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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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Posts
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portfolio
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publications
Analysis of drink-driving behavior: Considering the subjective and objective factors of drivers
Published in Traffic Injury Prevention, 2021
Drink-driving is one of the key behavioral risk factors in road traffic safety. The main purposes of this study are the identification of the influence of drivers’ subjective and objective factors on drink-driving behavior and the correlation between subjective and objective factors to design targeted measures for the prevention and control of drink-driving behavior.
Recommended citation: Jin Jie-Ling & Deng Yuan-Chang (2021) Analysis of drink-driving behavior: Considering the subjective and objective factors of drivers, Traffic Injury Prevention, 22:3, 183-188, DOI: 10.1080/15389588.2021.1873301 https://www.tandfonline.com/doi/full/10.1080/15389588.2021.1873301
Exploring influences on the intention of motorcyclists to drink and ride: An investigation in a fourth-tier city of China
Published in Traffic Injury Prevention , 2023
The analysis of motorcyclists’ intention to drink and ride can help reduce the possibility of accidents caused by the relevant behavior of motorcyclists. The main objectives of this study are to identify important factors in motorcyclists’ intention to drink and ride and to make some recommendations that could effectively reduce their riding intention after drinking.
Recommended citation: Yuanchang Deng, Chenjun Shi & Jieling Jin (2023) Exploring influences on the intention of motorcyclists to drink and ride: An investigation in a fourth-tier city of China, Traffic Injury Prevention, 24:2, 121-125, DOI: 10.1080/15389588.2022.2159763 https://doi.org/10.1080/15389588.2022.2159763
Variable speed limit modelling to improve traffic safety and efficiency of mixed traffic flow by a two-stage framework
Published in Transportmetrica A: Transport Science, 2023
A two-stage variable speed limits (VSL) modelling framework is proposed to enhance traffic safety and efficiency at freeway bottlenecks with mixed traffic flow comprising both human-driven and autonomous vehicles. The first-stage macroscopic VSL control framework is based on an extended cell transmission model (ECTM) and a VSL optimal control model. The ECTM serves as a traffic state predictor and characterises mixed traffic flow by modelling various characteristics such as driving behaviours and flow performance. The ECTM-based optimal control model aims to improve safety and efficiency. The second-stage microscopic validation framework is designed using the VISSIM_COM to validate the effectiveness and sophistication of the proposed VSL strategy. The proposed strategy is compared with a fixed speed limit strategy (FSL) and a VSL strategy considering only efficiency improvement (VSL_E). The results demonstrate that the proposed strategy improves traffic efficiency compared to the FSL and significantly outperforms VSL_E in traffic safety.
Recommended citation: Jieling Jin, Helai Huang, Ye Li, Gongquan Zhang, Yuxuan Dong, Bo Zhou & Hongli Xue (2023) Variable speed limit modelling to improve traffic safety and efficiency of mixed traffic flow by a two-stage framework, Transportmetrica A: Transport Science, DOI: 10.1080/23249935.2023.2253476 https://www.tandfonline.com/doi/full/10.1080/23249935.2023.2253476
Real-time crash risk prediction in freeway tunnels considering features interaction and unobserved heterogeneity: A two-stage deep learning modeling framework
Published in Analytic Methods in Accident Research, 2023
– Designed a two-stage framework blending statistical and deep learning models for real-time crash risk prediction in freeway tunnels which considering features interaction and unobserved heterogeneity. – Explored the unobserved heterogeneity and influence mechanism of precursors on real-time crash risk by a random parameters logit model with heterogeneity in means and variances. – Developed a random deep & cross network model considering feature interactions and unobserved heterogeneities to predict real-time crash risk. – The random deep & cross network model is explained by the shapley additive explanations approach, which discusses the mechanisms influencing the risk of tunnel crashes as well as the interactions and unobserved heterogeneity of features in the model.
Recommended citation: Jieling Jin, Helai Huang, Chen Yuan, Ye Li, Guoqing Zou, & Hongli Xue (2023) Real-time crash risk prediction in freeway tunnels considering features interaction and unobserved heterogeneity: A two-stage deep learning modeling framework , Analytic Methods in Accident Research, DOI: https://doi.org/10.1016/j.amar.2023.100306 https://www.sciencedirect.com/science/article/pii/S2213665723000416
Analysis of dynamic determinants of vehicles involved in crash affecting severity based on in-depth crash data
Published in Traffic injury prevention, 2024
The dynamic characteristics of vehicles involved in crashes may be an important factor affecting the crash severity. This study investigates the relationship between the dynamic characteristics of vehicles involved in crashes in the five seconds before the occurrence and the crash severity. The findings aim to offer insights for preventing severe crashes and advancing autonomous vehicle technology.
Recommended citation: Jin, J., Huang, H., Zhou, R., Chen, J., & Liu, P. (2024). Analysis of dynamic determinants of vehicles involved in crash affecting severity based on in-depth crash data. Traffic Injury Prevention, 25(3), 537–543. https://doi.org/10.1080/15389588.2024.2312517 https://www.tandfonline.com/doi/full/10.1080/15389588.2024.2312517
A variable speed limit control approach for freeway tunnels based on the model-based reinforcement learning framework with safety perception
Published in Accident Analysis & Prevention, 2024
– Developed a MBRL-based VSL strategy boosting sampling efficiency and transferability. – Developed a RDCN-based tunnel crash risk predictive model for safety perception. – Developed a MBRL-based VSL strategy for tunnel safety and efficiency enhancement.
Recommended citation: Jieling Jin, Ye Li, Helai Huang, Yuxuan Dong & Pan Liu (2024) A variable speed limit control approach for freeway tunnels based on the model-based reinforcement learning framework with safety perception , Accident Analysis & Prevention, DOI: https://doi.org/10.1016/j.aap.2024.107570 https://www.sciencedirect.com/science/article/abs/pii/S0001457524001155
Exploring master scenarios for autonomous driving tests from police-reported historical crash data using an adaptive search sampling framework
Published in Accident Analysis & Prevention, 2024
– Developed a novel approach using police-reported historical crash data to explore fundamental functional scenarios crucial for testing autonomous driving systems. – Introduced the concept of master scenario samples as representative subsets of historical crash data. Using deep generative models to synthesize diverse derived scenarios samples. – Proposed an adaptive search sampling method that iteratively refines sampling strategies based on the Similarity Score, ensuring comprehensive coverage of crash scenarios. – Established a surrogate model (SM) to expedite the process of scenario samples generation and evaluation, addressing the computational challenges associated with training deep generative models.
Recommended citation: Li, Y., Yang, Z., Jin, J., & Wu, D. (2024). Exploring master scenarios for autonomous driving tests from police-reported historical crash data using an adaptive search sampling framework. Accident Analysis & Prevention, 205, 107688. https://www.sciencedirect.com/science/article/pii/S0001457524002331
Variable speed limit control strategy for freeway tunnels based on a multi-objective deep reinforcement learning framework with safety perception
Published in Expert Systems with Applications, 2024
– Developing an RCR prediction model based on random deep and cross networks. This model aims to achieve the safety perception function of VSL systems in freeway tunnels, taking into account the current traffic detection conditions of most freeway tunnels. – Integrating the RCR prediction model into the reinforcement learning process of VSL agents to design an MDRL framework with safety perception. – Developing an application-oriented VSL strategy for traffic safety and efficiency improvement of freeway tunnels based on the safety-perception MDRL framework for the first time.
Recommended citation: Jin, J., Huang, H., Li, Y., Dong, Y., Zhang, G., & Chen, J. (2024). Variable speed limit control strategy for freeway tunnels based on a multi-objective deep reinforcement learning framework with safety perception. Expert Systems with Applications, 126277. https://www.sciencedirect.com/science/article/abs/pii/S0957417424031440
Collision causal discovery and real-time prediction of freeway tunnels: A novel dual-task approach
Published in Tunnelling and Underground Space Technology, 2025
– Employing a structural agnostic model with observational data to identify real-time traffic state causal precursors influencing collisions. This process involves uncovering causal relationships among these causal precursors to elucidate the mechanisms underlying collision occurrences. – Constructing collision causal graph data to address the interactive relationships among collision causes. The data utilizes causal relationships between causal precursors derived from causal discovery outcomes. – Developing a causal directed graph convolutional network model based on the collision causal graph to predict freeway tunnel collision risks in real time. The model improves prediction performance by capturing the causal relationship between features.
Recommended citation: Jin, J., Huang, H., Li, Y., & Dai, J. (2025). Collision causal discovery and real-time prediction of freeway tunnels: A novel dual-task approach. Tunnelling and Underground Space Technology, 155, 106216. https://www.sciencedirect.com/science/article/abs/pii/S0886779824006345
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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