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Title:

Understanding Spatial and Temporal Patterns of Urban Travel Demands with Call Detail Record Data

Accession Number:

01627780

Record Type:

Component

Abstract:

It is essential for effective transportation applications to understand spatial and temporal patterns of travel demands on urban scale. The high penetration of mobile phones in modern society and their ubiquitous usage in daily activities makes it practical to observe continuously the time-variant travel demands. This paper introduces an approach to understand spatial and temporal patterns in metropolitan areas and its pilot study with Call Detail Record (CDR) data in Beijing, China. To describe travel demand patterns in a quantitative way, normalized indexes of travel generation, attraction and tide phenomenon are introduced, which make the results more expressive for visualization. With this method, the spatial and temporal patterns of urban travel demands are discovered. Moreover, the urban structural travel demand patterns can be mined by a multi-layer analysis approach. As a result, patterns of urban travel demands can be depicted in spatial and temporal dimensions. All the analysis results show strong consistency with the empirical factors in Beijing and this data-driven processing method is very suitable for travel dynamic analysis and forecasting. The analysis framework shows significant application potential of transportation planning and intelligent transportation systems.

Supplemental Notes:

This paper was sponsored by TRB committee ABJ30 Standing Committee on Urban Transportation Data and Information Systems.

Monograph Accession #:

01618707

Report/Paper Numbers:

17-04534

Language:

English

Corporate Authors:

Transportation Research Board

500 Fifth Street, NW
Washington, DC 20001 United States

Authors:

Dong, Honghui
Liu, Kai
Zhenyu, Yang
Li, Zhibin
Jia, Limin
Qin, Yong
Wang, Yinhai

Pagination:

19p

Publication Date:

2017

Conference:

Transportation Research Board 96th Annual Meeting

Location: Washington DC, United States
Date: 2017-1-8 to 2017-1-12
Sponsors: Transportation Research Board

Media Type:

Digital/other

Features:

Figures; References

Candidate Terms:

Geospatial analysis

Geographic Terms:

Beijing (China)

Subject Areas:

Data and Information Technology; Highways; Planning and Forecasting

Source Data:

Transportation Research Board Annual Meeting 2017 Paper #17-04534

Files:

TRIS, TRB, ATRI

Created Date:

Dec 8 2016 11:44AM