Mode choice analysis of school trips using random forest technique

Jinit J.M. D'Cruz; Anu P. Alex; V. S. Manju

doi:10.5604/01.3001.0015.9175

Authors

Jinit J.M. D'Cruz Office of the Superintending Engineer, Kerala Water Authority, Trivandrum, Kerala Author https://orcid.org/0000-0002-9012-7595
Anu P. Alex Department of Civil Engineering, College of Engineering Trivandrum, Kerala Author https://orcid.org/0000-0001-7172-627X
V. S. Manju Department of Civil Engineering, College of Engineering Trivandrum, Kerala Author https://orcid.org/0000-0003-2084-561X

DOI:

https://doi.org/10.5604/01.3001.0015.9175

Keywords:

city transport, Multinomial Logit model, analysis of the travel model, school trips, peak hour traffic, commuting to schools

Abstract

Mode choice analysis of school trips becomes important due to the fact that these trips contribute to the second largest share of peak hour traffic. This scenario is more relevant in India, which has almost 265 million students enrolled in different accredited urban and rural schools of India, from Class I to XII as per the UDISE report of 2019-20. Thus, it becomes necessary to understand what mode of transport will be mostly used for school trips in order to design an efficient transportation system. Modal attributes and socio-economic characteristics are mostly considered as explanatory variables in travel mode choice models. Multinomial Logit (MNL) model is one of the classic models used in the development of mode choice models. These logistic regression models predict outcomes based on a set of independent variables. With the recent advances in machine learning, transportation problems are getting a wide arena of methods and solutions. Among them the method of ensemble learning is finding a prominent place in contemporary modelling. This study explores the potential of using ensembles of random decision trees in mode choice analysis by Random Forest Technique with a comparative analysis on conventional method. It was observed that Random Forest method outperforms MNL method in predicting the mode choice preference of students. The high accuracy of machine learning models is mainly due to its ability to consider complex nonlinear relationship between socio-economic attributes and travel mode choice. These models can learn and identify pattern characteristics extracted from sample data and form adaptive structures through computational process thereby offering insights into the relationships between variables that random utility models cannot recognize. This study considered activity -travel information, personal data and household characteristics of students as attributes for model development and observed that the age of the student and distance of school from home plays a significant role in deciding the mode choice of school trips.

References

Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S., (2019). Applications of Artificial Intelligence in Transport: An Overview, Sustainability, 11(1): 189.

Anas, A., (1983). Discrete choice theory, information theory and the multinomial logit and gravity models, Transportation Research Part B: Methodological, 17(1), 13-23.

Alex, A. P., Manju, V.S., Isaac, K. P., (2016). Modelling Of Daily Activity Schedule of Workers Using Unsupervised Machine Learning Technique, International Journal for Traffic and Transport Engineering, 6(1), 77-91.

Alex, A. P., Manju, V.S., Isaac, K. P., (2019). Modelling of travel behaviour of students using artificial intelligence. Archives of Transport, 51, 7-19.

Alex, A. P., Manju, V.S., Isaac, K. P., (2021). Modelling of Activity-Travel Pattern with Support Vector Machine, European Transport Trasporti Europei, 82, 1825-3997. https://doi.org/10.48295/ET.2021.82.2.

Ashalatha, R., Manju, V. S., Zacharia, A. B., (2013). Mode Choice Behavior of Commuters in Thiruvananthapuram City. Journal of Transportation Engineering, 139(5), 494-502.

Babu, D., Balan, S., Anjaneyulu, M. V. L. R., (2018). Activity-travel patterns of workers and students: a study from Calicut city, India. Archives of Transport, 46(2), 21-32.

Ben-Akiva, M., Lerman, S.R., (1985). Discrete choice analysis: theory and application to travel demand. (18th ed.). Cambridge: MIT Press, (Chapter 11).

Bhat, C.R., Sardesai, R., (2006). The Impact of Stop-Making and Travel Time Reliability on Commute Mode Choice. Transportation Research Part B: Methodological, 40 (9), 709-730.

Black, C., Collins, A., Snell, M., (2004). Encouraging Walking: The Case of Journey-to-School Trips in Compact Urban Areas. Urban Studies, 38 (7), 1121-1141.

Breiman, L., (2001). Random Forests. Machine Learning, 45, 5-32.

Ewing, R., Schroeer, W., Greene, W., (2004). School Location and Student Travel Analysis of Factors Affecting Mode Choice. Transportation Research Record, 1895(1), 55-63.

Gong, L., Kanamori, R., Yamamoto, T., (2018). Data selection in machine learning for identifying trip purposes and travel modes from longitudinal GPS data collection lasting for seasons. Travel Behaviour and Society. 11, 131-140.

Koppelman, F. S., (1983). Predicting transit ridership in response to transit service changes. Journal of Transportation Engineering, 109(4), 548-564.

Koppelman, F. S. Bhatt, C., (2006). A Self Instructing Course in Mode Choice Modeling: Multinomial and Nested Logit Models. U.S. Department of Transportation, Federal Transit Administration, (Chapter 3).

Li, Z., Hensher, D. A., Rose, J.M., (2010). Willingness to pay for travel time reliability in passenger transport: A review and some new empirical evidence. Transportation Research Part E: Logistics and Transportation Review, 46(3): 384-403.

Mcdonald, N. C., (2008). “Children’s mode choice for the school trip: the role of distance and school location in walking to school. Transportation , 35, 23-35.

Mcfadden, D., (1974). Conditional logit analysis of qualitative choice behaviour. In: Zarembka, P., (Eds.), Frontiers in Econometrics 105-142. Academic Press, NY.

Ortuzar, J.D., Willumsen, L.G., (2011). Model-ling Transport. (4th ed.). Chichester: Wiley, (Chapter 6).

Pinjari, A. R., Pendyala, R. M., Bhat, C.R., Waddell, P.A., (2007). Modelling residential sorting effects to understand the impact of the built environment on commute mode choice. Transportation, 34(5), 557-583.

Rahman, F.I., (2020). Analysing the factor influencing travel pattern and mode choice based on household interview survey data: a case study of Dhaka city, Bangladesh. Scientific Journal of Silesian University of Technology, Series Transport, 109, 153-162.

Rasouli, S., Timmermans, H.J., (2014). Using ensembles of decision trees to predict transport mode choice decisions: effects on predictive success and uncertainty estimates. European Journal of Transport and Infrastructure Re-search, 14 (4), 412-424.

Wilson, E.J., Marshall, J., Wilson, R., Krizek, K. J. (2010). By foot, bus or car: children's school travel and school choice policy. Environment and Planning A, 42(9), 2168-2185.

Yarlagadda, A.K., Srinivasan, S. (2008). Modelling Children’s School Travel Mode and Parental Escort Decisions. Transportation, 35(2), 201-218.

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