Jiancheng Weng, Kai Feng, Yu Fu, Jingjing Wang, Lizeng Mao
The exhaust emissions and frequent traffic incidents caused by traffic congestion have affected the operation and high-quality development of urban transport systems. Monitoring and accurately forecasting of urban traffic operation is a critical task to formulate pertinent strategies to alleviate traffic congestion. Compared with the traditional short-time traffic prediction, this study proposed a machine learning algorithm-based traffic forecasting model for the daily-level peak hour traffic operation status prediction by using abundant historical data of urban Traffic performance index (TPI). The paper also constructed a multi-dimensional influencing factor set to further investigate the relationship between different factors on the quality of road network operation, including day of week, time period, public holiday, car usage restriction policy, special events, etc. Based on long-term historical TPI data, this research proposed a daily dimensional road network TPI prediction model by using an extreme gradient boosting algorithm (XGBoost). The model validation results show that the model prediction accuracy can reach higher than 90%. Compared with other prediction models, including Bayesian Ridge, Linear Regression, ElatsicNet, SVR, the XGBoost model has a better performance, and proves its superiority in massive high-dimensional data set. The daily dimensional prediction model proposed in this paper has an important application value for predicting traffic status and improving the operation quality of urban road networks.
{"title":"Extreme Gradient Boosting Algorithm based Urban Daily Traffic Index Prediction Model: A Case Study of Beijing, China","authors":"Jiancheng Weng, Kai Feng, Yu Fu, Jingjing Wang, Lizeng Mao","doi":"10.48130/dts-2023-0018","DOIUrl":"https://doi.org/10.48130/dts-2023-0018","url":null,"abstract":"The exhaust emissions and frequent traffic incidents caused by traffic congestion have affected the operation and high-quality development of urban transport systems. Monitoring and accurately forecasting of urban traffic operation is a critical task to formulate pertinent strategies to alleviate traffic congestion. Compared with the traditional short-time traffic prediction, this study proposed a machine learning algorithm-based traffic forecasting model for the daily-level peak hour traffic operation status prediction by using abundant historical data of urban Traffic performance index (TPI). The paper also constructed a multi-dimensional influencing factor set to further investigate the relationship between different factors on the quality of road network operation, including day of week, time period, public holiday, car usage restriction policy, special events, etc. Based on long-term historical TPI data, this research proposed a daily dimensional road network TPI prediction model by using an extreme gradient boosting algorithm (XGBoost). The model validation results show that the model prediction accuracy can reach higher than 90%. Compared with other prediction models, including Bayesian Ridge, Linear Regression, ElatsicNet, SVR, the XGBoost model has a better performance, and proves its superiority in massive high-dimensional data set. The daily dimensional prediction model proposed in this paper has an important application value for predicting traffic status and improving the operation quality of urban road networks.","PeriodicalId":339219,"journal":{"name":"Digital Transportation and Safety","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135498089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jinxing Shen, Qinxin Liu, Zi Ye, Wenfeng Jiang, Changxi Ma
Implementing autonomous bus services in several cities worldwide has garnered substantial research attention. However, the benefits and challenges of this emerging mode remain insufficiently understood. Consequently, VOSviewer was employed for a bibliometric analysis involving 300 publications, investigating the associations among authors, journals, and keywords. Subsequently, we comprehensively reviewed the current state of research on two topics and proposed future recommendations. Results indicate that the first document related to autonomous bus services was published in 2009. Most user attitude -related research data are obtained via questionnaires and analyzed using statistical techniques. Autonomous bus services are expected to benefit passengers regarding travel time, cost, safety, etc., while passenger preferences are inconsistent. However, integrating the service into existing bus systems requires careful consideration of the schedule sequences. Notably, modular autonomous bus services present a new opportunity for the further optimization of bus services. In future studies, standardized data acquisition procedures should be developed to achieve comparable results. Regarding traveler choice behavior, the effect of specific autonomous bus service policies over time and the heterogeneity due to cultural or social contexts across regions should be assessed. To further promote autonomous bus services, based on fluctuating travel demands, the effects of vehicle capacity, speed, and cost on fleet composition should be evaluated comprehensively to optimize the bus network and schedule sequence. Owing to the protracted nature of the transition from conventional to fully autonomous buses, one should prioritize semi-autonomous bus services. Another essential future research direction is to integrate modular autonomous bus assembly or disassembly strategies with different fine-grained operation optimization techniques in various scenarios.
{"title":"Autonomous bus services: Current research status and future recommendations","authors":"Jinxing Shen, Qinxin Liu, Zi Ye, Wenfeng Jiang, Changxi Ma","doi":"10.48130/dts-2023-0019","DOIUrl":"https://doi.org/10.48130/dts-2023-0019","url":null,"abstract":"Implementing autonomous bus services in several cities worldwide has garnered substantial research attention. However, the benefits and challenges of this emerging mode remain insufficiently understood. Consequently, VOSviewer was employed for a bibliometric analysis involving 300 publications, investigating the associations among authors, journals, and keywords. Subsequently, we comprehensively reviewed the current state of research on two topics and proposed future recommendations. Results indicate that the first document related to autonomous bus services was published in 2009. Most user attitude -related research data are obtained via questionnaires and analyzed using statistical techniques. Autonomous bus services are expected to benefit passengers regarding travel time, cost, safety, etc., while passenger preferences are inconsistent. However, integrating the service into existing bus systems requires careful consideration of the schedule sequences. Notably, modular autonomous bus services present a new opportunity for the further optimization of bus services. In future studies, standardized data acquisition procedures should be developed to achieve comparable results. Regarding traveler choice behavior, the effect of specific autonomous bus service policies over time and the heterogeneity due to cultural or social contexts across regions should be assessed. To further promote autonomous bus services, based on fluctuating travel demands, the effects of vehicle capacity, speed, and cost on fleet composition should be evaluated comprehensively to optimize the bus network and schedule sequence. Owing to the protracted nature of the transition from conventional to fully autonomous buses, one should prioritize semi-autonomous bus services. Another essential future research direction is to integrate modular autonomous bus assembly or disassembly strategies with different fine-grained operation optimization techniques in various scenarios.","PeriodicalId":339219,"journal":{"name":"Digital Transportation and Safety","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135600394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A properly designed public transport system is expected to improve traffic efficiency. A high-frequency bus service would decrease the waiting time for passengers, but the interaction between buses and cars might result in more serious congestion. On the other hand, a low-frequency bus service would increase the waiting time for passengers and would not be able to reduce the use of private cars. It is important to strike a balance between high and low frequencies in order to minimize the total delays for all road users. It is critical to formulate the impacts of bus frequency on congestion dynamics and mode choices. However, as far as the authors know, most proposed bus frequency optimization formulations are based on static demand and the Bureau of Public Roads function, and do not properly consider the congestion dynamics and their impacts on mode choices. To fill this gap, this paper proposes a bi-level optimization model. A three-dimensional Macroscopic Fundamental Diagram based modeling approach is developed to capture the bi-modal congestion dynamics. A variational inequality model for the user equilibrium in mode choices is presented and solved using a double projection algorithm. A surrogate model-based algorithm is used to solve the bi-level programming problem.
{"title":"Bus frequency optimization in a large-scale multi-modal transportation system: Integrating 3D-MFD and dynamic traffic assignment","authors":"Kai Yuan, Dandan Cui, Jiancheng Long","doi":"10.48130/dts-2023-0020","DOIUrl":"https://doi.org/10.48130/dts-2023-0020","url":null,"abstract":"A properly designed public transport system is expected to improve traffic efficiency. A high-frequency bus service would decrease the waiting time for passengers, but the interaction between buses and cars might result in more serious congestion. On the other hand, a low-frequency bus service would increase the waiting time for passengers and would not be able to reduce the use of private cars. It is important to strike a balance between high and low frequencies in order to minimize the total delays for all road users. It is critical to formulate the impacts of bus frequency on congestion dynamics and mode choices. However, as far as the authors know, most proposed bus frequency optimization formulations are based on static demand and the Bureau of Public Roads function, and do not properly consider the congestion dynamics and their impacts on mode choices. To fill this gap, this paper proposes a bi-level optimization model. A three-dimensional Macroscopic Fundamental Diagram based modeling approach is developed to capture the bi-modal congestion dynamics. A variational inequality model for the user equilibrium in mode choices is presented and solved using a double projection algorithm. A surrogate model-based algorithm is used to solve the bi-level programming problem.","PeriodicalId":339219,"journal":{"name":"Digital Transportation and Safety","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136303785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Resilience analysis of road tunnels subject to refurbishment works","authors":"C. Caliendo, Isidoro Russo, G. Genovese","doi":"10.48130/dts-2023-0015","DOIUrl":"https://doi.org/10.48130/dts-2023-0015","url":null,"abstract":"","PeriodicalId":339219,"journal":{"name":"Digital Transportation and Safety","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123396914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Overview of the identification of traffic accident-prone locations driven by big data","authors":"Chunjiao Dong, Naixin Chang","doi":"10.48130/dts-2023-0006","DOIUrl":"https://doi.org/10.48130/dts-2023-0006","url":null,"abstract":"","PeriodicalId":339219,"journal":{"name":"Digital Transportation and Safety","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123918988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Countdown signals for motorized vehicles, which are intended to ensure safety on the road and regulate motor vehicle speed limits at road intersections, are still considered a relatively novel concept. These signals have been adopted by only a few countries, and the number of cities that use them is limited. This review aims to summarize the effects of countdown signals on traffic safety and efficiency and to determine the consistency and differences of existing research propositions on the matter. Based on the review, considerable research presents evidently different conclusions in the areas of driver red-light running and traffic safety. Particularly, some studies propose that countdown signals reinforce traffic safety, whereas others consider that such signals adversely affect traffic safety. Meanwhile, related literature provides varying conclusions on the aspect of traffic efficiency for vehicle headway. At present, the number of studies conducted regarding the driving behaviors of motorists toward countdown-signalized intersections is insufficient. Accordingly, such inadequate diversity in research causes difficulty in completely assessing the benefits and disadvantages of countdown signals. In this paper, an important future research direction on microcosmic driving psychological and physiological data combined with macro-driving behavior is proposed.
{"title":"Impact of countdown signals on traffic safety and efficiency: A review and proposal","authors":"Fuquan Pan, Jingzhou Yang, Lixia Zhang, Changxi Ma, Jinshun Yang, Pingxia Zhang","doi":"10.48130/dts-2023-0016","DOIUrl":"https://doi.org/10.48130/dts-2023-0016","url":null,"abstract":"Countdown signals for motorized vehicles, which are intended to ensure safety on the road and regulate motor vehicle speed limits at road intersections, are still considered a relatively novel concept. These signals have been adopted by only a few countries, and the number of cities that use them is limited. This review aims to summarize the effects of countdown signals on traffic safety and efficiency and to determine the consistency and differences of existing research propositions on the matter. Based on the review, considerable research presents evidently different conclusions in the areas of driver red-light running and traffic safety. Particularly, some studies propose that countdown signals reinforce traffic safety, whereas others consider that such signals adversely affect traffic safety. Meanwhile, related literature provides varying conclusions on the aspect of traffic efficiency for vehicle headway. At present, the number of studies conducted regarding the driving behaviors of motorists toward countdown-signalized intersections is insufficient. Accordingly, such inadequate diversity in research causes difficulty in completely assessing the benefits and disadvantages of countdown signals. In this paper, an important future research direction on microcosmic driving psychological and physiological data combined with macro-driving behavior is proposed.","PeriodicalId":339219,"journal":{"name":"Digital Transportation and Safety","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114504854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Driving risk assessment under the connected vehicle environment: A CNN-LSTM modeling approach","authors":"Yin Zheng, Lei Han, Jiqing Yu, Rongjie Yu","doi":"10.48130/dts-2023-0017","DOIUrl":"https://doi.org/10.48130/dts-2023-0017","url":null,"abstract":"","PeriodicalId":339219,"journal":{"name":"Digital Transportation and Safety","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121936960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xing Fu, Jun Liu, Zhitong Huang, A. Hainen, A. Khattak
{"title":"LSTM-based lane change prediction using waymo open motion dataset: The role of vehicle operating space","authors":"Xing Fu, Jun Liu, Zhitong Huang, A. Hainen, A. Khattak","doi":"10.48130/dts-2023-0009","DOIUrl":"https://doi.org/10.48130/dts-2023-0009","url":null,"abstract":"","PeriodicalId":339219,"journal":{"name":"Digital Transportation and Safety","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130448431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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