Abstract
This studyutilized the mobile signaling data to conductthe impact analysis of jobs-housing spatial mismatch on commuting behavior, with eight typical employment centers of three categories selected as the research subjects. Based on the analysis of the characteristics and indictors including commuting distance, accessibilities from cumulative opportunity model etc., this study demonstrates that (a) cumulative percentage of short commuting distance (e.g., less than 3 km) reflects the jobs-housing spatial match between employment centers and their peripheral areas; and (b) combining the indicators of employed population and area covered within a certain space-time range among indictors of accessibility, it is possible to identify the degree of jobs-housing balance and efficiency of the transport system. According to the evaluation radar maps, the authors believe that employment centers could be divided into three categories: those with a gathering power, those with improvable functions, and those with local adjustment potentials. Possible measures including controlling the gathering power of the city centers, improving the function mix and transport facilities, and optimizing the overall local environment, etc. could be made to achieve jobs-housing balance in central districts and their peripheral areas as a whole. Besides, the study, proceeding from the perspective of commuters, suggests that optimization of jobs-housing distribution along banded corridors would be more efficient than those within the traditional region so as to reduce commuting traffic load.
Highlights
To solve the problem of traffic congestion during peak commuting hours caused by the time-spatial shortage of transport resources is the current focus of multidisciplinary research
Origin-Destination (OD) analysis was conducted on the spatial information of the user’s place of residence and work yielded by mobile signaling data analysis to calculate the minimum length of path in a transport system, and this length was defined as the “commuting distance”
Suppose that Z is a collection of all the Traffic Analysis Zones (TAZs), the average commuting distance for residents or jobs in area I, which includes several TAZs, could be calculated using the following formula: Dw
Summary
To solve the problem of traffic congestion during peak commuting hours caused by the time-spatial shortage of transport resources is the current focus of multidisciplinary research. Nowlan et al (1991) [15] conducted research into Toronto CBD, Canada; Ewing (1995) [16] made regression analysis on statistics of the State of Florida, U.S; Weitz et al (1997) [17] compared data of two cities in Oregon, U.S and Lobyaem (2006) [18] took Bangkok as an example, all presenting evidence that a higher degree of jobs-housing spatial match helped ease the commuting traffic load. Loo (2011) [19] analyzed related data of Hongkong between 1992 and 2002, stating that the government could address issues regarding the commuting traffic and lowering their impact on the environment by implementing the strategy of decentralized employment centers in a synchronous manner This kind of research works revealed two facts: one is that the issues of jobs-housing imbalance and spatial mismatch are commonly faced by megacities; the other is that the degree of jobs-housing mismatch is positively correlated with the commuting traffic intensity
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