Spatiotemporal path inference model for urban rail transit passengers based on travel time data
Abstract Although the ‘one ticket transfer’ mode brings convenience to passengers, it also poses challenges to the passenger flow assignment. The current widely used multi‐path probability assignment model based on traffic cost realizes the passenger flow distribution from the macro‐perspective but...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-07-01
|
| Series: | IET Intelligent Transport Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/itr2.12332 |
| _version_ | 1827898681944178688 |
|---|---|
| author | Qin Luo Bin Lin Yitong Lyu Yuxin He Xiaochun Zhang Zhiqing Zhang |
| author_facet | Qin Luo Bin Lin Yitong Lyu Yuxin He Xiaochun Zhang Zhiqing Zhang |
| author_sort | Qin Luo |
| collection | DOAJ |
| description | Abstract Although the ‘one ticket transfer’ mode brings convenience to passengers, it also poses challenges to the passenger flow assignment. The current widely used multi‐path probability assignment model based on traffic cost realizes the passenger flow distribution from the macro‐perspective but lacks the consideration of passenger attributes and the time‐varying characteristics of passenger travel time. Here, a novel spatiotemporal path inference model is proposed for Urban Rail Transit (URT) passengers based on the travel time data and train operation information. In this study, the impact of passengers detained at the platform on passenger travel itinerary is considered by characterizing the passenger detention rate. The proposed method realizes the reverse inference of passenger path from the micro‐perspective, and can accurately describe the specific travel process of each passenger. Moreover, the real‐world data of Shenzhen Metro in China is taken to verify the rationality of the proposed model. The results show that the model is in good agreement with the existing clearance model and can accurately infer the passenger travel itinerary from the micro‐perspective. The proposed method provides a more refined solution for the spatial‐temporal assignment of URT passenger flow. |
| first_indexed | 2024-03-12T23:06:43Z |
| format | Article |
| id | doaj.art-6d974bc406854a439638c4e570633b2a |
| institution | Directory Open Access Journal |
| issn | 1751-956X 1751-9578 |
| language | English |
| last_indexed | 2024-03-12T23:06:43Z |
| publishDate | 2023-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Intelligent Transport Systems |
| spelling | doaj.art-6d974bc406854a439638c4e570633b2a2023-07-18T15:38:52ZengWileyIET Intelligent Transport Systems1751-956X1751-95782023-07-011771395141410.1049/itr2.12332Spatiotemporal path inference model for urban rail transit passengers based on travel time dataQin Luo0Bin Lin1Yitong Lyu2Yuxin He3Xiaochun Zhang4Zhiqing Zhang5College of Urban Transportation and Logistics Shenzhen Technology University Shenzhen People's Republic of ChinaCollege of Urban Transportation and Logistics Shenzhen Technology University Shenzhen People's Republic of ChinaShenzhen Urban Transport Planning Center Co., Ltd. Shenzhen People's Republic of ChinaCollege of Urban Transportation and Logistics Shenzhen Technology University Shenzhen People's Republic of ChinaShenzhen Urban Transport Planning Center Co., Ltd. Shenzhen People's Republic of ChinaShanghai Shentong Metro Group Co., Ltd. Technology Center Shanghai People's Republic of ChinaAbstract Although the ‘one ticket transfer’ mode brings convenience to passengers, it also poses challenges to the passenger flow assignment. The current widely used multi‐path probability assignment model based on traffic cost realizes the passenger flow distribution from the macro‐perspective but lacks the consideration of passenger attributes and the time‐varying characteristics of passenger travel time. Here, a novel spatiotemporal path inference model is proposed for Urban Rail Transit (URT) passengers based on the travel time data and train operation information. In this study, the impact of passengers detained at the platform on passenger travel itinerary is considered by characterizing the passenger detention rate. The proposed method realizes the reverse inference of passenger path from the micro‐perspective, and can accurately describe the specific travel process of each passenger. Moreover, the real‐world data of Shenzhen Metro in China is taken to verify the rationality of the proposed model. The results show that the model is in good agreement with the existing clearance model and can accurately infer the passenger travel itinerary from the micro‐perspective. The proposed method provides a more refined solution for the spatial‐temporal assignment of URT passenger flow.https://doi.org/10.1049/itr2.12332passenger detentionpassenger flow assignmentpath inferencetravel timeurban rail transit |
| spellingShingle | Qin Luo Bin Lin Yitong Lyu Yuxin He Xiaochun Zhang Zhiqing Zhang Spatiotemporal path inference model for urban rail transit passengers based on travel time data IET Intelligent Transport Systems passenger detention passenger flow assignment path inference travel time urban rail transit |
| title | Spatiotemporal path inference model for urban rail transit passengers based on travel time data |
| title_full | Spatiotemporal path inference model for urban rail transit passengers based on travel time data |
| title_fullStr | Spatiotemporal path inference model for urban rail transit passengers based on travel time data |
| title_full_unstemmed | Spatiotemporal path inference model for urban rail transit passengers based on travel time data |
| title_short | Spatiotemporal path inference model for urban rail transit passengers based on travel time data |
| title_sort | spatiotemporal path inference model for urban rail transit passengers based on travel time data |
| topic | passenger detention passenger flow assignment path inference travel time urban rail transit |
| url | https://doi.org/10.1049/itr2.12332 |
| work_keys_str_mv | AT qinluo spatiotemporalpathinferencemodelforurbanrailtransitpassengersbasedontraveltimedata AT binlin spatiotemporalpathinferencemodelforurbanrailtransitpassengersbasedontraveltimedata AT yitonglyu spatiotemporalpathinferencemodelforurbanrailtransitpassengersbasedontraveltimedata AT yuxinhe spatiotemporalpathinferencemodelforurbanrailtransitpassengersbasedontraveltimedata AT xiaochunzhang spatiotemporalpathinferencemodelforurbanrailtransitpassengersbasedontraveltimedata AT zhiqingzhang spatiotemporalpathinferencemodelforurbanrailtransitpassengersbasedontraveltimedata |