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01 A Cooperative Map Matching Algorithm Applied in Intelligent and Connected Vehicle Positioning
02 Indoor Sign-based Visual Localization Method
03 Intelligent Vehicles Localization Based on Semantic Map Representation from 3D Point Clouds
04 Companion Relationship Discovering Algorithm for Passengers in the Cruise Based on UWB Positioning
05 An Overview of Traffic Management in "Automatic+Manual" Driving Environment
06 An Image Generation Method for Automated Driving Based on Improved GAN
07 An Analysis of Injury Severities in School Bus Accidents Based on Random Parameter Logit Models
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2025, 43(4): 1-13.
doi: 10.3963/j.jssn.1674-4861.2025.04.001
Abstract:
The widespread adoption of autonomous driving technology depends not only on technological progress but also on public attitudes. In real-world operations, autonomous vehicles inevitably interact with vulnerable road users, including pedestrians, cyclists, elderly people, children, and persons with disabilities. Investigating these groups' attitudes toward autonomous driving and the factors that shape them is crucial for fostering social acceptance and ensuring the safe deployment of this technology. This review outlines the potential advantages of autonomous vehicles. It focuses on the attitudes of vulnerable road users and their influencing factors, aiming to provide guidance for researchers, technology developers, and policymakers in technological improvement and policy design. The findings indicate that most vulnerable road users generally hold positive attitudes toward autonomous vehicles. Younger male pedestrians and cyclists show stronger support. Persons with disabilities view them as opportunities for enhanced mobility, whereas older adults, due to lower adaptability, tend to prefer conventional vehicles. Familiarity with the technology further improves acceptance. Nevertheless, safety and reliability remain critical barriers to trust. Pedestrians and cyclists worry about road interaction risks. Persons with disabilities are concerned about design flaws and loss of independence. Older adults feel uneasy about interacting with new technologies, and parents express concerns over child safety features. The review also summarizes policy measures and practices in several countries aimed at protecting vulnerable road users, offering lessons for safety assurance in the era of autonomous driving. Finally, it highlights future research directions, including expanding study populations and geographical scope, adopting experiential research methods, conducting longitudinal studies, applying latent class analysis to identify subgroup differences, and differentiating between technical levels and operational models. These efforts will advance the understanding of how vulnerable road users' attitudes evolve and provide valuable insights for technology development and policy refinement.
The widespread adoption of autonomous driving technology depends not only on technological progress but also on public attitudes. In real-world operations, autonomous vehicles inevitably interact with vulnerable road users, including pedestrians, cyclists, elderly people, children, and persons with disabilities. Investigating these groups' attitudes toward autonomous driving and the factors that shape them is crucial for fostering social acceptance and ensuring the safe deployment of this technology. This review outlines the potential advantages of autonomous vehicles. It focuses on the attitudes of vulnerable road users and their influencing factors, aiming to provide guidance for researchers, technology developers, and policymakers in technological improvement and policy design. The findings indicate that most vulnerable road users generally hold positive attitudes toward autonomous vehicles. Younger male pedestrians and cyclists show stronger support. Persons with disabilities view them as opportunities for enhanced mobility, whereas older adults, due to lower adaptability, tend to prefer conventional vehicles. Familiarity with the technology further improves acceptance. Nevertheless, safety and reliability remain critical barriers to trust. Pedestrians and cyclists worry about road interaction risks. Persons with disabilities are concerned about design flaws and loss of independence. Older adults feel uneasy about interacting with new technologies, and parents express concerns over child safety features. The review also summarizes policy measures and practices in several countries aimed at protecting vulnerable road users, offering lessons for safety assurance in the era of autonomous driving. Finally, it highlights future research directions, including expanding study populations and geographical scope, adopting experiential research methods, conducting longitudinal studies, applying latent class analysis to identify subgroup differences, and differentiating between technical levels and operational models. These efforts will advance the understanding of how vulnerable road users' attitudes evolve and provide valuable insights for technology development and policy refinement.
2025, 43(4): 14-23.
doi: 10.3963/j.jssn.1674-4861.2025.04.002
Abstract:
To precisely quantify the dynamic evolution of vessel collision risks in inland bridge zones and enable tiered early warning, this study proposes an assessment method integrating vessel dynamic motion prediction with multi-dimensional potential field coupling. By adapting traditional safety potential field models to account for vessel navigation characteristics and bridge zone environmental constraints, the approach is applied to vessel-bridge collision risk evaluation. Based on risk causation theory, bridge zone risks are decomposed into four elements: static obstacles, channel constraints, human decision-making, and vessel kinetic energy. Static potential energy fields, boundary potential fields, behavioral potential fields, and time-varying kinetic energy fields are constructed respectively. Weighted allocation achieves coupling among these four potential fields. To address nonlinear vessel motion and prediction uncertainties caused by wind-induced flow disturbances, the Kalman filter algorithm processes automatic identification system (AIS) data in real time. This corrects process noise and observation noise to predict vessel dynamic deviations, which serve as correction parameters for the time-varying kinetic energy field, enhancing the potential field model's accuracy in representing dynamic risks. The time-varying kinetic energy field is superimposed with the improved potential field model to generate a comprehensive predicted field strength. This is combined with measured AIS data to produce an observed field strength, establishing a "prediction-observation" dual-field coupling early warning mechanism. Dynamic thresholds are set based on relevant regulations and historical cases to trigger graded warnings. Experimental validation conducted at the Chizhou Yangtze River Highway Bridge revealed: predicted field strengths at time points T2, T3, and T4 were 0.75, 0.64, and 0.45, respectively, while actual field strengths were 0.65, 0.59, and 0.40. The maximum relative error of 13.3% occurred at T2 during the bridge pier passage. The experiment confirmed the model's real-time capability and accuracy in collision risk warning for vessels passing under inland river bridges. The dual-field coupling mechanism enables controllable-error warnings in high-risk pier zones, providing dynamic risk quantification for vessel navigation decision-making.
To precisely quantify the dynamic evolution of vessel collision risks in inland bridge zones and enable tiered early warning, this study proposes an assessment method integrating vessel dynamic motion prediction with multi-dimensional potential field coupling. By adapting traditional safety potential field models to account for vessel navigation characteristics and bridge zone environmental constraints, the approach is applied to vessel-bridge collision risk evaluation. Based on risk causation theory, bridge zone risks are decomposed into four elements: static obstacles, channel constraints, human decision-making, and vessel kinetic energy. Static potential energy fields, boundary potential fields, behavioral potential fields, and time-varying kinetic energy fields are constructed respectively. Weighted allocation achieves coupling among these four potential fields. To address nonlinear vessel motion and prediction uncertainties caused by wind-induced flow disturbances, the Kalman filter algorithm processes automatic identification system (AIS) data in real time. This corrects process noise and observation noise to predict vessel dynamic deviations, which serve as correction parameters for the time-varying kinetic energy field, enhancing the potential field model's accuracy in representing dynamic risks. The time-varying kinetic energy field is superimposed with the improved potential field model to generate a comprehensive predicted field strength. This is combined with measured AIS data to produce an observed field strength, establishing a "prediction-observation" dual-field coupling early warning mechanism. Dynamic thresholds are set based on relevant regulations and historical cases to trigger graded warnings. Experimental validation conducted at the Chizhou Yangtze River Highway Bridge revealed: predicted field strengths at time points T2, T3, and T4 were 0.75, 0.64, and 0.45, respectively, while actual field strengths were 0.65, 0.59, and 0.40. The maximum relative error of 13.3% occurred at T2 during the bridge pier passage. The experiment confirmed the model's real-time capability and accuracy in collision risk warning for vessels passing under inland river bridges. The dual-field coupling mechanism enables controllable-error warnings in high-risk pier zones, providing dynamic risk quantification for vessel navigation decision-making.
2025, 43(4): 24-36.
doi: 10.3963/j.jssn.1674-4861.2025.04.003
Abstract:
Integrating manned and unmanned aircraft into shared airspace presents significant challenges to airspace safety assessment. Existing risk assessment studies primarily focus on tactical collision risk evaluation based on trajectory prediction, while strategic-level systematic risk assessment for airspace planning and design remains underdeveloped. To comprehensively evaluate airspace safety levels and support the safe large-scale integration of unmanned aircraft into controlled airspace, this study investigates a macroscopic traffic risk modeling and assessment method for integrated operations. Static risk indicators are constructed based on the structural characteristics of routes and intersections, incorporating geometric morphology and conflict-prone profiles. Dynamic risk indicators are proposed in both horizontal and vertical dimensions, based on traffic flow characteristics. By coupling static complexity with dynamic conflict risks, an integrated airspace traffic risk assessment model is established, reflecting both the static features of the airspace structure and the dynamic characteristics of aircraft operations. Using four sector areas in Shanghai as an example, a risk assessment is conducted under manned aircraft operational scenarios to validate the model's feasibility and effectiveness, and to determine the target safety level for the airspace. Simulation experiments are designed to explore the influence mechanism of the three parameters, speed difference, separation, and mix ratio, on risk under integrated operation. Based on the criterion of not exceeding the target safety level, an equivalent risk assessment approach is adopted to determine the feasibility of manned-unmanned integrated operations. The allowable number of unmanned aircraft that can be introduced into each manned route is evaluated. The results show that: ①Speed difference and separation are the core driving factors of risk. The influence of mix ratio on airspace traffic risk depends on the separation setting. When the minimum safety separation between manned and unmanned aircraft is significantly larger than that between manned aircraft or between unmanned aircraft, the risk peaks when unmanned aircraft account for about50% of the traffic mix. ②Complex interactions are observed among speed difference, separation, and mix ratio, with no additional higher-order coupling effects detected. ③High -risk initial periods are not conducive to the introduction of unmanned aircraft.
Integrating manned and unmanned aircraft into shared airspace presents significant challenges to airspace safety assessment. Existing risk assessment studies primarily focus on tactical collision risk evaluation based on trajectory prediction, while strategic-level systematic risk assessment for airspace planning and design remains underdeveloped. To comprehensively evaluate airspace safety levels and support the safe large-scale integration of unmanned aircraft into controlled airspace, this study investigates a macroscopic traffic risk modeling and assessment method for integrated operations. Static risk indicators are constructed based on the structural characteristics of routes and intersections, incorporating geometric morphology and conflict-prone profiles. Dynamic risk indicators are proposed in both horizontal and vertical dimensions, based on traffic flow characteristics. By coupling static complexity with dynamic conflict risks, an integrated airspace traffic risk assessment model is established, reflecting both the static features of the airspace structure and the dynamic characteristics of aircraft operations. Using four sector areas in Shanghai as an example, a risk assessment is conducted under manned aircraft operational scenarios to validate the model's feasibility and effectiveness, and to determine the target safety level for the airspace. Simulation experiments are designed to explore the influence mechanism of the three parameters, speed difference, separation, and mix ratio, on risk under integrated operation. Based on the criterion of not exceeding the target safety level, an equivalent risk assessment approach is adopted to determine the feasibility of manned-unmanned integrated operations. The allowable number of unmanned aircraft that can be introduced into each manned route is evaluated. The results show that: ①Speed difference and separation are the core driving factors of risk. The influence of mix ratio on airspace traffic risk depends on the separation setting. When the minimum safety separation between manned and unmanned aircraft is significantly larger than that between manned aircraft or between unmanned aircraft, the risk peaks when unmanned aircraft account for about50% of the traffic mix. ②Complex interactions are observed among speed difference, separation, and mix ratio, with no additional higher-order coupling effects detected. ③High -risk initial periods are not conducive to the introduction of unmanned aircraft.
2025, 43(4): 37-45.
doi: 10.3963/j.jssn.1674-4861.2025.04.004
Abstract:
To address the problem of the negative influence of visual blind spots on pilots'judgment of safe distances during emergency road landings of small fixed-wing aircraft, thereby increasing the probability of collisions with ground vehicles, this study constructs a low-altitude aircraft-ground vehicle collision model considering visual blind spots and uses SA60L as a research object. It quantitatively analyzes the effects of multiple factors on collision risks. Based on the landing characteristics of the SA60L aircraft, a three-dimensional visual blind spots model for the visual landing process is established. A three-dimensional coordinate system is constructed by using the pilot's position as the base point and combined with a 20° downward visual angle constraint to determine the projection range of the visual blind spots on the ground. Collision scenarios are classified into two categories by integrating parameters such as vehicle drivers'reaction time, thus a collision probability model is established. For collisions with rear vehicles, collision probability formulas are derived from three states: no braking, speed not reduced to 0 after braking, and speed reduced to 0 after braking. For collisions with front vehicles, the collision probability calculation logic is established under the conflict conditions that the aircraft landing roll distance covers the front vehicles. The three-dimensional visual blind spots are used as a pre-constraint for probability calculations, and the computation using the Monte Carlo method is activated to conduct 10, 000 simulations only when ground vehicles enter dangerous areas. These simulations analyze how ground vehicle speed, traffic flow, aircraft near-ground speed, and landing altitude affect collision risk and construct a multiple linear regression model. The results indicate that ground traffic flow (t =15.78) and ground vehicle speed (t =9.25) have the most significant impact on collision probability, with both factors showing approximately linear positive correlation with collision probability and increasing ground vehicle speed leads to an increase in collision probability amplitude. Landing altitude has a nonlinear"first increase then decrease"effect on collision probability. A high-risk zone is formed when ground vehicle speed exceeds 80 km/h and landing altitude is below 200 m, where collision probability increases from 0.12 in the safety threshold zone (speed < 40 km/h and altitude > 200 m) to 0.27, representing a 2.3-fold increase. The determination coefficient of the multiple linear regression model is R2 =0.965, indicating good fitting and significance.
To address the problem of the negative influence of visual blind spots on pilots'judgment of safe distances during emergency road landings of small fixed-wing aircraft, thereby increasing the probability of collisions with ground vehicles, this study constructs a low-altitude aircraft-ground vehicle collision model considering visual blind spots and uses SA60L as a research object. It quantitatively analyzes the effects of multiple factors on collision risks. Based on the landing characteristics of the SA60L aircraft, a three-dimensional visual blind spots model for the visual landing process is established. A three-dimensional coordinate system is constructed by using the pilot's position as the base point and combined with a 20° downward visual angle constraint to determine the projection range of the visual blind spots on the ground. Collision scenarios are classified into two categories by integrating parameters such as vehicle drivers'reaction time, thus a collision probability model is established. For collisions with rear vehicles, collision probability formulas are derived from three states: no braking, speed not reduced to 0 after braking, and speed reduced to 0 after braking. For collisions with front vehicles, the collision probability calculation logic is established under the conflict conditions that the aircraft landing roll distance covers the front vehicles. The three-dimensional visual blind spots are used as a pre-constraint for probability calculations, and the computation using the Monte Carlo method is activated to conduct 10, 000 simulations only when ground vehicles enter dangerous areas. These simulations analyze how ground vehicle speed, traffic flow, aircraft near-ground speed, and landing altitude affect collision risk and construct a multiple linear regression model. The results indicate that ground traffic flow (t =15.78) and ground vehicle speed (t =9.25) have the most significant impact on collision probability, with both factors showing approximately linear positive correlation with collision probability and increasing ground vehicle speed leads to an increase in collision probability amplitude. Landing altitude has a nonlinear"first increase then decrease"effect on collision probability. A high-risk zone is formed when ground vehicle speed exceeds 80 km/h and landing altitude is below 200 m, where collision probability increases from 0.12 in the safety threshold zone (speed < 40 km/h and altitude > 200 m) to 0.27, representing a 2.3-fold increase. The determination coefficient of the multiple linear regression model is R2 =0.965, indicating good fitting and significance.
2025, 43(4): 46-56.
doi: 10.3963/j.jssn.1674-4861.2025.04.005
Abstract:
The insufficiency of stopping sight distance (SSD) on highway curves due to roadside obstructions is a critical issue for mixed traffic flow composed of autonomous vehicles (AVs) and human-drive vehicles (HDVs). Traditional deterministic models for addressing this have limitations. Therefore, the minimum circular curve radius ensuring sight distance safety for this mixed traffic flow is investigated. The vehicle braking process is modeled in three stages, incorporating anti-lock braking system (ABS) and the lateral clearance method. Calculation models for SSD and available sight distance (ASD) are developed for various lanes and curve directions. This quantifies sight distance supply and demand under the most critical conditions. A reliability-based model is developed to assess sight distance for given curve radii. This model accounts for random variations in operating speed and braking reaction time of both drivers and autonomous systems, across different AVs penetration rates. The probability of SSD for the general minimum radius specified in the Design Specification for Highway Alignment (hereinafter referred to as standard values) is calculated. Using 95% as the target reliability probability, the recommended values for the minimum radius of circular curves and corresponding safe speed limits under various radii scenarios are proposed. The rationality of these recommended values is verified through SUMO simulations. Results indicate that when the AVs penetration rate is 0%, the reliability probability for the innermost lane of left-turning curves is lower than 95% when using the standard values. Higher AVs penetration increases the reliability probability, allowing for smaller minimum curve radii and higher safe speeds. SUMO simulations verify that the recommended values reduce traffic conflicts by 71.1% and improve traffic efficiency by 27.3% on average, compared to the standard values. Further increasing the curve radius provides no significant benefit.
The insufficiency of stopping sight distance (SSD) on highway curves due to roadside obstructions is a critical issue for mixed traffic flow composed of autonomous vehicles (AVs) and human-drive vehicles (HDVs). Traditional deterministic models for addressing this have limitations. Therefore, the minimum circular curve radius ensuring sight distance safety for this mixed traffic flow is investigated. The vehicle braking process is modeled in three stages, incorporating anti-lock braking system (ABS) and the lateral clearance method. Calculation models for SSD and available sight distance (ASD) are developed for various lanes and curve directions. This quantifies sight distance supply and demand under the most critical conditions. A reliability-based model is developed to assess sight distance for given curve radii. This model accounts for random variations in operating speed and braking reaction time of both drivers and autonomous systems, across different AVs penetration rates. The probability of SSD for the general minimum radius specified in the Design Specification for Highway Alignment (hereinafter referred to as standard values) is calculated. Using 95% as the target reliability probability, the recommended values for the minimum radius of circular curves and corresponding safe speed limits under various radii scenarios are proposed. The rationality of these recommended values is verified through SUMO simulations. Results indicate that when the AVs penetration rate is 0%, the reliability probability for the innermost lane of left-turning curves is lower than 95% when using the standard values. Higher AVs penetration increases the reliability probability, allowing for smaller minimum curve radii and higher safe speeds. SUMO simulations verify that the recommended values reduce traffic conflicts by 71.1% and improve traffic efficiency by 27.3% on average, compared to the standard values. Further increasing the curve radius provides no significant benefit.
2025, 43(4): 57-66.
doi: 10.3963/j.jssn.1674-4861.2025.04.006
Abstract:
To investigate the effects of different combinations of visual information on sidewalls on drivers'vehicle control abilities across various lanes in urban long tunnels, a driving simulation experiment was conducted. Statistical techniques and factor analysis were used to assess the influence of visual information types and lane positions. Results indicated that both combinations of visual information on sidewalls and lane positions significantly affected vehicle control performance, although no interaction effects were observed. Under the same lane condition, Scenario 1 (with horizontal stripes only) resulted in the highest driving speed, exceeding other three combination scenarios by 5.2~9.8 km/h. It also showed the highest longitudinal acceleration, surpassing others by 0.08~0.14 m/s2. In terms of lateral behavior, Scenario 1 exhibited greater lateral deviation than that in Scenarios 3 and 4 by 0.17 m and 0.16 m, respectively, and the maximum increase in lateral acceleration reached 0.051 m/s2. Under the same visual guidance condition, lane position also had a significant effect: driving speeds in the left and right lanes were 3.2 km/h and 2.1 km/h higher than that in the middle lane, respectively; the lateral acceleration in the left lane exceeded that of the middle and right lanes by 0.454 m/s2 and 0.495 m/s2, respectively. Overall, driving behavior indicators in the left lane were higher than those in the middle and right lanes, suggesting that the left and right lanes pose relatively higher driving risks. Further, factor analysis revealed that closed-type visual combinations were the most effective in enhancing vehicle control in the left and right lanes, while the wavy rhythmic pattern was better suited to improve control abilities in the middle lane. Therefore, it is recommended that closed-type visual combinations are prioritized in practical engineering applications, while wavy rhythmic patterns may be used in fatigue alert zones to enhance driving safety.
To investigate the effects of different combinations of visual information on sidewalls on drivers'vehicle control abilities across various lanes in urban long tunnels, a driving simulation experiment was conducted. Statistical techniques and factor analysis were used to assess the influence of visual information types and lane positions. Results indicated that both combinations of visual information on sidewalls and lane positions significantly affected vehicle control performance, although no interaction effects were observed. Under the same lane condition, Scenario 1 (with horizontal stripes only) resulted in the highest driving speed, exceeding other three combination scenarios by 5.2~9.8 km/h. It also showed the highest longitudinal acceleration, surpassing others by 0.08~0.14 m/s2. In terms of lateral behavior, Scenario 1 exhibited greater lateral deviation than that in Scenarios 3 and 4 by 0.17 m and 0.16 m, respectively, and the maximum increase in lateral acceleration reached 0.051 m/s2. Under the same visual guidance condition, lane position also had a significant effect: driving speeds in the left and right lanes were 3.2 km/h and 2.1 km/h higher than that in the middle lane, respectively; the lateral acceleration in the left lane exceeded that of the middle and right lanes by 0.454 m/s2 and 0.495 m/s2, respectively. Overall, driving behavior indicators in the left lane were higher than those in the middle and right lanes, suggesting that the left and right lanes pose relatively higher driving risks. Further, factor analysis revealed that closed-type visual combinations were the most effective in enhancing vehicle control in the left and right lanes, while the wavy rhythmic pattern was better suited to improve control abilities in the middle lane. Therefore, it is recommended that closed-type visual combinations are prioritized in practical engineering applications, while wavy rhythmic patterns may be used in fatigue alert zones to enhance driving safety.
2025, 43(4): 67-74.
doi: 10.3963/j.jssn.1674-4861.2025.04.007
Abstract:
Current studies suffer from the insufficient prediction accuracy for multi-classification prediction and unclear interaction mechanisms for accidents severity on two-lane highways in plateau mountainous region. To address these issues, this study proposes an XGBoost-based three-classification prediction framework, optimized by a genetic algorithm (GA). The framework is tested based on accident data from 2012 to 2017 on mountainous two-lane highways in Yunnan. It integrates 14 features, such as road geometry, traffic environment, and type of involved vehicle. The model performance is compared with random forest (RF), support vector machine (SVM), and the baseline XGBoost model. Additionally, partial dependence plots (PDP) are used to explore the influence mechanisms of different risk determinants on accident severity. The results show that: ①The proposed GA-XGBoost model has the best overall prediction performance, with accuracy, precision, and recall rates reaching 81.57%, 73.12%, and 82.68%, respectively. After optimization with the GA algorithm, the predictive accuracy for injury and fatal accidents improves by 14.58% and 50.00%, respectively, compared to those of the pre-optimization model. The number of correctly classified fatal accidents is three times than that of the RF and SVM models. All these show significant improvement of the ability to predict severe accidents. ②Factors reflecting vehicle characteristics and traffic environment have a more significant impact on accident occurrence. Among them, the type of causing-trouble vehicle, type of involved vehicle, accident type, and daily traffic volume are the top four risk factors. ③Regardless of the type of accident, when pedestrians or motorcycles are involved, the severity of the accident is significantly increased. Among them, pedestrian involvement increases the severity of the accident by 1.25 to 5 times higher than that of any other involvement type. Additionally, as traffic volume increases, the impact of side collisions on accident severity gradually increases.
Current studies suffer from the insufficient prediction accuracy for multi-classification prediction and unclear interaction mechanisms for accidents severity on two-lane highways in plateau mountainous region. To address these issues, this study proposes an XGBoost-based three-classification prediction framework, optimized by a genetic algorithm (GA). The framework is tested based on accident data from 2012 to 2017 on mountainous two-lane highways in Yunnan. It integrates 14 features, such as road geometry, traffic environment, and type of involved vehicle. The model performance is compared with random forest (RF), support vector machine (SVM), and the baseline XGBoost model. Additionally, partial dependence plots (PDP) are used to explore the influence mechanisms of different risk determinants on accident severity. The results show that: ①The proposed GA-XGBoost model has the best overall prediction performance, with accuracy, precision, and recall rates reaching 81.57%, 73.12%, and 82.68%, respectively. After optimization with the GA algorithm, the predictive accuracy for injury and fatal accidents improves by 14.58% and 50.00%, respectively, compared to those of the pre-optimization model. The number of correctly classified fatal accidents is three times than that of the RF and SVM models. All these show significant improvement of the ability to predict severe accidents. ②Factors reflecting vehicle characteristics and traffic environment have a more significant impact on accident occurrence. Among them, the type of causing-trouble vehicle, type of involved vehicle, accident type, and daily traffic volume are the top four risk factors. ③Regardless of the type of accident, when pedestrians or motorcycles are involved, the severity of the accident is significantly increased. Among them, pedestrian involvement increases the severity of the accident by 1.25 to 5 times higher than that of any other involvement type. Additionally, as traffic volume increases, the impact of side collisions on accident severity gradually increases.
Journal of Transport Information and Safety
(Founded in 1983 bimonthly )
Former Name：Computer and Communications
Supervised by：Ministry of Education of P. R. CHINA
Sponsored by：Wuhan University of Technology
Network of Computer Application Information in Transportation
In Association With：Intelligent Transportation Committee of China Association of Artificial Intelligence
Editor-in-Chief：ZHONG Ming
Edited and Published by：Editorial Office of Transport Information and Safety
Address：No. 1178，Heping Avenue, Wuchang, Wuhan, CHINA
Postcode：430063
Tel：027-86580355
E-mail：jtjsj@vip.163.com
Website：http://www.jtxa.net/
Postal Code：38-94
Domestic Issue：
CN 42-1781/U
Publication No.：ISSN 1674-4861
Indexed In
- Chinese Core Journal in “Integrated Transportation” category
- Chinese Science Citation Database (CSCD)
- Core Science and Technology Journals
- Chinese Scientific and Technological Papers and Citations (CSTPCD)
- Class A of Research Center for Chinese Science Evaluation (RCCSE)
- Chinese Academic Journal Comprehensive Evaluation Database (CAJ-CED)
- Chinese Core Journals (Selection) Database
- Chinese Scientific and Technological Periodicals Database
- China National Knowledge Infrastructure (CNKI)
- Chinese Academic Journals (CAJ-CD)
- Chinese Lifelong Education Academic Research Database
- Japan Science and Technology Agency (JST)
- World Journal Clout Index Report (2020 STM)
