Mobile Phone Data in Urban Customized Bus: A Network-based Hierarchical Location Selection Method with an Application to System Layout Design in the Urban Agglomeration
Abstract
:1. Introduction
1.1. Related Works
- First, the actual travel demand failed to be considered. In most existing CB systems, CB lines are planned based on travel demand data collected from on-line surveys, and the CB system layouts are designed based on the administrative division and organized by single cities. In the urban agglomeration area, survey data can hardly cover the area on a large scale. Moreover, even more importantly, the long tail travel demand in the urban agglomeration area, such as the long-distance commuting demands is unlikely to be discovered by the traditional survey data.
- Second, the hierarchical character of travel demand failed to be considered. The transportation demands in the urban agglomeration area are comprehensive [20], consisting of trips in cross-city, intrametropolitan, and intracity levels. The traveling patterns of residents are much complicated and generate more frequent cross-city movement. Without considering the entirety of urban agglomeration and hierarchically design transportation systems for travel demand, it is unable to build an efficient system to serve the actual demand.
1.2. Problem Description
1.2.1. Overview of the Dynamic CB Planning System
1.2.2. Customized Bus Service Scope Division
1.2.3. Customized Bus Station Location Selection
2. Methodology
2.1. Framework
Algorithm 1. Hierarchical commuting oriented CB system service scope and station location selection algorithm. |
Input: Jobs-housing relationship data |
Output: A tree storing information of CB service scope and CB station location |
Algorithm:
|
End while |
2.2. Constructing the Jobs-Housing Network
2.3. Service Scope Segmentation
2.3.1. Network Clusters Segmentation by Community Detection
- Modularity optimization: optimized modularity by allowing only local changes of communities;
- Community aggregation: the identified communities are aggregated in order to build a new network of communities.
2.3.2. Spatial Cluster Identification by DBSCAN
2.4. Location Selection Method for CB Stations
- Weighted mean center (WA).
- Weighted median center (MC).
- Weighted central element (CE).
3. Results and Discussion
3.1. Case Study
3.2. Result of Customized Bus Service Scope
3.3. Result of Customized Bus Station Location Selection
4. Conclusions and Future Prospects
- So long as the accurate and detailed data is provided, by applying the iterative algorithm, the service scopes can unlimitedly divide into smaller service scopes by changing the criteria, which enables us to choose an on-demand hierarchy structure.
- The iterative algorithm proposed in this paper is a powerful location selection tool, which is not limited to the scenario of CB system design. The methodology can be suitable for any OD flow dataset with uneven density in spatial cluster and have multiple application potentials (e.g., logistics center and rescue center location problem [43]).
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
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Yu, Q.; Li, W.; Zhang, H.; Yang, D. Mobile Phone Data in Urban Customized Bus: A Network-based Hierarchical Location Selection Method with an Application to System Layout Design in the Urban Agglomeration. Sustainability 2020, 12, 6203. https://doi.org/10.3390/su12156203
Yu Q, Li W, Zhang H, Yang D. Mobile Phone Data in Urban Customized Bus: A Network-based Hierarchical Location Selection Method with an Application to System Layout Design in the Urban Agglomeration. Sustainability. 2020; 12(15):6203. https://doi.org/10.3390/su12156203
Chicago/Turabian StyleYu, Qing, Weifeng Li, Haoran Zhang, and Dongyuan Yang. 2020. "Mobile Phone Data in Urban Customized Bus: A Network-based Hierarchical Location Selection Method with an Application to System Layout Design in the Urban Agglomeration" Sustainability 12, no. 15: 6203. https://doi.org/10.3390/su12156203