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
ITSLab, Massachusetts Institute of Technology
Kai Zhang
Graduate School at Shenzhen, Tsinghua University, Shenzhen, PRC
Shuang Zhang
Graduate School at Shenzhen, Tsinghua University, Shenzhen, PRC
Yizhou Chen
Shenzhen Rongheng Industry Group Co., Ltd.
DOI: https://doi.org/10.5198/jtlu.2020.1505
Keywords: Public Transit, Captive Transit Rider, Subjective Transit Evaluation, Dynamic Modal Accessibility Gap, Megacity Peripheral Relocatee’s Area, China
Abstract
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.
References
Beimborn, E., Greenwald, M., & Jin, X. (2003). Accessibility, connectivity, and captivity—impacts on transit choice. Transportation Research Record, 1835, 1–9.
Bierlaire, M. (2003). BIOGEME: A free package for the estimation of discrete choice models, Proceedings of the 3rd Swiss Transportation Research Conference, Ascona, Switzerland.
Brown, M. (1983). Public transit fare and subsidy policy in greater Vancouver, 1970-1983: Efficiency and equity implications. Vancouver, BC: School of Community and Regional Planning, University of British Columbia.
Cervero, R. (1990). Transit pricing research: A review and synthesis. Transportation, 17, 117–139.
Cervero, R., & Day, J. (2008). Suburbanization and transit-oriented development in China. Transport Policy, 15, 315–323.
Chen, X. (2003). Retrospect and prospect of public transit privatization in China, Journal of Advanced Transportation, 37(3), 319–347.
Chen, X. (2012). Shanghai public transport satisfaction score is over 80, nearly 90% residents rely on public transport, Liberation Daily. Retrieved from www.gov.cn/gzdt/2012-10/22/content_2248482.htm.
Chen, X., & Zhang, H. (2012). Evaluation of effects of car ownership policies in Chinese megacities: Beijing and Shanghai. Transportation Research Record, 2317, 32–39.
Day, J. (2009). Cost of suburbanization: Comparative effects of peri-urban residential relocation on household welfare measures in Shanghai (Doctoral dissertation), University of California, Berkeley.
Day, J., & Cervero, R. (2010). Effects of residential relocation on household and commuting expenditures in Shanghai, China. International Journal of Urban and Regional Research, 34, 481–508.
Deng T., & Nelson, J. (2012). The perception of bus rapid transit: A passenger survey from Beijing Southern Axis BRT Line 1. Transportation Planning and Technology, 35(2), 201–219.
De Vos, J., Mokhtarian, P. L., Schwanen, T., Van Acker, V., & Witlox, F. (2016). Travel mode choice and travel satisfaction: Bridging the gap between decision utility and experienced utility. Transportation, 43(5), 771–796.
De Vos, J. (2018). Do people travel with their preferred travel mode? Analyzing the extent of travel mode dissonance and its effect on travel satisfaction. Transportation Research Part A, 117, 261–274.
EmaoAuto. (2012). Various car prices. Retrieved from http://auto.emao.com.
Guan, J., & Zhang, P. (2011). The Shanghai periphery large-scale residential area residents’ commuting characteristics: Case study of Jinhexincheng, Jiangqiao, Jiading district, Paper presented at the 2011 Annual Meeting of China Urban Transport Planning, Wuhan, China.
Guan, J. (2012). Travel characteristics of different social groups in large-scale residential areas in the periphery of Shanghai: A case study of Jinhexincheng. Paper presented at the 12th Chinese Overseas Transportation Association International Conference of Transportation Professionals (CICTP 2012, ASCE publication), Beijing, China.
Guan, J. (2013). Travel behavior of two major groups in large scale residential areas in the periphery of Shanghai: A case study of Jinhexincheng, Jiading district. Paper presented at 92nd Annual Meeting of the Transportation Research Board, Washington, D.C.
Guan, J. (2015). Travel behavior characteristics of different social groups in large-scale residential areas on city periphery: Case study of Shanghai, China (Doctoral dissertation). Tongji University, Shanghai, China.
Guan, J., & Yang, D. (2015). Residents’ characteristics and transport policy analysis in large-scale residential areas on a city periphery: Case study of Jinhexincheng, Shanghai, China. Transportation Research Record, 2512, 11–21.
Guan, J. & Xu, C. (2018). Are relocatees different from others? Relocatees’ travel mode choice and travel equity analysis in large-scale residential areas on the periphery of megacity Shanghai, China Transportation Research Part A: Policy and Practice, 111(C), 162–173.
Habib, K., & Weiss, A. (2014). Evolution of latent modal captivity and mode choice patterns for commuting trips: A longitudinal analysis using repeated cross-sectional datasets, Transportation Research Part A, 66, 39–51.
Hess, D. B. (2005). Access to employment for adults in poverty in the Buffalo-Niagara region. Urban Studies, 42(7), 1177–1200.
Huzayyin, A., & Youssef, A. (2013). Analysis of the evolution of travelers’ mode captivity using logit modelling; with application on Greater Cairo. Paper presented at the 13th World Conference on Transportation Research Society, Rio de Janeiro.
Jacques, C., Manaugh, K., & El-Geneidy, A. (2013). Rescuing the captive [mode] user: An alternative approach to transport market segmentation. Transportation, 40, 625–645.
Kawabata, M. (2009). Spatiotemporal dimensions of modal accessibility disparity in Boston and San Francisco. Environment and Planning A, 41(1), 183–198.
Keefer, L. E. (1962) Characteristics of captive and choice transit trips in the Pittsburgh metropolitan area. 41st Annual Meeting of the Highway Research Board, 347, 24–33.
Krizek, K., & El-Geneidy, A. (2006). Better understanding the potential market of metro transit’s ridership and service (report no. CTS 06-09). Retrieved from its.umn.edu/Publications/ResearchReports/reportdetail.html?id=1118.
Kwok, R. C. W. & Yeh, A. G. O., (2004). The use of modal accessibility gap as an indicator for sustainable transport development. Environment and Planning A: Economy and Space, 36(5), 921–936.
Liang, Q. (2002). 30 affordable housing projects are developed, Beijing has no dilapidated building in 2005. Retrieved from people.com.cn/GB/shenghuo/81/99/20021124/873097.html
Mao, H., Fan, X., Guan, J., Chen, Y.-C., Su, H., Shi, W., … Xu, C. (2019). Customer attractiveness evaluation and classification of urban commercial centers by crowd intelligence. Computers in Human Behavior, 100, 218–230.
Morris, E. A., & Guerra, E. (2015). Mood and mode: Does how we travel affect how we feel? Transportation, 42(1), 25–43.
Pan, H., Wang, X., & Day, J. (2010). Travel characteristics and its impact on social segregation and urban livability. Urban Planning Forum, 6, 61–67.
Papaioannou, D., & Martinez, L. (2015). The role of accessibility and connectivity in mode choice. A structural equation modeling approach, 18th Euro Working Group on Transportation, EWGT 2015, July 2015, Delft, The Netherlands. Transportation Research Procedia, 10, 831–839.
Peng, Z., Yu, D., & Beimborn, E. (2002). Transit user perceptions of the benefits of automatic vehicle location. Transportation Research Record, 1791, 127–133.
Polzin, S., Chu, X., & Rey, J. (2000). Density and captivity in public transit success: Observations from the 1995 nationwide personal transportation study. Transportation Reseach Record, 1735, 10–18.
Salonen, M. & Toivonen, T., (2013). Modelling travel time in urban networks: Comparable measures for private car and public transport. Journal of Transport Geography, 31, 143–153.
Shanghai Municipal Statistics Bureau & Shanghai Statistics Press. (2012). Shanghai Statistical Yearbook. Shanghai: Shanghai Municipal Statistics Bureau, Shanghai Statistics Press.
Shanghai Urban Rural Construction and Transportation Development Research Institute and Shanghai City Comprehensive Transportation Planning Institute. (2014). Shanghai Comprehensive Transportation Annual Report 2014. Shanghai: Shanghai Urban Rural Construction and Transportation Development Research Institute and Shanghai City Comprehensive Transportation Planning Institute. Retrieved from wenku.baidu.com/view/5922ec06f46527d3250ce047.html.
Shen, Q. (1997). Urban transportation in Shanghai, China: Problems and planning implications. International Journal of Urban and Regional Research, 21, 589–606.
Shen, Q. (1998). Location characteristics of inner-city neighborhoods and employment accessibility of low-wage workers. Environment and Planning B: Urban analytics and City Science, 25(3), 345–365.
Srinivasan, K., Pradhan, G., & Naidu, G. (2007). Commute mode choice in a developing country role of subjective factors and variations in responsiveness across captive, semicaptive, and choice segments. Transportation Research Record, 2038, 53–61.
St-Louis, E., Manaugh, K., van Lierop, D., & El-Geneidy, A. (2014). The happy commuter: A comparison of commuter satisfaction across modes. Transportation Research Part F, 26, 160–170.
Tongji University. (2011). Residents travel demand analysis and transportation planning strategy in large-scale residential areas on the periphery of Shanghai central city. Shanghai: School of Transportation Engineering.
Vehicle license’s price tendency in Shanghai (2015). Retrieved from 51chepai.com.cn/paizhaojiage
Walker, J. (2001). Extended discrete choice models: Integrated framework, flexible error structures, and latent variables (Doctoral dissertation). Massachusetts Institute of Technology, Boston.
Yang, J. (2006). Transportation implications of land development in a transitional economy: Evidence from housing relocation in Beijing. Transportation Research Record, 1954, 7–14.
Yang, W., Chen, Y., Cao, X., Li., T., & Li, P. (2017). The spatial characteristics and influencing factors of modal accessibility gaps: A case study for Guangzhou, China. Journal of Transport Geography, 60, 21–32.
Ye, R., & Titheridge, H. (2017). Satisfaction with the commute: The role of travel mode choice, built environment and attitudes. Transportation Research Part D, 52B, 535–547.
Zhang, M., Shen, Q. & Sussman, J. (1999). Strategies to improve job accessibility: Case study of Tren Urbano in San Juan metropolitan region. Transportation Research Record, 1669(1), 53–60.
Zhao, P. (2011). Car use, commuting and urban form in a rapidly growing city: Evidence from Beijing, Transportation Planning and Technology, 34, 509–527.
Zhao, P., Lu, B., & de Roo G. (2011). The impact of urban growth on commuting patterns in a restructuring city: Evidence from Beijing. Regional Science, 90(4), 735–754.
Zhao, J, Webb, V., & Shah, P. (2014). Customer loyalty differences between captive and choice transit riders, Transportation Research Record, 2415, 80–88.
Zhou, Y. (1997). On the Suburbanization of Beijing. Chinese Geographical Science, 7, 208–219.
