How is public transit in the megacity peripheral relocatees’ area in China? Captive transit rider and dynamic modal accessibility gap analytics in a peripheral large-scale residential area in Shanghai, China - Jinping Guan; Kai Zhang; Shuang Zhang; Yizhou Chen (Journal of Transport and Land Use)
In the process of Chinese megacity suburbanization, central-city substandard housing is demolished. The government relocates residents to megacity peripheral relocatees’ areas. So far, few studies have focused on captive transit riders and analyzed the dynamic points of interest (POI) accessibility by public transit compared to the private mode in these areas. To fill this gap, this study conducts a survey in Jinhexincheng, one of these areas in Shanghai, China; analyzes captive-transit riders with multiple models; and computes the dynamic modal accessibility gap (DMAG) of public transit and private travel mode using multi-source heterogeneous data. Results show that 71.77% of transit-rider samples acknowledge they “have no other choice and have to travel by transit.” These captive transit riders are more often older, female, non-working, without a driving license, and with more complaints toward public transport. Subjective transit evaluation’s ordinal regression models show that waiting time, speed, operating hours, and number of lines/stops contribute to the low transit subjective evaluation. These things should be given a high priority in transit improvement. As for the captive transit riders, transit’s transfer and bicycle availability should be improved. Using big data analytics, a more fine-grained scale is brought in by computing a DMAG index. It shows a person mostly has a better POI accessibility by private mode for the 30-minute, real-travel-covered area for 24 hours of the average day. For the 60-minute, real-travel-covered area, public transit mostly has a better POI accessibility. This study supports transit planning and decision-making for megacity peripheral relocatees’ areas using multi-source heterogeneous data analytics.