A Novel Hierarchical Hybrid Model for Short-Term Bus Passenger Flow Forecasting
For the increasing travel demands and public transport problems, dynamically adjusting timetable or bus scheduling is necessary based on accurate real-time passenger flow forecasting. In order to get more accurate passenger flow in future, this paper proposes a novel hierarchical hybrid model based...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/7917353 |
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| _version_ | 1850210464419545088 |
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| author | Huawei Zhai Ruijie Tian Licheng Cui Xiaowei Xu Weishi Zhang |
| author_facet | Huawei Zhai Ruijie Tian Licheng Cui Xiaowei Xu Weishi Zhang |
| author_sort | Huawei Zhai |
| collection | DOAJ |
| description | For the increasing travel demands and public transport problems, dynamically adjusting timetable or bus scheduling is necessary based on accurate real-time passenger flow forecasting. In order to get more accurate passenger flow in future, this paper proposes a novel hierarchical hybrid model based on time series model, deep belief networks (DBNs), and improved incremental extreme learning machine (Im-ELM) to forecast short-term passenger flow. The proposed model is named HTSDBNE with two modelling steps. First, referring the idea of parallelization, the hybrid model, constructed by time series model, DBN, and Im-ELM, is used to forecast short-term passenger flow in different time scales hierarchically and parallel. Second, Im-ELM is utilized to analyse the relationship of forecasting results from the first step, and the weighted outputs of Im-ELM are as the final forecasting results. Comparing with single forecasting models and typical hybrid forecasting models, the testing results indicate that HTSDBNE has better performances. The mean absolute percent error of prediction results is around 10% and fully meets the application requirements of bus operation enterprise. |
| format | Article |
| id | doaj-art-928c8cd09bf54daa9d64d995dae90bc9 |
| institution | OA Journals |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-928c8cd09bf54daa9d64d995dae90bc92025-08-20T02:09:45ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/79173537917353A Novel Hierarchical Hybrid Model for Short-Term Bus Passenger Flow ForecastingHuawei Zhai0Ruijie Tian1Licheng Cui2Xiaowei Xu3Weishi Zhang4Information Science and Technology School, Dalian Maritime University, Dalian 116026, ChinaInformation Science and Technology School, Dalian Maritime University, Dalian 116026, ChinaPublic Security Information Department, Liaoning Police College, Dalian 116036, ChinaUniversity of Arkansas at Little Rock, Little Rock, AR, USAInformation Science and Technology School, Dalian Maritime University, Dalian 116026, ChinaFor the increasing travel demands and public transport problems, dynamically adjusting timetable or bus scheduling is necessary based on accurate real-time passenger flow forecasting. In order to get more accurate passenger flow in future, this paper proposes a novel hierarchical hybrid model based on time series model, deep belief networks (DBNs), and improved incremental extreme learning machine (Im-ELM) to forecast short-term passenger flow. The proposed model is named HTSDBNE with two modelling steps. First, referring the idea of parallelization, the hybrid model, constructed by time series model, DBN, and Im-ELM, is used to forecast short-term passenger flow in different time scales hierarchically and parallel. Second, Im-ELM is utilized to analyse the relationship of forecasting results from the first step, and the weighted outputs of Im-ELM are as the final forecasting results. Comparing with single forecasting models and typical hybrid forecasting models, the testing results indicate that HTSDBNE has better performances. The mean absolute percent error of prediction results is around 10% and fully meets the application requirements of bus operation enterprise.http://dx.doi.org/10.1155/2020/7917353 |
| spellingShingle | Huawei Zhai Ruijie Tian Licheng Cui Xiaowei Xu Weishi Zhang A Novel Hierarchical Hybrid Model for Short-Term Bus Passenger Flow Forecasting Journal of Advanced Transportation |
| title | A Novel Hierarchical Hybrid Model for Short-Term Bus Passenger Flow Forecasting |
| title_full | A Novel Hierarchical Hybrid Model for Short-Term Bus Passenger Flow Forecasting |
| title_fullStr | A Novel Hierarchical Hybrid Model for Short-Term Bus Passenger Flow Forecasting |
| title_full_unstemmed | A Novel Hierarchical Hybrid Model for Short-Term Bus Passenger Flow Forecasting |
| title_short | A Novel Hierarchical Hybrid Model for Short-Term Bus Passenger Flow Forecasting |
| title_sort | novel hierarchical hybrid model for short term bus passenger flow forecasting |
| url | http://dx.doi.org/10.1155/2020/7917353 |
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