Arriving on time: estimating travel time distributions on large-scale road networks

TL;DR

This paper introduces a scalable method for estimating travel time distributions on large road networks using GPS probe data, combining Markov models and Gaussian Markov random fields for improved on-time arrival predictions.

Contribution

The paper presents a novel scalable framework that efficiently estimates travel time distributions on large networks by leveraging topology and advanced probabilistic models.

Findings

Method scales linearly with network size

Accurate travel time distributions on a 505,000 link network

Efficient inference algorithms for large-scale data

Abstract

Most optimal routing problems focus on minimizing travel time or distance traveled. Oftentimes, a more useful objective is to maximize the probability of on-time arrival, which requires statistical distributions of travel times, rather than just mean values. We propose a method to estimate travel time distributions on large-scale road networks, using probe vehicle data collected from GPS. We present a framework that works with large input of data, and scales linearly with the size of the network. Leveraging the planar topology of the graph, the method computes efficiently the time correlations between neighboring streets. First, raw probe vehicle traces are compressed into pairs of travel times and number of stops for each traversed road segment using a `stop-and-go' algorithm developed for this work. The compressed data is then used as input for training a path travel time model, which couples a Markov model along with a Gaussian Markov random field. Finally, scalable inference algorithms are developed for obtaining path travel time distributions from the composite MM-GMRF model. We illustrate the accuracy and scalability of our model on a 505,000 road link network spanning the San Francisco Bay Area.