Journal of Transport and Land Use

Effects of the built environment on travel distance in bus-oriented, medium-sized cities in China

Xiaowei Li

School of Civil Engineering, Xi’an University of Architecture & Technology

Lanxin Shi

School of Civil Engineering, Xi’an University of Architecture & Technology

Junqing Tang

School of Urban Planning and Design, Shenzhen Graduate School, Peking University

Jiaying Li

School of Urban Planning and Design, Shenzhen Graduate School, Peking University

Pengjun Zhao

School of Urban Planning and Design, Shenzhen Graduate School, Peking University

Qian Liu

School of Civil Engineering, Xi’an University of Architecture & Technology

Jun Chen

School of Civil Engineering, Xi’an University of Architecture & Technology

Changxi Ma

School of Traffic and Transportation, Lanzhou Jiaotong University

DOI: https://doi.org/10.5198/jtlu.2024.2427

Keywords: Travel distance, Bus transit systems, Built environment, Medium-sized cities, Machine learning, Big data

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Abstract

The impact of the built environment and weather conditions on travel behavior has been widely studied. However, limited studies have focused on better understanding such effects in medium-sized cities with bus-oriented transit systems, particularly from a separate perspective of travelers’ origins and destinations. We took Weinan, China, as a representative of second-tier cities in developing countries that concentrate on bus-oriented development strategies. New evidence of feature importance and nonlinear effects of crucial factors were revealed by an interpretable machine learning-based approach combining XGBoost and Shapley Additive Explanation (SHAP) with multi-source data. Most key factors were critical at both origins and destinations, such as the density of residential and commercial facilities. However, several important factors, such as road density and boarding time, had strong imbalanced effects on travel behavior. These findings provide novel insights and empirical implications to support urban planning strategies in medium-sized cities.

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References

Abbasi, S., Ko, J., & Kim, J. (2022). Carsharing travel distance and its associated factors: A case study of Seoul, South Korea. Journal of Cleaner Production, 362, 132380.

Agreement, P. (2015). Paris Agreement. Retrieved from https://unfccc.int/sites/default/files/english_paris_agreement.pdf

Alabdullah, A. A., Iqbal, M., Zahid, M., Khan, K., Amin, M. N., & Jalal, F. E. (2022). Prediction of rapid chloride penetration resistance of metakaolin based high strength concrete using light GBM and XGBoost models by incorporating SHAP analysis. Construction and Building Materials, 345, 128296.

Allen, J., Muñoz, J. C., & Rosell, J. (2019). Effect of a major network reform on bus transit satisfaction. Transportation Research Part A: Policy and Practice, 124, 310–333.

Amap open platform. (2022). Retrieved from https://lbs.amap.com/demo/javascript-api/example/callapp/poisearch

Ao, Y., Chen, C., Yang, D., & Wang, Y. (2018). Relationship between rural built environment and household vehicle ownership: An empirical analysis in rural Sichuan, China. Sustainability, 10(5), 1566.

Auld, J., & Zhang, L. (2013). Inter-personal interactions and constraints in travel behavior within households and social networks. Transportation, 40(4), 751–754.

Baumgarte, F., Brandt, T., Keller, R., Röhrich, F., & Schmidt, L. (2021). You’ll never share alone: Analyzing carsharing user group behavior. Transportation Research Part D: Transport and Environment, 93, 102754.

Bi, H., Ye, Z., & Zhu, H. (2022). Data-driven analysis of weather impacts on urban traffic conditions at the city level. Urban Climate, 41, 101065.

Böcker, L., Prillwitz, J., & Dijst, M. (2013). Climate change impacts on mode choices and travelled distances: A comparison of present with 2050 weather conditions for the Randstad Holland. Journal of Transport Geography, 28, 176–185.

Bongardt, D., Breithaupt, M., & Creutzig, F. (2010). Beyond the fossil city: Towards low carbon transport and green growth. New York: United Nations Center for Regional Development.

Cao, X., Mokhtarian, P. L., & Handy, S. L. (2009). Examining the impacts of residential self-selection on travel behavior: A focus on empirical findings. Transport Reviews 29(3), 359–395.

Cervero, R. (1989). Jobs-housing balancing and regional mobility. Journal of the American Planning Association, 55(2), 136–150.

Cervero, R., & Duncan, M. (2006). Which reduces vehicle travel more: Jobs-housing balance or retail-housing mixing? Journal of the American Planning Association, 72(4), 475–490.

Cervero, R., & Kockelman, K. (1997). Travel demand and the 3Ds: Density, diversity, and design. Transportation Research Part D: Transport and Environment, 2(3), 199–219.

Chatman, D. G. (2005). How the built environment influences non-work travel: Theoretical and empirical essays. Los Angeles: University of California, Los Angeles.

Chen, J., & Yang, D. (2013). Estimating smart card commuters origin-destination distribution based on APTS data. Journal of Transportation Systems Engineering and Information Technology, 13(4), 47–53.

Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.

Chen, J., Lvu, Y. & Cui, M. (2018). Estimating alighting stops of smart card public transportation passengers based on travel patterns. Journal of Xi’an University of Architecture & Technology, 50(1), 23–29.

Dėdelė, A., Miškinytė, A., Andrušaitytė, S., & Nemaniūtė-Gužienė, J. (2020). Dependence between travel distance, individual socioeconomic and health-related characteristics, and the choice of the travel mode: A cross-sectional study for Kaunas, Lithuania. Journal of Transport Geography, 86, 102762.

Ding, C., Wang, D., Liu, C., Zhang, Y., & Yang, J. (2017). Exploring the influence of built environment on travel mode choice considering the mediating effects of car ownership and travel distance. Transportation Research Part A: Policy and Practice, 100, 65–80.

Ding, C., Cao, X., & Næss, P. (2018). Applying gradient boosting decision trees to examine non-linear effects of the built environment on driving distance in Oslo. Transportation Research Part A: Policy and Practice, 110, 107–117.

Ding, C., Cao, X., & Liu, C. (2019). How does the station-area built environment influence Metrorail ridership? Using gradient boosting decision trees to identify non-linear thresholds. Journal of Transport Geography, 77, 70–78.

Ding, C., Liu, T., Cao, X., & Tian, L. (2022). Illustrating nonlinear effects of built environment attributes on housing renters’ transit commuting. Transportation Research Part D: Transport and Environment, 112, 103503.

Dong, X., Lei, T., Jin, S., & Hou, Z. (2018). Short-term traffic flow prediction based on XGBoost. 2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS), 854–859.

Du, Q., Yin, F., & Li, Z. (2020). Base station traffic prediction using XGBoost‐LSTM with feature enhancement. IET Networks, 9(1), 29–37.

Durning, M., & Townsend, C. (2015). Direct ridership model of rail rapid transit systems in Canada. Transportation Research Record, 2537(1), 96–102.

Engelfriet, L., & Koomen, E. (2018). The impact of urban form on commuting in large Chinese cities. Transportation, 45(5), 1269–1295.

Ewing, R., & Cervero, R. (2010). Travel and the Built Environment. Journal of the American Planning Association, 76(3), 265–294.

Fan, J., Wang, X., Wu, L., Zhou, H., Zhang, F., Yu, X., … & Xiang, Y. (2018). Comparison of support vector machine and extreme gradient boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: A case study in China. Energy Conversion and Management, 164, 102–111.

Frank, L., Bradley, M., Kavage, S., Chapman, J., & Lawton, T. K. (2008). Urban form, travel time, and cost relationships with tour complexity and mode choice. Transportation, 35(1), 37–54.

Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5), 1189–1232.

Gao, K., Yang, Y., & Qu, X. (2021). Examining nonlinear and interaction effects of multiple determinants on airline travel satisfaction. Transportation Research Part D: Transport and Environment, 97, 102957.

Hallegatte, S., Green, C., Nicholls, R. J., & Corfee-Morlot, J. (2013). Future flood losses in major coastal cities. Nature Climate Change, 3(9), 802–806.

Handy, S. L., Boarnet, M. G., Ewing, R., & Killingsworth, R. E. (2002). How the built environment affects physical activity. American Journal of Preventive Medicine 23(2), 64–73.

Holz-Rau, C., Scheiner, J., & Sicks, K. (2013). Travel distances in daily travel and long-distance travel: What role is played by urban form? Environment and Planning A, 46(2), 488–507.

Hu, H., Xu, J., Shen, Q., Shi, F., & Chen, Y. (2018). Travel mode choices in small cities of China: A case study of Changting. Transportation Research Part D: Transport and Environment, 59, 361–374.

Jain, D., & Tiwari, G. (2019). Measuring density and diversity to model travel behavior in Indian context. Land Use Policy, 88, 104199.

Ji, S., Wang, X., Lyu, T., Liu, X., Wang, Y., Heinen, E., & Sun, Z. (2022). Understanding cycling distance according to the prediction of the XGBoost and the interpretation of SHAP: A non-linear and interaction effect analysis. Journal of Transport Geography, 103, 103414.

Jiang, F., Ma, L., Broyd, T., & Chen, K. (2021). Digital twin and its implementations in the civil engineering sector. Automation in Construction, 130, 103838.

Jiang, Y., Han, Y., Liu, M., & Ye, Y. (2022). Street vitality and built environment features: A data-informed approach from fourteen Chinese cities. Sustainable Cities and Society, 79, 103724.

Kamga, C., & Yazıcı, M. A. (2014). Temporal and weather related variation patterns of urban travel time: Considerations and caveats for value of travel time, value of variability, and mode choice studies. Transportation Research Part C: Emerging Technologies, 45, 4–16.

Ko, J., Lee, S., & Byun, M. (2019). Exploring factors associated with commute mode choice: An application of city-level general social survey data. Transport Policy, 75, 36–46.

Koushik, A. N., Manoj, M., & Nezamuddin, N. (2020). Machine learning applications in activity-travel behavior research: A review. Transport Reviews, 40(3), 288–311.

Leck, E. (2011). The impact of urban form on travel behavior: A meta-analysis. Berkeley Planning Journal, 19(1), 37–58.

Levinson, D. M., & Kumar, A. (1997). Density and the journey to work. Growth and Change, 28(2), 147–172.

Li, X., Fan, J., Wu, Y., Chen, J., & Deng, X. (2020). Exploring influencing factors of passenger satisfaction toward bus transit in small-medium city in China. Discrete Dynamics in Nature and Society, 2020, 1–11.

Li, X., Ma, Q., Wang, W., & Wang, B. (2021). Influence of weather conditions on the intercity travel mode choice: A case of Xi’an. Computational Intelligence and Neuroscience, 2021, 1–15.

Liu, C., Susilo, Y. O., & Karlström, A. (2017). Weather variability and travel behavior–What we know and what we do not know. Transport Reviews, 37(6). 715–741.

Liu, Y., So, E., Li, Z., Su, G., Gross, L., Li, X., …, & Wu, L. (2020). Scenario-based seismic vulnerability and hazard analyses to help direct disaster risk reduction in rural Weinan, China. International Journal of Disaster Risk Reduction, 48(4), 101577.

Lundberg, S., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Proceedings of the 31st International Conference on Neural Information Processing Systems, 4768–4777.

Ma, L., Xiong, H., Wang, Z., & Xie, K. (2019). Impact of weather conditions on middle school students’ commute mode choices: Empirical findings from Beijing, China. Transportation Research Part D: Transport and Environment, 68, 39–51.

Mangalathu, S., Hwang, S.-H., & Jeon, J.-S. (2020). Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach. Engineering Structures, 219, 110927.

Matzler, K., Sauerwein, E., & Heischmidt, K. (2003). Importance-performance analysis revisited: The role of the factor structure of customer satisfaction. The Service Industries Journal, 23(2), 112–129.

Mishra, G. S., Mokhtarian, P. L., Clewlow, R. R., & Widaman, K. F. (2017). Addressing the joint occurrence of self-selection and simultaneity biases in the estimation of program effects based on cross-sectional observational surveys: Case study of travel behavior effects in carsharing. Transportation, 46(1), 1–29.

Montgomery, J. (1998). Making a city: Urbanity, vitality and urban design. Journal of Urban Design, 3(1), 93–116.

Naess, P. (2010). Residential location, travel, and energy use in the Hangzhou Metropolitan Area. Journal of Transport and Land Use, 3(3), 27–59.

Nguyen-Phuoc, D. Q., Currie, G., Gruyter, C. D., Kim, I., & Young, W. (2018). Modelling the net traffic congestion impact of bus operations in Melbourne. Transportation Research Part A: Policy and Practice, 117, 1–12.

Ogra, A., & Ndebele, R. (2014). The role of 6Ds: Density, diversity, design, destination, distance, and demand management in transit oriented development (TOD). Paper presented at the Neo-International Conference on Habitable Environments, Oct. 31–Nov. 2, Jalandhar, Punjab, India.

Park, S., Yang, Y., & Wang, M. (2019). Travel distance and hotel service satisfaction: An inverted U-shaped relationship. International Journal of Hospitality Management, 76, 261–270.

Proboste, F., Muñoz, J. C., & Gschwender, A. (2020). Comparing social costs of public transport networks structured around an open and Closed BRT corridor in medium sized cities. Transportation Research Part A: Policy and Practice, 138, 187–212.

Reichert, A., Holz-Rau, C., & Scheiner, J. (2016). GHG emissions in daily travel and long-distance travel in Germany–Social and spatial correlates. Transportation Research Part D: Transport and Environment, 49, 25–43.

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). Model-agnostic interpretability of machine learning. Retrieved from https://arxiv.org/pdf/1606.05386

Rong, R., Liu, L., Jia, N., & Ma, S. (2022). Impact analysis of actual traveling performance on bus passenger’s perception and satisfaction. Transportation Research Part A: Policy and Practice, 160, 80–100.

Schäfer, A. W., & Yeh, S. (2020). A holistic analysis of passenger travel energy and greenhouse gas intensities. Nature Sustainability, 3(6), 459–462.

Sultana, S. (2002). Job/housing imbalance and commuting time in the Atlanta Metropolitan Area: Exploration of causes of longer commuting time. Urban Geography, 23(8), 728–749.

Sun, B., Ermagun, A., & Dan, B. (2017). Built environmental impacts on commuting mode choice and distance: Evidence from Shanghai. Transportation Research Part D: Transport and Environment, 52, 441–453.

Tang, T., Liu, R., & Choudhury, C. (2020). Incorporating weather conditions and travel history in estimating the alighting bus stops from smart card data. Sustainable Cities and Society, 53, 101927.

Tao, T., Wu, X., Cao, J., Fan, Y., Das, K., & Ramaswami, A. (2020). Exploring the nonlinear relationship between the built environment and active travel in the Twin Cities. Journal of Planning Education and Research, 43(3), 0739456X2091576.

Tennøy, A., Gundersen, F., & Øksenholt, K. V. (2022). Urban structure and sustainable modes’ competitiveness in small and medium-sized Norwegian cities. Transportation Research Part D: Transport and Environment, 105, 103225.

Tennøy, A., Knapskog, M., & Wolday, F. (2022). Walking distances to public transport in smaller and larger Norwegian cities. Transportation Research Part D: Transport and Environment, 103, 103169.

The People's Government of Linwei District, Weinan City. (2020). The comprehensive transportation development plan for the 14th Five-Year Plan period and the long-term objectives for 2035 in Weinan City. Retrieved from http://www.linwei.gov.cn/zfxxgk/fdzdgknr/ghjh/1628689084346040321.html

The People’s Government of Weinan City. (2023).Weinan City Public Transport Group Co., Ltd. Retrieved from https://www.weinan.gov.cn/zfxxgk/fdzdgknr/ggqsydw/jt/1643812198829346818.html

Tianjin Kuangwei Company. (2019). Weinan City (municipal district) public bus operation quantity and passenger volume: Three-year data report. Weinan, China: Weinan City.

Tu, M., Li, W., Orfila, O., Li, Y., & Gruyer, D. (2021). Exploring nonlinear effects of the built environment on ridesplitting: Evidence from Chengdu. Transportation Research Part D: Transport and Environment, 93, 102776.

Tuan, V. A., Truong, N. V., Tetsuo, S., & An, N. N. (2022). Public transport service quality: Policy prioritization strategy in the importance-performance analysis and the three-factor theory frameworks. Transportation Research Part A: Policy and Practice, 166, 118–134.

Wang, D., & Zhou, M. (2017). The built environment and travel behavior in urban China: A literature review. Transportation Research Part D: Transport and Environment, 52, 574–585.

Wang, R., Wu, J., & Qi, G. (2022). Exploring regional sustainable commuting patterns based on dockless bike-sharing data and POI data. Journal of Transport Geography, 102, 103395.

Wei, M. (2022). Investigating the influence of weather on public transit passenger’s travel behavior: Empirical findings from Brisbane, Australia. Transportation Research Part A: Policy and Practice, 156, 36–51.

Weinan City Transportation Bureau. (2020). Reply to proposal No. 138 of the first session of the Sixth Municipal Committee of the Chinese People’s Political Consultative Conference. Retrieved from https://jtj.weinan.gov.cn/zwxxgk/fdzdgknr/tajybl/1628290069586542594.html

Weinan City Transportation Bureau. (2018). The record of transportation work in Weinan in 2018. Retrieved from https://jtj.weinan.gov.cn/hdjl/zxft/1628289823745802241.html

Wu, Y., Mei, G., & Shao, K. (2022). Revealing influence of meteorological conditions and flight factors on delays using XGBoost. Journal of Computational Mathematics and Data Science, 3, 100030.

Yan, L., Wang, D., Zhang, S., & Ratti, C. (2021). Understanding urban centers in Shanghai with big data: Local and non-local function perspectives. Cities, 113, 103156.

Yang, C., Chen, M., & Yuan, Q. (2021). The application of XGBoost and SHAP to examining the factors in freight truck-related crashes: An exploratory analysis. Accident Analysis & Prevention, 158, 106153.

Yang, H., Zheng, R., Li, X., Huo, J., Yang, L., & Zhu, T. (2022). Nonlinear and threshold effects of the built environment on e-scooter sharing ridership. Journal of Transport Geography, 104, 103453.

Yin, J., Yu, D., Yin, Z., Liu, M., & He, Q. (2016). Evaluating the impact and risk of pluvial flash flood on intra-urban road network: A case study in the city center of Shanghai, China. Journal of Hydrology, 537, 138–145.

Yu, L., Xie, B., & Chan, E. H. W. (2019). Exploring impacts of the built environment on transit travel: Distance, time and mode choice, for urban villages in Shenzhen, China. Transportation Research Part E: Logistics and Transportation Review, 132, 57–71.

Yue, Y., Zhuang, Y., Yeh, A. G. O., Xie, J.-Y., Ma, C.-L., & Li, Q.-Q. (2017). Measurements of POI-based mixed use and their relationships with neighborhood vibrancy. International Journal of Geographical Information Science, 31(4), 658–675.

Zhang, M., & Zhao, P. (2017). The impact of land-use mix on residents’ travel energy consumption: New evidence from Beijing. Transportation Research Part D: Transport and Environment, 57, 224–236.