Arriving on time: estimating travel time distributions on large-scale road networks
This work addresses the need for reliable travel time distributions in routing applications, offering a scalable solution for urban planning and navigation systems, though it is incremental in improving existing statistical modeling approaches.
The authors tackled the problem of estimating travel time distributions on large-scale road networks to maximize on-time arrival probability, proposing a method that scales linearly with network size and demonstrated accuracy on a 505,000-link network in the San Francisco Bay Area.
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.