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Technology, User Behavior and Infrastructure for Sustainable Traffic Safety
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Technology, User Behavior and Infrastructure for Sustainable Traffic Safety

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

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 23866

Special Issue Editors

Dr. Quan Yuan

E-Mail Website1 Website2
Guest Editor
School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
Interests: traffic safety; accident analysis; intelligent and connected vehicles; human factors engineering; ITS
Special Issues, Collections and Topics in MDPI journals
Dr. Cong Chen

E-Mail Website
Guest Editor
Center for Urban Transportation Research, University of South Florida, Tampa, FL 33620, USA
Interests: crash data modeling; pedestrian and bicycle safety; advanced model development; human factor and behavior analysis; transportation geospatial and temporal analysis; education and workforce development
Dr. Weiwei Qi

E-Mail Website
Guest Editor
School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China
Interests: traffic flow; traffic safety; intelligent transportation system
Prof. Dr. Tao Wang

E-Mail Website
Guest Editor
School of Architecture and Transportation, Guilin University of Electronic Technology, Guilin 541004, China
Interests: traffic behavior and safety; traffic data analysis; intelligent transportation system

Special Issue Information

Dear Colleagues,

We would like to cordially invite you contribute to this Special Issue on “Technology, User Behavior and Infrastructure for Sustainable Traffic Safety”.

Sustainable traffic safety is an important way of preventing traffic accidents, maintaining traffic operation order and ensuring the stable development of the urban economy. Therefore, it is critical to study traffic safety from the aspects of intelligent transportation technologies, user behaviors and traffic infrastructures.

The robust performance of a transportation system relies on two components: safety and sustainability. Traditionally, transportation safety has focused on the reduction in crash frequency and injury severity, while sustainability has emphasized the social, mobile and environmental benefits. However, these two components are not disconnected, but interact with each other to bring synergy to enhance the system performance. It is understandable that a transportation system would not be sustainable if it is not safe, by generating a lot of crashes and causalities. In other words, safety is the first priority and prerequisite for mobility, social benefits and environmental accommodation. On the other hand, it is also necessary to balance between safety and mobility for multimodal transportation systems, given different land context classifications and roadway functions. Therefore, it should be our goal to achieve sustainable traffic safety for reliable transportation systems.

This Special Issue aims to offer a platform for professionals worldwide to share research insights and achievements towards safe and sustainable transportation systems, focusing on technological developments and applications, user behavior analyses and smart infrastructure design.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Human behavior analysis for safety of all road user types;
  • Smart Infrastructure to improve road user safety;
  • Safety and mobility trade-off in roadway safety design and analysis;
  • Theory modelling and innovative technologies in sustainable transportation system;
  • Modelling and application of emerging road traffic conflict technology;
  • Surrogate safety measures for collision avoidances;
  • Roles of human factors in sustainable road traffic safety;
  • Emerging path discovery and decision support algorithm for improving sustainable traffic safety;
  • Data-driven methods for road traffic evaluation and management;
  • Advanced technology on investigation, analysis and prevention of road traffic accidents.

We look forward to receiving your contributions.

Dr. Quan Yuan
Dr. Cong Chen
Dr. Weiwei Qi
Prof. Dr. Tao Wang
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

  • sustainable traffic safety
  • user behavior
  • infrastructure
  • safety and mobility trade-off

Published Papers (15 papers)

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29 pages, 25332 KiB  
Article
Analyzing Driving Safety on Prairie Highways: A Study of Drivers’ Visual Search Behavior in Varying Traffic Environments
by Xu Ding, Haixiao Wang, Chutong Wang and Min Guo
Sustainability 2023, 15(16), 12146; https://doi.org/10.3390/su151612146 - 8 Aug 2023
Cited by 1 | Viewed by 989
Abstract
This study aimed to investigate disparities in drivers’ visual search behavior across various typical traffic conditions on prairie highways and analyze driving safety at the visual search level. The study captured eye movement data from drivers across six real-world traffic environments: free driving, [...] Read more.
This study aimed to investigate disparities in drivers’ visual search behavior across various typical traffic conditions on prairie highways and analyze driving safety at the visual search level. The study captured eye movement data from drivers across six real-world traffic environments: free driving, vehicle-following, oncoming vehicles, rear vehicles overtaking cut-in, roadside risks, and driving through intersections, by carrying out a real vehicle test on a prairie highway. The drivers’ visual search area was divided into five areas using clustering principles. By integrating the Markov chain and information entropy theory, the information entropy of fixation distribution (IEFD) was constructed to quantify the complexity of drivers’ traffic information search. Additionally, the main area of visual search (MAVS) and the peak-to-average ratio of saccade velocity (PARSV) were introduced to measure visual search range and stability, respectively. The study culminated in the creation of a visual search load evaluation model that utilizes both VIKOR and improved CRITIC methodologies. The findings indicated that while drivers’ visual distribution and transfer modes vary across different prairie highway traffic environments, the current lane consistently remained their primary area of search for traffic information. Furthermore, it was found that each visual search indicator displayed significant statistical differences as traffic environments changed. Particularly when encountering roadside risks, drivers’ visual search load increased significantly, leading to a considerable decrease in driving safety. Full article
(This article belongs to the Special Issue Technology, User Behavior and Infrastructure for Sustainable Traffic Safety)
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14 pages, 3259 KiB  
Article
Interaction Patterns of Motorists and Cyclists at Intersections: Insight from a Vehicle–Bicycle Simulator Study
by Meng Zhang, Laura Quante, Kilian Gröne and Caroline Schießl
Sustainability 2023, 15(15), 11692; https://doi.org/10.3390/su151511692 - 28 Jul 2023
Viewed by 925
Abstract
At intersections, road users need to comprehend the intentions of others while also implicitly expressing their own intentions using dynamic information. Identifying patterns of this implicit communication between human drivers and vulnerable road users (VRUs) at intersections could enhance automated driving functions (ADFs), [...] Read more.
At intersections, road users need to comprehend the intentions of others while also implicitly expressing their own intentions using dynamic information. Identifying patterns of this implicit communication between human drivers and vulnerable road users (VRUs) at intersections could enhance automated driving functions (ADFs), enabling more human-like communication with VRUs. To this end, we conducted a coupled vehicle–bicycle simulator study to investigate interactions between right-turning motorists and crossing cyclists. This involved 34 participants (17 pairs of motorists and cyclists) encountering each other in a virtual intersection. The analysis focused on identifying interaction patterns between motorists and cyclists, specifically aiming to discern which patterns were more likely to be accepted by both parties. We found that in CM (vehicles overtaking), the post-encroachment time (PET) and the average speed of vehicles were higher than in the other two interaction patterns: C (bicycles always in front) and CMC (bicycles overtake). However, subjective ratings indicated that CM was viewed as more critical and less cooperative. Furthermore, this study unveiled the influence of crossing order and overtaking position on subjective ratings through ordered logistic regressions, suggesting that earlier overtaking could improve cyclists’ acceptance of the interaction. These findings may contribute to the optimization of communication strategies for ADF, thereby ensuring safety in interactions with VRUs. Full article
(This article belongs to the Special Issue Technology, User Behavior and Infrastructure for Sustainable Traffic Safety)
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25 pages, 10403 KiB  
Article
Exploring the Visual Attention Mechanism of Long-Distance Driving in an Underground Construction Cavern: Eye-Tracking and Simulated Driving
by Qin Zeng, Yun Chen, Xiazhong Zheng, Meng Zhang, Donghui Li and Qilin Hu
Sustainability 2023, 15(12), 9140; https://doi.org/10.3390/su15129140 - 6 Jun 2023
Cited by 2 | Viewed by 1234
Abstract
Prolonged driving is necessary in underground construction caverns to transport materials, muck, and personnel, exposing drivers to high-risk and complex environments. Despite previous studies on attention and gaze prediction at tunnel exit-inlet areas, a significant gap remains due to the neglect of dual [...] Read more.
Prolonged driving is necessary in underground construction caverns to transport materials, muck, and personnel, exposing drivers to high-risk and complex environments. Despite previous studies on attention and gaze prediction at tunnel exit-inlet areas, a significant gap remains due to the neglect of dual influences of long-distance driving and complex cues. To address this gap, this study establishes an experimental scenario in a construction environment, utilizing eye-tracking and simulated driving to collect drivers’ eye movement data. An analysis method is proposed to explore the visual change trend by examining the evolution of attention and calculating the possibility of visual cues being perceived at different driving stages to identify the attentional selection mechanism. The findings reveal that as driving time increases, fixation time decreases, saccade amplitude increases, and some fixations transform into unconscious saccades. Moreover, a phenomenon of “visual adaptation” occurs over time, reducing visual sensitivity to environmental information. At the start of driving, colorful stimuli and safety-related information compete for visual resources, while safety-related signs, particularly warning signs, always attract drivers’ attention. However, signs around intense light are often ignored. This study provides a scientific basis for transport safety in the construction environment of underground caverns. Full article
(This article belongs to the Special Issue Technology, User Behavior and Infrastructure for Sustainable Traffic Safety)
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26 pages, 7859 KiB  
Article
The Impact of In-Vehicle Traffic Lights on Driving Characteristics in the Presence of Obstructed Line-of-Sight
by Yunshun Zhang, Qishuai Xie, Minglei Gao and Yuchen Guo
Sustainability 2023, 15(10), 8416; https://doi.org/10.3390/su15108416 - 22 May 2023
Viewed by 1054
Abstract
In-vehicle traffic lights (IVTLs) have been identified as a potential means of eco-driving. However, the extent to which they influence driving characteristics in the event of obstructed on-road traffic lights (ORTLs) has yet to be fully examined. Firstly, the situation of partially deployed [...] Read more.
In-vehicle traffic lights (IVTLs) have been identified as a potential means of eco-driving. However, the extent to which they influence driving characteristics in the event of obstructed on-road traffic lights (ORTLs) has yet to be fully examined. Firstly, the situation of partially deployed IVTLs in both vehicles was analyzed to identify the factors that affect driving characteristics. Through the following distance model, relative vehicle speed, acceleration and deceleration, and following distance were recognized as the contributing factors. The evaluation indicators for driving characteristics were thereby further established. Then, a hardware-in-the-loop simulation platform was built using PreScan 8.5-MATLAB/Simulink R2018b joint simulation software and a Logitech G29 device. IVTLs were implemented using modules in the joint simulation software. Finally, under the scenarios of obstructed ORTLs and various deployment conditions of IVTLs, the original data were collected from 50 experimental subjects with simulated driving. The subjects included 25 males and 25 females, all of whom were non-professional drivers, with ages ranging from 20 to 40 years old. The conclusion was reached that IVTLs could improve driving comfort by approximately 10% in sunny weather (p = 0.008 < 0.05, p = 0.023 < 0.05; p = 0.046 < 0.05, p = 0.001 < 0.05), driving maneuverability by approximately 30% in foggy weather (p = 0.033 < 0.05), and driving safety by approximately 50% in the ORTLs obstructed by a truck scenario (p = 0.019 < 0.05). In general, even if only one vehicle was equipped with IVTLs, certain gain effects on the driving characteristics of both vehicles could still be provided. Full article
(This article belongs to the Special Issue Technology, User Behavior and Infrastructure for Sustainable Traffic Safety)
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22 pages, 2195 KiB  
Article
Investigation of Passengers’ Perceived Transfer Distance in Urban Rail Transit Stations Using XGBoost and SHAP
by Chengyuan Mao, Wenjiao Xu, Yiwen Huang, Xintong Zhang, Nan Zheng and Xinhuan Zhang
Sustainability 2023, 15(10), 7744; https://doi.org/10.3390/su15107744 - 9 May 2023
Cited by 2 | Viewed by 1265
Abstract
Providing high-quality public transport services and enhancing passenger experiences require efficient urban rail transit connectivity; however, passengers’ perceived transfer distance at urban rail transit stations may differ from the actual transfer distance, resulting in inconvenience and dissatisfaction. To address this issue, this study [...] Read more.
Providing high-quality public transport services and enhancing passenger experiences require efficient urban rail transit connectivity; however, passengers’ perceived transfer distance at urban rail transit stations may differ from the actual transfer distance, resulting in inconvenience and dissatisfaction. To address this issue, this study proposed a novel machine learning framework that measured the perceived transfer distance in urban rail transit stations and analyzed the significance of each influencing factor. The framework introduced the Ratio of Perceived Transfer Distance Deviation (R), which was evaluated using advanced XGBoost and SHAP models. To accurately evaluate R, the proposed framework considered 32 indexes related to passenger personal attributes, transfer facilities, and transfer environment. The study results indicated that the framework based on XGBoost and SHAP models can effectively measure the R of urban rail transit passengers. Key factors that affected R included the Rationality of Signs and Markings, Ratio of Escalators Length, Rationality of Traffic Organization outside The Station, Ratio of Stairs Length, and Degree of Congestion on Passageways. These findings can provide valuable theoretical references for designing transfer facilities and improving transfer service levels in urban rail transit stations. Full article
(This article belongs to the Special Issue Technology, User Behavior and Infrastructure for Sustainable Traffic Safety)
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25 pages, 8789 KiB  
Article
A Short-Term Parking Demand Prediction Framework Integrating Overall and Internal Information
by Tao Wang, Sixuan Li, Wenyong Li, Quan Yuan, Jun Chen and Xiang Tang
Sustainability 2023, 15(9), 7096; https://doi.org/10.3390/su15097096 - 24 Apr 2023
Cited by 1 | Viewed by 2342
Abstract
With the development of smart cities and smart transportation, cities can gradually provide people with more information to facilitate their life and travel, and parking is also inseparable from both of them. Accurate on-street parking demand prediction can improve parking resource utilization and [...] Read more.
With the development of smart cities and smart transportation, cities can gradually provide people with more information to facilitate their life and travel, and parking is also inseparable from both of them. Accurate on-street parking demand prediction can improve parking resource utilization and parking management efficiency, as well as potentially improve urban traffic conditions. Previous parking demand prediction methods seldom consider the correlation between the parking demand of a road section and its surroundings. Therefore, in order to capture the correlation of parking demand in the temporal and spatial dimensions as carefully as possible and enrich the relevant features in the prediction model so as to achieve more accurate prediction results, we designed a parking demand prediction structure that considers different features from two perspectives: overall and internal. We used gated recurrent units (GRU) to extract demand influences in the temporal dimension. The GRU is used in combination with a graph convolutional neural network (GCN) to extract demand influencing factors in the spatial dimension. Additionally, a more detailed representation is designed to express spatial dimensional features. Then, based on the historical parking demand features extracted using encoder–decoder, we fuse the extracted spatio-temporal features with them to finally obtain an on-street parking demand prediction model combining the overall and the internal information. By combining them, we can integrate more correlation factors to achieve a more accurate parking demand prediction. The performance of the model is evaluated by real parking data in Xiufeng District of Guilin. The results show that the proposed model achieves good prediction performance compared with other baselines. In addition, we also design feature ablation experiments. Through the comparison of the results, we find that each feature considered in the proposed model is important in parking demand prediction. Full article
(This article belongs to the Special Issue Technology, User Behavior and Infrastructure for Sustainable Traffic Safety)
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14 pages, 2612 KiB  
Article
Modeling Interactions of Autonomous/Manual Vehicles and Pedestrians with a Multi-Agent Deep Deterministic Policy Gradient
by Weichao Hu, Hongzhang Mu, Yanyan Chen, Yixin Liu and Xiaosong Li
Sustainability 2023, 15(7), 6156; https://doi.org/10.3390/su15076156 - 3 Apr 2023
Viewed by 1521
Abstract
This article focuses on the development of a stable pedestrian crash avoidance mitigation system for autonomous vehicles (AVs). Previous works have only used simple AV–pedestrian models, which do not reflect the actual interaction and risk status of intelligent intersections with manual vehicles. The [...] Read more.
This article focuses on the development of a stable pedestrian crash avoidance mitigation system for autonomous vehicles (AVs). Previous works have only used simple AV–pedestrian models, which do not reflect the actual interaction and risk status of intelligent intersections with manual vehicles. The paper presents a model that simulates the interaction between automatic driving vehicles and pedestrians on unsignalized crosswalks using the multi-agent deep deterministic policy gradient (MADDPG) algorithm. The MADDPG algorithm optimizes the PCAM strategy through the continuous interaction of multiple independent agents and effectively captures the inherent uncertainty in continuous learning and human behavior. Experimental results show that the MADDPG model can fully mitigate collisions in different scenarios and outperforms the DDPG and DRL algorithms. Full article
(This article belongs to the Special Issue Technology, User Behavior and Infrastructure for Sustainable Traffic Safety)
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16 pages, 2040 KiB  
Article
The Effect of Multifactor Interaction on the Quality of Human–Machine Co-Driving Vehicle Take-Over
by Yaxi Han, Tao Wang, Dong Shi, Xiaofei Ye and Quan Yuan
Sustainability 2023, 15(6), 5131; https://doi.org/10.3390/su15065131 - 14 Mar 2023
Cited by 1 | Viewed by 1557
Abstract
This paper investigates the effects of non-driving related tasks, take-over request time, and take-over mode interactions on take-over performance in human–machine cooperative driving in a highway environment. Based on the driving simulation platform, a human–machine collaborative driving simulation experiment was designed with various [...] Read more.
This paper investigates the effects of non-driving related tasks, take-over request time, and take-over mode interactions on take-over performance in human–machine cooperative driving in a highway environment. Based on the driving simulation platform, a human–machine collaborative driving simulation experiment was designed with various take-over quality influencing factors. The non-driving related tasks included no task, listening to the radio, watching videos, playing games, and listening to the radio and playing games; the take-over request time was set to 6, 5, 4, and 3 s, and the take-over methods include passive and active take-over. Take-over test data were collected from 65 drivers. The results showed that different take-over request times had significant effects on driver take-over performance and vehicle take-over steady state (p < 0.05). Driver reaction time and minimum TTC decreased with decreasing take-over request time, maximum synthetic acceleration increased with decreasing take-over request time, accident rate increased significantly at 3 s take-over request time, and take-over safety was basically ensured at 4 s request time. Different non-driving related tasks have a significant effect on driver take-over performance (p < 0.05). Compared with no task, non-driving related tasks significantly increase driver reaction time, but they only have a small effect on vehicle take-over steady state. Vehicle take-over mode has a significant effect on human–machine cooperative driving take-over quality; compared with passive take-over mode, the take-over quality under active take-over mode is significantly lower. Full article
(This article belongs to the Special Issue Technology, User Behavior and Infrastructure for Sustainable Traffic Safety)
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15 pages, 6265 KiB  
Article
Improving Traffic Safety through Traffic Accident Risk Assessment
by Zhenghua Hu, Jibiao Zhou and Enyou Zhang
Sustainability 2023, 15(4), 3748; https://doi.org/10.3390/su15043748 - 17 Feb 2023
Cited by 2 | Viewed by 1966
Abstract
The continuous development of sensors and the Internet of Things has produced a large amount of traffic data with location information. The improvement of traffic safety benefits from the availability of traffic accident data. Managers can patrol and control relevant areas in advance [...] Read more.
The continuous development of sensors and the Internet of Things has produced a large amount of traffic data with location information. The improvement of traffic safety benefits from the availability of traffic accident data. Managers can patrol and control relevant areas in advance with limited police resources, according to the short-term traffic accident predictions. As a result, the possibility of accidents can be reduced, and the level of traffic safety can be improved. The traditional approach to accident prediction relies too much on the subjective experience of traffic managers. Inspired by the deep learning technology in the field of computer vision, this study first divides the road network into regular grids and takes the number of traffic accidents in each grid as the pixel value of an image. Then, a traffic accident prediction approach based on a bi-directional ConvLSTM U-Net with densely connected convolutions (BCDU-Net) is proposed. This method mines the regular information hidden in the accident data and introduces densely connected convolutions to further extract the deep spatial-temporal features contained in the traffic accident sequence. Thus, the issues of gradient disappearance and model over-fitting caused by the traditional model in model training can be avoided. Finally, the simulation experiment is carried out on the historical traffic accident data of Yinzhou District, Ningbo City. Results show that BCDU-Net has better accuracy and precision than other models in three data sets: motor vehicle accidents, non-motor vehicle accidents, and single-vehicle accidents. Therefore, the BCDU-Net is more suitable for traffic accident prediction and has good application prospects for improving road safety. Full article
(This article belongs to the Special Issue Technology, User Behavior and Infrastructure for Sustainable Traffic Safety)
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23 pages, 11025 KiB  
Article
Study on Identification and Prevention of Traffic Congestion Zones Considering Resilience-Vulnerability of Urban Transportation Systems
by Xueting Zhao, Liwei Hu, Xingzhong Wang and Jiabao Wu
Sustainability 2022, 14(24), 16907; https://doi.org/10.3390/su142416907 - 16 Dec 2022
Cited by 8 | Viewed by 2114
Abstract
In order to solve the problem of urban short-term traffic congestion and temporal and spatial heterogeneity, it is important to scientifically delineate urban traffic congestion response areas to alleviate regional traffic congestion and improve road network efficiency. Previous urban traffic congestion zoning is [...] Read more.
In order to solve the problem of urban short-term traffic congestion and temporal and spatial heterogeneity, it is important to scientifically delineate urban traffic congestion response areas to alleviate regional traffic congestion and improve road network efficiency. Previous urban traffic congestion zoning is mostly divided by urban administrative divisions, which is difficult to reflect the difference of congestion degree within administrative divisions or traffic congestion zoning. In this paper, we introduce the Self-Organizing Feature Mapping (SOFM) model, construct the urban traffic congestion zoning index system based on the resilience and vulnerability of urban traffic systems, and establish the urban traffic congestion zoning model, which is divided into four, five, six, and seven according to the different structures of competition layer topology. The four vulnerability damage capacity indicators of traffic volume, severe congestion mileage, delay time and average operating speed, and two resilience supply capacity indicators of traffic systems, namely, road condition and number of lanes, are used as model input vectors; the data of Guiyang city from January to June 2021 are used as data sets to input four SOFM models for training and testing and the best SOFM model with six competitive topologies is constructed. Finally, the Support Vector Machine (SVM) is used to identify the optimal partition boundary line for traffic congestion. The results show that the four models predict the urban traffic congestion zoning level correctly over 95% on the test set, each traffic congestion zoning evaluation index in the urban area shows different obvious spatial clustering characteristics, the urban traffic congestion area is divided into six categories, and the city is divided into 16 zoning areas considering the urban traffic congestion control types (prevention zone, control zone, closure control zone). The spatial boundary is clear and credible, which helps to improve the spatial accuracy when predicting urban traffic congestion zoning and provides a new methodological approach for urban traffic congestion zoning and zoning boundary delineation. Full article
(This article belongs to the Special Issue Technology, User Behavior and Infrastructure for Sustainable Traffic Safety)
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17 pages, 3782 KiB  
Article
Sequence Calculation and Automatic Discrimination of Vehicle Merging Conflicts in Freeway Merging Areas
by Jinsong Hu, Huapeng Wang, Wei Wang and Weiwei Qi
Sustainability 2022, 14(24), 16834; https://doi.org/10.3390/su142416834 - 15 Dec 2022
Viewed by 1187
Abstract
The freeway is a continuous flow facility that improves the accessibility and operational efficiency of the road network. However; freeway merging areas are accident-prone areas. In order to investigate the reasons for the high occurrence of accidents in merging areas, this paper considers [...] Read more.
The freeway is a continuous flow facility that improves the accessibility and operational efficiency of the road network. However; freeway merging areas are accident-prone areas. In order to investigate the reasons for the high occurrence of accidents in merging areas, this paper considers the dynamic nature of traffic conflicts, constructs a sequence model of merging conflicts with Time Difference to Collision (TDTC) as the index, and implements automatic identification of merging conflicts based on the LightGBM algorithm. A UAV was used to collect vehicle trajectory data at the Guanghe Freeway in Guangzhou to verify the accuracy of automatic identification, with an accuracy rate of 91%. The results show that the most important feature of severe conflicts is the choice of the merging position. In addition, the most important feature of general conflicts is the standard deviation of speed before merging. Lastly, the most important feature of minor conflicts is the longitudinal speed difference between the ramp and mainline vehicles. Full article
(This article belongs to the Special Issue Technology, User Behavior and Infrastructure for Sustainable Traffic Safety)
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11 pages, 632 KiB  
Article
Correlation Analysis on Accident Injury and Risky Behavior of Vulnerable Road Users Based on Bayesian General Ordinal Logit Model
by Quan Yuan, Xianguo Zhai, Wei Ji, Tiantong Yang, Yang Yu and Shengnan Yu
Sustainability 2022, 14(23), 16048; https://doi.org/10.3390/su142316048 - 1 Dec 2022
Cited by 2 | Viewed by 1802
Abstract
Crashes involving vulnerable road users (VRUs) are types of traffic accidents which take up a large proportion and cause lots of casualties. With methods of statistics and accident reconstruction, this research investigates 378 actual traffic collisions between vehicles and VRUs in China in [...] Read more.
Crashes involving vulnerable road users (VRUs) are types of traffic accidents which take up a large proportion and cause lots of casualties. With methods of statistics and accident reconstruction, this research investigates 378 actual traffic collisions between vehicles and VRUs in China in 2021 to obtain human, vehicle, and road factors that affect the injury severity. The paper focuses on risky behaviors of VRUs and typical scenarios such as non-use of the crosswalk, violation of traffic lights, stepping into the motorway, and riding against traffic. Then, based on the Bayesian General Ordinal Logit model, influencing factors of injury severity in 168 VRU accidents are analyzed. Results demonstrate that the probability of death in an accident will rise when the motorist is middle-aged and the VRU is an e-bicycle rider; the probability of death in an accident will greatly decrease when the VRU bears minor responsibility. Therefore, middle-aged motorists and e-bicycle riders should strengthen safety consciousness and compliance with regulations to prevent accident and reduce injury for VRUs. In addition, helmet-wearing will help to reduce riders’ injuries. This research may provide ideas for intelligent vehicles to avoid collisions with risky VRUs. Full article
(This article belongs to the Special Issue Technology, User Behavior and Infrastructure for Sustainable Traffic Safety)
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15 pages, 2048 KiB  
Article
Modeling Urban Freeway Rear-End Collision Risk Using Machine Learning Algorithms
by Xiaolong Ma, Qiang Yu and Jianbei Liu
Sustainability 2022, 14(19), 12047; https://doi.org/10.3390/su141912047 - 23 Sep 2022
Cited by 5 | Viewed by 1399
Abstract
A large amount of traffic crash investigations have shown that rear-end collisions are the main type collisions on the freeway. The purpose of this study is to investigate the rear-end collision risk on the freeway. Firstly, a new framework was proposed to develop [...] Read more.
A large amount of traffic crash investigations have shown that rear-end collisions are the main type collisions on the freeway. The purpose of this study is to investigate the rear-end collision risk on the freeway. Firstly, a new framework was proposed to develop the rear-end collision probability (RCP) model between two vehicles based on Generalized Pareto Distribution (GPD). Secondly, the freeway rear-end collision risk (F-RCR) was defined as the sum of the rear-end collision probability of each vehicle and divided into three levels which was high, median, and low rear-end collision risk. Then, different machine learning algorithms were used to model F-RCR under the condition of an unbalanced dataset. The result of the RCP model showed continuous change and can identify the dangerous condition quickly compared to the traditional models even when the speed of the leading vehicle is faster than the following vehicle. When the vehicle distribution was unbalanced on road and the speed difference between adjacent lanes and the traffic volume was large, F-RCR will increase. Multi-Layer Perceptron (MLP) was found to be more suitable for modeling F-RCR. The framework provided in this research was transferrable and can be used in the freeway proactive traffic safety management system. Full article
(This article belongs to the Special Issue Technology, User Behavior and Infrastructure for Sustainable Traffic Safety)
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17 pages, 2479 KiB  
Article
Correlation Analysis of Real-Time Warning Factors for Construction Heavy Trucks Based on Electrified Supervision System
by Weiwei Qi, Shufang Zhu and Jinsong Hu
Sustainability 2022, 14(17), 10944; https://doi.org/10.3390/su141710944 - 1 Sep 2022
Cited by 1 | Viewed by 1137
Abstract
Due to inertia, heavy trucks are often involved in serious losses in accidents. To prevent such accidents, since 2020, the transportation department has promoted the free installation of intelligent video surveillance systems on key vehicles of “two passengers, one danger, and one cargo”. [...] Read more.
Due to inertia, heavy trucks are often involved in serious losses in accidents. To prevent such accidents, since 2020, the transportation department has promoted the free installation of intelligent video surveillance systems on key vehicles of “two passengers, one danger, and one cargo”. The system can provide real-time warnings to drivers for various risky driving behaviors. The data collected by the system are often managed by third-party platforms, and such platforms do not have authority beyond the information that the authority system can collect. Therefore, it is necessary to use the trajectory data and warning behavior records that the system can collect for behavior analysis and accident prevention. To analyze the correlation between different warning factors, 88,841 warning records and 1033 trip records of heavy trucks for construction in the second half of 2021 were collected from a third-party supervision platform. The research associated the warning records with the vehicle operation records according to the warning time and the license plate and established a multiple linear regression equation associated with operational attributes and warning factors. The factor selection results showed that only two warning factors, “too close distance” and “lane change across solid line”, can be used as dependent variables to construct a regression model. The results showed that many distracted behaviors had a significant impact on aggressive driving behavior. Companies need to focus on behaviors that are prone to other warning behaviors. This paper provides a theoretical basis for the optimization of the warning function of the electrified supervision system and the continuing education of drivers by exploring the internal correlation between different warning factors. Full article
(This article belongs to the Special Issue Technology, User Behavior and Infrastructure for Sustainable Traffic Safety)
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Knowledge Mapping with CiteSpace, VOSviewer, and SciMAT on Intelligent Connected Vehicles: Road Safety Issue
by Wei Ji, Shengnan Yu, Zefang Shen, Min Wang, Gang Cheng, Tiantong Yang and Quan Yuan
Sustainability 2023, 15(15), 12003; https://doi.org/10.3390/su151512003 - 4 Aug 2023
Cited by 4 | Viewed by 1868
Abstract
The rapid development of the Intelligent connected vehicle (ICV) industry has stimulated technological innovation in energy and communication while also highlighting the need for effective policies and road safety measures. Understanding and addressing road safety issues in the context of ICVs can contribute [...] Read more.
The rapid development of the Intelligent connected vehicle (ICV) industry has stimulated technological innovation in energy and communication while also highlighting the need for effective policies and road safety measures. Understanding and addressing road safety issues in the context of ICVs can contribute to ICV development and safe driving. This paper employs a knowledge mapping approach to scientifically and intuitively demonstrate research on the road safety issues of ICV over the last decade. By utilizing bibliometric tools such as CiteSpace, VOSviewer, and SciMAT, a total of 3661 original articles from the Web of Science are examined to explore three aspects. Firstly, the study investigates the collaborative relationships among authors and institutions within the industry. Secondly, it summarizes major research topics by analyzing and clustering keywords. Lastly, the paper identifies research hotspots and predicts future research directions. The findings reveal a dynamic field characterized by close collaboration among diverse institutions, with China and the United States emerging as the most active countries and mathematics and computer science journals becoming mainstream. According to three bibliometric tools, the research topics primarily revolve around three areas: Vehicular ad hoc Networks (VANET), intelligent transportation systems (ITS), and network security. Machine learning and V2X communication are predicted to be essential research topics in the next stage. Research on traffic accidents still has potential as the number of ICVs increases. Full article
(This article belongs to the Special Issue Technology, User Behavior and Infrastructure for Sustainable Traffic Safety)
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