A Novel Traffic Analysis Zone Division Methodology Based on Individual Travel Data
Urban rail transit passenger flow forecasting often relies on the traditional “four-step” method, where the division of traffic analysis zones (TAZs) is critical to ensuring prediction accuracy. As the fundamental units for describing trip origins and destinations, TAZs also encompass socioeconomic...
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MDPI AG
2024-12-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/1/156 |
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| author | Kai Du Jingni Song Dan Chen Ming Li Yadi Zhu |
| author_facet | Kai Du Jingni Song Dan Chen Ming Li Yadi Zhu |
| author_sort | Kai Du |
| collection | DOAJ |
| description | Urban rail transit passenger flow forecasting often relies on the traditional “four-step” method, where the division of traffic analysis zones (TAZs) is critical to ensuring prediction accuracy. As the fundamental units for describing trip origins and destinations, TAZs also encompass socioeconomic attributes such as land use, population, and employment. However, traditional TAZs, typically based on administrative boundaries, fail to reflect evolving urban travel behavior, particularly when transit stations are located near TAZ boundaries. Additionally, the emergence of urban big data allows for more refined spatial analyses based on individual travel patterns, addressing the limitations of administrative divisions. This study proposes an innovative TAZ aggregation model based on travel similarity, integrating public transit smart-card data and GIS data from bus networks. First, individual spatiotemporal travel patterns are mapped and discretized in both the spatial and temporal dimensions. Travel characteristic data are then extracted for spatial grid units. The TAZ division problem is defined as a multiobjective optimization problem, including factors such as travel similarity, the homogeneity of travel intensity, the statistical accuracy of the area, geographic information preservation, travel ratio constraints, and shape constraints. Multiple TAZ division schemes are produced and assessed using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), resulting in the selection of the optimal scheme. The proposed method is implemented on bus passenger travel data in Beijing, showing that the optimized scheme significantly reduces the number of zones with travel ratios exceeding 10%. Compared with existing schemes, the optimized division yields more uniform distributions of travel ratios, area, and travel density, while significantly minimizing the number of zones with a high travel concentration. These results demonstrate that the proposed method better reflects residents’ actual travel behaviors, offering a notable improvement over traditional approaches. This research provides a novel and practical framework for data-driven TAZ optimization. |
| format | Article |
| id | doaj-art-cd31fa4eb9c045e0b531f6779c2014ac |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-cd31fa4eb9c045e0b531f6779c2014ac2025-01-10T13:14:37ZengMDPI AGApplied Sciences2076-34172024-12-0115115610.3390/app15010156A Novel Traffic Analysis Zone Division Methodology Based on Individual Travel DataKai Du0Jingni Song1Dan Chen2Ming Li3Yadi Zhu4School of Electronics and Control Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Transportation Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Electronics and Control Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Civil Engineering, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Civil Engineering, Beijing Jiaotong University, Beijing 100044, ChinaUrban rail transit passenger flow forecasting often relies on the traditional “four-step” method, where the division of traffic analysis zones (TAZs) is critical to ensuring prediction accuracy. As the fundamental units for describing trip origins and destinations, TAZs also encompass socioeconomic attributes such as land use, population, and employment. However, traditional TAZs, typically based on administrative boundaries, fail to reflect evolving urban travel behavior, particularly when transit stations are located near TAZ boundaries. Additionally, the emergence of urban big data allows for more refined spatial analyses based on individual travel patterns, addressing the limitations of administrative divisions. This study proposes an innovative TAZ aggregation model based on travel similarity, integrating public transit smart-card data and GIS data from bus networks. First, individual spatiotemporal travel patterns are mapped and discretized in both the spatial and temporal dimensions. Travel characteristic data are then extracted for spatial grid units. The TAZ division problem is defined as a multiobjective optimization problem, including factors such as travel similarity, the homogeneity of travel intensity, the statistical accuracy of the area, geographic information preservation, travel ratio constraints, and shape constraints. Multiple TAZ division schemes are produced and assessed using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), resulting in the selection of the optimal scheme. The proposed method is implemented on bus passenger travel data in Beijing, showing that the optimized scheme significantly reduces the number of zones with travel ratios exceeding 10%. Compared with existing schemes, the optimized division yields more uniform distributions of travel ratios, area, and travel density, while significantly minimizing the number of zones with a high travel concentration. These results demonstrate that the proposed method better reflects residents’ actual travel behaviors, offering a notable improvement over traditional approaches. This research provides a novel and practical framework for data-driven TAZ optimization.https://www.mdpi.com/2076-3417/15/1/156intelligent transportationtraffic analysis zone divisionmalleable area unit problemsmart-card datamultiobjective optimization |
| spellingShingle | Kai Du Jingni Song Dan Chen Ming Li Yadi Zhu A Novel Traffic Analysis Zone Division Methodology Based on Individual Travel Data Applied Sciences intelligent transportation traffic analysis zone division malleable area unit problem smart-card data multiobjective optimization |
| title | A Novel Traffic Analysis Zone Division Methodology Based on Individual Travel Data |
| title_full | A Novel Traffic Analysis Zone Division Methodology Based on Individual Travel Data |
| title_fullStr | A Novel Traffic Analysis Zone Division Methodology Based on Individual Travel Data |
| title_full_unstemmed | A Novel Traffic Analysis Zone Division Methodology Based on Individual Travel Data |
| title_short | A Novel Traffic Analysis Zone Division Methodology Based on Individual Travel Data |
| title_sort | novel traffic analysis zone division methodology based on individual travel data |
| topic | intelligent transportation traffic analysis zone division malleable area unit problem smart-card data multiobjective optimization |
| url | https://www.mdpi.com/2076-3417/15/1/156 |
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