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Leveraging video data to better understand driver-pedestrian interactions in a smart city environment
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The World Health Organization characterizes traffic deaths as a preventable health epidemic. Despite the scale of the problem, this issue has not led to a concerted call to action. Why not? The field of media studies offers potential insight. Not only does media coverage help determine which issues merit attention, coverage also shapes how issues are framed. The aim of this paper is to examine local news coverage of vehicle crashes involving someone walking or biking. To that end, this paper used content analysis of 200 local news articles to answer the research questions: (1) How do articles apportion blame between vulnerable road users (VRUs) and drivers?; (2) To what extent do articles frame crashes as a public health issue? The results reveal that local news coverage tends to shift blame toward VRUs and away from drivers. Coverage almost always treats crashes as isolated incidents, obscuring the public health nature of the problem. This pattern of coverage likely contributes to the limited public outcry about pedestrian and bicyclist fatalities. Journalists can counteract these patterns by subtly altering their coverage. Planners can assist these efforts by making their expertise readily available to journalists. These simple changes would help the public identify links between seemingly isolated events and increase public pressure to reduce road deaths. © National Academy of Sciences: Transportation Research Board 2019.
Video based sensor systems can play a key role in delivering data for better road planning and traffic management. Smart road technologies will largely depend on data quality and quantity in the future. Video based detection systems, being an indispensable part of intelligent traffic systems (ITS), show huge potentials as they do not only offer a flexible way of data acquisition but are also being developed at a huge pace due to recent evolutions in hardware and software technology. In order to give a better understanding on the methods and potentials of this technology, a structured review is presented which not only includes current applications but also shows future use cases by analyzing the techniques of image processing and extrapolating their results to the future requirements of traffic engineering.
Video based vehicle detection technology is an integral part of Intelligent Transportation System (ITS), due to its non-intrusiveness and comprehensive vehicle behavior data collection capabilities. This paper proposes an efficient video based vehicle detection system based on Harris-Stephen corner detector algorithm. The algorithm was used to develop a standalonevehicle detection and tracking system that determines vehicle counts and speeds at arterial roadways and freeways. The proposed video based vehicle detection system was developed to eliminate the need of complex calibration, robustness to contrasts variations, and better performance with low resolutions videos. The algorithm performance for accuracy in vehicle counts and speed was evaluated. The performance of the proposed system is equivalent or better compared to a commercial vehicle detection system. Using the developed vehicle detection and tracking system an advance warning intelligent transportation system was designed and implemented to alert commuters in advance of speed reductions and congestions at work zones and special events. The effectiveness of the advance warning system was evaluated and the impact discussed.
For the proper understanding and modelling of pedestrian dynamics, reliable empirical data are necessary for analysis and verification. To this end, we have performed a series of experiments with a large number of persons. Such experiments give us the opportunity to selectively analyse parameters independent of undesired influences and adjust them to values seldom seen in field studies.We are developing software for the time-efficient automatic extraction of accurate pedestrian trajectories. Depending on the camera system the software is able to detect and track people on planar or uneven terrain with or without markers.In this paper, we summarise the experiments we have accomplished and the possibilities of our extraction techniques, in particular the newly introduced algorithm of markerless detection in stereo recordings. The markerless detection based on groups of ellipses approximating isolines of the same distance to an overhead stereo camera.
Pedestrian and bicycle volume data is one of the most fundamental types of data for active transportation planning. However, they are not yet well developed. This paper explores an innovative method that uses drone technology to collect pedestrian and bicycle volume data. It confirms the feasibility of the technology as an alternative method to collect complex movements of pedestrians and bicycles. Furthermore, this paper presents a method that converts the video footage to a spatiotemporal dataset. The dataset includes not only the pedestrian and bicycle count data, but also their behavior and characteristics. The spatiotemporal data can become a valuable resource for a variety of active transportation planning practices and research, including the collection of pedestrian and bicycle volume data in parks and recreational areas, the study of collisions between pedestrians and bicycles, and the analysis of the social path. Since it is possible to envision that the development of technologies can overcome current technical difficulties, such as battery lifespan, it is worth considering the application of drone technology to active transportation planning practice and research.
The new era of sharing information and “big data” has raised our expectations to make mobility more predictable and controllable through a better utilization of data and existing resources. The realization of these opportunities requires going beyond the existing traditional ways of collecting traffic data that are based either on fixed-location sensors or GPS devices with low spatial coverage or penetration rates and significant measurement errors, especially in congested urban areas. Unmanned Aerial Systems (UAS) or simply “drones” have been proposed as a pioneering tool of the Intelligent Transportation Systems (ITS) infrastructure due to their unique characteristics, but various challenges have kept these efforts only at a small size. This paper describes the system architecture and preliminary results of a first-of-its-kind experiment, nicknamed pNEUMA, to create the most complete urban dataset to study congestion. A swarm of 10 drones hovering over the central business district of Athens over multiple days to record traffic streams in a congested area of a 1.3 km2 area with more than 100 km-lanes of road network, around 100 busy intersections (signalized or not), many bus stops and close to half a million trajectories. The aim of the experiment is to record traffic streams in a multi-modal congested environment over an urban setting using UAS that can allow the deep investigation of critical traffic phenomena. The pNEUMA experiment develops a prototype system that offers immense opportunities for researchers many of which are beyond the interests and expertise of the authors. This open science initiative creates a unique observatory of traffic congestion, a scale an-order-of-magnitude higher than what was available till now, that researchers from different disciplines around the globe can use to develop and test their own models.
This study is, to our knowledge, the first in the literature to introduce a modeling framework for analyzing traffic crash frequency based on a series of ensemble machine learning (EML) methods. The main objectives of this study are fourfold: (a) to design a systematic EML‐based framework for crash frequency analysis, (b) to comprehensively compare the performance in analyzing crash frequency by different optimized EML models, (c) to identify significant contributors to crash frequency, and (d) to propose the approach to construct schemes to reduce traffic crashes. To achieve the research goal, the Highway Safety Information System database that includes records of over 1.5 million crashes is employed for model estimation and validation. We first optimize the EML models for crash analysis via the k‐fold cross‐training, including the two averaging methods of random forest and extremely randomized trees, and the two boosting methods of adaptive boosting and gradient tree boosting. Then, we assess the behavior of the optimized models, and conduct a sensitivity test to validate the stability of model performance. Furthermore, we evaluate the relative importance of features to crash frequency by using the Gini diversity index. The results indicate that the two averaging EML models can achieve desirable performance in crash frequency analysis, which outperform the two boosting EML models, in terms of predictive accuracy, generalization ability, and stability. From the results, we explore new insights into the significance of contributors to crash occurrence. Finally, we present the approach of safety improvements for transport facilities.
Big data is becoming a research focus in intelligent transportation systems (ITS), which can be seen in many projects around the world. Intelligent transportation systems will produce a large amount of data. The produced big data will have profound impacts on the design and application of intelligent transportation systems, which makes ITS safer, more efficient, and profitable. Studying big data analytics in ITS is a flourishing field. This paper first reviews the history and characteristics of big data and intelligent transportation systems. The framework of conducting big data analytics in ITS is discussed next, where the data source and collection methods, data analytics methods and platforms, and big data analytics application categories are summarized. Several case studies of big data analytics applications in intelligent transportation systems, including road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plan, rail transportation management and control, and assets maintenance are introduced. Finally, this paper discusses some open challenges of using big data analytics in ITS.
A report of a study of pedestrian accident experience at unsignalized intersections and whether it is less in marked or unmarked crosswalks. Accident experience covering a 5-year period was studied at 400 intersections, each having one marked and one unmarked crosswalk crossing the main thoroughfare. The study showed that, in terms of the number of pedestrians using the crosswalks, approximately twice as many pedestrian accidents occur in marked crosswalks as in unmarked crosswalks.