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Safety and Sustainability in Future Transportation

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

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 7230

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Special Issue Editors

Dr. Linjun Lu

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Guest Editor

School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: transportation safety and security; intelligent transportation systems; autonomous driving and intelligent connected vehicles

Prof. Dr. Qingchang Lu

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Guest Editor

Department of Traffic Information and Control Engineering, Chang’an University, Xi’an 710064, China
Interests: transportation network resilience and vulnerability; transportation and climate change; travel behavior analysis; traffic information and control

Prof. Dr. Chen Wang

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Guest Editor

Intelligent Transportation Research Center, Southeast University, Nanjing, 210096, China
Interests: transportation safety and security; intelligent transportation systems; traffic simulation; big data and data mining
Special Issues, Collections and Topics in MDPI journals

Dr. Tao Lu

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Guest Editor

College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China
Interests: intelligent design of traffic infrastructure; intelligent network system testing

Special Issue Information

Dear Colleagues,

Transportation systems face remarkable opportunities and challenges with the rapid development and application of emerging technologies such as intelligent perception, extensive data analysis, supercomputing, and next-generation communication. Emerging technologies are making transportation systems more transparent: real-time dynamic operational data are collected and will influence management and decision-making; information exchanges between autonomous vehicles and other participants and infrastructure enable system optimization; the popularity of new energy vehicles has made the transportation industry greener, which relies on traditional fossil energy. These revolutionary trends are well known, but more intensive and intelligent transportation systems have also brought unknown safety and sustainability concerns while improving efficiency. Multi-perspective research based on accurate data and cases can better reveal laws and impacts and provide strong support for forming transportation policies. The topics of this Special Issue mainly include, but are not limited to:

(1) Research on the development and application of emerging technologies in the field of transportation;

(2) Next-generation intelligent transportation technologies represented by autonomous driving and intelligent connected vehicles;

(3) Research on multi-modal traffic big data and data mining;

(4) Opportunities and challenges brought by next-generation intelligent transportation technology to traffic safety and security;

(5) Opportunities and challenges brought by next-generation intelligent transportation technologies to green transportation and transportation sustainability;

(6) Research on the evolution mechanism of transportation network resilience (including safety and sustainability) under the influence of disasters.

Dr. Linjun Lu
Prof. Dr. Qingchang Lu
Prof. Dr. Chen Wang
Dr. Tao Lu
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.

Keywords

emerging technologies in transportation
network resilience considering safety and sustainability
big data and data mining
autonomous driving and intelligent networked systems

Published Papers (5 papers)

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Research

19 pages, 4831 KiB

Open AccessArticle

Traffic Manager Development for a Roundabout Crossed by Autonomous and Connected Vehicles Using V2I Architecture

by Ouafae El Ganaoui-Mourlan, Stephane Camp, Charles Verhas, Nicolas Pollet, Benjamin Ortega and Baptiste Robic

Sustainability 2023, 15(12), 9247; https://0-doi-org.brum.beds.ac.uk/10.3390/su15129247 - 07 Jun 2023

Cited by 1 | Viewed by 1225

Abstract

Connected Autonomous Vehicle (CAV) is considered as a proposal toward sustainable mobility. In order to succeed in a sustainable mobility solution, “CAV” or more precisely “CAV Transport System” should prove to be low energy, safe, and allow better performances than human-driven vehicles. This paper will propose a system architecture for a sustainable CAV Transport System on a standard scenario: crossing a roundabout. Nowadays, roundabouts are very common and practical crossing alternatives to improve the traffic flow and increase safety. This study aims to simulate and analyze the behavior of connected autonomous vehicles crossing a roundabout using a V2I (vehicle-to-infrastructure) architecture. The vehicles are exchanging information with a so-called central signaling unit. All vehicles are exchanging their position, speed, and target destination. The central signaling unit has a global view of the system compared to each ego vehicle (has more local than global information); thus, can safely and efficiently manage the traffic of the vehicles in the roundabout using a standard signaling block strategy. This strategy of decision of the central signaling unit (CSU) is performed by dividing the roundabout into several zones/blocks which can be booked by only one vehicle at a time. A solver, reproducing a vehicle’s behavior and dynamics, computes the trajectory and velocity of each vehicle depending on its surroundings. Finally, a graphical representation is used and implemented to facilitate the analysis and visualization of the roundabout crossing. The vehicle flow performance of the developed traffic control model is compared with SUMO. Full article

(This article belongs to the Special Issue Safety and Sustainability in Future Transportation)

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17 pages, 3027 KiB

Open AccessArticle

Collaborative Determination Method of Metro Train Plan Adjustment and Passenger Flow Control under the Impact of COVID-19

by Fuquan Pan, Jingshuang Li, Hailiang Tang, Changxi Ma, Lixia Zhang and Xiaoxia Yang

Sustainability 2023, 15(2), 1128; https://0-doi-org.brum.beds.ac.uk/10.3390/su15021128 - 06 Jan 2023

Viewed by 1090

Abstract

Aiming at the problem of metro operation and passenger transport organization under the impact of the novel coronavirus (COVID-19), a collaborative determination method of train planning and passenger flow control is proposed to reduce the train load rate in each section and decrease the risk of spreading COVID-19. The Fisher optimal division method is used to determine reasonable passenger flow control periods, and based on this, different flow control rates are adopted for each control period to reduce the difficulty of implementing flow control at stations. According to the actual operation and passenger flow changes, a mathematical optimization model is established. Epidemic prevention risk values (EPRVs) are defined based on the standing density criteria for trains to measure travel safety. The optimization objectives of the model are to minimize the EPRV of trains in each interval, the passenger waiting time and the operating cost of the corporation. The decision variables are the number of running trains during the study period and the flow control rate at each station. The original model is transformed into a single-objective model by the linear weighting of the target, and the model is solved by designing a particle swarm optimization and genetic algorithm (PSO-GA). The validity of the method and the model is verified by actual metro line data. The results of the case study show that when a line is in the moderate-risk area of COVID-19, two more trains should be added to the full-length and short-turn routes after optimization. Combined with the flow control measures for large passenger flow stations, the maximum train load rate is reduced by 35.18%, and the load rate of each section of trains is less than 70%, which meets the requirements of COVID-19 prevention and control. The method can provide a theoretical basis for related research on ensuring the safety of metro operation during COVID-19. Full article

(This article belongs to the Special Issue Safety and Sustainability in Future Transportation)

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15 pages, 4880 KiB

Open AccessArticle

Travel Time Reliability Analysis Considering Bus Bunching: A Case Study in Xi’an, China

by Yanan Zhang, Hongke Xu, Qing-Chang Lu and Xiaohui Fan

Sustainability 2022, 14(23), 15583; https://0-doi-org.brum.beds.ac.uk/10.3390/su142315583 - 23 Nov 2022

Cited by 2 | Viewed by 1350

Abstract

Bus bunching occurring at stops has an unstable impact on bus travel time. In order to evaluate urban bus travel time effectively, the travel time reliability (TTR) addressing bus bunching is analyzed. This paper focuses on the delayed time caused by bus bunching in the dwelling process at bus stops and uses the coefficient of variation of time headway to evaluate the degree of bus bunching. Moreover, the travel time deviation (TTD) indicator and travel time on-time accuracy (OTA) model are proposed to evaluate the bus TTR. The proposed model is used to analyze 113 runs of a bus route in Xi’an city, China. Real-time GPS data are used to analyze the operation of each run from the origin to the destination stops. The results show that 74.34% of the runs are delayed. When the value of TTD is higher than |0.1|, 64.2% of runs are delayed with bus bunching. Based on the measuring of OTA in two situations, the value of TTR considering bus bunching is reduced by 20%. In addition, the number of stopping routes at peak periods has a significant impact on the occurrence of bus bunching. The research results would have practical implications for the operation and management of buses. Full article

(This article belongs to the Special Issue Safety and Sustainability in Future Transportation)

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18 pages, 3490 KiB

Open AccessArticle

Traffic Sign Detection Based on Lightweight Multiscale Feature Fusion Network

by Shan Lin, Zicheng Zhang, Jie Tao, Fan Zhang, Xing Fan and Qingchang Lu

Sustainability 2022, 14(21), 14019; https://0-doi-org.brum.beds.ac.uk/10.3390/su142114019 - 27 Oct 2022

Cited by 3 | Viewed by 1323

Abstract

Traffic sign detection is a research hotspot in advanced assisted driving systems, given the complex background, light transformation, and scale changes of traffic sign targets, as well as the problems of slow result acquisition and low accuracy of existing detection methods. To solve the above problems, this paper proposes a traffic sign detection method based on a lightweight multiscale feature fusion network. Since a lightweight network model is simple and has fewer parameters, it can greatly improve the detection speed of a target. To learn more target features and improve the generalization ability of the model, a multiscale feature fusion method can be used to improve recognition accuracy during training. Firstly, MobileNetV3 was selected as the backbone network, a new spatial attention mechanism was introduced, and a spatial attention branch and a channel attention branch were constructed to obtain a mixed attention weight map. Secondly, a feature-interleaving module was constructed to convert the single-scale feature map of the specified layer into a multiscale feature fusion map to realize the combined encoding of high-level semantic information and low-level semantic information. Then, a feature extraction base network for lightweight multiscale feature fusion with an attention mechanism based on the above steps was constructed. Finally, a key-point detection network was constructed to output the location information, bias information, and category probability of the center points of traffic signs to achieve the detection and recognition of traffic signs. The model was trained, validated, and tested using TT100K datasets, and the detection accuracy of 36 common categories of traffic signs reached more than 85%, among which the detection accuracy of five categories exceeded 95%. The results showed that, compared with the traditional methods of Faster R-CNN, CornerNet, and CenterNet, traffic sign detection based on a lightweight multiscale feature fusion network had obvious advantages in the speed and accuracy of recognition, significantly improved the detection performance for small targets, and achieved a better real-time performance. Full article

(This article belongs to the Special Issue Safety and Sustainability in Future Transportation)

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19 pages, 3546 KiB

Open AccessArticle

A Study on Safety Evaluation of Pedestrian Flows Based on Partial Impact Dynamics by Real-Time Data in Subway Stations

by Xianing Wang, Zhan Zhang, Ying Wang, Jun Yang and Linjun Lu

Sustainability 2022, 14(16), 10328; https://0-doi-org.brum.beds.ac.uk/10.3390/su141610328 - 19 Aug 2022

Viewed by 1382

Abstract

With the rapid development of urban rail transit, the scientific assurance of pedestrian safety has become an important issue. Orderly behavior is a crucial factor affecting pedestrian safety. Therefore, in-depth research into pedestrian behavior is needed. This study carries out an evaluation of safety in pedestrian flows by establishing a new force model based on real-time data. In this model, we consider the microscopic characteristics of pedestrians and define four force influence mechanisms for simulating pedestrian behavior. Compared with existing models, this model incorporates partial impact dynamics to make it applicable to the particular environment of subway stations. Through the validation of real-world data, it is demonstrated that the model can accurately describe pedestrian behavior and better reproduce the characteristics of pedestrians. The influence of pedestrians and of environmental factors on the model are also discussed. Using our model, we propose a risk evaluation system based on pedestrian volatility. By using real-time pedestrian information from subway stations, the potential risk to pedestrians can be discerned and assessed in advance. This research advances the management of pedestrian safety and provides a framework for studying behavior models and for safety evaluation. Full article

(This article belongs to the Special Issue Safety and Sustainability in Future Transportation)

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