Computer Vision for Transit Travel Time Prediction: An End-to-End Framework Using Roadside Urban Imagery

TL;DR

This paper introduces an innovative end-to-end framework that uses roadside urban imagery combined with traditional transit data to accurately predict transit travel times, enhancing real-time transit information systems.

Contribution

It is the first to utilize roadside urban imagery directly for transit travel time prediction, integrating vision transformers with real-time data for automated and scalable travel time estimation.

Findings

Vision Transformer model achieves 80-85% accuracy in predicting travel time bands.

The framework effectively combines GTFS, AVL, and roadside imagery for travel time estimation.

The approach improves the scalability and automation of transit travel time prediction.

Abstract

Accurate travel time estimation is paramount for providing transit users with reliable schedules and dependable real-time information. This paper is the first to utilize roadside urban imagery for direct transit travel time prediction. We propose and evaluate an end-to-end framework integrating traditional transit data sources with a roadside camera for automated roadside image data acquisition, labeling, and model training to predict transit travel times across a segment of interest. First, we show how the GTFS real-time data can be utilized as an efficient activation mechanism for a roadside camera unit monitoring a segment of interest. Second, AVL data is utilized to generate ground truth labels for the acquired images based on the observed transit travel time percentiles across the camera-monitored segment during the time of image acquisition. Finally, the generated labeled image dataset is used to train and thoroughly evaluate a Vision Transformer (ViT) model to predict a discrete transit travel time range (band). The results illustrate that the ViT model is able to learn image features and contents that best help it deduce the expected travel time range with an average validation accuracy ranging between 80%-85%. We assess the interpretability of the ViT model's predictions and showcase how this discrete travel time band prediction can subsequently improve continuous transit travel time estimation. The workflow and results presented in this study provide an end-to-end, scalable, automated, and highly efficient approach for integrating traditional transit data sources and roadside imagery to improve the estimation of transit travel duration. This work also demonstrates the value of incorporating real-time information from computer-vision sources, which are becoming increasingly accessible and can have major implications for improving operations and passenger real-time information.