Modeling Stated Preference for Mobility-on-Demand Transit: A Comparison of Machine Learning and Logit Models

11/04/2018
by   Xilei Zhao, et al.
0

Logit models are usually applied when studying individual travel behavior, i.e., to predict travel mode choice and to gain behavioral insights on traveler preferences. Recently, some studies have applied machine learning to model travel mode choice and reported higher out-of-sample prediction accuracy than conventional logit models (e.g., multinomial logit). However, there has not been a comprehensive comparison between logit models and machine learning that covers both prediction and behavioral analysis. This paper aims at addressing this gap by examining the key differences in model development, evaluation, and behavioral interpretation between logit and machine-learning models for travel-mode choice modeling. To complement the theoretical discussions, we also empirically evaluated the two approaches on stated-preference survey data for a new type of transit system integrating high-frequency fixed routes and micro-transit. The results show that machine learning can produce significantly higher predictive accuracy than logit models and are better at capturing the nonlinear relationships between trip attributes and mode-choice outcomes. On the other hand, compared to the multinomial logit model, the best-performing machine-learning model, the random forest model, produces less reasonable behavioral outputs (i.e. marginal effects and elasticities) when they were computed from a standard approach. By introducing some behavioral constraints into the computation of behavioral outputs from a random forest model, however, we obtained better results that are somewhat comparable with the multinomial logit model. We believe that there is great potential in merging ideas from machine learning and conventional statistical methods to develop refined models for travel-behavior research and suggest some possible research directions.

READ FULL TEXT
research
02/08/2019

Modeling Heterogeneity in Mode-Switching Behavior Under a Mobility-on-Demand Transit System: An Interpretable Machine Learning Approach

Recent years have witnessed an increased focus on interpretability and t...
research
10/30/2019

Distilling Black-Box Travel Mode Choice Model for Behavioral Interpretation

Machine learning has proved to be very successful for making predictions...
research
04/06/2021

A novel activity pattern generation incorporating deep learning for transport demand models

Activity generation plays an important role in activity-based demand mod...
research
07/03/2020

PsychFM: Predicting your next gamble

There is a sudden surge to model human behavior due to its vast and dive...
research
03/07/2023

Deep hybrid model with satellite imagery: how to combine demand modeling and computer vision for behavior analysis?

Classical demand modeling analyzes travel behavior using only low-dimens...
research
02/01/2021

Comparing hundreds of machine learning classifiers and discrete choice models in predicting travel behavior: an empirical benchmark

Researchers have compared machine learning (ML) classifiers and discrete...

Please sign up or login with your details

Forgot password? Click here to reset