Journal of Transport and Land Use

Understanding household VMT generation: A comparative analysis with traditional statistical models and a machine-learning approach

Guang Tian

University of New Orleans

https://orcid.org/0000-0002-4023-3912

Bob Danton

University of New Orleans

https://orcid.org/0009-0006-9344-6889

Bin Li

Louisiana State University

https://orcid.org/0000-0003-0831-092X

VJ Gopu

University of Louisiana at Lafayette and Louisiana Transportation Research Center

https://orcid.org/0000-0001-5718-4654

Julius A. Codjoe

University of Louisiana at Lafayette and Louisiana Transportation Research Center

https://orcid.org/0000-0003-1958-8695

DOI: https://doi.org/10.5198/jtlu.2024.2531

Keywords: VMT, Travel behavior, Land use, Built environment, Machine learning, Nonlinear

---

Abstract

Planners and policy makers have long been interested in predicting people’s travel behaviors, including the number of vehicle miles traveled (VMT) they generate. Reducing VMT has come to be seen as a key strategy for lowering greenhouse gas emissions and mitigating their associated health effects, as well as for increasing sustainability and equity within communities and on a global scale. Emerging machine learning methods such as boosted regression trees (BRT) allow for the identification of comparative influences of different factors as well as their nonlinear and threshold effects on travel outcomes, but studies comparing the results of these methods with traditional statistical regressions have been scarce. This study is the first to compare these methods’ indications of the impacts of land-use patterns on VMT generation using a large multiregional dataset. The results indicate that the two methods perform similarly in predicting whether households generate any VMT and in predicting the number of VMT generated by those households that generate any. The results of the BRT model validate the statistical models’ indications that built environmental variables contribute significantly to VMT production, while further allowing for the identification of nonlinear impacts. Key thresholds and nonlinear effects of land-use variables on household VMT generation are identified from the BRT model. Both models indicate that land-use patterns that are denser, more diverse, and have increased access to transit result in reductions of vehicular trips and overall VMT, while the BRT model provides effective thresholds for these variables useful for developing planning solutions.

---

References

Barri, E. Y., Farber, S., Jahanshahi, H., & Beyazit, E. (2022). Understanding transit ridership in an equity context through a comparison of statistical and machine learning algorithms. Journal of Transport Geography, 105, 103482.

Breiman, L. (2001). Statistical modeling: The two cultures. Statistical Science, 16(3), 199–231.

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

Cervero, R., & Murakami, J. (2010). Effects of built environments on vehicle miles traveled: Evidence from 370 US urbanized areas. Environment and Planning A: Economy and Space, 42, 400–418.

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., 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., Chen, P., & Jiao, J. (2018). Non-linear effects of the built environment on automobile-involved pedestrian crash frequency: A machine learning approach. Accident Analysis and Prevention, 112, 116–126.

Du, Q., Zhou, Y., Huang, Y., Wang, Y., & Bai, L. (2022). Spatiotemporal exploration of the non-linear impacts of accessibility on metro ridership. Journal of Transport Geography, 102, 103380.

Ewing, R., & Cervero, R. (2001). Travel and the built environment: A synthesis. Transportation Research Record, 1780, 87–114.

Ewing, R., & Cervero, R. (2010). Travel and the built environment: A meta-analysis. Journal of the American Planning Association, 76(3), 265–294.

Ewing, R., Hamidi, S., Gallivan, F., Nelson, A. C., & Grace, J. B. (2014). Structural equation models of VMT growth in US urbanized areas. Urban Studies, 51(14), 3079–3096.

Ewing, R., Tian, G., Goates, J., Zhang, M., Greenwald, M. J., Joyce, A., . . . Greene, W. (2015). Varying influences of the built environment on household travel in 15 diverse regions of the United States. Urban Studies, 52(13), 2330–2348.

Federal Highway Administration. (2021, May). FHWA forecasts of vehicle miles traveled (VMT): Spring 2021. Retrieved from https://www.fhwa.dot.gov/policyinformation/tables/vmt/2021_vmt_forecast_sum.pdf

Friedman, J. H., & Meulman, J. J. (2003). Multiple additive regression trees with application in epidemiology. Statistics in Medicine, 22, 1365–1381.

Galster, G. C. (2018). Nonlinear and threshold effects related to neighborhood: Implications for planning and policy. Journal of Planning Literature, 33(4), 492–508.

Gan, Z., Yang, M., Feng, T., & Timmermans, H. J. (2020). Examining the relationship between built environment and metro ridership at station-to-station level. Transportation Research Part D: Transport and Environment, 82, 102332.

Greene, W. (2018). Econometric analysis (8th Edition). London: Pearson.

Greenwell, B. M. (2017, June). pdp: An R package for constructing partial dependence plots. The R Journal, 9(1), 421–436.

Hu, X., Cao, Y., Peng, T., Gao, R., & Dai, G. (2021). Nonlinear influence model of built environment of residential area on electric vehicle miles traveled. World Electric Vehicle Journal, 12, 247.

Ihlanfeldt, K. (2020). Vehicle miles traveled and the built environment: New evidence from panel data. Journal of Transport and Land Use, 13(1), 23–48.

Lee, A. E., & Handy, S. L. (2018). Leaving level-of-service behind: The implications of a shift to VMT impact metrics. Research in Transportation Business & Management, 29, 14–25.

Li, W., & Kockelman, K. M. (2022). How does machine learning compare to conventional econometrics for transport data sets? A test of ML versus MLE. Growth & Change, 53(1), 342–376.

Li, Z. (2022). Extracting spatial effects from machine learning model using local interpretation method: An example of SHAP and XGBoost. Computers, Environment and Urban Systems, 96, 101845.

Liu, J., Wang, B., & Xiao, L. (2021). Non-linear associations between built environment and active travel for working and shopping: An extreme gradient boosting approach. Journal of Transport Geography, 92, 103034.

Manel, S., Williams, H. C., & Ormerod, S. J. (2001). Evaluating presence–absence models in ecology: The need to account for prevalence. Journal of Applied Ecology, 38(5), 921–931.

Nasri, A., & Zhang, L. (2012). Impact of metropolitan-level built environment on travel behavior. Transportation Research Record, 2323, 75–79.

Nasri, A., & Zhang, L. (2014). Assessing the impact of metropolitan-level, county-level, and local-level built environment on travel behavior: Evidence from 19 U.S. urban areas. Journal of Urban Planning and Development, 141(3), 04014031.

Rui, A., Tong, Z., Ding, Y., Tan, B., Wu, Z., Xiong, Q., & Liu, Y. (2022). Examining non-linear built environment effects on injurious traffic collisions: A gradient boosting decision tree analysis. Journal of Transport & Health, 24(5), 101296.

Salon, D., Boarnet, M. G., Handy, S., Spears, S., & Tal, G. (2012). How do local actions affect VMT? A critical review of the empirical evidence. Transportation Research Part D: Transport and Environment, 17(7), 495–508.

Stevens, M. R. (2017). Does compact development make people drive less? Journal of the American Planning Association, 83(1), 7–18.

Tao, T., & Næss, P. (2022). Exploring nonlinear built environment effects on driving with a mixed-methods approach. Transportation Research Part D: Transport and Environment, 111, 103443.

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), 637–652.

Tian, G., & Ewing, R. (2017). A walk trip generation model for Portland, OR. Transportation Research Part D: Transport and Environment, 52, 340–353.

Tian, G., Park, K., Ewing, R., Watten, M., & Walters, J. (2020). Traffic generated by mixed-use developments—A follow-up 31-region study. Transportation Research Part D: Transport and Environment, 78 (2020), 102205.

U.S. Environmental Protection Agency. (2022, May 19). Carbon pollution from transportation. Retrieved from https://www.epa.gov/transportation-air-pollution-and-climate-change/carbon-pollution-transportation

Wali, B., Frank, L. D., Chapman, J. E., & Fox, E. H. (2021). Developing policy thresholds for objectively measured environmental features to support active travel. Transportation Research Part D: Transport and Environment, 90, 102678.

Xiao, W., & Wei, Y. D. (2023). Assess the non-linear relationship between built environment and active travel around light-rail transit stations. Applied Geography, 151, 102862.

Xie, C., Lu, J., & Parkany, E. (2003). Work travel mode choice modeling with data mining: Decision trees and neural networks. Transportation Research Record, 1854(1), 50–61.

Xu, Y., Yan, X., Liu, X., & Zhao, X. (2021). Identifying key factors associated with ridesplitting adoption rate. Transportation Research Part A: Policy and Practice, 144, 170–188.

Yan, X., Liu, X., & Zhao, X. (2020). Using machine learning for direct demand modeling of ridesourcing services. Journal of Transport Geography, 83, 102661.

Zhang, L., Hong, J., Nasri, A., & Shen, Q. (2012). How built environment affects travel behavior: A comparative analysis of the connections between land use and vehicle miles traveled in US cities. Journal of Transport and Land Use, 5(3), 40–52.

Zhang, M., & Zhang, W. (2020). When context meets self-selection: The built environment–Travel connection revisited. Journal of Planning Education and Research, 40(3), 304–319.

Zhao, X., Yan, X., Yu, A., & Van Hentenryck, P. (2020). Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models. Travel Behavior and Society, 20, 22–35.

Zhu, M. (2008). Kernels and ensembles: Perspectives on statistical learning. The American Statistician, 62(2), 97–109.