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Emerging Transportation Solutions for Safer and Greener Future Mobility

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601).

Deadline for manuscript submissions: closed (12 April 2023) | Viewed by 36246

Special Issue Editors

Dr. Bo Yu

E-Mail Website
Guest Editor
College of Transportation Engineering, Tongji University, Shanghai 201804, China
Interests: road safety; driving behavior modeling; eco-driving; connected and autonomous vehicle; intelligent transportation system
Special Issues, Collections and Topics in MDPI journals
Dr. Kun Gao

E-Mail Website
Guest Editor
Department of Architecture and Civil Engineering, Chalmers University of Technology, SE-412 96 Goteburg, Sweden
Interests: transportation electrification; shared mobility and connected vehicles
Special Issues, Collections and Topics in MDPI journals
Dr. Yueru Xu

E-Mail Website
Guest Editor
Intelligent Transportation System Research Center, Southeast University, Nanjing 211189, China
Interests: road safety and environment; intelligent transportation system; transportation energy consumption
Dr. You Kong

E-Mail Website
Guest Editor
College of Transport and Communication, Shanghai Maritime University, Shanghai 201306, China
Interests: autonomous vehicles; parking management; intelligent transportation system

Special Issue Information

Dear Colleagues,

With the emergence of new technologies such as connected and automated vehicles, big data, shared mobility, and electric vehicles, intelligent transportation systems (ITS) are constantly being updated and upgraded to provide comprehensive services that are safer, more sustainable, and more efficient. However, opportunities are always accompanied by challenges. New technologies bring a great deal of new research and practical questions as well, which need collective efforts from interdisciplinary fields.

At present, safety issues in ITS have attracted extensive attention, and human factors have been found to be one of the most important sources of risk. More in-depth investigations on potential impacting factors and prevention methods of human-factor-related traffic accidents are still needed—for example, the driving styles and risk compensation behavior under ITS, the effectiveness of advanced driver-assistance systems in preventing traffic accidents, behavior modeling and safety protection of vulnerable road users (e.g., cyclists, pedestrians), dangerous scenarios under autonomous driving, etc. Meanwhile, ITS bring a large amount of multi-source heterogeneous data, including natural driving data, crash data of autonomous vehicles, and wide-range vehicle trajectory data. This leads to the extension of the corresponding analysis methods, from traditional statistical methods to machine learning and deep learning. The application of these novel methods will further improve traffic safety in emerging ITS.

COP27 has further urged the world to take solid actions to reduce greenhouse emissions from different sectors. Electric vehicles, shared mobility, and intelligent connected vehicles are potential sustainable solutions in the transportation sector, but need further scientific planning, operation optimization, promotion strategies, and regulations to fulfill their sustainable merits. For instance, many cities are in the early stages of electrifying transportation systems, with insufficient planning and inadequate charging facilities. The rapid increase in loads from electric vehicle charging is not incorporated into the power grid maintenance and upgrade plans. Additionally, due to range limitations and charging needs, electric buses operate with lower availability than conventional buses, and many users are not willing to shift towards sustainable mobility alternatives. Although some studies have investigated these topics to some extent, more explorations should be made to advance these areas by leveraging emerging methods such as big data, machine learning, and edge computing, which are expected to have superior performances.

This Special Issue is devoted to addressing challenges in the modeling, optimization, analysis, and policymaking of utilizing emerging transportation solutions for improving transportation safety and reducing emissions. This Special Issue especially welcomes studies that utilize machine learning, new data resources, and interdisciplinary solutions to promote the safety and sustainability of transportation systems. It is open to any subject area of the related theme; of particular interest are traffic safety and risk analysis, human factors in ITS, eco-driving, transportation electrification and shared mobility, connected and automated vehicles, advanced driver-assistance systems, etc.

Dr. Bo Yu
Dr. Kun Gao
Dr. Yueru Xu
Dr. You Kong
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. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly 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 2500 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

  • driving behavior and risk evaluation
  • traffic safety and human factors in ITS
  • environmental impact analysis
  • transportation electrification
  • shared mobility
  • interdisciplinary solutions
  • travel behavior regarding sustainable transportation modes
  • eco-driving
  • intelligent transportation systems (ITS)
  • connected and automated vehicles (CAV)
  • advanced driver-assistance systems (ADAS)
  • machine learning
  • new data resources
  • edge computing

Published Papers (19 papers)

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29 pages, 2608 KiB  
Article
The Effect of Travel-Chain Complexity on Public Transport Travel Intention: A Mixed-Selection Model
by Yuan Yuan, Chunfu Shao, Zhichao Cao and Chaoying Yin
Int. J. Environ. Res. Public Health 2023, 20(5), 4547; https://doi.org/10.3390/ijerph20054547 - 3 Mar 2023
Viewed by 1774
Abstract
With urban expansion and traffic environment improvement, travel chains continue to grow, and the combination of travel purposes and modes becomes more complex. The promotion of mobility as a service (MaaS) has positive effects on facilitating the public transport traffic environment. However, public [...] Read more.
With urban expansion and traffic environment improvement, travel chains continue to grow, and the combination of travel purposes and modes becomes more complex. The promotion of mobility as a service (MaaS) has positive effects on facilitating the public transport traffic environment. However, public transport service optimization requires an accurate understanding of the travel environment, selection preferences, demand prediction, and systematic dispatch. Our study focused on the relationship between the trip-chain complexity environment and travel intention, combining the Theory of Planned Behavior (TPB) with travelers’ preferences to construct a bounded rationality theory. First, this study used K-means clustering to transform the characteristics of the travel trip chain into the complexity of the trip chain. Then, based on the partial least squares structural equation model (PLS-SEM) and the generalized ordered Logit model, a mixed-selection model was established. Finally, the travel intention of PLS-SEM was compared with the travel sharing rate of the generalized ordered Logit model to determine the trip-chain complexity effects for different public transport modes. The results showed that (1) the proposed model, which transformed travel-chain characteristics into travel-chain complexity using K-means clustering and adopted a bounded rationality perspective, had the best fit and was the most effective with comparison to the previous prediction approaches. (2) Compared with service quality, trip-chain complexity negatively affected the intention of using public transport in a wider range of indirect paths. Gender, vehicle ownership, and with children/without children had significant moderating effects on certain paths of the SEM. (3) The research results obtained by PLS-SEM indicated that when travelers were more willing to travel by subway, the subway travel sharing rate corresponding to the generalized ordered Logit model was only 21.25–43.49%. Similarly, the sharing rate of travel by bus was only 32–44% as travelers were more willing to travel by bus obtained from PLS-SEM. Therefore, it is necessary to combine the qualitative results of PLS-SEM with the quantitative results of generalized ordered Logit. Moreover, when service quality, preferences, and subjective norms were based on the mean value, with each increase in trip-chain complexity, the subway travel sharing rate was reduced by 3.89–8.30%, while the bus travel sharing rate was reduced by 4.63–6.03%. Full article
(This article belongs to the Special Issue Emerging Transportation Solutions for Safer and Greener Future Mobility)
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20 pages, 2014 KiB  
Article
Exploring Factors Affecting Crash Injury Severity with Consideration of Secondary Collisions in Freeway Tunnels
by Younshik Chung and Jong-Jin Kim
Int. J. Environ. Res. Public Health 2023, 20(4), 3723; https://doi.org/10.3390/ijerph20043723 - 20 Feb 2023
Cited by 1 | Viewed by 1434
Abstract
Although there have been several studies conducted exploring the factors affecting injury severity in tunnel crashes, most studies have focused on identifying factors that directly influence injury severity. In particular, variables related to crash characteristics and tunnel characteristics affect the injury severity, but [...] Read more.
Although there have been several studies conducted exploring the factors affecting injury severity in tunnel crashes, most studies have focused on identifying factors that directly influence injury severity. In particular, variables related to crash characteristics and tunnel characteristics affect the injury severity, but the inconvenient driving environment in a tunnel space, characterized by narrow space and dark lighting, can affect crash characteristics such as secondary collisions, which in turn can affect the injury severity. Moreover, studies on secondary collisions in freeway tunnels are very limited. The objective of this study was to explore factors affecting injury severity with the consideration of secondary collisions in freeway tunnel crashes. To account for complex relationships between multiple exogenous variables and endogenous variables by considering the direct and indirect relationships between them, this study used a structural equation modeling with tunnel crash data obtained from Korean freeway tunnels from 2013 to 2017. Moreover, based on high-definition closed-circuit televisions installed every 250 m to monitor incidents in Korean freeway tunnels, this study utilized unique crash characteristics such as secondary collisions. As a result, we found that tunnel characteristics indirectly affected injury severity through crash characteristics. In addition, one variable regarding crashes involving drivers younger than 40 years old was associated with decreased injury severity. By contrast, ten variables exhibited a higher likelihood of severe injuries: crashes by male drivers, crashes by trucks, crashes in March, crashes under sunny weather conditions, crashes on dry surface conditions, crashes in interior zones, crashes in wider tunnels, crashes in longer tunnels, rear-end collisions, and secondary collisions with other vehicles. Full article
(This article belongs to the Special Issue Emerging Transportation Solutions for Safer and Greener Future Mobility)
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16 pages, 1422 KiB  
Article
A Study on the Entire Take-Over Process-Based Emergency Obstacle Avoidance Behavior
by Yi Li, Zhaoze Xuan and Xianyu Li
Int. J. Environ. Res. Public Health 2023, 20(4), 3069; https://doi.org/10.3390/ijerph20043069 - 9 Feb 2023
Viewed by 1100
Abstract
Nowadays, conditional automated driving vehicles still need drivers to take-over in the scenarios such as emergency hazard events or driving environments beyond the system’s control. This study aimed to explore the changing trend of the drivers’ takeover behavior under the influence of traffic [...] Read more.
Nowadays, conditional automated driving vehicles still need drivers to take-over in the scenarios such as emergency hazard events or driving environments beyond the system’s control. This study aimed to explore the changing trend of the drivers’ takeover behavior under the influence of traffic density and take-over budget time for the entire take-over process in emergency obstacle avoidance scenarios. In the driving simulator, a 2 × 2 factorial design was adopted, including two traffic densities (high density and low density) and two kinds of take-over budget time (3 s and 5 s). A total of 40 drivers were recruited, and each driver was required to complete four simulation experiments. The driver’s take-over process was divided into three phases, including the reaction phase, control phase, and recovery phase. Time parameters, dynamics parameters, and operation parameters were collected for each take-over phase in different obstacle avoidance scenarios. This study analyzed the variability of traffic density and take-over budget time with take-over time, lateral behavior, and longitudinal behavior. The results showed that in the reaction phase, the driver’s reaction time became shorter as the scenario urgency increased. In the control phase, the steering wheel reversal rate, lateral deviation rate, braking rate, average speed, and takeover time were significantly different at different urgency levels. In the recovery phase, the average speed, accelerating rate, and take-over time differed significantly at different urgency levels. For the entire take-over process, the entire take-over time increased with the increase in urgency. The lateral take-over behavior tended to be aggressive first and then became defensive, and the longitudinal take-over behavior was defensive with the increase in urgency. The findings will provide theoretical and methodological support for the improvement of take-over behavior assistance in emergency take-over scenarios. It will also be helpful to optimize the human-machine interaction system. Full article
(This article belongs to the Special Issue Emerging Transportation Solutions for Safer and Greener Future Mobility)
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29 pages, 11217 KiB  
Article
Modelling and Mitigating Secondary Crash Risk for Serial Tunnels on Freeway via Lighting-Related Microscopic Traffic Model with Inter-Lane Dependency
by Shanchuan Yu, Yu Chen, Lang Song, Zhaoze Xuan and Yi Li
Int. J. Environ. Res. Public Health 2023, 20(4), 3066; https://doi.org/10.3390/ijerph20043066 - 9 Feb 2023
Cited by 2 | Viewed by 1196
Abstract
This paper models and mitigates the secondary crash (SC) risk for serial tunnels on the freeway which is incurred by traffic turbulence after primary crash (PC) occurrence and location-heterogeneous lighting conditions along serial tunnels. A traffic conflict approach is developed where SC risk [...] Read more.
This paper models and mitigates the secondary crash (SC) risk for serial tunnels on the freeway which is incurred by traffic turbulence after primary crash (PC) occurrence and location-heterogeneous lighting conditions along serial tunnels. A traffic conflict approach is developed where SC risk is quantified using a surrogate safety measure based on the simulated vehicle trajectories after PC occurs from a lighting-related microscopic traffic model with inter-lane dependency. Numerical examples are presented to validate the model, illustrate SC risk pattern over time, and evaluate the countermeasures for SC, including adaptive tunnel lighting control (ATLC) and advanced speed and lane-changing guidance (ASLG) for connected vehicles (CVs). The results demonstrate that the tail of the stretching queue on the PC occurrence lane, the adjacent lane of the PC-incurred queue, and areas near tunnel portals are high-risk locations. In serial tunnels, creating a good lighting condition for drivers is more effective than advanced warnings in CVs to mitigate SC risk. Combined ATLC and ASLG is promising since ASLG informs CVs of an immediate response to traffic turbulence on the lane where PC occurs and ATLC alleviates SC risks on adjacent lanes via smoothing the lighting condition variations and reducing inter-lane dependency. Full article
(This article belongs to the Special Issue Emerging Transportation Solutions for Safer and Greener Future Mobility)
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18 pages, 4452 KiB  
Article
Safe, Efficient, and Comfortable Autonomous Driving Based on Cooperative Vehicle Infrastructure System
by Jing Chen, Cong Zhao, Shengchuan Jiang, Xinyuan Zhang, Zhongxin Li and Yuchuan Du
Int. J. Environ. Res. Public Health 2023, 20(1), 893; https://doi.org/10.3390/ijerph20010893 - 3 Jan 2023
Cited by 15 | Viewed by 3951
Abstract
Traffic crashes, heavy congestion, and discomfort often occur on rough pavements due to human drivers’ imperfect decision-making for vehicle control. Autonomous vehicles (AVs) will flood onto urban roads to replace human drivers and improve driving performance in the near future. With the development [...] Read more.
Traffic crashes, heavy congestion, and discomfort often occur on rough pavements due to human drivers’ imperfect decision-making for vehicle control. Autonomous vehicles (AVs) will flood onto urban roads to replace human drivers and improve driving performance in the near future. With the development of the cooperative vehicle infrastructure system (CVIS), multi-source road and traffic information can be collected by onboard or roadside sensors and integrated into a cloud. The information is updated and used for decision-making in real-time. This study proposes an intelligent speed control approach for AVs in CVISs using deep reinforcement learning (DRL) to improve safety, efficiency, and ride comfort. First, the irregular and fluctuating road profiles of rough pavements are represented by maximum comfortable speeds on segments via vertical comfort evaluation. A DRL-based speed control model is then designed to learn safe, efficient, and comfortable car-following behavior based on road and traffic information. Specifically, the model is trained and tested in a stochastic environment using data sampled from 1341 car-following events collected in California and 110 rough pavements detected in Shanghai. The experimental results show that the DRL-based speed control model can improve computational efficiency, driving efficiency, longitudinal comfort, and vertical comfort in cars by 93.47%, 26.99%, 58.33%, and 6.05%, respectively, compared to a model predictive control-based adaptive cruise control. The results indicate that the proposed intelligent speed control approach for AVs is effective on rough pavements and has excellent potential for practical application. Full article
(This article belongs to the Special Issue Emerging Transportation Solutions for Safer and Greener Future Mobility)
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21 pages, 9323 KiB  
Article
Analysis of Perception Accuracy of Roadside Millimeter-Wave Radar for Traffic Risk Assessment and Early Warning Systems
by Cong Zhao, Delong Ding, Zhouyang Du, Yupeng Shi, Guimin Su and Shanchuan Yu
Int. J. Environ. Res. Public Health 2023, 20(1), 879; https://doi.org/10.3390/ijerph20010879 - 3 Jan 2023
Cited by 11 | Viewed by 2699
Abstract
Millimeter-wave (MMW) radar is essential in roadside traffic perception scenarios and traffic safety control. For traffic risk assessment and early warning systems, MMW radar provides real-time position and velocity measurements as a crucial source of dynamic risk information. However, due to MMW radar’s [...] Read more.
Millimeter-wave (MMW) radar is essential in roadside traffic perception scenarios and traffic safety control. For traffic risk assessment and early warning systems, MMW radar provides real-time position and velocity measurements as a crucial source of dynamic risk information. However, due to MMW radar’s measuring principle and hardware limitations, vehicle positioning errors are unavoidable, potentially causing misperception of the vehicle motion and interaction behavior. This paper analyzes the factors influencing the MMW radar positioning accuracy that are of major concern in the application of transportation systems. An analysis of the radar measuring principle and the distributions of the radar point cloud on the vehicle body under different scenarios are provided to determine the causes of the positioning error. Qualitative analyses of the radar positioning accuracy regarding radar installation height, radar sampling frequency, vehicle location, posture, and size are performed. The analyses are verified through simulated experiments. Based on the results, a general guideline for radar data processing in traffic risk assessment and early warning systems is proposed. Full article
(This article belongs to the Special Issue Emerging Transportation Solutions for Safer and Greener Future Mobility)
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16 pages, 1592 KiB  
Article
Risk Assessment of Import Cold Chain Logistics Based on Entropy Weight Matter Element Extension Model: A Case Study of Shanghai, China
by Qiang Fu, Yurou Sun and Lei Wang
Int. J. Environ. Res. Public Health 2022, 19(24), 16892; https://doi.org/10.3390/ijerph192416892 - 15 Dec 2022
Cited by 1 | Viewed by 2011
Abstract
The development of world trade and fresh-keeping technology has led to the rapid development of international cold chain logistics. However, the novel coronavirus epidemic continues to spread around the world at the present stage, which challenges disease transmission control and safety supervision of [...] Read more.
The development of world trade and fresh-keeping technology has led to the rapid development of international cold chain logistics. However, the novel coronavirus epidemic continues to spread around the world at the present stage, which challenges disease transmission control and safety supervision of international cold chain logistics. Constructing an Import Cold Chain Logistics Safety Supervision System (ICCL-SSS) is helpful for detecting and controlling disease import risk. This paper constructs an evaluation index system of ICCL safety that comprehensively considers the potential risk factors of three ICCL processes: the logistics process in port, the customs clearance process, and the logistics process from port to door. The risk level of ICCL-SSS is evaluated by combining the Extension Decision-making Model and the Entropy Weight Method. The case study of Shanghai, China, the world’s largest city of ICCL, shows that the overall risk level of ICCL-SSS in Shanghai is at a moderate level. However, the processes of loading and unloading, inspection and quarantine, disinfection and sterilization, and cargo storage are at high risk specifically. The construction and risk assessment of ICCL-SSS can provide theoretical support and practical guidance for improving the safety supervision ability of ICCL regulation in the post-epidemic era, and helps the local government to scientifically formulate ICCL safety administration policies and accelerate the development of world cold chain trade. Full article
(This article belongs to the Special Issue Emerging Transportation Solutions for Safer and Greener Future Mobility)
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17 pages, 774 KiB  
Article
Sensitivity of a Dynamic Model of Air Traffic Emissions to Technological and Environmental Factors
by Francisco A. Buendia-Hernandez, Maria J. Ortiz Bevia, Francisco J. Alvarez-Garcia and Antonio Ruizde Elvira
Int. J. Environ. Res. Public Health 2022, 19(22), 15406; https://doi.org/10.3390/ijerph192215406 - 21 Nov 2022
Viewed by 1298
Abstract
In this study, we introduce a sensitivity analysis of modelled CO2 aviation emissions to changes in the model parameters, which is intended as a contribution to the understanding of the atmospheric composition stabilization issue. The two variable dynamic model incorporates the effects [...] Read more.
In this study, we introduce a sensitivity analysis of modelled CO2 aviation emissions to changes in the model parameters, which is intended as a contribution to the understanding of the atmospheric composition stabilization issue. The two variable dynamic model incorporates the effects of the technological innovations on the emissions rate, the environmental feedback, and a non-linear control term on the passengers rate. The model parameters, estimated from different air traffic sources, are subject to considerable uncertainty. The stability analysis of Monte Carlo simulations revealed that, for certain values of the non-linear term parameter and depending on the type of flight, the passengers number at some equilibrium points exceeded its initial value, while the emissions level was below the initial corresponding one. The results of two global sensitivity analyses indicated that the influence of the non-linear term prevailed on the passengers number rate, followed distantly by the environmental feedback. For the emissions rate, the non-linear term contribution dominated, with the technological term influence placing second. Full article
(This article belongs to the Special Issue Emerging Transportation Solutions for Safer and Greener Future Mobility)
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21 pages, 5231 KiB  
Article
An Environmentally Sustainable Software-Defined Networking Data Dissemination Method for Mixed Traffic Flows in RSU Clouds with Energy Restriction
by Hongming Li, Dongxiu Ou and Yuqing Ji
Int. J. Environ. Res. Public Health 2022, 19(22), 15112; https://doi.org/10.3390/ijerph192215112 - 16 Nov 2022
Cited by 1 | Viewed by 1212
Abstract
The connected multi road side unit (RSU) environment can be envisioned as the RSU cloud. In this paper, the Software-Defined Networking (SDN) framework is utilized to dynamically reconfigure the RSU clouds for the mixed traffic flows with energy restrictions, which are composed of [...] Read more.
The connected multi road side unit (RSU) environment can be envisioned as the RSU cloud. In this paper, the Software-Defined Networking (SDN) framework is utilized to dynamically reconfigure the RSU clouds for the mixed traffic flows with energy restrictions, which are composed of five categories of vehicles with distinctive communication demands. An environmentally sustainable SDN data dissemination method for safer and greener transportation solutions is thus proposed, aiming to achieve the lowest overall SDN cloud delay with the least working hosts and minimum energy consumption, which is a mixed integer linear programming problem (MILP). To solve the problem, Joint optimization algorithms with Finite resources (JF) in three hyperparameters versions, JF (DW = 0.3, HW = 0.7), JF (DW = 0.5, HW = 0.5) and JF (DW = 0.7, HW = 0.3), were proposed, which are in contrast with single-objective optimization algorithms, the Host Optimization (H) algorithm, and the Delay optimization (D) algorithm. Results show that JF (DW = 0.3, HW = 0.7) and JF (DW = 0.5, HW = 0.5), when compared with the D algorithm, usually had slightly larger cloud delays, but fewer working hosts and energy consumptions, which has vital significance for enhancing energy efficiency and environmental protection, and shows the superiority of JFs over the D algorithm. Meanwhile, the H algorithm had the least working hosts and fewest energy consumptions under the same conditions, but completely ignored the explosive surge of delay, which is not desirable for most cases of the SDN RSU cloud. Further analysis showed that the larger the network topology of the SDN cloud, the harder it was to find a feasible network configuration. Therefore, when designing an environmentally sustainable SDN RSU cloud for the greener future mobility of intelligent transportation systems, its size should be limited or partitioned into a relatively small topology. Full article
(This article belongs to the Special Issue Emerging Transportation Solutions for Safer and Greener Future Mobility)
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13 pages, 685 KiB  
Article
MGCAF: A Novel Multigraph Cross-Attention Fusion Method for Traffic Speed Prediction
by Tian Ma, Xiaobao Wei, Shuai Liu and Yilong Ren
Int. J. Environ. Res. Public Health 2022, 19(21), 14490; https://doi.org/10.3390/ijerph192114490 - 4 Nov 2022
Cited by 1 | Viewed by 1441
Abstract
Traffic speed prediction is an essential part of urban transportation systems that contributes to minimizing the environmental pollution caused by vehicle emissions. The existing traffic speed prediction studies have achieved good results, but some challenges remain. Most previously developed methods only account for [...] Read more.
Traffic speed prediction is an essential part of urban transportation systems that contributes to minimizing the environmental pollution caused by vehicle emissions. The existing traffic speed prediction studies have achieved good results, but some challenges remain. Most previously developed methods only account for road network characteristics such as distance while ignoring road directions and time patterns, resulting in lower traffic speed prediction accuracy. To address this issue, we propose a novel model that utilizes multigraph and cross-attention fusion (MGCAF) mechanisms for traffic speed prediction. We construct three graphs for distances, position relationships, and temporal correlations to adequately capture road network properties. Furthermore, to adaptively aggregate multigraph features, a multigraph attention mechanism is embedded into the network framework, enabling it to better connect the traffic features between the temporal and spatial domains. Experiments are performed on real-world datasets, and the results demonstrate that our method achieves positive performance and outperforms other baselines. Full article
(This article belongs to the Special Issue Emerging Transportation Solutions for Safer and Greener Future Mobility)
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14 pages, 963 KiB  
Article
Key Factors, Planning Strategy and Policy for Low-Carbon Transport Development in Developing Cities of China
by Liu Yang, Yuanqing Wang, Yujun Lian, Zhongming Guo, Yuanyuan Liu, Zhouhao Wu and Tieyue Zhang
Int. J. Environ. Res. Public Health 2022, 19(21), 13746; https://doi.org/10.3390/ijerph192113746 - 22 Oct 2022
Cited by 2 | Viewed by 1790
Abstract
Exploring key impact factors and their effects on urban residents’ transport carbon dioxide (CO2) emissions is significant for effective low-carbon transport planning. Researchers face the model uncertainty problem to seek a rational and better explanatory model and the key variables in [...] Read more.
Exploring key impact factors and their effects on urban residents’ transport carbon dioxide (CO2) emissions is significant for effective low-carbon transport planning. Researchers face the model uncertainty problem to seek a rational and better explanatory model and the key variables in the model set containing various factors after they are arranged and combined. This paper uses the Bayesian Model Averaging method to solve the above problem, explore the key variables, and determine their relative significance and averaging effects. Beijing, Xi’an, and Wuhan are selected as three case cities for their representation of developing Chinese cities. We found that the initial key factor increasing transport emissions is the high dependence on cars, and the second is the geographical location factor that much more suburban residents suffer longer commuting. Developing satellite city rank first for reducing transport emissions due to more local trips with an average short distance, the second is the metro accessibility, and the third is polycentric form. Key planning strategies and policies are proposed: (i) combining policies of car restriction based on vehicle plate number, encouraging clean fuel cars, a carbon tax on oil uses, and rewarding public transit passengers; (ii) fostering subcenters’ strong industries to develop self-contained polycentric structures and satellite cities, and forming employment and life circle within 5 km radius; and (iii) integrating bus and rail transit services in the peripheral areas and suburbs and increasing the integration level of muti-modes transferring in transport hubs. The findings will offer empirical evidence and reference value in developing cities globally. Full article
(This article belongs to the Special Issue Emerging Transportation Solutions for Safer and Greener Future Mobility)
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17 pages, 4454 KiB  
Article
Spatiotemporal Feature Enhancement Aids the Driving Intention Inference of Intelligent Vehicles
by Huiqin Chen, Hailong Chen, Hao Liu and Xiexing Feng
Int. J. Environ. Res. Public Health 2022, 19(18), 11819; https://doi.org/10.3390/ijerph191811819 - 19 Sep 2022
Cited by 3 | Viewed by 1594
Abstract
In order that fully self-driving vehicles can be realized, it is believed that systems where the driver shares control and authority with the intelligent vehicle offer the most effective solution. An understanding of driving intention is the key to building a collaborative autonomous [...] Read more.
In order that fully self-driving vehicles can be realized, it is believed that systems where the driver shares control and authority with the intelligent vehicle offer the most effective solution. An understanding of driving intention is the key to building a collaborative autonomous driving system. In this study, the proposed method incorporates the spatiotemporal features of driver behavior and forward-facing traffic scenes through a feature extraction module; the joint representation was input into an inference module for obtaining driver intentions. The feature extraction module was a two-stream structure that was designed based on a deep three-dimensional convolutional neural network. To accommodate the differences in video data inside and outside the cab, the two-stream network consists of a slow pathway that processes the driver behavior data with low frame rates, along with a fast pathway that processes traffic scene data with high frame rates. Then, a gated recurrent unit, based on a recurrent neural network, and a fully connected layer constitute an intent inference module to estimate the driver’s lane-change and turning intentions. A public dataset, Brain4Cars, was used to validate the proposed method. The results showed that compared with modeling using the data related to driver behaviors, the ability of intention inference is significantly improved after integrating traffic scene information. The overall accuracy of the intention inference of five intents was 84.92% at a time of 1 s prior to the maneuver, indicating that making full use of traffic scene information was an effective way to improve inference performance. Full article
(This article belongs to the Special Issue Emerging Transportation Solutions for Safer and Greener Future Mobility)
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22 pages, 1522 KiB  
Article
Divergent Effects of Factors on Crash Severity under Autonomous and Conventional Driving Modes Using a Hierarchical Bayesian Approach
by Weixi Ren, Bo Yu, Yuren Chen and Kun Gao
Int. J. Environ. Res. Public Health 2022, 19(18), 11358; https://doi.org/10.3390/ijerph191811358 - 9 Sep 2022
Cited by 6 | Viewed by 1716
Abstract
Influencing factors on crash severity involved with autonomous vehicles (AVs) have been paid increasing attention. However, there is a lack of comparative analyses of those factors between AVs and human-driven vehicles. To fill this research gap, the study aims to explore the divergent [...] Read more.
Influencing factors on crash severity involved with autonomous vehicles (AVs) have been paid increasing attention. However, there is a lack of comparative analyses of those factors between AVs and human-driven vehicles. To fill this research gap, the study aims to explore the divergent effects of factors on crash severity under autonomous and conventional (i.e., human-driven) driving modes. This study obtained 180 publicly available autonomous vehicle crash data, and 39 explanatory variables were extracted from three categories, including environment, roads, and vehicles. Then, a hierarchical Bayesian approach was applied to analyze the impacting factors on crash severity (i.e., injury or no injury) under both driving modes with considering unobserved heterogeneities. The results showed that some influencing factors affected both driving modes, but their degrees were different. For example, daily visitors’ flowrate had a greater impact on the crash severity under the conventional driving mode. More influencing factors only had significant impacts on one of the driving modes. For example, in the autonomous driving mode, mixed land use increased the severity of crashes, while daytime had the opposite effects. This study could contribute to specifying more appropriate policies to reduce the crash severity of both autonomous and human-driven vehicles especially in mixed traffic conditions. Full article
(This article belongs to the Special Issue Emerging Transportation Solutions for Safer and Greener Future Mobility)
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20 pages, 5354 KiB  
Article
A Framework for Assessing Resilience in Urban Mobility: Incorporating Impact of Ridesharing
by Dawei Li, Yiping Liu, Yuchen Song, Zhenghao Ye and Dongjie Liu
Int. J. Environ. Res. Public Health 2022, 19(17), 10801; https://doi.org/10.3390/ijerph191710801 - 30 Aug 2022
Viewed by 1316
Abstract
To a certain degree, the resilience of the transportation system expresses the safety of the transportation system, because it reflects the ability of the system to maintain its function in the face of disturbance events. In the current research, the assessment of the [...] Read more.
To a certain degree, the resilience of the transportation system expresses the safety of the transportation system, because it reflects the ability of the system to maintain its function in the face of disturbance events. In the current research, the assessment of the resilience of urban mobility is attractive and challenging. Apart from this, the concept of green mobility has been popular in recent years. As a representative way of shared mobility, the implementation of ridesharing will affect the level of urban mobility resilience to a certain extent. In this paper, we use a data low-intensity method to evaluate the urban traffic resilience under the circumstance of restricted car use. In addition, we incorporate the impact of ridesharing services. The research in this paper can be regarded as an evaluation framework, which can help policy makers and relevant operators to grasp the overall resilience characteristics of cities in emergencies, identify weak sectors, and formulate the best response plan. This method has been successfully applied to two cities in China, demonstrating its potential for practice. Finally, we also explored the relationship between urban traffic resilience and the pattern of population distribution. The analysis shows that population density has an impact on the level of transportation resilience. And the incorporation of ridesharing will bring an obvious increment in resilience of most areas. Full article
(This article belongs to the Special Issue Emerging Transportation Solutions for Safer and Greener Future Mobility)
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14 pages, 38292 KiB  
Article
Traffic Risk Environment Impact Analysis and Complexity Assessment of Autonomous Vehicles Based on the Potential Field Method
by Ying Cheng, Zhen Liu, Li Gao, Yanan Zhao and Tingting Gao
Int. J. Environ. Res. Public Health 2022, 19(16), 10337; https://doi.org/10.3390/ijerph191610337 - 19 Aug 2022
Cited by 10 | Viewed by 1782
Abstract
Although autonomous vehicles have introduced a promising potential for improving traffic safety and efficiency, ensuring the safety of autonomous vehicles in complex road traffic environments is still a huge challenge to be tackled. To quickly quantify the potential risk factors of autonomous vehicles [...] Read more.
Although autonomous vehicles have introduced a promising potential for improving traffic safety and efficiency, ensuring the safety of autonomous vehicles in complex road traffic environments is still a huge challenge to be tackled. To quickly quantify the potential risk factors of autonomous vehicles in traffic environments, this paper focuses mainly on the influence of the depth and breadth of the environment elements on the autonomous driving system, uses the potential field theory to establish a model of the impact of the environmental elements on the autonomous driving system, and combines AHP to quantify equivalent virtual electric quantity of each environment element, so as to realize the quantitative evaluation of the traffic environment complexity. The proposed method comprehensively considers the physical attributes and state parameters of the environmental elements, which compensates for the fact that the shortage of the factors considered in the traffic environment complexity assessment is not comprehensive. Finally, a series of experiments was carried out to verify the reliability of our proposed method. The results show that the complexity of the static elements is determined only by the physical attributes and shape of the obstacle; the complexity of the dynamic elements is determined by the movement of the obstacle and the movement of the autonomous vehicle, and the comprehensive complexity mainly depends on the complexity of their dynamic elements. Compared with other methods, the complexity evaluation values are generally consistent, the absolute percentage error of the majority of samples was within ±5%, and the degree of deviation was −1.143%, which provides theoretical support for autonomous vehicles on safety and the risk assessment in future. Full article
(This article belongs to the Special Issue Emerging Transportation Solutions for Safer and Greener Future Mobility)
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26 pages, 6409 KiB  
Article
Microscopic Simulating the Impact of Cruising for Parking on Traffic Efficiency and Emission with Parking-and-Visit Test Data
by Xinliu Sui, Xiaofei Ye, Tao Wang, Xingchen Yan, Jun Chen and Bin Ran
Int. J. Environ. Res. Public Health 2022, 19(15), 9127; https://doi.org/10.3390/ijerph19159127 - 26 Jul 2022
Cited by 2 | Viewed by 1536
Abstract
Cruising for parking creates a moving queue of cars that are waiting for vacated parking spaces, but no one can see how many cruisers are in the queue because they are mixed with normal cars. In order to mitigate the influence of cruising [...] Read more.
Cruising for parking creates a moving queue of cars that are waiting for vacated parking spaces, but no one can see how many cruisers are in the queue because they are mixed with normal cars. In order to mitigate the influence of cruising for parking on normal cars, the simulation framework based on VISSIM was proposed for reproducing the cruising vehicles and normal traffic flows. The car-following model of cruising vehicles was calibrated by the GPS and video data. The scenarios under different cruising ratios were analyzed to evaluate the influence of cruising for parking on traffic efficiency and emissions. Finally, the layout optimization changing the parking locations and positions of entrance-exit gates were discussed to mitigate the negative effect. The results indicated that cruising for parking deteriorates the traffic congestion and emissions on the road sections, intersections and network. The closer distances the intersections and sections are to the parking lot, the greater the negative impact is. But the negative effect after the 30% proportion on traffic performance only illustrates the slight deterioration, because the carrying capacity of the network is reached. The research results provide a quantitative method for the hidden contribution of cruising for parking on traffic congestion and emissions. Full article
(This article belongs to the Special Issue Emerging Transportation Solutions for Safer and Greener Future Mobility)
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18 pages, 10146 KiB  
Article
Evaluation of Highway Hydroplaning Risk Based on 3D Laser Scanning and Water-Film Thickness Estimation
by Wenchen Yang, Bijiang Tian, Yuwei Fang, Difei Wu, Linyi Zhou and Juewei Cai
Int. J. Environ. Res. Public Health 2022, 19(13), 7699; https://doi.org/10.3390/ijerph19137699 - 23 Jun 2022
Cited by 4 | Viewed by 1646
Abstract
Hydroplaning risk evaluation plays a pivotal role in highway safety management. It is also an important component in the intelligent transportation system (ITS) ensuring human driving safety. Water-film is the widely accepted vital factor resulting in hydroplaning and thus continuously gained researchers’ attention [...] Read more.
Hydroplaning risk evaluation plays a pivotal role in highway safety management. It is also an important component in the intelligent transportation system (ITS) ensuring human driving safety. Water-film is the widely accepted vital factor resulting in hydroplaning and thus continuously gained researchers’ attention in recent years. This paper provides a new framework to evaluate the hydroplaning potential based on emerging 3D laser scanning technology and water-film thickness estimation. The 3D information of the road surface was captured using a vehicle-mounted Light Detection and Ranging (LiDAR) system and then processed by a wavelet-based filter to remove the redundant information (surrounding environment: trees, buildings, and vehicles). Then, the water film thickness on the given road surface was estimated based on a proposed numerical algorithm developed by the two-dimensional depth-averaged Shallow Water Equations (2DDA-SWE). The effect of the road surface geometry was also investigated based on several field test data in Shanghai, China, in January 2021. The results indicated that the water-film is more likely to appear on the rutting tracks and the pavement with local unevenness. Based on the estimated water-film, the hydroplaning speeds were then estimated to represent the hydroplaning risk of asphalt pavement in rainy weather. The proposed method provides new insights into the water-film estimation, which can help drivers make effective decisions to maintain safe driving. Full article
(This article belongs to the Special Issue Emerging Transportation Solutions for Safer and Greener Future Mobility)
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12 pages, 1659 KiB  
Article
Analysis of the Effect of Human-Machine Co-Driving Vehicle on Pedestrian Crossing Speed at Uncontrolled Mid-Block Road Sections: A VR-Based Case Study
by Kun Wang, Liang Xu and Han Jiang
Int. J. Environ. Res. Public Health 2022, 19(12), 7208; https://doi.org/10.3390/ijerph19127208 - 12 Jun 2022
Viewed by 1684
Abstract
The current study investigates the effects of speed and time headway of human-machine co-driving vehicles on pedestrian crossing speed at uncontrolled mid-block road sections. A VR-based simulation study is conducted to study pedestrian crossing behaviour when facing human-machine co-driving vehicles. A total of [...] Read more.
The current study investigates the effects of speed and time headway of human-machine co-driving vehicles on pedestrian crossing speed at uncontrolled mid-block road sections. A VR-based simulation study is conducted to study pedestrian crossing behaviour when facing human-machine co-driving vehicles. A total of 30 college students are recruited, and each participant is required to complete 5 street-crossing simulator trials facing human-machine co-driving vehicles with varying time headway levels and speeds. The correlations and differences between demographic information, time headway, vehicle speed, and pedestrian crossing speed are analyzed. The results show that gender and pedestrian’s trust in human-machine co-driving vehicles are significantly correlated with pedestrian crossing speed. The pedestrian crossing speed increases with the increase in vehicle speed and decreases with the increase in vehicle time headway. In addition, the time headway has a stronger correlation with the pedestrian crossing speed than the vehicle speed. The findings will provide theoretical and methodological support for the formulation of pedestrian crossing control measures in the stage of human-machine co-driving. Full article
(This article belongs to the Special Issue Emerging Transportation Solutions for Safer and Greener Future Mobility)
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Review

Jump to: Research

14 pages, 1119 KiB  
Review
Effective and Acceptable Eco-Driving Guidance for Human-Driving Vehicles: A Review
by Ran Tu, Junshi Xu, Tiezhu Li and Haibo Chen
Int. J. Environ. Res. Public Health 2022, 19(12), 7310; https://doi.org/10.3390/ijerph19127310 - 14 Jun 2022
Cited by 8 | Viewed by 2846
Abstract
Eco-driving guidance refers to courses, warnings, or suggestions provided to human drivers to improve driving behaviour to enable less energy use and emissions. This paper reviews existing eco-driving guidance studies and identifies challenges to tackle in the future. We summarize two categories of [...] Read more.
Eco-driving guidance refers to courses, warnings, or suggestions provided to human drivers to improve driving behaviour to enable less energy use and emissions. This paper reviews existing eco-driving guidance studies and identifies challenges to tackle in the future. We summarize two categories of current guidance systems, static and dynamic, distinguished by whether real-world driving records are used to generate behaviour guidance or not. We find that influencing factors, such as the content of suggestions, the display methods, and drivers’ socio-demographic characteristics, have varied effects on the guidance results across studies. Drivers are reported to have basic eco-driving knowledge, while the question of how to motivate the acceptance and practice of such behaviour, especially in the long term, is overlooked. Adaptive driving suggestions based on drivers’ individual habits can improve the effectiveness and acceptance while this field is under investigation. In-vehicle assistance presents potential safety issues, and visualized in-vehicle assistance is reported to be most distractive. Given existing studies focusing on the operational level, a common agreement on the guidance design and associated influencing factors has yet to be reached. Research on the systematic and tactical design of eco-driving guidance and in-vehicle interaction is advised. Full article
(This article belongs to the Special Issue Emerging Transportation Solutions for Safer and Greener Future Mobility)
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