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Emerging Transportation in Sustainable Development Environment: Multi-Modal Transportation and Connected Automated Vehicle (CAV) Technology

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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: 10 October 2024 | Viewed by 14859

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Special Issue Editors

Prof. Dr. Wuhong Wang

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Guest Editor

School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100811, China
Interests: intelligent vehicle; connected automated transportation system; green mobility behavior and traffic safety
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Lisheng Jin

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Guest Editor

School of Vehicle and Energy, Yanshan University, Qinhuangdao 066004, China
Interests: intelligent vehicle perception, decision and control; human–vehicle–road collaboration and vehicle networking technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the support of information technology, the transportation system is developing towards safety, intelligence, and sustainability at an extremely fast speed. It not only brings much more travel choices, but also brings an increasingly complex traffic environment. At present, the transportation system is in the development stage of multi-modal transportation.

As a critical carrier of multi-modal transportation, connected automated vehicles (CAVs) are regarded as the best solution for completely driverless technology. From the micro perspective, it solves the insurmountable problems of single vehicle intelligence through collaborative perception, control, and decision-making. From the macro perspective, it can centrally and efficiently regulate traffic flow, which is very important for sustainable development. In the foreseeable future, there will be mixed traffic of traditional vehicles, connected automated vehicles, and other types of vehicles in the transportation system. Therefore, the related technologies of multi-modal transportation and CAV have important research value and significance.

Many existing studies have laid the foundation for traffic sustainability. However, there is little discussion on the topic of emerging transportation technologies. For example, how does multi-modal transportation affect the sustainable development environment when considering various traffic participants and complex traffic environment; how do artificial intelligence and big data enable emerging transportation and intelligent transportation system; and can CAV technology better serve multi-modal transportation and sustainable transportation?

The objective of this Special Issue is to discuss the new problems and methods of green mobility, sustainable transportation, emerging transportation, multi-modal transportation, and CAV technology. Original research articles and review papers are welcome. Research areas may include (but are not limited to) the following:

Green transportation and low carbon mobility system;
Solutions of emerging technologies for traffic safety;
Energy and emission solutions for transportation systems;
Policies and strategies for sustainable development of transportation;
System architecture, modeling, and simulation of multi-modal transportation;
Modeling and analysis of connected automate driving behavior;
Perception, control and decision technology of CAV;
Collaborative management of CAV in complex traffic environment.

We look forward to receiving your contributions.

Prof. Dr. Wuhong Wang
Prof. Dr. Lisheng Jin
Guest Editors

Manuscript Submission Information

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Keywords

green intelligent transportation
sustainable development of transportation
traffic safety and management
emerging transportation and multi-modal transportation
CAV technology

Published Papers (11 papers)

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Research

20 pages, 8339 KiB

Open AccessArticle

Heterogeneous Traffic Flow Signal Control and CAV Trajectory Optimization Based on Pre-Signal Lights and Dedicated CAV Lanes

by Jixiang Wang, Haiyang Yu, Siqi Chen, Zechang Ye and Yilong Ren

Sustainability 2023, 15(21), 15295; https://doi.org/10.3390/su152115295 - 26 Oct 2023

Cited by 2 | Viewed by 1044

Abstract

This paper proposes a control system to address the efficiency and pollutant emissions of heterogeneous traffic flow composed of human-operated vehicles (HVs) and connected and automated vehicles (CAVs). Based on the comprehensive collection of information on the flow of heterogeneous traffic, the control system uses a two-layer optimization model for signal duration calculation and CAV trajectory planning. The upper model optimizes the phase duration in real time based on the actual total number and type of vehicles entering the control adjustment zone, while the lower model optimizes CAV lane-changing strategies and vehicle acceleration optimization curves based on the phase duration optimized by the upper model. The target function accounts for reducing fuel usage, carbon emission lane-changing costs, and vehicle travel delays. Based on the Webster optimal cycle formula, an improved cuckoo algorithm with strong search performance is created to solve the model. The numerical data confirmed the benefits of the suggested signal control and CAV trajectory optimization method based on pre-signal lights and dedicated CAV lanes for heterogeneous traffic flow. Intersection capacity was significantly enhanced, CAV average fuel consumption, carbon emission and lane-changing frequency were significantly reduced, and traffic flow speed and delay were significantly improved. Full article

(This article belongs to the Special Issue Emerging Transportation in Sustainable Development Environment: Multi-Modal Transportation and Connected Automated Vehicle (CAV) Technology)

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18 pages, 5113 KiB

Open AccessArticle

Optimization of Roadside Unit Deployment on Highways under the Evolution of Intelligent Connected-Vehicle Permeability

by Luyu Zhang, Youfu Lu, Ning Chen, Peng Wang, Weilin Kong, Qingbin Wang, Guizhi Qin and Zhenhua Mou

Sustainability 2023, 15(14), 11112; https://doi.org/10.3390/su151411112 - 17 Jul 2023

Viewed by 975

Abstract

With the increasing number of Connected and Autonomous Vehicles (CAVs), the heterogeneous traffic flow on highways now consists of a mix of CAVs and Non-networked Autonomous Vehicles (NAVs). The current deployment of Roadside Units (RSUs) on highways is mostly based on uniform or hotspot locations. However, when the permeability of CAVs on the road varies, the communication network may face challenges such as excessive energy consumption due to closely spaced RSU deployments at high CAV permeability or communication interruptions due to widely spaced RSU deployments at low CAV permeability. To address this issue, this paper proposes an improved D-LEACH clustering algorithm based on vehicle clustering; analyzes the impact of RSU and vehicle communication radius, mixed traffic density, and different CAV permeabilities in the heterogeneous traffic flow on the RSU deployment interval; and calculates the rational and effective RSU deployment interval schemes under different CAV permeabilities on highways in the heterogeneous traffic flow. When the heterogeneous traffic flow density is stable and CAV continues to penetrate, the RSU communication radius and deployment interval can be adjusted to ensure that the network connectivity is maintained at a high level. When the RSU and vehicle communication radius are stable, the mixed traffic density is 0.05, and the CAV permeability is 0.2, the RSU deployment interval can be set to 1235 m; when the mixed traffic density is 0.08 and the CAV penetration rate is 0.7, the RSU deployment interval can be set to 1669 m to ensure that the network connectivity is maintained at a high level. Full article

(This article belongs to the Special Issue Emerging Transportation in Sustainable Development Environment: Multi-Modal Transportation and Connected Automated Vehicle (CAV) Technology)

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17 pages, 392 KiB

Open AccessArticle

Enhancing Intersection Performance for Tram and Connected Vehicles through a Collaborative Optimization

by Ali Louati and Elham Kariri

Sustainability 2023, 15(12), 9231; https://doi.org/10.3390/su15129231 - 07 Jun 2023

Cited by 4 | Viewed by 1109

Abstract

This article tackles a pervasive problem in connected transportation networks: the issue of conflicting right-of-way between trams and Connected Vehicles (CV) at intersections. Trams are typically granted a semi-exclusive right-of-way, leading to a clash with CV. To resolve this challenge, the study introduces a Transit Signal Priority (TSP) system and a guidance framework that seeks to minimize unintended delays for trams while minimizing the negative impact on CV, passenger comfort, energy consumption, and overall travel time. The proposed framework employs a collaborative optimization system and an improved genetic algorithm to adjust both the signal phase duration and the operating path. The study is based on data collected from a simulated intersection that includes the signal phase sequence and duration. The findings demonstrate that the proposed framework was able to reduce the transit time for trams by 45.8% and the overall transit time for trams 481 and CVs by 17.1% compared to the conventional method. Additionally, the system was able to reduce energy consumption by 34.7% and the non-comfort index by 25.8%. Overall, this research contributes to the development of a more efficient and sustainable transportation system for the future. Full article

(This article belongs to the Special Issue Emerging Transportation in Sustainable Development Environment: Multi-Modal Transportation and Connected Automated Vehicle (CAV) Technology)

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14 pages, 3992 KiB

Open AccessArticle

Vehicle Tracking Algorithm Based on Deep Learning in Roadside Perspective

by Guangsheng Han, Qiukun Jin, Hui Rong, Lisheng Jin and Libin Zhang

Sustainability 2023, 15(3), 1950; https://doi.org/10.3390/su15031950 - 19 Jan 2023

Viewed by 1507

Abstract

Traffic intelligence has become an important part of the development of various countries and the automobile industry. Roadside perception is an important part of the intelligent transportation system, which mainly realizes the effective perception of road environment information by using sensors installed on the roadside. Vehicles are the main road targets in most traffic scenes, so tracking a large number of vehicles is an important subject in the field of roadside perception. Considering the characteristics of vehicle-like rigid targets from the roadside view, a vehicle tracking algorithm based on deep learning was proposed. Firstly, we optimized a DLA-34 network and designed a block-N module, then the channel attention and spatial attention modules were added in the front of the network to improve the overall feature extraction ability and computing efficiency of the network. Next, the joint loss function was designed to improve the intra-class and inter-class discrimination ability of the tracking algorithm, which can better discriminate objects of similar appearance and the color of vehicles, alleviate the IDs problem and improve algorithm robustness and the real-time performance of the tracking algorithm. Finally, the experimental results showed that the method had a good tracking effect for the vehicle tracking task from the roadside perspective and could meet the practical application demands of complex traffic scenes. Full article

(This article belongs to the Special Issue Emerging Transportation in Sustainable Development Environment: Multi-Modal Transportation and Connected Automated Vehicle (CAV) Technology)

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15 pages, 2564 KiB

Open AccessArticle

Influence of Risky Driving Behavior and Road Section Type on Urban Expressway Driving Safety

by Huacai Xian, Yujia Hou, Yu Wang, Shunzhong Dong, Junying Kou and Zewen Li

Sustainability 2023, 15(1), 398; https://doi.org/10.3390/su15010398 - 26 Dec 2022

Cited by 1 | Viewed by 1348

Abstract

The causes of traffic crashes are complex and uncertain, among which the risky driving behaviors of drivers and the types of road sections in high-crash areas are all critical influencing factors. We used ArcGIS software to draw traffic heat maps under different thresholds to prevent the occurrence of traffic crashes accurately and effectively according to the vehicle GPS data of urban expressways in Jinan City, Shandong Province. This paper studied the relationship between risky driving behaviors (rapid acceleration, rapid deceleration, and overspeed) and road types with traffic crashes. The traffic safety evaluation model of urban expressways based on ordered logistic was established to predict the safety level of the urban expressway. The model’s accuracy was 85.71%, and the applicability was good. The research results showed that rapid deceleration was the most significant influencing factor of crashes on urban expressways. When the vehicle deceleration was less than or equal to −4 m/s², the probability of a crash was 22.737 times greater than when the vehicle deceleration was at −2 to −2.5 m/s²; when the vehicle acceleration was greater than or equal to 3 m/s², the probability of a crash was 19.453 times greater than when the vehicle acceleration was at 1 to 1.5 m/s². The likelihood of a crash at a road section with a ramp opening was 8.723 times greater than that of a crash at a non-ramp opening; the crash probability of a speeding vehicle was 7.925 times greater than that of a non-speeding vehicle; the likelihood of a crash on a curve was 6.147 times greater than that on a straight. The research results can provide adequate technical support for identifying high-risk sections of expressways and active early warning of traffic crashes. Full article

(This article belongs to the Special Issue Emerging Transportation in Sustainable Development Environment: Multi-Modal Transportation and Connected Automated Vehicle (CAV) Technology)

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14 pages, 979 KiB

Open AccessArticle

Evaluation of Social Stability Risk of Adjusting Goods Vehicle Calculation Method Based on Optimal Combination Weighting—Cloud Model

by Pengyun Chong, Hui Yin, Chaofeng Wang, Pengcheng Wang, Linqing Li, Di Wu, Jingwei Li and Dong Ding

Sustainability 2022, 14(24), 17057; https://doi.org/10.3390/su142417057 - 19 Dec 2022

Cited by 1 | Viewed by 1532

Abstract

In order to solve the social stability risk problem of the adjusting goods vehicle calculation method (AGVCM) in the cancellation of toll stations at expressway provincial boundaries, a social stability risk evaluation model was proposed. Firstly, based on the possible time stages of the AGVCM, we built the social stability risk evaluation index system of the AGVCM including 4 levels, 3 grades, and 60 indicators. Secondly, we used the semantic information of cloud model to transform qualitative description and quantitative evaluation, calculated the risk level for social stability risk evaluation of AGVCM according to an integrated cloud algorithm, and proposed an optimal combination weighting—cloud model for the social stability risk evaluation of AGVCM. Finally, the feasibility of the evaluation model is verified by a case study in Yunnan province in China. The results showed that the cloud model’s numerical characteristic of social stability risk evaluation of AGVCM in Yunnan based on the optimal combination weighting—cloud model is (0.28, 0.073, 0.0008), implying the risk level is “small” and the conclusion is basically consistent with current standards. The model contains rich process information, which is helpful to better effectively eliminate the subjectivity and arbitrariness in the evaluation process of identifying the key factors affecting the project risk level. Full article

(This article belongs to the Special Issue Emerging Transportation in Sustainable Development Environment: Multi-Modal Transportation and Connected Automated Vehicle (CAV) Technology)

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15 pages, 3573 KiB

Open AccessArticle

Research on Influencing Factors of Urban Road Traffic Casualties through Support Vector Machine

by Huacai Xian, Yu Wang, Yujia Hou, Shunzhong Dong, Junying Kou and Huili Zeng

Sustainability 2022, 14(23), 16203; https://doi.org/10.3390/su142316203 - 05 Dec 2022

Cited by 2 | Viewed by 1194

Abstract

Urban road traffic safety has always been vital in transportation research. This paper analyzed the factors influencing the degree of traffic accident casualties on Jinan Jingshi Road and its branch roads, taking them as the study area for urban road traffic safety problems. Additionally, it used the application of Particle Swarm Optimization (PSO), a Support Vector Machine (SVM) model, and a recursive feature elimination (RFE) to rank the contribution degree of the influencing factors. The results showed that driving on rainy days has a high probability of casualties, while the type of collision was a minimum influence factor. Additionally, on rainy days, cars were accident-prone road vehicles, and 8:00–12:00 and 18:00–22:00 were accident-prone periods. Based on the results, preventive measures were further put forward regarding the driver, road drainage capacity, policy management, and autopilot technology. This study aimed to guide urban traffic safety planning and provide a basis for developing traffic safety measures. Full article

(This article belongs to the Special Issue Emerging Transportation in Sustainable Development Environment: Multi-Modal Transportation and Connected Automated Vehicle (CAV) Technology)

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16 pages, 4503 KiB

Open AccessArticle

A Novel Multi-Objective Energy Management Strategy for Fuel Cell Buses Quantifying Fuel Cell Degradation as Operating Cost

by Menglin Li, Haoran Liu, Mei Yan, Hongyang Xu and Hongwen He

Sustainability 2022, 14(23), 16190; https://doi.org/10.3390/su142316190 - 04 Dec 2022

Cited by 4 | Viewed by 1222

Abstract

Fluctuation in a fuel cell’s output power affects its service life. This paper aims to explore the relationship between power output fluctuation and energy consumption and the cost of the fuel cell system. Hence, based on the actual driving information of vehicles, a novel multi-objective energy management strategy (EMS) for fuel cell buses (FCBs) that quantifies fuel cell life as operating cost is proposed. The actual driving data of FCBs on bus line 727 in Zhengzhou, China, were collected. Based on this, considering the degradation factors of the fuel cell and power battery hybrid energy system, a multi-objective cost framework was established to quantify the life degradation as consumption cost. Furthermore, the influence of different power change limits on the performance of the EMS was analysed based on real-world driving data and the typical Chinese city bus driving cycle, respectively. The simulation results show that the degradation cost of the fuel cell can be effectively reduced when the power change limit is 1 kW, and the simulation results obtained using real-world driving data are very different from those obtained using typical city bus driving cycles. This study provides a reference for the application of a vehicle energy management strategy in real-world scenarios as well as highlights its significance. Full article

(This article belongs to the Special Issue Emerging Transportation in Sustainable Development Environment: Multi-Modal Transportation and Connected Automated Vehicle (CAV) Technology)

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22 pages, 6095 KiB

Open AccessArticle

RSU Cluster Deployment and Collaboration Storage of IoV Based Blockchain

by Chen Chen and Shi Quan

Sustainability 2022, 14(23), 16152; https://doi.org/10.3390/su142316152 - 02 Dec 2022

Cited by 2 | Viewed by 1281

Abstract

The Internet of Vehicles is increasingly becoming an indispensable platform to make interactions among vehicles, humans, and roadside infrastructures, and it is continuing to evolve with improvements on its application scenarios and service robustness. Academia and industry have also been concerned with the security issue of Internet of Vehicles (IoV). The Blockchain technology provides a new solution to improve security of the IoV, and it has drawn increased attention. Blockchain technology deals with the network model, identity authentication, trust management, and access control. However, there are insufficient studies on strategy of nodes deployment in the existing BIoV (Blockchain-based IoV). Based on the principle of partition, this paper studies the Blockchain-based IoV model in which the vehicle network system consists of vehicles and Road Side Unit (RSU) nodes. The Blockchain network is formed by the RSU nodes. By optimizing the LEACH algorithm, we partition RSUs and select cluster heads in BIoV, which has good scalability while maintaining a reasonable scale of Blockchain. Simulation results indicated that the improved-LEACH algorithm (I-LEACH) is more effective than the LEACH, DEEC, and the improved-DEEC algorithms (I-DEEC) with respect to the network life cycle and data transmission. Additionally, in order to reduce dependence of the storage resources from BIoV, we designed an intra-cluster collaborative storage scheme by adopting three algorithms for comparison—the genetic algorithm (GA), particle swarm optimization (PSO) algorithm, and the model quantum genetic algorithm (QGA). It revealed that the intra-cluster collaborative storage scheme is effective to ease the bottleneck of storage space demanded in BIoV, and verified by the simulation experiments. Full article

(This article belongs to the Special Issue Emerging Transportation in Sustainable Development Environment: Multi-Modal Transportation and Connected Automated Vehicle (CAV) Technology)

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19 pages, 33143 KiB

Open AccessArticle

Automatic ROI Setting Method Based on LSC for a Traffic Congestion Area

by Yang He, Lisheng Jin, Huanhuan Wang, Zhen Huo, Guangqi Wang and Xinyu Sun

Sustainability 2022, 14(23), 16126; https://doi.org/10.3390/su142316126 - 02 Dec 2022

Cited by 2 | Viewed by 1271

Abstract

Congested regions in videos put forward higher requirements for target detection algorithms, and the key detection of congested regions provides optimization directions for improving the accuracy of detection algorithms. In order to make the target detection algorithm pay more attention to the congested area, an automatic selection method of a traffic congestion area based on surveillance videos is proposed. Firstly, the image is segmented with superpixels, and a superpixel boundary map is extracted. Then, the mean filtering method is used to process the superpixel boundary map, and a fixed threshold is used to filter pixels with high texture complexity. Finally, a maximin method is used to extract the traffic congestion area. Monitoring data of night and rainy days were collected to expand the UA-DETRAC data set, and experiments were carried out on the extended data set. The results show that the proposed method can realize automatic setting of the congestion area under various weather conditions, such as full light, night and rainy days. Full article

(This article belongs to the Special Issue Emerging Transportation in Sustainable Development Environment: Multi-Modal Transportation and Connected Automated Vehicle (CAV) Technology)

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15 pages, 2198 KiB

Open AccessArticle

Research on the Impacts of Vehicle Type on Car-Following Behavior, Fuel Consumption and Exhaust Emission in the V2X Environment

by Junyan Han, Xiaoyuan Wang, Huili Shi, Bin Wang, Gang Wang, Longfei Chen and Quanzheng Wang

Sustainability 2022, 14(22), 15231; https://doi.org/10.3390/su142215231 - 16 Nov 2022

Cited by 5 | Viewed by 1338

Abstract

The type of vehicles in realistic traffic systems are not homogeneous. Impacts of the preceding vehicle’s type on the car-following behavior, fuel consumption and exhaust emissions are still unclear. This paper presents a study on the impacts of two types of preceding vehicles, heavy vehicles and new energy vehicles, on car-following behavior, traffic flow characteristics, fuel consumption and exhaust emissions. Firstly, an extended car-following model was proposed by incorporating the influence of the preceding vehicle’s type. Secondly, impacts of the preceding vehicle’s type on platoon stability were analyzed by applying linear stability theory. Finally, numerical simulations were carried out to analyze impacts of the preceding vehicles’ type on the characteristics of the car-following platoon, traffic flow operation, and vehicle’s fuel consumption and exhaust emissions. The results reveal that, compared with the normal preceding vehicle, there are negative impacts of the heavy and new-energy preceding vehicles on the platoon stability, traffic flow operation, and vehicle’s fuel consumption and exhaust emissions, and these impacts are related to the corresponding sensitivity parameters and the penetration percentages of the two types of preceding vehicle. The research results of this paper can provide a reference for understanding car-following behavior and traffic-flow characteristics affected by the type of preceding vehicles in the V2X environment. Full article

(This article belongs to the Special Issue Emerging Transportation in Sustainable Development Environment: Multi-Modal Transportation and Connected Automated Vehicle (CAV) Technology)

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