28
Views
0
CrossRef citations to date
0
Altmetric
Research Article

How does the built environment affect transit use under different urban village renewal strategies?

, , &
Received 28 Oct 2023, Accepted 16 Apr 2024, Published online: 29 Apr 2024

References

  • Ao, Y., Y. Zhang, Y. Wang, et al. 2020. “Influences of Rural Built Environment on Travel Mode Choice of Rural Residents: The Case of Rural Sichuan.” Journal of Transport Geography 85: 102708. https://doi.org/10.1016/j.jtrangeo.2020.102708.
  • Ayoub, J., X. J. Yang, and F. Zhou. 2021. “Modeling Dispositional and Initial Learned Trust in Automated Vehicles with Predictability and Explainability.” Transportation Research Part F: Traffic Psychology and Behaviour 77: 102–116. https://doi.org/10.1016/j.trf.2020.12.015.
  • Badland, H. M., G. M. Schofield, and N. Garrett. 2008. “Travel Behavior and Objectively Measured Urban Design Variables: Associations for Adults Traveling to Work.” Health & Place 14 (1): 85–95. https://doi.org/10.1016/j.healthplace.2007.05.002.
  • Bhatta, B. P., and O. I. Larsen. 2011. “Errors in Variables in Multinomial Choice Modeling: A Simulation Study Applied to a Multinomial Logit Model of Travel Mode Choice.” Transport Policy 18 (2): 326–335. https://doi.org/10.1016/j.tranpol.2010.10.002.
  • Bo, Z., J. Zhi-Cai, and N. I. An-Ning. 2014. “An Evolutionary Game Model for the Dynamic Traffic Flow Based on Cumulative Prospect Theory.” Journal of Industrial Engineering and Engineering Management.
  • Can, V. V. 2013. “Estimation of Travel Mode Choice for Domestic Tourists to Nha Trang Using the Multinomial Probit Model.” Transportation Research Part A: Policy and Practice 49: 149–159. https://doi.org/10.1016/j.tra.2013.01.025.
  • Cao, X., and J. E. Schoner. 2019. “Travel Time Perception and Modal Choices in Multimodal Trips.” Transportation Research Part A: Policy and Practice 125: 193–210.
  • Chang, I., H. Park, E. Hong, et al. 2022. “Predicting Effects of Built Environment on Fatal Pedestrian Accidents at Location-Specific Level: Application of XGBoost and SHAP.” Accident Analysis & Prevention 166: 106545. https://doi.org/10.1016/j.aap.2021.106545.
  • Chen, C., and S. Chen. 2016. “The Relationship between Urban Form and Household Travel Carbon Emissions in China Cities.” Journal of Cleaner Production 137: 1672–1682.
  • Chen, X., H. Gong, and J. Wang. 2012. “BRT Vehicle Travel Time Prediction Based on SVM and Kalman Filter.” Journal of Transportation Systems Engineering and Information Technology 12 (4): 29–34. https://doi.org/10.1016/S1570-6672(11)60211-0.
  • Chen, T., and C. Guestrin. 2016. “Xgboost: A Scalable Tree Boosting System.” In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.
  • Chen, J. L., Y. L. Ma, and N. Zhu. 2011. “A Study on Bus Travel Mode Choice Behavior Based on a Nested Logit Model.” Journal of Transportation Systems Engineering and Information Technology 11: 120–125.
  • Chen, J., and F. L. Wu. 2020. “Housing and Land Financialization under the State Ownership of Land in China.” Land Use Policy 104844 (112): 1–10.
  • Ding, C., X. Cao, and C. Liu. 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. https://doi.org/10.1016/j.jtrangeo.2019.04.011.
  • Ding, C., X. J. Cao, and P. Naess. 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. https://doi.org/10.1016/j.tra.2018.02.009.
  • Ding, C., X. Cao, B. Yu, et al. 2021. “Non-Linear Associations between Zonal Built Environment Attributes and Transit Commuting Mode Choice Accounting for Spatial Heterogeneity.” Transportation Research Part A: Policy and Practice 148: 22–35. https://doi.org/10.1016/j.tra.2021.03.021.
  • Ding, C., D. Wang, C. Liu, et al. 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. https://doi.org/10.1016/j.tra.2017.04.008.
  • Elias, W., G. Newmark, and Y. Shiftan. 2008. “Gender and Travel Behavior in Two Arab Communities in Israel.” Transportation Research Record: Journal of the Transportation Research Board 2067 (1): 75–83. https://doi.org/10.3141/2067-09.
  • Ewing, R., K. Bartholomew, S. Winkelman, et al. 2008. “Growing Cooler: The Evidence on Urban Development and Climate Change.” Journal of the American Planning Association 75 (1): 6–13.
  • Ewing, R., and R. Cervero. 2001. “Travel and the Built Environment: A Synthesis.” Transportation Research Record: Journal of the Transportation Research Board 1780 (1): 87–114. https://doi.org/10.3141/1780-10.
  • Galich, A., S. Nieland, B. Lenz, et al. 2021. “How Would We Cycle Today If We Had the Weather of Tomorrow? An Analysis of the Impact of Climate Change on Bicycle Traffic.” Sustainability 13: 10254. https://doi.org/10.3390/su131810254.
  • Guan, X., and D. Wang. 2019. “Residential Self-Selection in the Built Environment-Travel Behavior Connection: Whose Self-Selection?” Transportation Research Part D: Transport and Environment 67: 16–32. https://doi.org/10.1016/j.trd.2018.10.015.
  • He, J., M. Li, J. Qiu, et al. 2023. “HOPEXGB: A Consensual Model for Predicting miRNA/lncRNA-Disease Associations Using a Heterogeneous Disease-miRNA-lncRNA Information Network.” Journal of Chemical Information and Modeling 64 (7): 2863–2877.
  • Huo, W., Z. Zhu, H. Sun, et al. 2022. “Development of Machine Learning Models for the Prediction of the Compressive Strength of Calcium-Based Geopolymers.” ournal of Cleaner Production 380: 1–18.
  • Kim, K., K. Kwon, and M. W. Horner. 2021. “Examining the Effects of the Built Environment on Travel Mode Choice across Different Age Groups in Seoul using a Random Forest Method.” Transportation Research Record: Journal of the Transportation Research Board 2675 (8): 670–683. https://doi.org/10.1177/03611981211000750.
  • Kong, X., Y. Zhang, W. L. Eisele, et al. 2022. “Using an Interpretable Machine Learning Framework to Understand the Relationship of Mobility and Reliability Indices on Truck Drivers’ Route Choices.” IEEE Transactions on Intelligent Transportation Systems 23: 13419–13428. https://doi.org/10.1109/TITS.2021.3124221.
  • Lee, C., and S. Lee. 2022. “Exploring the Contributions by Transportation Features to Urban Economy: An Experiment of a Scalable Tree-Boosting Algorithm with Big Data.” Land 11 (4): 1–30.
  • Lee, J. S., J. Nam, and S. S. Lee. 2014. “Built Environment Impacts on Individual Mode Choice: An Empirical Study of the Houston-Galveston Metropolitan Area.” International Journal of Sustainable Transportation 8 (6): 447–470. https://doi.org/10.1080/15568318.2012.716142.
  • Litman, T. A. 2015. Evaluating Public Transit Benefits and Costs. Victoria Transport Policy Institute. https://api.semanticscholar.org/
  • Liu, J., B. Wang, and L. Xiao. 2021. “Non-Linear Associations between Built Environment and Active Travel for Working and Shopping: An Extreme Gradient Boosting Approach.” Journal of Transport Geography 50: 254–268.
  • Lubo-Robles, D., D. Devegowda, V. Jayaram, et al. 2022. “Quantifying the Sensitivity of Seismic Facies Classification to Seismic Attribute Selection: An Explainable Machine-Learning Study.” Interpretation 10 (3): 1–29.
  • Lundberg, S. M., and S. I. Lee. 2017. “A Unified Approach to Interpreting Model Predictions.” In NIPS’17: Proceedings of the 31st International Conference on Neural Information Processing System, 4768–4777. Long Beach, CA: Curran Associates Inc.
  • Manoj, M., and A. Verma. 2016. “Effect of Built Environment Measures on Trip Distance and Mode Choice Decision of Non-Workers from a City of a Developing Country, India.” Transportation Research Part D: Transport and Environment 46: 351–364. https://doi.org/10.1016/j.trd.2016.04.013.
  • Molnar, C. 2020. Interpretable Machine Learning. Leanpub.
  • Munshi, T. 2016. “Built Environment and Mode Choice Relationship for Commute Travel in the City of Rajkot, India.” Transportation Research Part D: Transport and Environment 44: 239–253. https://doi.org/10.1016/j.trd.2015.12.005.
  • Pan, Wenjian, and Juan Du. 2021. “Towards Sustainable Urban Transition: A Critical Review of Strategies and Policies of Urban Village Renewal in Shenzhen, China.” Land Use Policy 111: 1–20.
  • Paulssen, M., D. Temme, A. Vij, et al. 2014. “Values, Attitudes and Travel Behavior: A Hierarchical Latent Variable Mixed Logit Model of Travel Mode Choice.” Transportation 41 (4): 873–888. https://doi.org/10.1007/s11116-013-9504-3.
  • POLPLAN Company, Limited. 2001. “Mobility, Safety, and Life after Driving.” In International Conference on Transed towards Safety.
  • Shapley, L. 1953. “A Value for n-Person Games.” In Contributions to the Theory of Games II, 307–317. Princeton, NJ, USA: Princeton University Press.
  • Song, Y., J. Li, and X. Li. 2018. “Built Environment and Transit Use: Empirical Evidence from Nanjing, China.” Cities 72: 1–11.
  • Taubenböck, H., N. J. Kraff, and M. Wurm. 2018. “The Morphology of the Arrival City – A Global Categorization Based on Literature Surveys and Remotely Sensed Data.” Applied Geography 92: 150–167. https://doi.org/10.1016/j.apgeog.2018.02.002.
  • Van Oostrum, M. 2020. “Informal Laneway Encroachment: Reassessing Public/Private Interface Transformation in Urban Villages.” Habitat International 104: 102259. https://doi.org/10.1016/j.habitatint.2020.102259.
  • Wang, D., and T. Lin. 2014. “Residential Self-Selection, Built Environment, and Travel Behavior in the Chinese Context.” Journal of Transport and Land Use 7 (3): 5–14. https://doi.org/10.5198/jtlu.v7i3.486.
  • Wang, W., Y. Wang, G. Correia, et al. 2020. “A Network-Based Model of Passenger Transfer Flow between Bus and Metro: An Application to the Public Transport System of Beijing.” Journal of Advanced Transportation 15: 1–12.
  • Wang, Y. P., Y. L. Wang, and J. S. Wu. 2009. “Urbanization and Informal Development in China: Urban Villages in Shenzhen.” International Journal of Urban and Regional Research 33 (4): 957–973. https://doi.org/10.1111/j.1468-2427.2009.00891.x.
  • Wang, L., C. Zhao, X. Liu, et al. 2021. “Non-Linear Effects of the Built Environment and Social Environment on Bus Use among Older Adults in China: An Application of the XGBoost Model.” International Journal of Environmental Research and Public Health 18 (18): 9592. https://doi.org/10.3390/ijerph18189592.
  • Yu, L., B. Xie, and E. Chan. 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. https://doi.org/10.1016/j.tre.2019.11.004.
  • Zhang, M. 2004. “The Role of Land Use in Travel Mode Choice: Evidence from Boston and Hong Kong.” Journal of the American Planning Association 70 (3): 344–360. https://doi.org/10.1080/01944360408976383.
  • Zhang, M., and J. Hong. 2019. “Evaluating the Joint Effects of Built Environment and Demographic Factors on Public Transit Ridership: A Case Study in Austin, Texas.” Journal of Transport Geography 77: 40–49.
  • Zhang, Y., W. Wu, Y. Li, Q. Liu, and C. Li. 2014. “Does the Built Environment Make a Difference? An Investigation of Household Vehicle Use in Zhongshan Metropolitan Area, China.” Sustainability 6 (8): 4910–4930. https://doi.org/10.3390/su6084910.
  • Zhang, F., and P. Zhao. 2018. “The Effect of Built Environment on Public Transport Use: Evidence from Changsha, China.” Journal of Transport Geography 71: 48–58.
  • Zhao, P. 2013. “The Impact of the Built Environment on Individual Workers’ Commuting Behavior in Beijing.” International Journal of Sustainable Transportation 7 (5): 389–415. https://doi.org/10.1080/15568318.2012.692173.
  • Zhao, T., Z. Huang, W. Tu, et al. 2022. “Coupling Graph Deep Learning and Spatial-Temporal Influence of Built Environment for Short-Term Bus Travel Demand Prediction.” Computers, Environment and Urban Systems 94: 101776. https://doi.org/10.1016/j.compenvurbsys.2022.101776.
  • Zou, L., S. Shu, X. Lin, et al. 2022. “Passenger Flow Prediction Using Smart Card Data from Connected Bus System Based on Interpretable XGBoost.” Wireless Communications and Mobile Computing 2022 (3): 1–13.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.