Assessing the Distribution of Commuting Trips and Jobs-Housing Balance Using Smart Card Data: A Case Study of Nanjing, China

Abstract

The purpose of this research is to assess the distribution of commuting trips and the level of jobs-housing balance with Nanjing smart card data. A new approach is presented using the Lorenz curve and Gini coefficient based on the commuting time. This article also quantifies and visualizes Nanjing’s jobs-housing balance in each urban, suburban and exurban district. The core findings from this research are summarized as follows. First, the Gini coefficient of commuting time is 0.251 in urban areas, 0.258 for suburban areas and 0.267 for exurban areas. The gap of each non-urban district in commuting time is larger than urban districts. Second, the result of jobs-housing ratio (JHR) shows that jobs of Xuanwu district are far more than the working population of this district, whereas jobs and working population in other urban districts are relatively matched. The value of JHR is less than 0.8 in all suburban and exurban districts but Yuhuatai district, which suggests that jobs in these suburban districts (excluding Yuhuatai district) are in short supply compared with their working population. Third, the JHR within a particular district may be different according to the specific locations, especially those areas close to the boundary between two different kinds of districts.

Research on the Jobs-Housing Balance of Residents in Peri-Urbanization Areas in China: A Case Study of Zoucheng County

“…Excess commuting based on the cost of commuting time is also a heated research topic of jobs-housing balance [35][36][37]. Regarding the standard of commuting time and distance, it is generally believed that if the commuting time is within 30 min [38,39], the commuting distance is within 5 km [40,41], the individual is in a comfortable commuting state, that is, the jobs-housing balance has been achieved. However, there are certain limitations in the measurement criteria of jobs-housing balance in the above study, as only considering commuting time and commuting distance cannot fully reflect the individual's jobs-housing balance status.…”

Investigating spatio-temporal mobility patterns and changes in metro usage under the impact of COVID-19 using Taipei Metro smart card data

“…Metro smart card trip data has been combined with socio-economic data gained from surveys to predict the demographic attributes of smart card users (Zhang and Cheng 2018 ), to infer the employment status of passengers (Zhang and Cheng 2020 ), to analyze the associations between demographic attributes and travel patterns (Goulet-Langlois et al 2016 ), and to identify commuters among passengers of a metro line (Mei et al 2020 ). Other studies have used metro smart card trip data to predict lifestyles of passengers based on trip patterns (Shin 2020 ), to identify home and/or work locations of passengers (Li et al 2015 ; Sari Aslam et al 2019 ; Zou et al 2018 ), to assess the spatial distribution of commuting trips and jobs-housing ratios (Zheng et al 2019 ), and to infer trip purpose and activity (Lee and Hickman 2014 ; Zou et al 2018 ).…”

Investigate Jobs–Housing Spatial Relationship with Individual-Based Mobility Big Data of Public Housing Tenants: A Case Study in Chongqing, China

“…In recent years, with the rapid development of information technology, big data has been widely applied in related research fields. Using data such as government data [11], bus card swiping data [12], cell phone signal data [7,[13][14][15][16], Baidu heat map data [17], etc., more and more studies on the jobs-housing relationship have been carried out in cities such as Beijing, Shanghai, Wuhan, Chongqing, and so on. In addition, Zhang and other scholars tried to combine big data with small data.…”