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Modeling Pedestrian Behavior and Safety in Interactions with Conventional and...
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Modeling Pedestrian Behavior and Safety in Interactions with Conventional and Automated Traffic

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Transportation".

Deadline for manuscript submissions: closed (15 October 2020) | Viewed by 12674

Special Issue Editor

Dr. Eleonora Papadimitriou

E-Mail Website
Guest Editor
TU Delft, Faculty of Technology, Policy and Management, Building 31, Jaffalaan 5, 2628 BX Delft, The Netherlands
Interests: road safety; behavior and human factors; transportation engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to highlight advances in pedestrian behavior and safety research, with a focus on the establishment of a stronger link between the understanding of pedestrian behavior when interacting with motorized traffic and the related safety implications. Research questions related to the deployment of automated vehicles and the respective changes in the expectations, risk perception, and behavior of pedestrians, as well as their safety consequences, are also relevant. Topics of interest include (but are not limited to) the following:

  • Discrete choice models of pedestrian behavior;
  • Expectations, perceptions, behavior, and safety of pedestrians;
  • Pedestrian risk exposure and safety metrics;
  • Pedestrian interaction with conventional and automated traffic;
  • The integration of safety in the simulation of pedestrian behavior;
  • Safety issues of vulnerable groups (children, elderly, disabled).

Contributions may include both literature reviews and original studies; results from virtual or field testing are especially welcome.

Dr. Eleonora Papadimitriou
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • pedestrian behavior
  • vehicle–pedestrian interaction
  • pedestrian safety

Published Papers (3 papers)

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Research

17 pages, 1545 KiB  
Article
Investigating the Factors Influencing Pedestrian–Vehicle Crashes by Age Group in Seoul, South Korea: A Hierarchical Model
by Seunghoon Park and Dongwon Ko
Sustainability 2020, 12(10), 4239; https://doi.org/10.3390/su12104239 - 21 May 2020
Cited by 5 | Viewed by 2780
Abstract
Walking is the most natural form of moving in everyday life. However, the urban environment is not always safe for walking. Hence, it is appropriate to create pedestrian safety-oriented neighborhood environments to promote walkability. This study identifies factors that can affect the severity [...] Read more.
Walking is the most natural form of moving in everyday life. However, the urban environment is not always safe for walking. Hence, it is appropriate to create pedestrian safety-oriented neighborhood environments to promote walkability. This study identifies factors that can affect the severity of pedestrian injuries by considering and comparing the individual characteristics of pedestrian crashes and the built environment of neighborhoods using a hierarchical model. The main results of this study are as follows. Those under 18 and over 65 years of age, which are recognized as vulnerable road users, are more affected by the neighborhood environments than other age groups. Parks, convenience stores, and restaurants are likely to play a significant role in reducing the severity of pedestrian injuries. However, for pedestrians under 18 years of age, convenience stores that students often visit after school are likely to increase the severity of pedestrian injuries. The severity of pedestrian injuries is likely to be lower at non-signalized crosswalks and intersections than at signalized crosswalks and intersections. The study is expected to contribute to existing literature on the topic and guide policy makers and planners to arrive at consensus approaches when making decisions on how to build pedestrian safety-oriented neighborhoods. Full article
(This article belongs to the Special Issue Modeling Pedestrian Behavior and Safety in Interactions with Conventional and Automated Traffic)
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25 pages, 9770 KiB  
Article
Spatio-Semantic Road Space Modeling for Vehicle–Pedestrian Simulation to Test Automated Driving Systems
by Benedikt Schwab, Christof Beil and Thomas H. Kolbe
Sustainability 2020, 12(9), 3799; https://doi.org/10.3390/su12093799 - 7 May 2020
Cited by 22 | Viewed by 6056
Abstract
Automated driving technologies offer the opportunity to substantially reduce the number of road accidents and fatalities. This requires the development of systems that can handle traffic scenarios more reliable than the human driver. The extreme number of traffic scenarios, though, causes enormous challenges [...] Read more.
Automated driving technologies offer the opportunity to substantially reduce the number of road accidents and fatalities. This requires the development of systems that can handle traffic scenarios more reliable than the human driver. The extreme number of traffic scenarios, though, causes enormous challenges in testing and proving the correct system functioning. Due to its efficiency and reproducibility, the test procedure will involve environment simulations to which the system under test is exposed. A combination of traffic, driving and Vulnerable Road User (VRU) simulation is therefore required for a holistic environment simulation. Since these simulators have different requirements and support various formats, a concept for integrated spatio-semantic road space modeling is proposed in this paper. For this purpose, the established standard OpenDRIVE, which describes road networks with their topology for submicroscopic driving simulation and HD maps, is combined with the internationally used semantic 3D city model standard CityGML. Both standards complement each other, and their combination opens the potentials of both application domains—automotive and 3D GIS. As a result, existing HD maps can now be used by model processing tools, enabling their transformation to the target formats of the respective simulators. Based on this, we demonstrate a distributed environment simulation with the submicroscopic driving simulator Virtual Test Drive and the pedestrian simulator MomenTUM at a sensitive crossing in the city of Ingolstadt. Both simulators are coupled at runtime and the architecture supports the integration of automated driving functions. Full article
(This article belongs to the Special Issue Modeling Pedestrian Behavior and Safety in Interactions with Conventional and Automated Traffic)
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15 pages, 251 KiB  
Article
Prioritizing Safety or Traffic Flow? Qualitative Study on Highly Automated Vehicles’ Potential to Prevent Pedestrian Crashes with Two Different Ambitions
by Roni Utriainen and Markus Pöllänen
Sustainability 2020, 12(8), 3206; https://doi.org/10.3390/su12083206 - 15 Apr 2020
Cited by 7 | Viewed by 3091
Abstract
Interaction between drivers and pedestrians enables pedestrians to cross the street without conflicts. When highly automated vehicles (HAVs) become prevalent, interaction will change. Although HAVs manage to identify pedestrians, they may not be able to assess pedestrians’ intentions. This study discusses two different [...] Read more.
Interaction between drivers and pedestrians enables pedestrians to cross the street without conflicts. When highly automated vehicles (HAVs) become prevalent, interaction will change. Although HAVs manage to identify pedestrians, they may not be able to assess pedestrians’ intentions. This study discusses two different ambitions: Prioritizing pedestrian safety and prioritizing efficient traffic flow; and how these two affect the possibilities to avoid fatal crashes between pedestrians and passenger cars. HAVs’ hypothetical possibilities to avoid different crash scenarios are evaluated based on 40 in-depth investigated fatal pedestrian crashes, which occurred with manually-driven cars in Finland in 2014–2016. When HAVs prioritize pedestrian safety, they decrease speed near pedestrians as a precaution which affects traffic flow due to frequent decelerations. When HAVs prioritize efficient traffic flow, they only decelerate, when pedestrians are in a collision course. The study shows that neither of these approaches can be applied in all traffic environments, and all of the studied crashes would not likely be avoidable with HAVs even when prioritizing pedestrian safety. The high expectations of HAVs’ safety benefits may not be realized, and in addition to safety and traffic flow, there are many other objectives in traffic which need to be considered. Full article
(This article belongs to the Special Issue Modeling Pedestrian Behavior and Safety in Interactions with Conventional and Automated Traffic)
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