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Novel Approaches to Transportation Safety Planning in Sustainable Transportation
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Novel Approaches to Transportation Safety Planning in Sustainable Transportation

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

Deadline for manuscript submissions: closed (30 December 2021) | Viewed by 7500

Special Issue Editors

Prof. Dr. Jaeyoung Lee

E-Mail Website1 Website2
Chief Guest Editor
School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China
Interests: traffic safety; transportation planning; travel behavior; driving behavior
Special Issues, Collections and Topics in MDPI journals
Prof. Alfonso Montella

E-Mail Website
Guest Editor
Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy
Interests: highway design; highway safety management; highway safety modelling; road safety audits and inspections; drivers’ behaviour investigations by driving simulator experiments
Dr. Ling Wang

E-Mail Website
Guest Editor
College of Transportation Engineering, Tongji University, Shanghai (201804), China
Interests: traffic safety; traffic control and management
Dr. Qing Cai

E-Mail
Guest Editor
Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando (32816), USA
Interests: transportation safety; big data; machine learning; driving behavior

Special Issue Information

Dear Colleagues,

Transportation safety planning is defined as a comprehensive, system-wide, multimodal, proactive process that integrates safety into transportation planning. It is often termed macroscopic (or macro-level) safety analysis, as it aims to explore traffic crashes at a large scale. Numerous studies on transportation safety planning have been conducted in the past decades, and many of them have focused on the safety of sustainable modes of transportation such as walking, bicycle, environment-friendly public transportation. In the recent decade, there have been considerable changes in transportation. The changes include new statistical methodologies (e.g., machine learning), multimodal policy plans, growth of sustainable transportation modes, and intelligent connected vehicles. Furthermore, the current unprecedented crisis of COVID-19 is immeasurably affecting transportation systems as well as traffic safety. These recent transformations in transportation systems should be reflected in future studies of transportation safety planning. Therefore, the purpose of the Special Issue is to exchange innovative ideas and findings in transportation safety planning while taking the new tides in transportation systems into consideration. The potential topics include but are not limited to: (1) macroscopic analysis of traffic crashes using novel methodologies for sustainable modes; (2) Big Data applications for transportation safety planning; (3) proactive countermeasures to prevent or reduce crashes involving sustainable modes; (4) taking intelligent connected vehicle technologies into consideration; (5) long-term effects of transportation policies and laws; and (6) effects of a large-scale events (e.g., pandemic) on traffic safety. Other topics related to transportation safety planning are also welcomed. This Special Issue is expected to contribute to the existing knowledge by considering the innovative perspective and provides useful and practical knowledge to researchers and policy-makers.

Prof. Jaeyoung Lee
Chief Guest Editor

Prof. Alfonso Montella
Dr. Ling Wang
Dr. Qing Cai
Guest Editors

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.

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Published Papers (2 papers)

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Research

17 pages, 3053 KiB  
Article
Feature Extraction and Representation of Urban Road Networks Based on Travel Routes
by Shichen Huang, Chunfu Shao, Juan Li, Xiong Yang, Xiaoyu Zhang, Jianpei Qian and Shengyou Wang
Sustainability 2020, 12(22), 9621; https://doi.org/10.3390/su12229621 - 18 Nov 2020
Cited by 2 | Viewed by 2533
Abstract
Extraction of traffic features constitutes a key research direction in traffic safety planning. In previous traffic tasks, road network features are extracted manually. In contrast, Network Representation Learning aims to automatically learn low-dimensional node representations. Enlightened by feature learning in Natural Language Processing, [...] Read more.
Extraction of traffic features constitutes a key research direction in traffic safety planning. In previous traffic tasks, road network features are extracted manually. In contrast, Network Representation Learning aims to automatically learn low-dimensional node representations. Enlightened by feature learning in Natural Language Processing, representation learning of urban nodes is studied as a supervised task in this paper. Following this line of thinking, a deep learning framework, called StreetNode2VEC, is proposed for learning feature representations for nodes in the road network based on travel routes, and then model parameter calibration is performed. We explain the effectiveness of features from visualization, similarity analysis, and link prediction. In visualization, the features of nodes naturally present a clustered pattern, and different clusters correspond to different regions in the road network. Meanwhile, the features of nodes still retain their spatial information in similarity analysis. The proposed method StreetNode2VEC obtains a AUC score of 0.813 in link prediction, which is greater than that obtained from Graph Convolutional Network (GCN) and Node2vec. This suggests that the features of nodes can be used to effectively and credibly predict whether a link should be established between two nodes. Overall, our work provides a new way of representing road nodes in the road network, which have potential in the traffic safety planning field. Full article
(This article belongs to the Special Issue Novel Approaches to Transportation Safety Planning in Sustainable Transportation)
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18 pages, 5304 KiB  
Article
How Does Heterogeneity Affect Freeway Safety? A Simulation-Based Exploration Considering Sustainable Intelligent Connected Vehicles
by Yuntao Shi, Ye Li, Qing Cai, Hao Zhang and Dan Wu
Sustainability 2020, 12(21), 8941; https://doi.org/10.3390/su12218941 - 28 Oct 2020
Cited by 10 | Viewed by 2730
Abstract
Intelligent connected vehicles (ICVs) are recognized as a new sustainable transportation mode, which could be promising for reducing crashes. However, the mixed traffic consisting of manually driven vehicles and ICVs may negatively affect road safety due to individual heterogeneity. This study investigated heterogeneity [...] Read more.
Intelligent connected vehicles (ICVs) are recognized as a new sustainable transportation mode, which could be promising for reducing crashes. However, the mixed traffic consisting of manually driven vehicles and ICVs may negatively affect road safety due to individual heterogeneity. This study investigated heterogeneity effects on freeway safety-based simulation experiments. Two types of vehicle dynamic models were employed to depict dynamic behaviors of manually driven vehicles and adaptive cruise control (ACC) vehicles (a simplified version of ICVs), respectively. Real vehicle trajectories were utilized to calibrate model parameters based on genetic algorithms. Surrogate safety measures were applied to establish the relationship between vehicle behaviors and longitudinal collision risks. Simulation results indicate that the heterogeneity has negative effects on longitudinal safety. With the higher degree of heterogeneity, longitudinal collision risks are increased. Compared to traffic flow consisting of human drivers only, mixed traffic flow may be more dangerous when the market penetration rate of ACC is low, since the ACC system can be recognized as a new source of individual heterogeneity. Findings of this study show that necessary countermeasures should be developed to improve safety for mixed traffic flow from the perspective of transportation safety planning in the near future. Full article
(This article belongs to the Special Issue Novel Approaches to Transportation Safety Planning in Sustainable Transportation)
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