GPS phone program to capitalize on the authenticity of instant traffic congestion approximation and resolve security issues. ...
With increasing urbanization across the world, the demand for smart transportation methods to support everyone, as well as freight, becomes more vital. To tackle the challenges of growing congestion on the roads, big data analytics (BDA) strategies can be used to offer insights for real decision-making, and policy designing. This study has two primary goals. First, this study evaluates academic literature regarding BDA for smart commuter routes programs; and next based upon the studies, it suggests a framework that is effective, but comprehensive in making recommendation to drive down the congestion and increase efficiency of shared transportation system. The study believes that the framework suggested is solid, versatile, and adaptive enough to be implemented in transportation systems in large cities. Using the framework, system will be managed in a centralized system, allowing much more efficient transportation across cities. Further studies should be conducted over a long period, in smaller cities as well, to make improvement on the framework.
... Преимущества анализа больших данных для систем общественного транспорта были представлены в работе [2]. Авторы проанализировали различные источники информации: траектория движения транспорта (координаты GPS, скорость передвижения), отчеты о неисправностях, передвижения людей (с помощью GPS и Wi-Fi сигналов), социальные сети (текстовые записи, адреса) и веб-логи (идентификаторы пользователей, комментарии). ...
Целью исследования является разработка подхода для выявления и автоматического устранения противоречий в больших данных для задач аналитики городского пассажирского транспорта. Актуальность выявления и устранения противоречий в больших данных транспортных потоков обусловлена тем фактом, что они преобразуются в значительную информацию для использования в задачах управления. Особенность предложенного подхода состоит в реализации универсального модуля двухуровневого обнаружения противоречий в выбранном наборе данных с использованием статистических методов и алгоритмов машинного обучения. Разработанный модуль «Выявление и устранение противоречий» включает три метода выявления и устранения противоречивых данных: 1) методы статистической обработки; 2) критерий Граббса; 3) алгоритмы машинного обучения: k- средних и многослойная нейронная сеть. Программная реализация системы для предложенного подхода выявления и автоматического устранения противоречий используется для решения задачи формирования расписания городских автобусных маршрутов в пилотном режиме. В статье приведены этапы и результаты численных экспериментов для выбранного набора данных в разработанной системе для предложенного подхода выявления и автоматического устранения противоречий при составлении и корректировке расписания автобусных маршрутов города Алматы.
... Even when feature maps are dynamic with complex bindings, deep learning models outperform traditional learning methods as developed in [11][12][13]. The encoder-based work in [29][30][31][32][33][34][35][36][37] has done a detailed study on the autoencoder model to better predict traffic flow. Autoencoder is used by many researchers to denoise feature points in large datasets. ...
Due to recent advances in the Vehicular Internet of Things (VIoT) large volume of traffic trajectory data is generated. The trajectory data is highly unstructured and pre-processing it is a very cumbersome task, due to the complexity of the traffic data. However, the accuracy of traffic flow learning models depends on the quantity and quality of preprocessed data. Hence, there is a significant gap between the size and quality of benchmarked traffic datasets and the respective learning models. Also, generating a custom traffic dataset with required feature points in a constrained environment is very difficult. This research work aims to harness the power of the deep learning hybrid model with datasets that have fewer feature points. Therefore, a hybrid deep learning model, that extracts the optimal feature points from the existing dataset using a stacked autoencoder, is presented. Handcrafted feature points are fed into the hybrid deep neural network to predict the travel path and travel time between two geographic points. The Chengdu1 and Chengdu2 standard reference datasets are used to realize our hypothesis of the evolution of a hybrid deep neural network with minimal feature points. The hybrid model includes the Graph Neural Networks (GNN) and the Residual Networks (ResNet) preceded by the Stacked Autoencoder (SAE). This hybrid model simultaneously learns temporal and spatial characteristics of the traffic data. Temporal feature points are optimally reduced using Stacked Autoencoder to improve the accuracy of the deep neural network. The proposed GNN+Resnet model performance was compared to models in literature using Root Mean Square Error (RMSE) loss, Mean Absolute Error (MAE) and Mean Absolute Percentile Error (MAPE). The proposed model was found to perform better by improving the travel time prediction loss on Chengdu1 and Chengdu2 datasets. An in-depth comprehension of the proposed GNN+Resnet model, for predicting travel time during peak and off-peak periods, is also presented. The model's RMSE loss was improved up to 22.59% for peak hour traffic data and up to 11.05% for off-peak hours traffic data in the chengdu1 dataset.
... Recently artificial intelligence (AI) technologies have attracted a lot of research attention from both academia and industry [2][3][4]. Traffic analysis and forecasting using AI and big data analysis, the internet of things (IoT), vehicles to vehicles (V2X), are becoming emerging research areas of intelligent transportation [5][6][7][8]. Moreover, the introduction of AI-based traffic control systems not only can dynamically coordinate the urban traffic network operation and equalize the traffic flow at each intersection, thereby improving the road traffic capacity but also reasonably optimizing and configuring the signal phase and timing of each intersection in traffic signal control systems. ...
Edge computing supported vehicle networks have attracted considerable attention in recent years both from industry and academia due to their extensive applications in urban traffic control systems. We present a general overview of Artificial Intelligence (AI)-based traffic control approaches which focuses mainly on dynamic traffic control via edge computing devices. A collaborative edge computing network embedded in the AI-based traffic control system is proposed to process the massive data from roadside sensors to shorten the real-time response time, which supports efficient traffic control and maximizes the utilization of computing resources in terms of incident levels associated with different rescue schemes. Furthermore, several open research issues and indicated future directions are discussed.
... In transportation, these efforts have been mainly focusing on the aspects of data acquisitionmostly in terms of data collection, information extraction and cleaning and modelling analysis. The analyses most commonly performed are basedto name but a fewon Floating Car Data (Li et al., 2021;Chen et al., 2021b;Astarita et al., 2019Astarita et al., , 2020, mobile phone data (Franco et al., 2020;Zhao et al., 2020;Huang et al., 2018;Wang et al., 2018;Zhou et al., 2018), payment and transit card data (Arbex and Cunha, 2020;Tavassoli et al., 2020;Sulis et al., 2018;Yap et al., 2018;Utsunomiya et al., 2006), GPS enabled mobile phone data (Bachir et al., 2019;Bwambale et al., 2017) and social media (Liao et al., 2021;Yao and Qian, 2021;Lock and Pettit, 2020;Hu et al., 2020;Chaniotakis and Antoniou, 2015;Zheng et al., 2016). Of particular interest in regards to the increased data availability is the evolution of pervasive systems (e.g., GPS handsets, cellular networks) and especially the connectivity that has been available to a growing number of individuals, that allow the sharing of different information types such as spatial, temporal, and textual information. ...
Social Media have increasingly provided data about the movement of people in cities making them useful in understanding the daily life of people in different geographies. Particularly useful for travel analysis is when Social Media users allow (voluntarily or not) tracing their movement using geotagged information of their communication with these online platforms. In this paper we use geotagged tweets from 10 cities in the European Union and United States of America to extract spatiotemporal patterns, study differences and commonalities among these cities, and explore the nature of user location recurrence. The analysis here shows the distinction between residents and tourists is fundamental for the development of city-wide models. Identification of repeated rates of location (recurrence) can be used to define activity spaces. Differences and similarities across different geographies emerge from this analysis in terms of local distributions but also in terms of the worldwide reach among the cities explored here. The comparison of the temporal signature between geotagged and non-geotagged tweets also shows similar temporal distributions that capture in essence city rhythms of tweets and activity spaces.
... One of the most traditional problems in transportation systems is the management of tra c ow. Zheng et al. (2015)apply the concept of Cyber-Physical-Social Systems (CPSS) to achieve signals from both the physical and social spaces. The proposed CPSSbased Transportation System (CTS) is a software-de ned transportation system that creates an environment where human factors, transportation systems, and computing technologies are integrated and interact to provide intelligent responses that affect the real world (Fig. 3). ...
The big data concept has been gaining strength over the last years. With the arise and dissemination of social media and high access easiness to information through applications, there is a necessity for all kinds of service providers to collect and analyze data, improving the quality of their services and products. In this regard, the relevance and coverage of this niche of study is notorious. It is not a coincidence that governments, supported by companies and startups, are investing in platforms to collect and analyze data, aiming at the better efficiency of the services provided to the citizens. Considering the aforementioned aspects, this work makes a contextualization of the Big Data and ITS (Intelligent Transportation System) concepts by gathering recently published articles, from 2017 to 2021, taking survey and case studies into consideration with the objective of demonstrating the importance of those themes in current days. Within the scope of big data applied to ITS, this study proposes a database for the public transportation in the city of Campinas (Brazil), enabling its improvement according to the population demands. Finally, this study tries to present clearly and objectively the methodology employed with the maximum number of characteristics, applying statistical analyses (box-and-whisker diagrams and Pearson correlation), highlighting the limitations, and expanding the studied concepts to describe the application of an Advanced Traveler Information System (ATIS), a branch of Intelligent Transportation System (ITS), in a real situation. Therefore, besides the survey of the applied concepts, this work develops a specific case study, highlighting the identified deficiencies and proposing solutions. Future works are also contemplated with the objective of expand this study and improve the accuracy of the achieved results.
... The effect of online facilities on transport devices is examined in who signified the possibility of GPS furnished phones as widely obtainable web traffic congestion observing methods to offer dependable visitors information instantly (Keskar et al., 2021). Yang et al. (2017) suggested a visitor observing technique contingent on GPS enabled cell devices, applying the comprehensive scope offered by the connected network in numerous cities. Zheng et al. (2015) unveiled a visitor observing technique dependent on virtual transport channels and executed it with a GPS phone program to capitalize on the authenticity of instant traffic congestion approximation and resolve security issues. ...
Big data has a huge impact on urban planning and cities morphology. Big data is utilized to appraise the requirements of the shared transport structure, by focusing on funding and portability plans inside the key cities. The research provides a recommendation-making system (RMS) focused on suggesting transport methods to automobile consumption by detailing a huge volume of transport methods information originating from various products. The research focuses on the utilization of big data to come down with shared transport, and presents a structural understanding for gathering, combining, aggregating, incorporating, disseminating, and controlling information from numerous origins. Information extraction methods are utilized, allowing the evaluation of both organized big data, that follows developed benchmarks like CRISP-DM, and disorganized, readily offered big data. Investigational information has been gathered from a representative of phones and automatic vehicle location devices in the region. The suggested RMS allowed to examine the temporal and spatial scope of shared transport facilities, and suggested plans to enhance the transportation.
... The data sources of research related to short-term traffic flow prediction mainly include GPS Floating Car Data [3][4], fixed loop detector data [5] and vehicle electronic identification data. However, at present, the short-term prediction of travel time at home and abroad mostly focuses on the data based on GPS floating car and fixed loop detector. ...
In order to make short-term prediction of the direction of traffic flow in urban roads, a short-term prediction method of urban road travel time based on K nearest neighbor algorithm and vissim simulation is constructed. First, the intersection of Shiji Road and Yingbin Road was selected as the survey site, and the number of vehicles in each direction of each entrance lane of the intersection was investigated using manual counting, and the signal timing of each phase of the intersection was investigated. Input the survey data into the vissim simulation software to get the travel time of each entrance lane in each direction. Then build a vissim simulation traffic flow direction prediction model based on the KNN algorithm, including the construction of feature vectors, cross-validation methods to determine K values, and local estimation methods. The experimental results show that the average relative error between the predicted traffic flow direction and the actual traffic flow direction tends to 0.27. Due to the small amount of data, the prediction result is more accurate.
... For example, location data can improve the effectiveness of services, help to improve traffic safety, traffic operation and traffic planning; a large amount of data generated by logistics and manufacturing process can optimize the logistics distribution path, save transportation costs and improve operational efficiency. [4] Among them, sharing data is the key to the realization and management of such a system. It creates a complex, flexible and interconnected transportation system by aggregating data from smart phones, vehicles and infrastructure, and generates real-time images of the city and its traffic by accessing data from different sources. ...
With the construction of intelligent transportation, big data with heterogeneous, multi-source and massive characteristics has become an important carrier of cooperative intelligent transportation systems (C-ITS) and plays an important role. Big data in C-ITS can break through the restrictions between regions and entities and then learning cooperatively by sharing data. In addition, the combined efficiency and information integration advantages of big data are conducive to the construction of a comprehensive and three-dimensional traffic information system and can enhance traffic prediction. However, such substantial sensitive data, mainly on the cloud infrastructure, exposes several vulnerabilities like data leakages and privacy breaks, especially when data is shared for cooperative learning purposes. To address this, this paper proposes a forward privacy-preserving scheme, named AFFIRM, for multi-party encrypted sample alignment adopting cooperative learning in C-ITS. By introducing the searchable encryption method, we realize the sample alignment of cooperative learning in the multi-party encrypted data space. AFFIRM ensures encrypted sample alignment under the condition of forward privacy security. We have formally proved that the proposed scheme satisfies both forward security and validity. We have assessed AFFIRM by validating the potential threat of malicious tampering by privacy attackers and malicious personnel search for the aligned sample data and verify it. Finally, we numerically tested and compared AFFIRM against the corresponding ones of some state-of-the-art schemes under various record sizes, servers and processing.
... Zheng, Xinhu, et al [17] summarized analytical methods, data sources, and application methods for social transportation, along with recommended a few potential investigate guidelines in the field of social transportation. ...
Abstract— Road traffic accidents are very essential for common people, consequential an estimated 1.2 million deaths and 50 million
injuries all over the world every year. In this emerging world, the road accidents are among the principal reason of fatality and
injury. The concern of traffic safety has heaved immense alarms across the manageable enhancement of contemporary traffic and
transportation. The analysis on road traffic accident grounds can detect the major aspects quickly, professionally and afford
instructional techniques to the prevention of traffic accidents and reduction of road traffic accident, which might significantly
decrease personal victim by means of road traffic accidents. The current research represents that the Data Mining techniques that
have employed in the field of transport have been investigated. Through this comprehensive investigation, the techniques of Data
Mining in the traffic study can enhance the administration level of road traffic safety productively.
Keywords— Transportation, Data Mining, Road Traffic, Accidents, Road Safety
... The data collection for transportation and traffic-related studies can be done either by manual methods (field-based observations) or by automated technology-based methods (Tourangeau et al., 1997;Hummer, 1994;Zheng et al., 2016). The traditional manual methods of data collection necessitate the investment of a substantial amount of human resources, efforts and time. ...
The implementation of Intelligent Transportation Systems (ITS) as a part of smart mobility is crucial for solving the current problems of the transportation industry. The setting up and maintenance of ITS requires not only the current passenger demand but also the future passenger demand. The future passenger demand can be obtained with time-series forecasting carried out with different techniques. With the advancements in the technological field, modern and more advanced methods of time-series forecasting using deep learning are being preferred over traditional forecasting techniques. However, the research carried out in this regard is quite limited, particularly considering the Indian scenario. Hence this research work focuses on exploring the performance of deep learning forecasting techniques considering the aspects mentioned previously. Here, the forecasting of passenger demand was done with Long Short-Term Memory (LSTM) using the three months Automatic Passenger Counter (APC) data of the Hubballi-Dharwad Bus Rapid Transit System (HDBRTS) as part of a case study. Then the forecasting of passenger demand was also done with Seasonal Autoregressive Integrated Moving Average (SARIMA), and the comparison of the forecasting accuracy of both methods was made using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Furthermore, to validate the results, novel approach has been adopted for the process, by following some more time-series resampled with different time intervals. Study shows that LSTMs will be used satisfactorily in the traffic conditions of developing counties, for forecasting passenger demand using APC data. Study also provides detailed guiding methodologies of advanced methods of passenger forecasting along with conventional ones.
... Some studies done in western countries like Canada and Sweden show contrasting scenario as those suggest that users of active transportation are the most satisfied, with high satisfaction scores being assigned to cyclists followed by pedestrians [19,20]. Previous study indicates that perception factors have a greater influence on satisfaction [21]. This analogy is somewhat exemplified in Sydney, Australia, where pedestrians or bicyclists in the inner city core enjoyed their commute more than private vehicle users while going to work or study [22]. ...
India’s National Urban Transport Policy lays out guidelines and strategies to strike a balance between vehicle usage and environmental awareness. An estimated one-third of the all urban trips in India are walking trips, and other non-motorized transport (NMT) modes such as cycle/e-rickshaws are also hugely popular. However, facilities are rarely designed, keeping in mind the needs of such NMT users, which have resulted in a poor level of service offered and subsequently a steady decline of their modal share. The study results show that although the average walking and rickshaw trip lengths were significantly large which indicates a propensity to use NMT modes, but the overall satisfaction with the facilities/services were average or below average. A majority of respondents are dissatisfied by the air quality while walking or with the location of pick-up/drop-off points while availing the cycle rickshaw service. The modeling results show that removal of obstructions is expected to have the highest impact of improving the overall satisfaction of pedestrians i.e., odds are 1.62 times higher than footpath continuity which is the least influential one. Additionally, sturdier/cleaner cycle rickshaws are likely to have the most significant impact i.e., odds are 1.46 times more than passenger comfort (passenger load), which is the least influential, in case of rickshaw users. Lastly, the study concludes that specific and targeted NMT improvements will likely be required, even in newer parts of a city, and in several instances, multiple improvements shall be warranted, to enhance the satisfaction of a NMT user.KeywordsNon-motorized transportWalking tripCycle/e-rickshawsDesign and operational factorsUsers’ perception
... In recent times, research in several domains is heavily focusing on large-scale data analysis utilizing sophisticated computing capabilities and machine learning [11,12,13,14,15,16,17]. However, causal analysis of data for prediction purposes has received limited attention in the literature. ...
Since the increasing outspread of COVID-19 in the U.S., with the highest number of confirmed cases and deaths in the world as of September 2020, most states in the country have enforced travel restrictions resulting in sharp reductions in mobility. However, the overall impact and long-term implications of this crisis to travel and mobility remain uncertain. To this end, this study develops an analytical framework that determines and analyzes the most dominant factors impacting human mobility and travel in the U.S. during this pandemic. In particular, the study uses Granger causality to determine the important predictors influencing daily vehicle miles traveled and utilize linear regularization algorithms, including Ridge and LASSO techniques, to model and predict mobility. State-level time-series data were obtained from various open-access sources for the period starting from March 1, 2020 through June 13, 2020 and the entire data set was divided into two parts for training and testing purposes. The variables selected by Granger causality were used to train the three different reduced order models by ordinary least square regression, Ridge regression, and LASSO regression algorithms. Finally, the prediction accuracy of the developed models was examined on the test data. The results indicate that the factors including the number of new COVID cases, social distancing index, population staying at home, percent of out of county trips, trips to different destinations, socioeconomic status, percent of people working from home, and statewide closure, among others, were the most important factors influencing daily VMT. Also, among all the modeling techniques, Ridge regression provides the most superior performance with the least error, while LASSO regression also performed better than the ordinary least square model.
Today, the problem of processing big data in real time is observed not only in unstructured big data but also in dealing with structured data in databases of small businesses and organizations due to the rapid increase in data volume. Traditional methods and approaches are not considered effective to solve the problem. Moreover, most of the modern effective approaches are based on the cooperation of several computers, and they require plenty of expenses, so it is not suitable for small organizations. The approach proposed in this chapter aims to effectively process big data in real time, bypassing the shortcomings above. The proposed approach is based on the use of a distributed computing mechanism on a single server. The chapter reveals the architecture of this approach, the functional scheme, the essence of the approach, and the effectiveness of the approach. Moreover, in the chapter improving the effectiveness of the approach through machine learning is discussed. Experimental results have been obtained based on the approach and they compared with the traditional approach.
The construction of transportation 5.0 or the so-called society-centered intelligent transportation systems (ITS) has aroused higher requirements for the intelligent sensing capability to seamlessly integrate Cyber-Physical-Social Systems (CPSS). Crowd Sensing Intelligence (CSI), as a promising paradigm, leverages the collective intelligence of heterogeneous sensing resources to gather data and information from CPSS. Our first Distributed/Decentralized Hybrid Workshop on Crowd Sensing Intelligence (DHW-CSI) has been focused on principles and high-level processes of organizing and operating CSI. This letter reports the discussion results of the second DHW-CSI addressing the participants, methods, and stages of CSI for ITS. We categorized sensing participants into three kinds, i.e., biological , digital, and robotic. Then we summarized three methods to enable sensing intelligence, i.e., foundation models, scenarios engineering, and human-oriented operating systems. Finally, we anticipated that the progression of CSI will experience three stages, from algorithmic intelligence to linguistic intelligence, and eventually to imaginative intelligence.
The booming development of Internet of Vehicles (IoV) has brought new vitality to the construction of intelligent transportation systems (ITS). At the same time, a huge amount of data has been generated due to the gradual development of IoV towards large-scale, complex, and diversified. These data are owned by the companies that vehicles belonging to or service providers, such as taxi companies own taxi data. Due to interest and privacy considerations, data owners are not willing to share data, thus a serious data isolated island problem is created, which is detrimental to the development of ITS. Therefore, this paper focuses on how to prevent privacy disclosure of vehicles while sharing vehicle data to improve the service. Considering the amount of interactive data and privacy disclosure during data release, vehicle data is abstracted from text form into graph-structured data form. At the same time, graph differential privacy together with anonymity protection is proposed innovatively to firmly protect vehicle privacy. Moreover, to solve the high complexity of big data graph structure transformation, an Accelerated nodes and edges Combined Graph Differential Privacy algorithm (ACGDP) is proposed. Based on the simulations of real-world data that combine electric and non-electric taxies, it is verified that our proposed scheme has a tradeoff between information availability and privacy protection. With the graph differential privacy processed data, our proposed scheme reduces the average wasted mileage for charging by 3.87% and achieves a 44.28% increase in drivers’ income. Drivers’ satisfaction of receiving orders and charging preference reaches 68% after the graph-structured data reuse.
Detailed understanding of multi-modal mobility patterns within urban areas is crucial for public infrastructure planning , transportation management, and designing public transport (PT) services centred on users' needs. Yet, even with the rise of ubiquitous computing, sensing urban mobility patterns in a timely fashion remains a challenge. Traditional data sources fail to fully capture door-to-door trajectories and rely on a set of models and assumptions to fill their gaps. This study focuses on a new type of data source that is collected through the mobile ticketing app of HSL, the local PT operator of the Helsinki capital region. HSL's dataset called TravelSense, records anonymized travelers' movements within the Helsinki region by means of Bluetooth beacons, mobile phone GPS, and phone OS activity detection. In this study, TravelSense dataset is processed and analyzed to reveal spatio-temporal mobility patterns as part of investigating its potentials in mobility sensing efforts. The representativeness of the dataset is validated with two external data sources-mobile phone trip data (for demand patterns) and travel survey data (for modal share). Finally, practical perspectives that this dataset can yield are presented through a preliminary analysis of PT transfers in multimodal trips within the study area.
To manage growing urbanization, there is the development of smart cities with an aim for environment preservation, improvement of the socio‐economical standard of living of people by adopting technological advancement in information and communication technology (ICT). For design, implementation, and deployment of smart cities leads to an exploration of artificial intelligence (AI), machine learning (ML), and deep learning (DL). In this work, the application of machine learning or deep learning is explored for applications of smart cities such as smart transportation systems (STSs), smart grids (SGs), smart healthcare, etc. Major challenges that are faced while designing smart city plans are such as the plant should be energy efficient network architecture, privacy‐preserving as well as data needed to be efficiently analyzed of big data. To explore more accurate and precise decision‐making system ML/AI techniques have shown their proficiency in the improvement of efficiency as well as to deploy low‐cost smart network architecture design and management. In this chapter, an analytical study will be presented with the application of AI, ML, and DL in different sectors/application areas of smart cities. The main aim is to focus on and explore the efficiency level of ML/AI techniques. This chapter will also provide an in‐depth analysis of innovative development, deployment, analysis, security, and management in smart cities. So, this chapter will help in the exploration of research challenges and future direction for researchers.
Origin-destination (OD) datasets are often represented as ‘desire lines’ between zone centroids. This paper presents a ‘jittering’ approach to pre-processing and conversion of OD data into geographic desire lines that (1) samples unique origin and destination locations for each OD pair, and (2) splits ‘large’ OD pairs into ‘sub-OD’ pairs. Reproducible findings, based on the open source odjitter Rust crate, show that route networks generated from jittered desire lines are more geographically diffuse than route networks generated by ‘unjittered’ data. We conclude that the approach is a computationally efficient and flexible way to simulate transport patterns, particularly relevant for modelling active modes. Further work is needed to validate the approach and to find optimal settings for sampling and disaggregation.
Aiming at the problems of strong coupling, low efficiency and poor flexibility of the traditional expressway toll system, a new expressway toll system based on the “Cloud Wan Edge Terminal (CWET)" architecture is designed which builds computing, storage and APP centers on the cloud. The highway data are transmitted to the cloud through the SD-WAN network, and develops the edge operating system which realize the ubiquitous interconnection of all kinds of toll system terminals. The new toll collection system has achieved good results in the application of Jiangsu Expressway.KeywordsExpresswayToll systemCloud wan edge terminalSD-WAN
In day-to-day life transportation plays a major role in cities. Present day traffic management is a complex task for transportation agencies through traditional approaches, hence Intelligent Transportation systems is applied to give traffic management solutions like parking, E-toll charge and traffic control by analyzing data from related sources. Data is collected from various sources for analyzing transportation need’s, yet transportation issues remain one of the major tribulations in cities. Unstructureddata gives enormous information load for big data analytics, but the unstructured content processing is a challenge in industry. Passive data like social media data is a major data sources for Intelligent Systems, social media applications such as Twitter, Facebook where user can share live comments based on their interaction with the world is a rich source for passive data. Social media data helps in analyzing traffic issues like traffic jam, accident locations, road condition etc. Major issue with social media data is processing and analysis of data is very complex because of volume and data format. Big data architecture helps in extracting, processing, loading in database and analyzing this unstructured data. To identify thesentimentalanalysis is majorly classified based onpositive, negative and neutral tweets. As the polarity of neutral tweets is zero it cannot be used for Opinion mining. So, this paper is focused on Neutral tweets classification based on feature selection. Part of Speech (PoS) tagging is used for labeling the words of the text in the tweets to find nouns example location, date and time are compared with the other attribute values for improving the classification of neutral tweets. Research work shown in this paper has taken social media speech data (Tweets) from twitter as input and preprocessing techniques are applied on the data collected, Methods such as feature selection are then used to extract the features related to tweets for classifying neutral tweets for better understanding on road condition, identification of traffic patterns and finally traffic behavior is analyzed by using Ensemble machine learning algorithm. In the proposed model to measure the sentimental analysis a new approach is provided based on feature selection. The findings disclose with SentiWordNetopinion lexicon approach gives 56% accuracy of positive or negative opinion using twitter dataset, the results of feature selection-based opinion mining proposed model increased substantially with 88% accuracy.
Analysis of traffic conduct, mainly in densely populated urban areas, provides an excellent opportunity to study traffic patterns and extract useful information to help in planning and development. During activities that draw in a massive number of people, such as religious pilgrimages or sporting events, collisions of automotive traffic flows can result in interruptions and unsafe situations for the subjects, often creating chaos and congestion. The scenario becomes more ambitious in Hajj when millions of pilgrims move in a restricted area during a fixed period of time. Hajj is a 5-day Islamic pilgrimage whereby millions of pilgrims from across the globe assemble in Makkah to perform a number of spatiotemporal rituals every year on fixed dates. This chapter presents an interactive platform that utilizes large-scale GPS traces to detect the motion of buses during Hajj. For a period of 2 months, GPS traces are gathered for over 17,000 buses used to carry pilgrims performing Hajj activities. An interactive big data platform was developed to analyze and visualize the massive amount of spatial data. The analysis was done for various stakeholders, including the bus companies. Using our map-based visualization, they were able to visualize the movement of buses; identify drivers’ behavior, speed violations, and location of the violations; and determine the quality of data provided by various AVL providers. The information extracted can be used to generate an intelligent transportation system featuring schedule, evacuation, sustainability, resource optimization, and environmental and economic efficiencies to benefit stakeholders and improve the mobility of pilgrims throughout Hajj.KeywordsGPS dataTrajectoryHajjCategorizationBig dataClustering
Assessment of drought impact on the socio-economic fabric is a critical issue worldwide. Many studies have attempted to obtain socio-economic drought information. However, not enough attention has been paid to reducing the awareness gap between the monitoring of data of drought by public institutions, and local situations. This study proposes a socio-economic drought information (SEDI) based on Internet news articles that can consider droughts in situ. Based on 20,999 processed news articles, the SEDI is classified into four categories: water deficit, water security and support, economic damage and impact, and environmental and sanitation impact. In the moderately and severely dry conditions, the relationships between SEDI and monitoring data were evident with the receiver operating characteristic and the area under the curve of above 0.7. The evaluation results showed that SEDI could realistically reflect the lack of precipitation and social impact on South Korea. SEDI can successfully detect drought situations, thereby allowing understanding of the spatial temporal patterns of drought impact. The proposed SEDI can help effectively disseminate public safety information, thereby providing helpful data on local areas.
Collaborative unmanned systems have emerged to meet our society’s wide-ranging grand challenges, with their advantages including high performance, efficiency, flexibility, and inherent resilience. Increasing levels of group/team autonomy have also been achieved due to the embodiment of artificial intelligence (AI). However, the current networked unmanned systems still do not have sufficient human-level intelligence and human needs fulfillment for the challenging missions in our lives. We propose in this paper a vision of human-centric networked unmanned systems: Unmanned Intelligent Clusters (UnIC). Within this vision, distributed unmanned systems and humans are connected via knowledge to achieve cognition. This paper details UnIC’s concept, sources of intelligence, and layered architecture, and review enabling technologies for achieving this vision. In addition to the technological aspects, the social acceptance is highlighted.
Government authorities of Delhi define transit-oriented development (TOD) as a micro- or macro-development around transit nodes, which can be served by people through walking over private transport. As metro rail transit (MRT) network expands, planning agencies are interested in facilitating TOD along existing and future transit corridors. However, the proposed definition lacks clear motivation, and there is less agreement among stakeholders on which criterion to focus, what levels are existing, why TOD is important, and what TOD should accomplish. A comprehensive TOD measurement tool is necessary to answer these questions at policy level, which enables standards for TOD planning and implementation. The present study fills this gap by proposing a TOD scoring tool based on personal interviews with stakeholders and suitable analytical analysis. Despite different perspectives of experts in decision making, there appeared to be a consensus among experts in believing that TOD planning in Delhi must focus on accessibility to jobs, proximity to transit, pedestrian and cycling facilities, and travel demand management. Besides, application of TOD scoring tool on 48 potential neighborhoods in Delhi showed that Uttam Nagar (0.77) has the highest TOD score, and Chanakyapuri (0.13) scored least. This scoring tool will guide stakeholders in future TOD policy, planning, and implementation. Establishing and understanding this TOD measurement framework can help stakeholders of other Indian cities to implement TOD, more strategically. Conclusively, this study recommended action plans through a set of strategies that help fortify TOD planning in neighborhoods of Delhi.KeywordsTOD planningNeighborhoodsPriority criterionTOD score
Financial capability of a Public Private Participation (PPP) Project for a public road project is an exceptionally significant for personal area since it is a risk free allotment for the government. In this study, financial capability of a PPP road project with real option valuation is proposed considering conventional discounted cash flow analysis. The monetary feasibility of a road project in PPP mode is found to be more strongly and expansively than present traditional approach by allowing the uniqueness of the scheme considering the future opportunity and ambiguity that the present model shall be revised. A hypothetical case study has been considered and it is found that proposed method is very useful which yields 17.3% financial internal rate of return (FIRR) against 12.5% as obtained from conventional method.KeywordsBOT projectFinancial viabilityNPVIRRDCF
Driven by the massive number of connected vehicles and the stringent requirements of data-intensive applications, logistics transportation systems have evolved to fully comprehend its effectiveness and quality to meet public transportation needs. As a result, to meet public transportation requirements and analyze the data efficiently at the edge of the networks, an advanced artificial intelligent technique needs to be introduced to make the transportation system intelligent by supporting efficient decision making, intelligent traffic control, and intrusion and misuse detection.Motivated by the challenges mentioned above, in this paper, we develop a logistic agent-based model for analyzing public transports such as cars, bus or trains in the intelligent transportation system. The intelligent logistic framework is built on a parallel neural network structure, known as a Swarm-Neural Network (SWNN). The proposed SWNN model analyzes the sensory data and recognizes the public transportation at the edge of the networks. The SWNN model is constructed so that it fits within the intelligent logistic transportation framework, and the proposed model shortens the transit time of every small-scale logistics delivery to its destination. The performance of the proposed SWNN model is evaluated using a standard TMD dataset, where the SWNN model is trained using data, retrieved multiple sensors such as accelerometer, gyroscope, magnetometer, and audio sensors. The features of the sensory data are extracted based on a 5-s time interval. The performance of the proposed SWNN model is studied over various standard machine learning techniques such as Random Forest, XGBoost, and Decision Tree. As per the simulation results, the proposed technique achieves 78–98% accuracy over a real-time dataset’s different sets of features.
With the recent increase in urban drift, which has led to an unprecedented surge in urban population, the smart city (SC) transportation industry faces a myriad of challenges, including the development of efficient strategies to utilize available infrastructures and minimize traffic. There is, therefore, the need to devise efficient transportation strategies to tackle the issues affecting the SC transportation industry. This paper reviews the state-of-the-art for SC transportation techniques and approaches. The paper gives a comprehensive review and discussion with a focus on emerging technologies from several information and data-driven perspectives including (1) geoinformation approaches; (2) data analytics approaches; (3) machine learning approaches; (4) integrated deep learning approaches; (5) artificial intelligence (AI) approaches. The paper contains core discussions on the impacts of geo-information on SC transportation, data-driven transportation and big data technology, machine learning approaches for SC transportation, innovative artificial intelligence (AI) approaches for SC transportation, and recent trends revealed by using integrated deep learning towards SC transportation. This survey paper aimed to give useful insights to researchers regarding the roles that data-driven approaches can be utilized for in smart cities (SCs) and transportation. An objective of this paper was to acquaint researchers with the recent trends and emerging technologies for SC transportation applications, and to give useful insights to researchers on how these technologies can be exploited for SC transportation strategies. To the best of our knowledge, this is the first comprehensive review that examines the impacts of the various five driving technological forces—geoinformation, data-driven and big data technology, machine learning, integrated deep learning, and AI—in the context of SC transportation applications.
Today, a company continues its activities in a highly competitive environment regardless of the sector in which it operates. An important point has been emphasized in many developments by experienced managers and academics which have been released to the public. From marketing to finance, human resource management, auditing and planning, all business processes have entered an incredible innovative process. One of the topics in this process is big data. When cumulative data are not used, they cannot transcend being huge piles of garbage. However, it is not possible to analyze such large, complex, and dynamic data via conventional methods. At this point, the concept of big data has emerged. In this study, after the explanation and definition of the concept, a vast literature review was conducted in order to present the relationship of big data with IoT, big data-related topics, and academic researches on big data. Afterwards, real-life enterprise applications were exemplified from various industries.
Big data and the Internet of Things (IoT) are the recent innovations in this era of smart world. Both of these technologies are proving very beneficial for today's fast-moving lifestyle. Both technologies are connected to each other and used together in many real-world applications. Big data and IoT have their uses and applications in almost every area from homes to industries, from agriculture to manufacturing, from transportation to warehousing, from food industries to entertainment industry, even from our shoe to robotics. This chapter discusses various applications of big data and IoT in detail and also discusses how both the technologies are affecting our daily life and how it can make things better.
A cyber-physical system (CPS) is composed of a physical system and its corresponding cyber systems that are tightly fused at all scales and levels. CPS is helpful to improve the controllability, efficiency and reliability of a physical system, such as vehicle collision avoidance and zero-net energy buildings systems. It has become a hot R&D and practical area from US to EU and other countries. In fact, most of physical systems and their cyber systems are designed, built and used by human beings in the social and natural environments. So, social systems must be of the same importance as their CPSs. The indivisible cyber, physical and social parts constitute the cyber-physical-social system (CPSS), a typical complex system and it's a challengeable problem to control and manage it under traditional theories and methods. An artificial systems, computational experiments and parallel execution (ACP) methodology is introduced based on which data-driven models are applied to social system. Artificial systems, i.e., cyber systems, are applied for the equivalent description of physical-social system (PSS). Computational experiments are applied for control plan validation. And parallel execution finally realizes the stepwise control and management of CPSS. Finally, a CPSS-based intelligent transportation system (ITS) is discussed as a case study, and its architecture, three parts, and application are described in detail.
Agents in many transportation systems prefer to use the shortest paths, which may not guarantee the optimal transporting efficiency. Given that passengers in some big urban rail transit (URT) systems experience severe congestions, here we developed a congestion avoidance routing model. Based on the Beijing Subway data, the usage patterns of the URT network were comprehensively analyzed under the scenarios of shortest path (SP) routing and minimum cost (MC) routing. We found that MC routing can considerably reduce congestion in the URT network with a tiny increase of travel time. Interestingly, encouraging a small fraction of passengers who experience the most congestion to adopt MC routing achieves nearly the same effect with pure MC routing on mitigating congestion. Hence, a hybrid routing model was proposed to offer practical solutions for alleviating passenger congestion in the URT networks.
In this paper, we present a visual analysis system to explore sparse traffic trajectory data recorded by transportation cells. Such data contains the movements of nearly all moving vehicles on the major roads of a city. Therefore it is very suitable for macro-traffic analysis. However, the vehicle movements are recorded only when they pass through the cells. The exact tracks between two consecutive cells are unknown. To deal with such uncertainties, we first design a local animation, showing the vehicle movements only in the vicinity of cells. Besides, we ignore the micro-behaviors of individual vehicles, and focus on the macro-traffic patterns. We apply existing trajectory aggregation techniques to the dataset, studying cell status pattern and inter-cell flow pattern. Beyond that, we propose to study the correlation between these two patterns with dynamic graph visualization techniques. It allows us to check how traffic congestion on one cell is correlated with traffic flows on neighbouring links, and with route selection in its neighbourhood. Case studies show the effectiveness of our system.
Recent advances in localization techniques have fundamentally enhanced social networking services, allowing users to share their locations and location-related contents, such as geo-tagged photos and notes. We refer to these social networks as location-based social networks (LBSNs). Location data bridges the gap between the physical and digital worlds and enables a deeper understanding of users’ preferences and behavior. This addition of vast geo-spatial datasets has stimulated research into novel recommender systems that seek to facilitate users’ travels and social interactions. In this paper, we offer a systematic review of this research, summarizing the contributions of individual efforts and exploring their relations. We discuss the new properties and challenges that location brings to recommender systems for LBSNs. We present a comprehensive survey analyzing 1) the data source used, 2) the methodology employed to generate a recommendation, and 3) the objective of the recommendation. We propose three taxonomies that partition the recommender systems according to the properties listed above. First, we categorize the recommender systems by the objective of the recommendation, which can include locations, users, activities, or social media. Second, we categorize the recommender systems by the methodologies employed, including content-based, link analysis-based, and collaborative filtering-based methodologies. Third, we categorize the systems by the data sources used, including user profiles, user online histories, and user location histories. For each category, we summarize the goals and contributions of each system and highlight the representative research effort. Further, we provide comparative analysis of the recommender systems within each category. Finally, we discuss the available data-sets and the popular methods used to evaluate the performance of recommender systems. Finally, we point out promising research topics for future work. This article presents a panorama of the recommender systems in location-based social networks with a balanced depth, facilitating research into this important research theme.
Social networks such as Twitter and Facebook are popular, personal, and real-time in nature. We found that there exists a significant number of traffic information such as traffic congestion, incidents, and weather in Twitter. However, an algorithm is needed to extract and classify the traffic information before publishing (re-tweeting) and becoming useful for others. Traffic information was extracted from Twitter using syntactic analysis and then further classified into two categories: point and link. This method can classify 2,942 traffic tweets into the point category with 76.85% accuracy and classify 331 traffic tweets into the link category with 93.23% accuracy. Our system can report traffic information real-time.
Vehicle electrification is envisioned to be a significant component of the forthcoming smart grid. In this paper, a smart grid vision of the electric vehicles for the next 30 years and beyond is presented from six perspectives pertinent to intelligent transportation systems: 1) vehicles; 2) infrastructure; 3) travelers; 4) systems, operations, and scenarios; 5) communications; and 6) social, economic, and political.
The popularity of location-based social networks provide us with a new platform to understand users' preferences based on their location histories. In this paper, we present a location-based and preference-aware recommender system that offers a particular user a set of venues (such as restaurants) within a geospatial range with the consideration of both: 1) User preferences, which are automatically learned from her location history and 2) Social opinions, which are mined from the location histories of the local experts. This recommender system can facilitate people's travel not only near their living areas but also to a city that is new to them. As a user can only visit a limited number of locations, the user-locations matrix is very sparse, leading to a big challenge to traditional collaborative filtering-based location recommender systems. The problem becomes even more challenging when people travel to a new city. To this end, we propose a novel location recommender system, which consists of two main parts: offline modeling and online recommendation. The offline modeling part models each individual's personal preferences with a weighted category hierarchy (WCH) and infers the expertise of each user in a city with respect to different category of locations according to their location histories using an iterative learning model. The online recommendation part selects candidate local experts in a geospatial range that matches the user's preferences using a preference-aware candidate selection algorithm and then infers a score of the candidate locations based on the opinions of the selected local experts. Finally, the top-k ranked locations are returned as the recommendations for the user. We evaluated our system with a large-scale real dataset collected from Foursquare. The results confirm that our method offers more effective recommendations than baselines, while having a good efficiency of providing location recommendations.
Massive amount of movement data, such as daily trips made by millions of passengers in a city, are widely available nowadays. They are a highly valuable means not only for unveiling human mobility patterns, but also for assisting transportation planning, in particular for metropolises around the world. In this paper, we focus on a novel aspect of visualizing and analyzing massive movement data, i.e., the interchange pattern, aiming at revealing passenger redistribution in a traffic network. We first formulate a new model of circos figure, namely the interchange circos diagram, to present interchange patterns at a junction node in a bundled fashion, and optimize the color assignments to respect the connections within and between junction nodes. Based on this, we develop a family of visual analysis techniques to help users interactively study interchange patterns in a spatiotemporal manner: 1) multi-spatial scales: from network junctions such as train stations to people flow across and between larger spatial areas; and 2) temporal changes of patterns from different times of the day. Our techniques have been applied to real movement data consisting of hundred thousands of trips, and we present also two case studies on how transportation experts worked with our interface.
In this work, we present an interactive system for visual analysis of urban traffic congestion based on GPS trajectories. For these trajectories we develop strategies to extract and derive traffic jam information. After cleaning the trajectories, they are matched to a road network. Subsequently, traffic speed on each road segment is computed and traffic jam events are automatically detected. Spatially and temporally related events are concatenated in, so-called, traffic jam propagation graphs. These graphs form a high-level description of a traffic jam and its propagation in time and space. Our system provides multiple views for visually exploring and analyzing the traffic condition of a large city as a whole, on the level of propagation graphs, and on road segment level. Case studies with 24 days of taxi GPS trajectories collected in Beijing demonstrate the effectiveness of our system.
This article presents the Mobile Century fleld experiment, performed on February 8, 2008, to demonstrate the feasibility of a prototype location-based service: real-time tra-c estimation using GPS data from cellular phones only. Mobile Century consisted of 100 vehicles carrying a GPS-equipped Nokia N95 cell phone driving loops on a 10-mile stretch of I-880 between Hayward and Fremont, California. The data obtained in the experiment was processed in real-time and broadcasted on the internet for 8 hours. Travel time and velocity contour estimates were shown in real-time using a privacy-preserving architecture developed to provide this new service in an environment acceptable to users and participants. The quality of the data proves to be accurate against data obtained independently from the experiment. The experiment also shows that it is not necessary to have a high proportion of equipped vehicles to obtain accurate results, conflrming that GPS-enabled cell phones can realistically be used as tra-c sensors, while preserving individuals' privacy. 1. Background The convergence of sensing, communication and multi-media platforms has enabled a key capability: mobility tracking using GPS. Major cellular phone manufacturers plan to embed GPS receivers in most of their phones in the near future. This trend has major implications for the tra-c engineering community. Currently, tra-c monitoring is most commonly based on flxed detectors, which provide vehicle counts, roadway occupancy, and often speed. Unfortunately, their high installation and maintenance costs prohibit more widespread deployments, particularly in developing countries. Moreover, the reliability and accuracy of this type of detectors vary. GPS-equipped mobile phones can provide speed and position measurements to the transportation engi- neering community by leveraging infrastructure deployed by phone manufacturing companies and network providers. Because this technology is market driven, it will penetrate transportation networks at a very rapid pace, soon covering rural areas with a signiflcant impact in developing countries where there is a lack of public tra-c monitoring infrastructure. The present study describes a fleld experiment to assess the feasibility of this new tra-c monitoring system based on GPS equipped phones.
Optimizing paths on networks is crucial for many applications, ranging from subway traffic to Internet communication. Because global path optimization that takes account of all path choices simultaneously is computationally hard, most existing routing algorithms optimize paths individually, thus providing suboptimal solutions. We use the physics of interacting polymers and disordered systems to analyze macroscopic properties of generic path optimization problems and derive a simple, principled, generic, and distributed routing algorithm capable of considering all individual path choices simultaneously. We demonstrate the efficacy of the algorithm by applying it to: (i) random graphs resembling Internet overlay networks, (ii) travel on the London Underground network based on Oyster card data, and (iii) the global airport network. Analytically derived macroscopic properties give rise to insightful new routing phenomena, including phase transitions and scaling laws, that facilitate better understanding of the appropriate operational regimes and their limitations, which are difficult to obtain otherwise.
In this paper, we combine the most complete record of daily mobility, based on large-scale mobile phone data, with detailed Geographic Information System (GIS) data, uncovering previously hidden patterns in urban road usage. We find that the major usage of each road segment can be traced to its own - surprisingly few - driver sources. Based on this finding we propose a network of road usage by defining a bipartite network framework, demonstrating that in contrast to traditional approaches, which define road importance solely by topological measures, the role of a road segment depends on both: its betweeness and its degree in the road usage network. Moreover, our ability to pinpoint the few driver sources contributing to the major traffic flow allows us to create a strategy that achieves a significant reduction of the travel time across the entire road system, compared to a benchmark approach.
Crowd-powered search is a new form of search and problem solving scheme that involves collaboration among a potentially large number of voluntary Web users. Human flesh search (HFS), a particular form of crowd-powered search originated in China, has seen tremendous growth since its inception in 2001. HFS presents a valuable test-bed for scientists to validate existing and new theories in social computing, sociology, behavioral sciences, and so forth.
In this research, we construct an aggregated HFS group, consisting of the participants and their relationships in a comprehensive set of identified HFS episodes. We study the topological properties and the evolution of the aggregated network and different sub-groups in the network. We also identify the key HFS participants according to a variety of measures.
We found that, as compared with other online social networks, HFS participant network shares the power-law degree distribution and small-world property, but with a looser and more distributed organizational structure, leading to the diversity, decentralization, and independence of HFS participants. In addition, the HFS group has been becoming increasingly decentralized. The comparisons of different HFS sub-groups reveal that HFS participants collaborated more often when they conducted the searches in local platforms or the searches requiring a certain level of professional knowledge background. On the contrary, HFS participants did not collaborate much when they performed the search task in national platforms or the searches with general topics that did not require specific information and learning. We also observed that the key HFS information contributors, carriers, and transmitters came from different groups of HFS participants.
Using an algorithm to analyze opportunistically collected mobile phone location data, the authors estimate weekday and weekend travel patterns of a large metropolitan area with high accuracy.
Agent-based traffic management systems can use the autonomy, mobility, and adaptability of mobile agents to deal with dynamic traffic environments. Cloud computing can help such systems cope with the large amounts of storage and computing resources required to use traffic strategy agents and mass transport data effectively. This article reviews the history of the development of traffic control and management systems within the evolving computing paradigm and shows the state of traffic control and management systems based on mobile multiagent technology.
Finding 10 balloons across the U.S. illustrates how the Internet has changed the way we solve highly distributed problems.
Timely updates, increased citizen engagement, and more effective marketing are just a few of the reasons transportation agencies have already started to adopt social media networking tools. Best Practices for Transportation Agency Use of Social Media offers real-world advice for planning and implementing social media from leading government practitioners, academic researchers, and industry experts. The book provides an overview of the various social media platforms and tools, with examples of how transportation organizations use each platform. It contains a series of interviews that illustrate what creative agencies are doing to improve service, provide real-time updates, garner valuable information from their customers, and better serve their communities. It reveals powerful lessons learned from various transportation agencies, including a regional airport, city and state departments of transportation, and municipal transit agencies. Filled with examples from transportation organizations, the text provides ideas that can apply to all modes of transportation including mass transit, highways, aviation, ferries, bicycling, and walking. It describes how to measure the impact of your social media presence and also examines advanced uses of social media for obtaining information by involving customers and analyzing their social media use. The book outlines all the resources you will need to maintain a social media presence and describes how to use social media analytical tools to assess service strengths and weaknesses and customer sentiment. Explaining how to overcome the digital divide, language barriers, and accessibility challenges for patrons with disabilities, it provides you with the understanding of the various social media technologies along with the knowhow to determine which one is best for a specific situation and purpose.
With the ubiquity of mobile communication devices, people experiencing traffic jams share real-time information and interact with each other on social media sites, which provide new channels to monitor, estimate and manage traffic flows. In this paper, we use natural language processing and data mining technologies to extract traffic jam related information from Tianya.cn, analyze the content of people's talk to discover the 'talking point' of people when facing traffic jams, and to provide data support for relevant authorities to make successful and effective decisions for real-time traffic jam response and management.
Studying human movement citywide is important for understanding mobility and transportation patterns. Rather than investigating the trajectories of individuals, we employ an Eulerian approach to analyze the crowd flows among a geographical network and a social network, which are extracted from mobile phone data. We design a suite of visualization techniques to illustrate the dynamic evolutions of the flow over the networks. We contribute the design and implementation of a visual analytics system, which is called Mobility Viewer, that supports situation-aware understanding and visual reasoning of human mobility. We exemplify our approach with a real citywide data set of seven million users in two months.
Transport assessment plays a vital role in urban planning and traffic control, which are influenced by multi-faceted traffic factors involving road infrastructure and traffic flow. Conventional solutions can hardly meet the requirements and expectations of domain experts. In this paper we present a data-driven solution by leveraging a visual analysis system to evaluate the real traffic situations based on taxi trajectory data. A sketch-based visual interface is designed to support dynamic query and visual reasoning of traffic situations within multiple coordinated views. In particular, we propose a novel road-based query model for analysts to interactively conduct evaluation tasks. This model is supported by a bi-directional hash structure, TripHash, which enables real-time responses to the data queries over a huge amount of trajectory data. Case studies with a real taxi GPS trajectory dataset (> 30GB) show that our system performs well for on-demand transport assessment and reasoning.
Intelligent transportation systems (ITS) are becoming a crucial component of our society, whereas reliable and efficient vehicular communications consist of a key enabler of a well-functioning ITS. To meet a wide variety of ITS application needs, vehicular-to-vehicular and vehicular-to-infrastructure communications have to be jointly considered, configured, and optimized. The effective and efficient coexistence and cooperation of the two give rise to a dynamic spectrum management problem. One recently emerged and rapidly adopted solution of a similar problem in cellular networks is the so-termed device-to-device (D2D) communications. Its potential in the vehicular scenarios with unique challenges, however, has not been thoroughly investigated to date. In this paper, we for the first time carry out a feasibility study of D2D for ITS based on both the features of D2D and the nature of vehicular networks. In addition to demonstrating the promising potential of this technology, we will also propose novel remedies necessary to make D2D technology practical as well as beneficial for ITS.
Urban transportation is an important factor in energy consumption and pollution, and is of increasing concern due to its complexity and economic significance. Its importance will only increase as urbanization continues around the world. In this article, we explore drivers' refueling behavior in urban areas. Compared to questionnaire-based methods of the past, we propose a complete data-driven system that pushes towards real-time sensing of individual refueling behavior and citywide petrol consumption. Our system provides the following: detection of individual refueling events (REs) from which refueling preference can be analyzed; estimates of gas station wait times from which recommendations can be made; an indication of overall fuel demand from which macroscale economic decisions can be made, and a spatial, temporal, and economic view of urban refueling characteristics. For individual behavior, we use reported trajectories from a fleet of GPS-equipped taxicabs to detect gas station visits. For time spent estimates, to solve the sparsity issue along time and stations, we propose context-aware tensor factorization (CATF), a factorization model that considers a variety of contextual factors (e.g., price, brand, and weather condition) that affect consumers' refueling decision. For fuel demand estimates, we apply a queue model to calculate the overall visits based on the time spent inside the station. We evaluated our system on large-scale and real-world datasets, which contain 4-month trajectories of 32,476 taxicabs, 689 gas stations, and the self-reported refueling details of 8,326 online users. The results show that our system can determine REs with an accuracy of more than 90%, estimate time spent with less than 2 minutes of error, and measure overall visits in the same order of magnitude with the records in the field study.
The participation of a large and varied group of people in the planning process has long been encouraged to increase the effectiveness and acceptability of plans. However, in practice, participation by affected stakeholders has often been limited to small groups, both because of the lack of reach on the part of planners and because of a sense of little or no ownership of the process on the part of citizens. Overcoming these challenges to stakeholder participation is particularly important for any transportation planning process because the success of the system depends primarily on its ability to cater to the requirements and preferences of the people whom the system serves. Crowdsourcing uses the collective wisdom of a crowd to achieve a solution to a problem that affects the crowd. This paper proposes the use of crowdsourcing as a possible mechanism to involve a large group of stakeholders in transportation planning and operations. Multiple case studies show that crowdsourcing was used to collect data from a wide range of stakeholders in transportation projects. Two distinct crowdsourcing usage types are identified: crowdsourcing for collecting normally sparse data on facilities such as bike routes and crowdsourcing for soliciting feedback on transit quality of service and real-time information quality. A final case study exemplifies the use of data quality auditors for ensuring the usability of crowd-sourced data, one of many potential issues in crowdsourcing presented in the paper. These case studies show that crowdsourcing has immense potential to replace or augment traditional ways of collecting data and feedback from a wider group of a transportation system's users without creating an additional financial burden.
Social networks have been recently employed as a source of information for event detection, with particular reference to road traffic congestion and car accidents. In this paper, we present a real-time monitoring system for traffic event detection from Twitter stream analysis. The system fetches tweets from Twitter according to several search criteria; processes tweets, by applying text mining techniques; and finally performs the classification of tweets. The aim is to assign the appropriate class label to each tweet, as related to a traffic event or not. The traffic detection system was employed for real-time monitoring of several areas of the Italian road network, allowing for detection of traffic events almost in real time, often before online traffic news web sites. We employed the support vector machine as a classification model, and we achieved an accuracy value of 95.75% by solving a binary classification problem (traffic versus nontraffic tweets). We were also able to discriminate if traffic is caused by an external event or not, by solving a multiclass classification problem and obtaining an accuracy value of 88.89%.
In this paper, we propose a novel centralized time-division multiple access (TDMA)-based scheduling protocol for practical vehicular networks based on a new weight-factor-based scheduler. A roadside unit (RSU), as a centralized controller, collects the channel state information and the individual information of the communication links within its communication coverage, and it calculates their respective scheduling weight factors, based on which scheduling decisions are made by the RSU. Our proposed scheduling weight factor mainly consists of three parts, i.e., the channel quality factor, the speed factor, and the access category factor. In addition, a resource-reusing mode among multiple vehicle-to-vehicle (V2V) links is permitted if the distances between every two central vehicles of these V2V links are larger than a predefined interference interval. Compared with the existing medium-access-control protocols in vehicular networks, the proposed centralized TDMA-based scheduling protocol can significantly improve the network throughput and can be easily incorporated into practical vehicular networks.
Two microscopic simulation methods are compared for driver behavior: the Gazis-Herman-Rothery (GHR) car-following model and a proposed agent-based neural network model. To analyze individual driver characteristics, a back-propagation neural network is trained with car-following episodes from the data of one driver in the naturalistic driving database to establish action rules for a neural agent driver to follow under perceived traffic conditions during car-following episodes. The GHR car-following model is calibrated with the same data set, using a genetic algorithm. The car-following episodes are carefully extracted and selected for model calibration and training as well as validation of the calibration rules. Performances of the two models are compared, with the results showing that at less than 10-Hz data resolution the neural agent approach outperforms the GHR model significantly and captures individual driver behavior with 95% accuracy in driving trajectory.
Ubiquitous mobile devices, such as smartphones, led to an increased popularity of pedestrian-related routing applications over the past few years. Because pedestrians typically aim to minimize their walking distance, especially in nonrecreational and multimodal trips, pedestrian routing systems will be fully used only if they can find the correct shortest path and thus help to avoid unnecessary detours. The standard equipment of car navigation systems based on the Global Positioning System several years ago led to the availability of accurate street network data for car-based routing applications. However, pedestrian routing applications should consider pedestrian-related network segments besides those used by motorized traffic, including footpaths and pedestrian bridges. The authors of this paper performed a shortest-path analysis of pedestrian routes for cities in Germany and the United States. For a set of 1,000 randomly generated origin-destination pairs, the authors compared the lengths of pedestrian routes that were computed by different freely available network sources, such as OpenStreetMap and TIGER/Line data, and proprietary data sets, such as TomTom, NAVTEQ, and ATKIS. The results showed that freely available data sources such as OpenStreetMap provided a relatively comprehensive option for cities in which commercial pedestrian data sets were not yet available.
In this paper, we propose a citywide and real-time model for estimating the travel time of any path (represented as a sequence of connected road segments) in real time in a city, based on the GPS trajectories of vehicles received in current time slots and over a period of history as well as map data sources. Though this is a strategically important task in many traffic monitoring and routing systems, the problem has not been well solved yet given the following three challenges. The first is the data sparsity problem, i.e., many road segments may not be traveled by any GPS-equipped vehicles in present time slot. In most cases, we cannot find a trajectory exactly traversing a query path either. Second, for the fragment of a path with trajectories, they are multiple ways of using (or combining) the trajectories to estimate the corresponding travel time. Finding an optimal combination is a challenging problem, subject to a tradeoff between the length of a path and the number of trajectories traversing the path (i.e., support). Third, we need to instantly answer users' queries which may occur in any part of a given city. This calls for an efficient, scalable and effective solution that can enable a citywide and real-time travel time estimation. To address these challenges, we model different drivers' travel times on different road segments in different time slots with a three dimension tensor. Combined with geospatial, temporal and historical contexts learned from trajectories and map data, we fill in the tensor's missing values through a context-aware tensor decomposition approach. We then devise and prove an object function to model the aforementioned tradeoff, with which we find the most optimal concatenation of trajectories for an estimate through a dynamic programming solution. In addition, we propose using frequent trajectory patterns (mined from historical trajectories) to scale down the candidates of concatenation and a suffix-tree-based index to manage the trajectories received in the present time slot. We evaluate our method based on extensive experiments, using GPS trajectories generated by more than 32,000 taxis over a period of two months. The results demonstrate the effectiveness, efficiency and scalability of our method beyond baseline approaches.
Provides an overview of the technical articles and features presented in this issue.
Data dissemination is a promising application for the vehicular network. Existing data dissemination schemes are generally built upon some random-access protocol, which results in the unavoidable collision problem. To address this problem, in this paper we design a novel data dissemination strategy from the scheduling perspective. A data dissemination scheduling framework is then proposed. In the proposed framework, the main challenge is how best to assign the transmission opportunity to nodes with maximum dissemination utility and to avoid the collision problem. We then propose a novel and practical relay selection strategy and adopt the space–time network coding (STNC) with low detection complexity and space–time diversity gain to improve the dissemination efficiency. Compared with the random-access dissemination such as CodeOn-Basic and the noncooperative transmission, our proposed data dissemination strategy performs better in terms of the dissemination delay. In addition, the proposed strategy works even better in the dense network than the sparse scenario, benefitting from the space–time diversity gain of STNC and no-collision transmissions. This is in sharp contrary to the CodeOn-Basic method.
Performance evaluation is considered as an important part of the unmanned ground vehicle (UGV) development; it helps to discover research problems and improves driving safety. In this paper, a task-specific performance evaluation model of UGVs applied in the Intelligent Vehicle Future Challenge (IVFC) annual competitions is discussed. It is defined in functional levels with a formal evaluation process, including metrics analysis, metrics preprocessing, weights calculation, and a technique for order of preference by similarity to ideal solution and fuzzy comprehensive evaluation methods. IVFC 2012 is selected as a case study and overall performances of five UGVs are evaluated with specific analyzed autonomous driving tasks of environment perception, structural on-road driving, unstructured zone driving, and dynamic path planning. The model is proved to be helpful in IVFC serial competition UGVs performance evaluation.
Significance
Recent advances in information technologies have increased our participation in “sharing economies,” where applications that allow networked, real-time data exchange facilitate the sharing of living spaces, equipment, or vehicles with others. However, the impact of large-scale sharing on sustainability is not clear, and a framework to assess its benefits quantitatively is missing. For this purpose, we propose the method of shareability networks, which translates spatio-temporal sharing problems into a graph-theoretic framework that provides efficient solutions. Applying this method to a dataset of 150 million taxi trips in New York City, our simulations reveal the vast potential of a new taxi system in which trips are routinely shareable while keeping passenger discomfort low in terms of prolonged travel time.
Presents summary abstracts of the papers in this issue of IEEE Transactions on Intelligent Transportation Systems.
Presents abstracts of the articles included in this issue of IEEE Transactions on Intelligent Transportation Systems.
The advances in mobile computing and social networking services enable people to probe the dynamics of a city. In this paper, we address the problem of detecting and describing traffic anomalies using crowd sensing with two forms of data, human mobility and social media. Traffic anomalies are caused by accidents, control, protests, sport events, celebrations, disasters and other events. Unlike existing traffic-anomaly-detection methods, we identify anomalies according to drivers' routing behavior on an urban road network. Here, a detected anomaly is represented by a sub-graph of a road network where drivers' routing behaviors significantly differ from their original patterns. We then try to describe the detected anomaly by mining representative terms from the social media that people posted when the anomaly happened. The system for detecting such traffic anomalies can benefit both drivers and transportation authorities, e.g., by notifying drivers approaching an anomaly and suggesting alternative routes, as well as supporting traffic jam diagnosis and dispersal. We evaluate our system with a GPS trajectory dataset generated by over 30,000 taxicabs over a period of 3 months in Beijing, and a dataset of tweets collected from WeiBo, a Twitter-like social site in China. The results demonstrate the effectiveness and efficiency of our system.
Path prediction is useful in a wide range of applications. Most of the existing solutions, however, are based on eager learning methods where models and patterns are extracted from historical trajectories and then used for future prediction. Since such approaches are committed to a set of statistically significant models or patterns, problems can arise in dynamic environments where the underlying models change quickly or where the regions are not covered with statistically significant models or patterns.
We propose a "semi-lazy" approach to path prediction that builds prediction models on the fly using dynamically selected reference trajectories. Such an approach has several advantages. First, the target trajectories to be predicted are known before the models are built, which allows us to construct models that are deemed relevant to the target trajectories. Second, unlike the lazy learning approaches, we use sophisticated learning algorithms to derive accurate prediction models with acceptable delay based on a small number of selected reference trajectories. Finally, our approach can be continuously self-correcting since we can dynamically re-construct new models if the predicted movements do not match the actual ones.
Our prediction model can construct a probabilistic path whose probability of occurrence is larger than a threshold and which is furthest ahead in term of time. Users can control the confidence of the path prediction by setting a probability threshold. We conducted a comprehensive experimental study on real-world and synthetic datasets to show the effectiveness and efficiency of our approach.
In the surveillance of road tunnels, video data plays an important role for a detailed inspection and as an input to systems for an automated detection of incidents. In disaster scenarios like major accidents, however, the increased amount of detected incidents may lead to situations where human operators lose a sense of the overall meaning of that data, a problem commonly known as a lack of situation awareness. The primary contribution of this paper is a design study of AlVis, a system designed to increase situation awareness in the surveillance of road tunnels. The design of AlVis is based on a simplified tunnel model which enables an overview of the spatiotemporal development of scenarios in real-time. The visualization explicitly represents the present state, the history, and predictions of potential future developments. Concepts for situation-sensitive prioritization of information ensure scalability from normal operation to major disaster scenarios. The visualization enables an intuitive access to live and historic video for any point in time and space. We illustrate AlVis by means of a scenario and report qualitative feedback by tunnel experts and operators. This feedback suggests that AlVis is suitable to save time in recognizing dangerous situations and helps to maintain an overview in complex disaster scenarios.