Pub Date : 2025-03-01DOI: 10.1016/j.ijtst.2024.04.004
Yongqi Deng , Jiaorong Wu , Chengcheng Yu , Jihao Deng , Meiting Tu , Yuqin Wang
Employing flow space theory and multi-source data, this study examines the spatial network structure and factors influencing railway passenger flow, which is crucial for rail planning in densely populated megalopolises. Focusing on China's Yangtze River Delta (YRD) megalopolis, we utilize social network analysis (SNA) to explore the characteristics of various flow networks and their interactions with the railway passenger flow network. Key findings include: (1) a pronounced polarization effect and core-periphery structure exist in the YRD, notably within industry and railway flow networks; (2) industry and corporation flow significantly contributes to rail passenger flow, with corporation networks in commerce, technical services, and finance showing higher similarity to the railway passenger flow network; (3) there is significant heterogeneity in the correlation between rail passenger flow and other flows within sub-networks formed by connections among nodes of different levels; (4) enhancing railway services at lower-level nodes is essential to mitigate the disparity between population mobility and rail passenger flow and to promote rail transportation equity. This research offers valuable insights for policymakers in developing countries to strategically plan railroad networks in megalopolises.
{"title":"Investigating influential factors on railway passenger flow utilizing multi-source data fusion and flow space theory: A case study of the Yangtze River Delta megalopolis, China","authors":"Yongqi Deng , Jiaorong Wu , Chengcheng Yu , Jihao Deng , Meiting Tu , Yuqin Wang","doi":"10.1016/j.ijtst.2024.04.004","DOIUrl":"10.1016/j.ijtst.2024.04.004","url":null,"abstract":"<div><div>Employing flow space theory and multi-source data, this study examines the spatial network structure and factors influencing railway passenger flow, which is crucial for rail planning in densely populated megalopolises. Focusing on China's Yangtze River Delta (YRD) megalopolis, we utilize social network analysis (SNA) to explore the characteristics of various flow networks and their interactions with the railway passenger flow network. Key findings include: (1) a pronounced polarization effect and core-periphery structure exist in the YRD, notably within industry and railway flow networks; (2) industry and corporation flow significantly contributes to rail passenger flow, with corporation networks in commerce, technical services, and finance showing higher similarity to the railway passenger flow network; (3) there is significant heterogeneity in the correlation between rail passenger flow and other flows within sub-networks formed by connections among nodes of different levels; (4) enhancing railway services at lower-level nodes is essential to mitigate the disparity between population mobility and rail passenger flow and to promote rail transportation equity. This research offers valuable insights for policymakers in developing countries to strategically plan railroad networks in megalopolises.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"17 ","pages":"Pages 192-207"},"PeriodicalIF":4.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140783058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01DOI: 10.1016/j.ijtst.2024.07.007
Yu Bai , Pengyue Tu , Ghim Ping Ong
While the advantages of Autonomous vehicles (AVs) and their impact on manually-driven vehicles (MVs) have been widely discussed in continuous flow conditions, their performance under mixed traffic, intermitted flow conditions has yet to be properly studied. One of the representative scenarios is that vehicular flow is interrupted by roadside crossing obstacles such as pedestrians or cyclists. Since such interruption makes vehicles stop and go more frequently and creates random and complex traffic conflict, it has become a critical factor that can affect the driving performance of AVs. Therefore, this paper proposes a uniform traffic model (Pre_IDM+) to include roadside crossing impact in traffic flow analysis. The classical intelligent driving model (IDM) is extended into an obstacle-avoiding case, in which a novel pre-reaction workflow is introduced to describe yielding behavior and generate a reasonable braking trajectory. A real mixed traffic data near an un-signalized mid-block crosswalk is used to calibrate Pre_IDM+ and an accordingly microscope mixed traffic simulation platform is constructed. The simulation results show that discreet AVs can greatly avoid hard braking (−83.61%) and slightly improve passing speed (+5.11%) compared with MVs, while competitive AVs can maximize traffic efficiency (+7.03%) but will also deteriorate driving smoothness and comfort (−31.66%). Maintaining a sparse distribution of crossing sites along the road may contribute more to traffic stability and driving continuity compared with gathering all obstacles crossing at one location. This paper may help better understand the impact of AVs on general intermitted flow and give a reference to mixed traffic modeling towards a complex road condition.
{"title":"An extended intelligent driving model for autonomous and manually driven vehicles in a mixed traffic environment with consideration to roadside crossing","authors":"Yu Bai , Pengyue Tu , Ghim Ping Ong","doi":"10.1016/j.ijtst.2024.07.007","DOIUrl":"10.1016/j.ijtst.2024.07.007","url":null,"abstract":"<div><div>While the advantages of Autonomous vehicles (AVs) and their impact on manually-driven vehicles (MVs) have been widely discussed in continuous flow conditions, their performance under mixed traffic, intermitted flow conditions has yet to be properly studied. One of the representative scenarios is that vehicular flow is interrupted by roadside crossing obstacles such as pedestrians or cyclists. Since such interruption makes vehicles stop and go more frequently and creates random and complex traffic conflict, it has become a critical factor that can affect the driving performance of AVs. Therefore, this paper proposes a uniform traffic model (Pre_IDM+) to include roadside crossing impact in traffic flow analysis. The classical intelligent driving model (IDM) is extended into an obstacle-avoiding case, in which a novel pre-reaction workflow is introduced to describe yielding behavior and generate a reasonable braking trajectory. A real mixed traffic data near an un-signalized mid-block crosswalk is used to calibrate Pre_IDM+ and an accordingly microscope mixed traffic simulation platform is constructed. The simulation results show that discreet AVs can greatly avoid hard braking (−83.61%) and slightly improve passing speed (+5.11%) compared with MVs, while competitive AVs can maximize traffic efficiency (+7.03%) but will also deteriorate driving smoothness and comfort (−31.66%). Maintaining a sparse distribution of crossing sites along the road may contribute more to traffic stability and driving continuity compared with gathering all obstacles crossing at one location. This paper may help better understand the impact of AVs on general intermitted flow and give a reference to mixed traffic modeling towards a complex road condition.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"17 ","pages":"Pages 375-391"},"PeriodicalIF":4.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141847683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The application of a new liquid soil material and the treatment effect of backfilling an underpass tunnel in an airport are studied. The deformation and mechanical properties of liquid soil and conventional soil under load are comprehensively compared and analyzed via a numerical simulation with finite element software. The effects of the buried depth of overlying fill, tunnel height, and traffic load on the backfilling of liquid soil abutment are analyzed. The research results show that under the action of load, the overall deformation and stress distribution of the liquid soil and conventional soil show similar laws. However, liquid soil backfilling has great advantages over conventional soil backfilling in all aspects. Liquid soil backfilling can reduce the deformation and the compressive stress at the corner of the backfilling area by approximately 13% and 15%, respectively. The overburden buried depth has a great impact on the subgrade deformation. In the actual construction, the overburden buried depth should be 1.5 m. The overburden depth has a greater impact on the vertical deformation of the road, and the self-weight of the overburden will act as an additional load on the overall roadbed, compared with conventional soil backfill. The overburden depth of 2.0 m conventional soil backfill is about equal to the overburden depth of 1.5 m liquid soil backfill. The use of liquid soil backfill is equivalent to the use of the overburden fill in reducing the additional load of 0.5 m. The height of the box culvert has a greater impact on the stress, but this change is not linear. The actual construction in the case of meeting the specific requirements of use should try to control in the vicinity of 8.4 m, and at the same time the use of liquid soil backfill can reduce the compressive stress of about 14%. The compressive stress increases first and then decreases with the increase in the liquid soil modulus. The liquid soil modulus should be controlled to 180 MPa. Moreover, liquid soil backfilling can reduce the compressive stress in the backfilling area by approximately 25%. The trapezoidal slope of the backfill area is proportional to the deformation amount. Although an obvious correlation with compressive stress exists, the regularity is not strong. Thus, the trapezoidal slope should be set to 1:1 during construction. Traffic load slightly affects the overall deformation and compressive stress of the road. However, the distribution trends of deformation and stress change obviously under the action of aircraft load. In the actual design, only one load form of aircraft load should be considered.
{"title":"Mechanical response and numerical simulation of liquid soil abutment backfill","authors":"Chongwei Huang , Chuan Zhao , Yu Sun , Shengfei Guan","doi":"10.1016/j.ijtst.2024.02.001","DOIUrl":"10.1016/j.ijtst.2024.02.001","url":null,"abstract":"<div><div>The application of a new liquid soil material and the treatment effect of backfilling an underpass tunnel in an airport are studied. The deformation and mechanical properties of liquid soil and conventional soil under load are comprehensively compared and analyzed via a numerical simulation with finite element software. The effects of the buried depth of overlying fill, tunnel height, and traffic load on the backfilling of liquid soil abutment are analyzed. The research results show that under the action of load, the overall deformation and stress distribution of the liquid soil and conventional soil show similar laws. However, liquid soil backfilling has great advantages over conventional soil backfilling in all aspects. Liquid soil backfilling can reduce the deformation and the compressive stress at the corner of the backfilling area by approximately 13% and 15%, respectively. The overburden buried depth has a great impact on the subgrade deformation. In the actual construction, the overburden buried depth should be 1.5 m. The overburden depth has a greater impact on the vertical deformation of the road, and the self-weight of the overburden will act as an additional load on the overall roadbed, compared with conventional soil backfill. The overburden depth of 2.0 m conventional soil backfill is about equal to the overburden depth of 1.5 m liquid soil backfill. The use of liquid soil backfill is equivalent to the use of the overburden fill in reducing the additional load of 0.5 m. The height of the box culvert has a greater impact on the stress, but this change is not linear. The actual construction in the case of meeting the specific requirements of use should try to control in the vicinity of 8.4 m, and at the same time the use of liquid soil backfill can reduce the compressive stress of about 14%. The compressive stress increases first and then decreases with the increase in the liquid soil modulus. The liquid soil modulus should be controlled to 180 MPa. Moreover, liquid soil backfilling can reduce the compressive stress in the backfilling area by approximately 25%. The trapezoidal slope of the backfill area is proportional to the deformation amount. Although an obvious correlation with compressive stress exists, the regularity is not strong. Thus, the trapezoidal slope should be set to 1:1 during construction. Traffic load slightly affects the overall deformation and compressive stress of the road. However, the distribution trends of deformation and stress change obviously under the action of aircraft load. In the actual design, only one load form of aircraft load should be considered.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"17 ","pages":"Pages 1-20"},"PeriodicalIF":4.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01DOI: 10.1016/j.ijtst.2024.03.004
Hsien-Wen Deng , M Sabbir Salek , Mizanur Rahman , Mashrur Chowdhury , Mitch Shue , Amy W. Apon
In this study, we developed a real-time connected vehicle (CV) speed advisory application that uses public cloud services, and tested it on a simulated signalized corridor for different roadway traffic conditions. First, we developed a scalable serverless cloud computing architecture leveraging public cloud services offered by Amazon Web Services (AWS) to support the requirements of a real-time CV application. Second, we developed an optimization-based real-time CV speed advisory algorithm by taking a modular design approach, which makes the application automatically scalable and deployable in the cloud using the serverless architecture. Third, we developed a cloud-in-the-loop simulation testbed using AWS and an open-source microscopic roadway traffic simulator called simulation of urban mobility (SUMO). Our analyses based on different roadway traffic conditions showed that the serverless CV speed advisory application meets the latency requirement of real-time CV mobility applications. Besides, our serverless CV speed advisory application reduced the average stopped delay (by 77%) and the aggregated risk of collision (by 21%) at the signalized intersections of a corridor. These prove the feasibility as well as the efficacy of utilizing public cloud infrastructure to implement real-time roadway traffic management applications in a CV environment.
{"title":"Leveraging public cloud infrastructure for real-time connected vehicle speed advisory at a signalized corridor","authors":"Hsien-Wen Deng , M Sabbir Salek , Mizanur Rahman , Mashrur Chowdhury , Mitch Shue , Amy W. Apon","doi":"10.1016/j.ijtst.2024.03.004","DOIUrl":"10.1016/j.ijtst.2024.03.004","url":null,"abstract":"<div><div>In this study, we developed a real-time connected vehicle (CV) speed advisory application that uses public cloud services, and tested it on a simulated signalized corridor for different roadway traffic conditions. First, we developed a scalable serverless cloud computing architecture leveraging public cloud services offered by Amazon Web Services (AWS) to support the requirements of a real-time CV application. Second, we developed an optimization-based real-time CV speed advisory algorithm by taking a modular design approach, which makes the application automatically scalable and deployable in the cloud using the serverless architecture. Third, we developed a cloud-in-the-loop simulation testbed using AWS and an open-source microscopic roadway traffic simulator called simulation of urban mobility (SUMO). Our analyses based on different roadway traffic conditions showed that the serverless CV speed advisory application meets the latency requirement of real-time CV mobility applications. Besides, our serverless CV speed advisory application reduced the average stopped delay (by 77%) and the aggregated risk of collision (by 21%) at the signalized intersections of a corridor. These prove the feasibility as well as the efficacy of utilizing public cloud infrastructure to implement real-time roadway traffic management applications in a CV environment.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"17 ","pages":"Pages 131-147"},"PeriodicalIF":4.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Over-speeding is a pivotal factor in fatal traffic crashes globally, necessitating robust speed management strategies to augment road safety. In 2021, the National Highway Traffic Safety Administration reported over 12 000 speed-related fatalities in the United States alone. Previous studies aggregated over-speeding tendencies; however, the extent of over-speeding has a significant implication on the crash outcome. This study delves into the prevalence and magnitude of over-speeding in various scenarios, utilizing data from traffic cameras in Edmonton, Canada, and employing a negative binomial statistical model for analysis. The model elucidates the significance and likelihood of over-speeding tendencies by incorporating temporal and built environment variables, i.e., year, month, number of lanes, dwelling unit types, school-related, and open green space. Study results indicated that the aggregation of the over-speeding data tends to underestimate the influence of various factors. Notably, the estimated impact of the posted speed limit for the disaggregated models is up to over two times that for the aggregated model. Further, the summer months exhibit a roughly 25% uptick in speed limit violations for aggregated models while about a 40% uptick in the speed limit violations for disaggregated approaches. Conversely, a discernible decline in over-speeding tendencies is observed with camera enforcement, showcasing a 25% reduction over four years. Built environment variables presented mixed results, with one-unit dwellings associated with a 12% increase in over-speeding, while proximity to schools indicated a 10% decrease. These pivotal findings provide policymakers and practitioners with valuable insights to formulate targeted interventions and countermeasures to curtail speed limit violations and bolster overall road safety conditions.
{"title":"The influence of roadway characteristics and built environment on the extent of over-speeding: An exploration using mobile automated traffic camera data","authors":"Boniphace Kutela , Frank Ngeni , Cuthbert Ruseruka , Tumlumbe Juliana Chengula , Norris Novat , Hellen Shita , Abdallah Kinero","doi":"10.1016/j.ijtst.2024.03.003","DOIUrl":"10.1016/j.ijtst.2024.03.003","url":null,"abstract":"<div><div>Over-speeding is a pivotal factor in fatal traffic crashes globally, necessitating robust speed management strategies to augment road safety. In 2021, the National Highway Traffic Safety Administration reported over 12 000 speed-related fatalities in the United States alone. Previous studies aggregated over-speeding tendencies; however, the extent of over-speeding has a significant implication on the crash outcome. This study delves into the prevalence and magnitude of over-speeding in various scenarios, utilizing data from traffic cameras in Edmonton, Canada, and employing a negative binomial statistical model for analysis. The model elucidates the significance and likelihood of over-speeding tendencies by incorporating temporal and built environment variables, i.e., year, month, number of lanes, dwelling unit types, school-related, and open green space. Study results indicated that the aggregation of the over-speeding data tends to underestimate the influence of various factors. Notably, the estimated impact of the posted speed limit for the disaggregated models is up to over two times that for the aggregated model. Further, the summer months exhibit a roughly 25% uptick in speed limit violations for aggregated models while about a 40% uptick in the speed limit violations for disaggregated approaches. Conversely, a discernible decline in over-speeding tendencies is observed with camera enforcement, showcasing a 25% reduction over four years. Built environment variables presented mixed results, with one-unit dwellings associated with a 12% increase in over-speeding, while proximity to schools indicated a 10% decrease. These pivotal findings provide policymakers and practitioners with valuable insights to formulate targeted interventions and countermeasures to curtail speed limit violations and bolster overall road safety conditions.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"17 ","pages":"Pages 120-130"},"PeriodicalIF":4.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140275145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01DOI: 10.1016/j.ijtst.2024.04.005
Yan Liu , Quan Zhang , Xuan Li , Yang Shi
Metro plays a vital role in managing passenger distribution at intercity railway (IR) stations, particularly during holidays when there is a surge in tourist traffic. To efficiently accommodate the high demand for intercity travel, it becomes imperative for metro agencies to optimize holiday timetables. This paper focuses on designing holiday timetables of the first service period for the metro network that connects to an IR station, aiming to enhance multimodal collaboration with IR timetables while ensuring seamless coordination among various metro lines at the network level. A bi-objective model is proposed to maximize the temporal availability of metro network and minimize transfer waiting times for IR passengers traveling in early morning. To solve the model, an improved artificial bee colony algorithm (ABC) is designed, incorporating adaptive neighbour search and simulated annealing techniques. The effectiveness of the model and algorithm is verified using the Shanghai metro network and Hongqiao Railway Station. Results indicate a 9.46% increase in the temporal availability of metro network for IR passengers, coupled with a 9.68% reduction in passenger transfer waiting times. Notably, the study reveals that solely advancing operations of the IR-connected metro lines is inefficient. Instead, optimizing train timetables for the entire metro network proves to be a cost-effective approach to enhancing the overall service level of early-morning operations. Furthermore, the study emphasizes the significance of even-numbered train headways in reducing passenger transfer waiting times.
{"title":"Optimizing multimodal timetable synchronization of intercity railway and metro for the first service period during holidays","authors":"Yan Liu , Quan Zhang , Xuan Li , Yang Shi","doi":"10.1016/j.ijtst.2024.04.005","DOIUrl":"10.1016/j.ijtst.2024.04.005","url":null,"abstract":"<div><div>Metro plays a vital role in managing passenger distribution at intercity railway (IR) stations, particularly during holidays when there is a surge in tourist traffic. To efficiently accommodate the high demand for intercity travel, it becomes imperative for metro agencies to optimize holiday timetables. This paper focuses on designing holiday timetables of the first service period for the metro network that connects to an IR station, aiming to enhance multimodal collaboration with IR timetables while ensuring seamless coordination among various metro lines at the network level. A bi-objective model is proposed to maximize the temporal availability of metro network and minimize transfer waiting times for IR passengers traveling in early morning. To solve the model, an improved artificial bee colony algorithm (ABC) is designed, incorporating adaptive neighbour search and simulated annealing techniques. The effectiveness of the model and algorithm is verified using the Shanghai metro network and Hongqiao Railway Station. Results indicate a 9.46% increase in the temporal availability of metro network for IR passengers, coupled with a 9.68% reduction in passenger transfer waiting times. Notably, the study reveals that solely advancing operations of the IR-connected metro lines is inefficient. Instead, optimizing train timetables for the entire metro network proves to be a cost-effective approach to enhancing the overall service level of early-morning operations. Furthermore, the study emphasizes the significance of even-numbered train headways in reducing passenger transfer waiting times.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"17 ","pages":"Pages 208-223"},"PeriodicalIF":4.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140778647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01DOI: 10.1016/j.ijtst.2024.05.002
Wei Sun , Lili Nurliyana Abdullah , Fatimah binti Khalid , Puteri Suhaiza binti Sulaiman
The TrafficRiskClassifier model proposed in this study adopts an innovative approach integrating migration learning, image classification, and self-supervised learning, with the goal of significantly enhancing the accuracy and efficiency of traffic accident risk analysis. Compared with traditional traffic safety analysis techniques, this model focuses on utilizing contextual information and situational data from traffic accidents to achieve higher risk classification accuracy. The core of this approach is to deeply mine and analyze the detailed information in the accident environment, to provide more scientific and effective support for traffic accident risk prevention and response. Initially, by integrating migration learning with image classification techniques, the model efficiently extracts pivotal features from complex traffic scenarios and forms initial risk assessments. Subsequently, self-supervised learning is incorporated in this study, augmenting the model's capability to comprehend and categorize accident imagery. The TrafficRiskClassifier model exhibits a generalization ability of 91.82%, 85.16%, and 80.92% on individual classification tasks, respectively, signifying its robust learning capacity and proficiency in managing unseen data. Furthermore, the TrafficRiskClassifier model delineates a functional nexus between accident risk and variables such as weather, road conditions, and personal factors, employing a polynomial regression approach. This methodology not only amplifies the predictive precision of the model but also renders it versatile across diverse scenarios. Through analyzing various polynomial functions, the model achieves improved accuracy in classifying different risk levels. The outcomes demonstrate that the TrafficRiskClassifier model can efficaciously amalgamate contextual information within traffic scenarios, thereby achieving more precise classification of traffic accident risks, and consequently serving as an invaluable instrument for urban traffic safety management.
{"title":"Classification of traffic accidents’ factors using TrafficRiskClassifier","authors":"Wei Sun , Lili Nurliyana Abdullah , Fatimah binti Khalid , Puteri Suhaiza binti Sulaiman","doi":"10.1016/j.ijtst.2024.05.002","DOIUrl":"10.1016/j.ijtst.2024.05.002","url":null,"abstract":"<div><div>The TrafficRiskClassifier model proposed in this study adopts an innovative approach integrating migration learning, image classification, and self-supervised learning, with the goal of significantly enhancing the accuracy and efficiency of traffic accident risk analysis. Compared with traditional traffic safety analysis techniques, this model focuses on utilizing contextual information and situational data from traffic accidents to achieve higher risk classification accuracy. The core of this approach is to deeply mine and analyze the detailed information in the accident environment, to provide more scientific and effective support for traffic accident risk prevention and response. Initially, by integrating migration learning with image classification techniques, the model efficiently extracts pivotal features from complex traffic scenarios and forms initial risk assessments. Subsequently, self-supervised learning is incorporated in this study, augmenting the model's capability to comprehend and categorize accident imagery. The TrafficRiskClassifier model exhibits a generalization ability of 91.82%, 85.16%, and 80.92% on individual classification tasks, respectively, signifying its robust learning capacity and proficiency in managing unseen data. Furthermore, the TrafficRiskClassifier model delineates a functional nexus between accident risk and variables such as weather, road conditions, and personal factors, employing a polynomial regression approach. This methodology not only amplifies the predictive precision of the model but also renders it versatile across diverse scenarios. Through analyzing various polynomial functions, the model achieves improved accuracy in classifying different risk levels. The outcomes demonstrate that the TrafficRiskClassifier model can efficaciously amalgamate contextual information within traffic scenarios, thereby achieving more precise classification of traffic accident risks, and consequently serving as an invaluable instrument for urban traffic safety management.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"17 ","pages":"Pages 328-344"},"PeriodicalIF":4.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141039967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01DOI: 10.1016/j.ijtst.2024.04.006
Sanghuiyu Yan , Pengju Ren , Guohong Wu , Jian Li
Bus electrification is widely cited as a key technique to reduce carbon emissions and mitigate climate change. In some Chinese cities, the electrification rate of buses has even reached 100%. However, compared with diesel buses (DBs), electric buses (EBs) cause scheduling problems during urban flash floods due to their limited wading performance. This study proposes a mixed EB-DB fleet strategy to mitigate bus fleet problems during urban flash floods. The difference in wading height thresholds between EBs and DBs is considered in the fleet schedule. Utilizing a two-fleet strategy, the requisite number of DBs for bus depots is calculated. The mixed bus assignment problem is then solved by linear programming to establish the optimal dispatch scheme for the individual bus route. This study emphasizes the advantages of mixed bus fleets in emergency response, and contributes to improving urban transportation resilience.
{"title":"Electric bus fleets during urban flash floods: A mixed bus fleet strategy","authors":"Sanghuiyu Yan , Pengju Ren , Guohong Wu , Jian Li","doi":"10.1016/j.ijtst.2024.04.006","DOIUrl":"10.1016/j.ijtst.2024.04.006","url":null,"abstract":"<div><div>Bus electrification is widely cited as a key technique to reduce carbon emissions and mitigate climate change. In some Chinese cities, the electrification rate of buses has even reached 100%. However, compared with diesel buses (DBs), electric buses (EBs) cause scheduling problems during urban flash floods due to their limited wading performance. This study proposes a mixed EB-DB fleet strategy to mitigate bus fleet problems during urban flash floods. The difference in wading height thresholds between EBs and DBs is considered in the fleet schedule. Utilizing a two-fleet strategy, the requisite number of DBs for bus depots is calculated. The mixed bus assignment problem is then solved by linear programming to establish the optimal dispatch scheme for the individual bus route. This study emphasizes the advantages of mixed bus fleets in emergency response, and contributes to improving urban transportation resilience.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"17 ","pages":"Pages 224-237"},"PeriodicalIF":4.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140770271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01DOI: 10.1016/j.ijtst.2024.03.001
Siqi Wan , Huaqiao Mu , Ke Han , Taesu Cheong , Chi Xie
Multi-source track-to-track association (TTTA), which identifies trajectories from multiple sensors or data sources of the same dynamic vehicle, is an important data fusion technique widely applied to vehicle detection in the fields of road, marine, and aviation transportation. However, issues such as time asynchrony, heterogeneous sampling intervals, and random sensing errors have posed considerable challenges to the accuracy and robustness of TTTA. Aiming to address these issues in an integrated manner, this paper proposes a TTTA algorithm that comprehensively calculates the similarity between trajectories using multiple trajectory features through dynamic time warping (DTW) and Cauchy distribution degree of membership function. Multiple experimental datasets were generated by randomly sampling real AIS trajectory data into two trajectory data sources and adding random errors. The average association accuracy of all scenarios and error levels of the proposed method reached 97.33%, far higher than other benchmark methods. Experimental results demonstrated the advantage of the proposed algorithm in various TTTA scenarios, especially its robustness in intricate trajectory situations. The results also indicated that more features can maintain the stability of associations in the presence of larger random errors, and DTW can improve association accuracy in intricate scenarios. This study provides a practical solution for the problem of time asynchrony, heterogeneous sampling intervals, and random errors in multi-source trajectory data fusion, showcasing promising applications across diverse domains.
{"title":"A fuzzy track-to-track association algorithm with dynamic time warping for trajectory-level vehicle detection","authors":"Siqi Wan , Huaqiao Mu , Ke Han , Taesu Cheong , Chi Xie","doi":"10.1016/j.ijtst.2024.03.001","DOIUrl":"10.1016/j.ijtst.2024.03.001","url":null,"abstract":"<div><div>Multi-source track-to-track association (TTTA), which identifies trajectories from multiple sensors or data sources of the same dynamic vehicle, is an important data fusion technique widely applied to vehicle detection in the fields of road, marine, and aviation transportation. However, issues such as time asynchrony, heterogeneous sampling intervals, and random sensing errors have posed considerable challenges to the accuracy and robustness of TTTA. Aiming to address these issues in an integrated manner, this paper proposes a TTTA algorithm that comprehensively calculates the similarity between trajectories using multiple trajectory features through dynamic time warping (DTW) and Cauchy distribution degree of membership function. Multiple experimental datasets were generated by randomly sampling real AIS trajectory data into two trajectory data sources and adding random errors. The average association accuracy of all scenarios and error levels of the proposed method reached 97.33%, far higher than other benchmark methods. Experimental results demonstrated the advantage of the proposed algorithm in various TTTA scenarios, especially its robustness in intricate trajectory situations. The results also indicated that more features can maintain the stability of associations in the presence of larger random errors, and DTW can improve association accuracy in intricate scenarios. This study provides a practical solution for the problem of time asynchrony, heterogeneous sampling intervals, and random errors in multi-source trajectory data fusion, showcasing promising applications across diverse domains.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"17 ","pages":"Pages 95-108"},"PeriodicalIF":4.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140277952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01DOI: 10.1016/j.ijtst.2024.04.003
S. Tejeshwini, K.H. Mamatha, S.V. Dinesh
Aging of binder affects the service life of flexible pavements. Binder’s aging involves changes in its physical, rheological, chemical, and morphological characteristics, making it hard and brittle and resulting in pavement deterioration. Oxidation and volatalization are two main mechanisms involved in aging. Aging stiffens the binder as it increases the viscosity of binder resulting in pavement distresses like fatigue cracking, ravelling, and thermal cracking. Hence, it is obligatory to examine the aging effects on binder to predict the service life of flexible pavements. In this study, core samples are collected from 15 years aged in-service pavement section, and fresh binder was aged for varying aging duration in the laboratory to simulate field aging condition. Field aged and laboratory aged binders were subjected to physical, chemical, rheological, and morphological investigations for evaluating the variation of these properties with long-term aging (LTA). It is evident that the traffic speed influences the dynamic viscosity of binders. With aging, the rutting resistance of binders improved, and fatigue resistance of binders diminished considerably. 15 years field aging condition is simulated in the laboratory, and the results showed that the severity of aging in binder increases with the pavement depth in the field.
{"title":"Performance characterization of long-term aged bitumen: Field and laboratory investigation","authors":"S. Tejeshwini, K.H. Mamatha, S.V. Dinesh","doi":"10.1016/j.ijtst.2024.04.003","DOIUrl":"10.1016/j.ijtst.2024.04.003","url":null,"abstract":"<div><div>Aging of binder affects the service life of flexible pavements. Binder’s aging involves changes in its physical, rheological, chemical, and morphological characteristics, making it hard and brittle and resulting in pavement deterioration. Oxidation and volatalization are two main mechanisms involved in aging. Aging stiffens the binder as it increases the viscosity of binder resulting in pavement distresses like fatigue cracking, ravelling, and thermal cracking. Hence, it is obligatory to examine the aging effects on binder to predict the service life of flexible pavements. In this study, core samples are collected from 15 years aged in-service pavement section, and fresh binder was aged for varying aging duration in the laboratory to simulate field aging condition. Field aged and laboratory aged binders were subjected to physical, chemical, rheological, and morphological investigations for evaluating the variation of these properties with long-term aging (LTA). It is evident that the traffic speed influences the dynamic viscosity of binders. With aging, the rutting resistance of binders improved, and fatigue resistance of binders diminished considerably. 15 years field aging condition is simulated in the laboratory, and the results showed that the severity of aging in binder increases with the pavement depth in the field.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"17 ","pages":"Pages 175-191"},"PeriodicalIF":4.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140763798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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