Travel Time Prediction from Sparse Open Data

TL;DR

This paper presents a free, open-source travel time prediction model that balances accuracy and resource efficiency, using open data and machine learning, suitable for large-scale urban analysis.

Contribution

It introduces a minimally-congested driving time prediction model that requires minimal data and computational resources, filling a gap for resource-constrained researchers.

Findings

The model outperforms naive travel time estimates.

It achieves comparable accuracy with minimal data inputs.

The approach is interpretable and scalable for urban analysis.

Abstract

Travel time prediction is central to transport geography and planning's accessibility analyses, sustainable transportation infrastructure provision, and active transportation interventions. However, calculating accurate travel times, especially for driving, requires either extensive technical capacity and bespoke data, or resources like the Google Maps API that quickly become prohibitively expensive to analyze thousands or millions of trips necessary for metropolitan-scale analyses. Such obstacles particularly challenge less-resourced researchers, practitioners, and community advocates. This article argues that a middle-ground is needed to provide reasonably accurate travel time predictions without extensive data or computing requirements. It introduces a free, open-source minimally-congested driving time prediction model with minimal cost, data, and computational requirements. It trains and tests this model using the Los Angeles, California urban area as a case study by calculating naive travel times from open data then developing a random forest model to predict travel times as a function of those naive times plus open data on turns and traffic controls. Validation shows that this interpretable machine learning method offers a superior middle-ground technique that balances reasonable accuracy with minimal resource requirements.