Sustainable Public Transport in Urban Areas and Accident Prevention

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: closed (10 December 2023) | Viewed by 1343

Special Issue Editors

Department of Civil Engineering, Faculty of Engineering, University Putra Malaysia (UPM), Serdang 43400, Malaysia
Interests: road safety; travel behavior analysis; transportation planning and policy; travel demand modeling
Civil Engineering Department, Faculty of Engineering, Universiti Pertahanan Nasional Malaysia, Kem Sungai Besi, Kuala Lumpur 57000, Malaysia
Interests: highway; railway; transport; traffic accident; disaster

Special Issue Information

Dear Colleagues,

In a continent with a high urbanization rate and population density, public transportation is an integral part of many people's daily lives. They provide a reliable and egalitarian alternative to personal transportation. Increased public transportation usage reduces the number of privately owned vehicles, hence lowering greenhouse gas emissions.

In a number of circumstances, public transportation allows for travel that is both faster and more convenient than private transportation, such as cars and motorcycles. Trains, buses, and even light rail systems are prone to the same types of collision-related damage as cars and motorcycles.

In contrast to smaller vehicles that accept a limited number of passengers at once, the majority of these modes of public transportation are built to accommodate large groups of individuals. When a crash or collision occurs, a significant number of persons may experience harm and injury in a single incident. The severity of injuries is proportional to the impact force or malfunction. In emerging nations, the issue worsens as a result of the absence of effective and comprehensive solutions.

In light of the aforementioned development, this Special Issue aims to collect the most recent ideas and emerging research in sustainable transportation in urban areas and crash prevention. Interest-related topics include, but are not restricted to, the following:

  • Risk modelling and simulation for analysis of public transport safety.
  • Data-driven approaches to evaluating and managing public transport safety.
  • Road safety and urban public transport mobility patterns.
  • Roles of human and environment factors in public transport safety.
  • The impact of emerging technologies on public transport safety.
  • The application of big data to enhance public transport safety.

Prof. Dr. Teik Hua Law
Dr. Choy Peng Ng
Guest Editors

Manuscript Submission Information

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  • road safety
  • urban mobility
  • sustainability
  • risk management
  • road infrastructure
  • safety performance public

Published Papers (1 paper)

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19 pages, 3711 KiB  
Study on Traffic Accident Forecast of Urban Excess Tunnel Considering Missing Data Filling
Appl. Sci. 2023, 13(11), 6773; - 02 Jun 2023
Cited by 3 | Viewed by 954
Urban highway tunnels are frequent accident locations, and predicting and analyzing road conditions after accidents to avoid traffic congestion is a key measure for tunnel traffic operation management. In this paper, 200 traffic accident data from the YingTian Street Tunnel in Nanjing city [...] Read more.
Urban highway tunnels are frequent accident locations, and predicting and analyzing road conditions after accidents to avoid traffic congestion is a key measure for tunnel traffic operation management. In this paper, 200 traffic accident data from the YingTian Street Tunnel in Nanjing city were analyzed and encoded to extract the main factors affecting tunnel traffic conditions from three aspects: time, traffic flow, and tunnel environment. Next, graph convolution long short-term memory networks were used to predict and fill in missing traffic flow data. Finally, seven independent variables selected by Pearson correlation analysis were input into the constructed BP neural network and random forest model to predict tunnel traffic conditions during accidents and accident duration. Experimental results show that the accuracy of random forest and BP neural networks in predicting traffic flow is 83.39% and 82.94%, respectively, and that the absolute error of the two models in predicting accident duration is 75% and 60% within 25 min, respectively. Both models perform well in predicting traffic conditions, and the random forest models perform better in terms of robustness and generalization in predicting crash duration. The experimental results have important implications for tunnel operation management during accidents. Full article
(This article belongs to the Special Issue Sustainable Public Transport in Urban Areas and Accident Prevention)
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