Hillel, T., Elshafie, M., and Ying, J.

A comparison of classification methods for modelling urban mode choice

Speaker: Hillel Tim

Workshop on Discrete Choice Models 2017, EPFL

June 22, 2017

Current transport models used to inform policy and network decisions in an urban area typically rely on multinomial logit (MNL) models to predict passenger mode choice. These models make efficient use of sparse data, and can be calibrated to aggregate survey data and passenger/vehicle counts. However, the recent adoption of several notable technologies, including online journey-planners, contactless payment cards, and mobile-phone-based location services, has enabled a step-change of several orders of magnitude in the availability of passenger movement data. This data presents the possibility to make use of machine-learning based classifiers to predict mode choice at an individual level, with higher accuracy than the currently employed techniques. We present a new dataset, covering on the one hand complete trip diaries with personal profile data from the London Travel Demand Survey, and on the other hand the path, distance and duration reconstructed from an online directions service for each transport mode (walking, cycling, combined public transport, bus, rail, and driving). We use this dataset to compare the predictive performance of a complete suite of machine-learning classification algorithms with MNL. We then use the highest performing models to determine the key factors driving urban passenger mode choice.