Bi-objective bus scheduling optimization with passenger perception in mind
Abstract With the development of big traffic data, bus schedules should be changed from the traditional "empirical" rough scheduling to "responsive" accurate scheduling to meet the travel needs of passengers. Based on passenger flow distribution, considering passengers' feel...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-04-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-32997-4 |
| _version_ | 1827966308149362688 |
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| author | Shuai Liu Lin Liu Dongmei Pei Jue Wang |
| author_facet | Shuai Liu Lin Liu Dongmei Pei Jue Wang |
| author_sort | Shuai Liu |
| collection | DOAJ |
| description | Abstract With the development of big traffic data, bus schedules should be changed from the traditional "empirical" rough scheduling to "responsive" accurate scheduling to meet the travel needs of passengers. Based on passenger flow distribution, considering passengers' feelings of congestion and waiting time at the station, we establish a Dual-Cost Bus Scheduling Optimization Model (Dual-CBSOM) with the optimization objectives of minimizing bus operation and passenger travel costs. Improving the classical Genetic Algorithm (GA) by adaptively determining the crossover probability and mutation probability of the algorithm. We use an Adaptive Double Probability Genetic Algorithm (A_DPGA) to solve the Dual-CBSOM. Taking Qingdao city as an example for optimization, the constructed A_DPGA is compared with the classical GA and Adaptive Genetic Algorithm (AGA). By solving the arithmetic example, we get the optimal solution that can reduce the overall objective function value by 2.3%, improve the bus operation cost by 4.0%, and reduce the passenger travel cost by 6.3%. The conclusions show that the Dual_CBSOM built can better meet the passenger travel demand, improve passenger travel satisfaction, and reduce the passenger travel cost and waiting for cost. It is demonstrated that the A_DPGA built in this research has faster convergence and better optimization results. |
| first_indexed | 2024-04-09T17:47:55Z |
| format | Article |
| id | doaj.art-2145039ba1404c9daa2422682de1db43 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-09T17:47:55Z |
| publishDate | 2023-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-2145039ba1404c9daa2422682de1db432023-04-16T11:13:12ZengNature PortfolioScientific Reports2045-23222023-04-0113111610.1038/s41598-023-32997-4Bi-objective bus scheduling optimization with passenger perception in mindShuai Liu0Lin Liu1Dongmei Pei2Jue Wang3College of Geodesy and Geomatics, Shandong University of Science and TechnologyCollege of Geodesy and Geomatics, Shandong University of Science and TechnologyCollege of Geodesy and Geomatics, Shandong University of Science and TechnologyQingDao ZhenQing Bus Group Co., LtdAbstract With the development of big traffic data, bus schedules should be changed from the traditional "empirical" rough scheduling to "responsive" accurate scheduling to meet the travel needs of passengers. Based on passenger flow distribution, considering passengers' feelings of congestion and waiting time at the station, we establish a Dual-Cost Bus Scheduling Optimization Model (Dual-CBSOM) with the optimization objectives of minimizing bus operation and passenger travel costs. Improving the classical Genetic Algorithm (GA) by adaptively determining the crossover probability and mutation probability of the algorithm. We use an Adaptive Double Probability Genetic Algorithm (A_DPGA) to solve the Dual-CBSOM. Taking Qingdao city as an example for optimization, the constructed A_DPGA is compared with the classical GA and Adaptive Genetic Algorithm (AGA). By solving the arithmetic example, we get the optimal solution that can reduce the overall objective function value by 2.3%, improve the bus operation cost by 4.0%, and reduce the passenger travel cost by 6.3%. The conclusions show that the Dual_CBSOM built can better meet the passenger travel demand, improve passenger travel satisfaction, and reduce the passenger travel cost and waiting for cost. It is demonstrated that the A_DPGA built in this research has faster convergence and better optimization results.https://doi.org/10.1038/s41598-023-32997-4 |
| spellingShingle | Shuai Liu Lin Liu Dongmei Pei Jue Wang Bi-objective bus scheduling optimization with passenger perception in mind Scientific Reports |
| title | Bi-objective bus scheduling optimization with passenger perception in mind |
| title_full | Bi-objective bus scheduling optimization with passenger perception in mind |
| title_fullStr | Bi-objective bus scheduling optimization with passenger perception in mind |
| title_full_unstemmed | Bi-objective bus scheduling optimization with passenger perception in mind |
| title_short | Bi-objective bus scheduling optimization with passenger perception in mind |
| title_sort | bi objective bus scheduling optimization with passenger perception in mind |
| url | https://doi.org/10.1038/s41598-023-32997-4 |
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