Authors: Mengya Li*, Geography, University Of Illinois, Urbana Champain, Mei-Po Kwan, Geography and Geographical Information Science, University of Illinois, Urbana Champaign, Fahui Wang, Department of Geography and Anthropology, Louisiana State University, Jun Wang , School of Geographical Sciences, East China Normal University
Topics: Urban Geography, Transportation Geography, Geographic Information Science and Systems
Keywords: commuting pattern; gravity model; POI data; central urban area; Shanghai
Session Type: Paper
Start / End Time: 10:00 AM / 11:40 AM
Room: Muses, Sheraton, 8th Floor
Presentation File: No File Uploaded
Commuting is an essential part of urban life. Regardless of the reasons for long commutes, the increasing length of work journeys in many cities will contribute to negative social and environmental impacts, such as traffic congestion and air pollution. In contrast, due to the lack of flexible work schedules, commuters are also more likely to encounter adverse events such as bad weather and high exposure to pollutants. Commuting patterns may assist researchers to better understand the interaction between commuting behaviors and urban structure, and to alleviate the aforementioned negative effects through appropriate measures. However, commuting studies are inadequate in quite a few cities due to the lack of suitable datasets that include information on commute distance, duration, departure/arrival time, and origins/destinations. This study implements a gravity-based model to estimate interzonal commuting patterns in central Shanghai, China. The gravity model uses data of workers, employment data (estimated using points of interest [POIs]), and O-D travel time matrix. The results revealed a "busy corridor" in the west of the central urban area, especially during the morning peak hours. This pattern corresponds well with the real-time traffic status released by the Shanghai Traveling website and other studies that investigate the overall commuting patterns in Shanghai. Importantly, our methodology offers an alternative when there are limited data for studying commuting patterns, rather than a challenge or an optimization of the methods or datasets used in existed studies.