Machine Learning-Based Ranking of Factors Influencing Human Movement Purposes for Supporting Human-Infrastructure Interaction Modeling
Publication: Computing in Civil Engineering 2023
ABSTRACT
Modeling, predicting, and controlling the interactions between humans and civil infrastructure systems can simultaneously improve the operational efficiency of infrastructure systems and the satisfaction of infrastructure users. The first step toward achieving this goal is to model human-to-infrastructure interaction, which in most cases is driven by human movements (e.g., moving from an origin location to a destination requires using the transportation infrastructure connecting the two). To this end, this paper aims to conduct a machine learning-based data-driven analysis to rank the importance of factors influencing human movement purposes, thereby identifying highly influential factors to support subsequent human-to-infrastructure interaction modeling. The research methodology included: (1) representing movement instances using spatial and land use, temporal, and demographic features; and (2) conducting feature ranking per movement purpose type using the logistic regression algorithm. As a preliminary work, this paper focuses on presenting the research methodology, and analyzing and discussing the feature ranking results.
Get full access to this article
View all available purchase options and get full access to this chapter.
REFERENCES
ASCE. (2021). 2021 Report Card for America’s Infrastructure.
Bryan Conroy and Paul Sajda. (2012). “Fast, Exact Model Selection and Permutation Testing for L2-Regularized Logistic Regression.” in Proceedings of the 15th International Conference on Artificial Intelligence and Statistics 2012, La Palma, Canary Islands.
Chen, C., Gong, H., Lawson, C., and Bialostozky, E. (2010). “Evaluating the feasibility of a passive travel survey collection in a complex urban environment: Lessons learned from the New York City case study.” Transp. Res. Part A Policy Pract., 44(10), 830–840.
CMPA (Chicago Metropolitan Agency for Planning). (2023). My Daily Travel Survey.
FHDA (Federal Highway Administration). (2023). National Household Travel Survey.
Gong, L., Morikawa, T., Yamamoto, T., and Sato, H. (2014). “Deriving personal trip data from GPS DATA: A literature review on the existing methodologies.” Procedia - Social and Behavioral Sciences, 138, 557–565.
Haughwout, A. F., Orr, J., and Bedoll, D. (2008). “The Price of Land in the New York Metropolitan Area.” Economics and Finance.
Kim, G., Miller, P. A., and Nowak, D. J. (2018). “Urban vacant land typology: A tool for managing urban vacant land.” Sustainable Cities Soc., 36, 144–156.
NYC DOT (New York City Department of Transportation). (2023). Citywide Mobility Survey.
Oliveira, M. G., Vovsha, P., Wolf, J., and Mitchell, M. (2014). “Evaluation of two methods for identifying trip purpose in GPS-based household travel surveys.” Transp. Res. Rec.: Journal of the Transportation Research Board, 2405(1), 33–41.
Pereira, F., Carrion, C., Zhao, F., Cottrill, C. D., Zegras, C., and Ben-Akiva, M. (2013). The Future Mobility Survey: Overview and Preliminary Evaluation. Proceedings of the Eastern Asia Society for Transportation Studies, 9, 1–13.
Stopher, P., FitzGerald, C., and Zhang, J. (2008). “Search for a global positioning system device to measure person travel.” Transp. Res. Part C Emerging Technol., 16(3), 350–369.
Wu, J., Jiang, C., Houston, D., Baker, D., and Delfino, R. (2011). “Automated Time Activity Classification based on Global Positioning System tracking data.” Environmental Health.
Xiao, G., Juan, Z., and Zhang, C. (2016). “Detecting trip purposes from smartphone-based travel surveys with artificial neural networks and particle swarm optimization.” Transp. Res. Part C Emerging Technol., 71, 447–463.
Information & Authors
Information
Published In
History
Published online: Jan 25, 2024
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Business management
- Computer programming
- Computing in civil engineering
- Data analysis
- Engineering fundamentals
- Equipment and machinery
- Freight transportation
- Human and behavioral factors
- Infrastructure
- Land use
- Logistics
- Management methods
- Methodology (by type)
- Practice and Profession
- Ratings
- Research methods (by type)
- Transportation engineering
- Urban and regional development
- Urban areas
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.