Bus Network Adjustment Pre-Evaluation Based on Biometric Recognition and Travel Spatio-Temporal Deduction
A critical component of bus network adjustment is the accurate prediction of potential risks, such as the likelihood of complaints from passengers. Traditional simulation methods, however, face limitations in identifying passengers and understanding how their travel patterns may change. To address t...
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MDPI AG
2024-11-01
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| author | Qingbo Wei Nanfeng Zhang Yuan Gao Cheng Chen Li Wang Jingfeng Yang |
| author_facet | Qingbo Wei Nanfeng Zhang Yuan Gao Cheng Chen Li Wang Jingfeng Yang |
| author_sort | Qingbo Wei |
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| description | A critical component of bus network adjustment is the accurate prediction of potential risks, such as the likelihood of complaints from passengers. Traditional simulation methods, however, face limitations in identifying passengers and understanding how their travel patterns may change. To address this issue, a pre-evaluation method has been developed, leveraging the spatial distribution of bus networks and the spatio-temporal behavior of passengers. The method includes stage of travel demand analysis, accessible path set calculation, passenger assignment, and evaluation of key indicators. First, we explore the actual passengers’ origin and destination (OD) stop from bus card (or passenger Code) payment data and biometric recognition data, with the OD as one of the main input parameters. Second, a digital bus network model is constructed to represent the logical and spatial relationships between routes and stops. Upon inputting bus line adjustment parameters, these relationships allow for the precise and automatic identification of the affected areas, as well as the calculation of accessible paths of each OD pair. Third, the factors influencing passengers’ path selection are analyzed, and a predictive model is built to estimate post-adjustment path choices. A genetic algorithm is employed to optimize the model’s weights. Finally, various metrics, such as changes in travel routes and ride times, are analyzed by integrating passenger profiles. The proposed method was tested on the case of the Guangzhou 543 route adjustment. Results show that the accuracy of the number of predicted trips after adjustment is 89.6%, and the predicted flow of each associated bus line is also consistent with the actual situation. The main reason for the error is that the path selection has a certain level of irrationality, which stems from the fact that the proportion of passengers who choose the minimum cost path for direct travel is about 65%, while the proportion of one-transfer passengers is only about 50%. Overall, the proposed algorithm can quantitatively analyze the impact of rigid travel groups, occasional travel groups, elderly groups, and other groups that are prone to making complaints in response to bus line adjustment. |
| format | Article |
| id | doaj-art-239fdeb185e04aac89f44dfdf10ba2cf |
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| issn | 1999-4893 |
| language | English |
| publishDate | 2024-11-01 |
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| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-239fdeb185e04aac89f44dfdf10ba2cf2025-08-20T02:26:44ZengMDPI AGAlgorithms1999-48932024-11-01171151310.3390/a17110513Bus Network Adjustment Pre-Evaluation Based on Biometric Recognition and Travel Spatio-Temporal DeductionQingbo Wei0Nanfeng Zhang1Yuan Gao2Cheng Chen3Li Wang4Jingfeng Yang5Guangdong Provincial Key Laboratory of Intelligent Port Security Inspection, Huangpu Customs District, Guangzhou 510700, ChinaGuangdong Provincial Key Laboratory of Intelligent Port Security Inspection, Huangpu Customs District, Guangzhou 510700, ChinaGuangzhou Public Transport Data Management Center Co., Ltd., Guangzhou 510620, ChinaGuangzhou Public Transport Data Management Center Co., Ltd., Guangzhou 510620, ChinaGuangdong Zhongke Zhenheng Information Technology Co., Ltd., Foshan 528225, ChinaGuangdong Zhongke Zhenheng Information Technology Co., Ltd., Foshan 528225, ChinaA critical component of bus network adjustment is the accurate prediction of potential risks, such as the likelihood of complaints from passengers. Traditional simulation methods, however, face limitations in identifying passengers and understanding how their travel patterns may change. To address this issue, a pre-evaluation method has been developed, leveraging the spatial distribution of bus networks and the spatio-temporal behavior of passengers. The method includes stage of travel demand analysis, accessible path set calculation, passenger assignment, and evaluation of key indicators. First, we explore the actual passengers’ origin and destination (OD) stop from bus card (or passenger Code) payment data and biometric recognition data, with the OD as one of the main input parameters. Second, a digital bus network model is constructed to represent the logical and spatial relationships between routes and stops. Upon inputting bus line adjustment parameters, these relationships allow for the precise and automatic identification of the affected areas, as well as the calculation of accessible paths of each OD pair. Third, the factors influencing passengers’ path selection are analyzed, and a predictive model is built to estimate post-adjustment path choices. A genetic algorithm is employed to optimize the model’s weights. Finally, various metrics, such as changes in travel routes and ride times, are analyzed by integrating passenger profiles. The proposed method was tested on the case of the Guangzhou 543 route adjustment. Results show that the accuracy of the number of predicted trips after adjustment is 89.6%, and the predicted flow of each associated bus line is also consistent with the actual situation. The main reason for the error is that the path selection has a certain level of irrationality, which stems from the fact that the proportion of passengers who choose the minimum cost path for direct travel is about 65%, while the proportion of one-transfer passengers is only about 50%. Overall, the proposed algorithm can quantitatively analyze the impact of rigid travel groups, occasional travel groups, elderly groups, and other groups that are prone to making complaints in response to bus line adjustment.https://www.mdpi.com/1999-4893/17/11/513urban trafficbus network adjustmentspatio-temporal modelpre-evaluation |
| spellingShingle | Qingbo Wei Nanfeng Zhang Yuan Gao Cheng Chen Li Wang Jingfeng Yang Bus Network Adjustment Pre-Evaluation Based on Biometric Recognition and Travel Spatio-Temporal Deduction Algorithms urban traffic bus network adjustment spatio-temporal model pre-evaluation |
| title | Bus Network Adjustment Pre-Evaluation Based on Biometric Recognition and Travel Spatio-Temporal Deduction |
| title_full | Bus Network Adjustment Pre-Evaluation Based on Biometric Recognition and Travel Spatio-Temporal Deduction |
| title_fullStr | Bus Network Adjustment Pre-Evaluation Based on Biometric Recognition and Travel Spatio-Temporal Deduction |
| title_full_unstemmed | Bus Network Adjustment Pre-Evaluation Based on Biometric Recognition and Travel Spatio-Temporal Deduction |
| title_short | Bus Network Adjustment Pre-Evaluation Based on Biometric Recognition and Travel Spatio-Temporal Deduction |
| title_sort | bus network adjustment pre evaluation based on biometric recognition and travel spatio temporal deduction |
| topic | urban traffic bus network adjustment spatio-temporal model pre-evaluation |
| url | https://www.mdpi.com/1999-4893/17/11/513 |
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