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Travel time estimation in real-time using buses as speed probes
... Tomaras [120] analysed the heterogeneous urban data to identify the key points for the operation of crowdsourcing technique for sustainable smart city application. The time efficiency [130] for travel time estimation of the real time bus network is analysed with crowdsourcing. Faber et al. [113] developed a visual analytic system (VAS)-based crowdsourced smart city mobility model. ...
Connected vehicles and fully automated driving systems are the main objectives of the future transportation system. A safe interactive system that interacts with people and things is essential to achieve these objectives. In this context, a crowd intelligence system plays a key role in interactive system development. Crowd intelligence is a combined method of data collection, integration and analysis from devices such as the smartphones, wearables, vehicles and a wide range of Internet of Things applications to use them as sensors. This collective feedback-driven interactive method is opportunistic for the development of the future transportation system. In this study, a survey is conducted considering crowd-intelligence techniques for the transportation system. From this survey, various challenges of the intelligent transportation system have been outlined and crowd-intelligent solutions have been discussed. A layered structure of transportation system architecture is suggested considering various problems in each layer and its crowd-intelligent solutions. The crowd-intelligence-based mobility, traffic control, traffic prediction, parking solutions have been discussed in this survey. Moreover, the importance of crowd-intelligent techniques and its applicability is discussed for sustainable development of futuristic transport infrastructure.
This paper presents a participatory sensing-based urban traffic monitoring system. Different from existing works that heavily rely on intrusive sensing or full cooperation from probe vehicles, our system exploits the power of participatory sensing and crowdsources the traffic sensing tasks to bus riders' mobile phones. The bus riders are information source providers and, meanwhile, major consumers of the final traffic output. The system takes public buses as dummy probes to detect road traffic conditions, and collects the minimum set of cellular data together with some lightweight sensing hints from the bus riders' mobile phones. Based on the crowdsourced data from participants, the system recovers the bus travel information and further derives the instant traffic conditions of roads covered by bus routes. The real-world experiments with a prototype implementation demonstrate the feasibility of our system, which achieves accurate and fine-grained traffic estimation with modest sensing and computation overhead at the crowd.
Crowdsourcing has emerged as an attractive paradigm in recent years for information collection for disaster response, which utilizes data received from the human crowd, to provide critical information collection and dissemination during emergency situations and visualize this data to generate emergency maps for the human crowd. In this paper we investigate the use of crowdsourcing mechanisms for real-time emergency response and describe our approach for developing a crowdsourcing tool that can be effectively used to formulate questions and seek answers from the human crowd using a MapReduce programming model, and integrate this information into a novel spatiotemporal data structure and create a visual emergency map. Our experimental evaluation shows that our approach is practical, efficient and can be used for applications with real-time demands.
In order to handle spatial data efficiently, as required in computer aided design and geo-data applications, a database system needs an index mechanism that will help it retrieve data items quickly according to their spatial locations However, traditional indexing methods are not well suited to data objects of non-zero size located m multi-dimensional spaces In this paper we describe a dynamic index structure called an R-tree which meets this need, and give algorithms for searching and updating it. We present the results of a series of tests which indicate that the structure performs well, and conclude that it is useful for current database systems in spatial applications
The rise of GPS and broadband-speed wireless devices has led to tremendous excitement about a range of applications broadly characterized as ¿location based services¿. Current database storage systems, however, are inadequate for manipulating the very large and dynamic spatio-temporal data sets required to support such services. Proposals in the literature either present new indices without discussing how to cluster data, potentially resulting in many disk seeks for lookups of densely packed objects, or use static quadtrees or other partitioning structures, which become rapidly suboptimal as the data or queries evolve. As a result of these performance limitations, we built TrajStore, a dynamic storage system optimized for efficiently retrieving all data in a particular spatiotemporal region. TrajStore maintains an optimal index on the data and dynamically co-locates and compresses spatially and temporally adjacent segments on disk. By letting the storage layer evolve with the index, the system adapts to incoming queries and data and is able to answer most queries via a very limited number of I/Os, even when the queries target regions containing hundreds or thousands of different trajectories.
The domain of spatiotemporal applications is a treasure trove of new types of data and queries. However, work in this area is guided by related research from the spatial and temporal domains, so far, with little attention towards the true nature of spatiotemporal phenomena. In this work, the focus is on a spatiotemporal sub-domain, namely the trajectories of moving point objects. We present new types of spatiotemporal queries, as well as algorithms to process those. Further, we introduce two access methods this kind of data, namely the Spatio-Temporal R-tree (STR-tree) and the Trajectory-Bundle tree (TB-tree). The former is an R-tree based access method that considers the trajectory identity in the index as well, while the latter is a hybrid structure, which preserves trajectories as well as allows for R-tree typical range search in the data. We present performance studies that compare the two indices with the R-tree (appropriately modified, for a fair comparison) under a varying set of spatiotemporal queries, and we provide guidelines for a successful choice among them. 1
Reduction in greenhouse gas emissions from transportation is essential in combating global warming and climate change. Eco-routing enables drivers to use the most eco-friendly routes and is effective in reducing vehicle emissions. The EcoTour system assigns eco-weights to a road network based on GPS and fuel consumption data collected from vehicles to enable ecorouting. Given an arbitrary source-destination pair in Denmark, EcoTour returns the shortest route, the fastest route, and the eco-route, along with statistics for the three routes. EcoTour also serves as a testbed for exploring advanced solutions to a range of challenges related to eco-routing.
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.
StarTrack was the first service designed to manage tracks of GPS location coordinates obtained from mobile devices and to facilitate the construction of track-based applications. Our early attempts to build practical applications on StarTrack revealed substantial efficiency and scalability problems, including frequent client-server roundtrips, unnecessary data transfers, costly similarity comparisons involving thousands of tracks, and poor fault-tolerance. To remedy these limitations, we revised the overall system architecture, API, and implementation. The API was extended to operate on collections of tracks rather than individual tracks, delay query execution, and permit caching of query results. New data structures, namely track trees, were introduced to speed the common operation of searching for similar tracks. Map matching algorithms were adopted to convert each track into a more compact and canonical sequence of road segments. And the underlying track database was partitioned and replicated among multiple servers. Altogether, these changes not only simplified the construction of track-based applications, which we confirmed by building applications using our new API, but also resulted in considerable performance gains. Measurements of similarity queries, for example, show two to three orders of magnitude improvement in query times.
Multi-cost optimal route planning under time-varying uncertainty
- B Yang
- C Guo
- C S Jensen
- M Kaul
- S Shang