With the growing volumes of vehicle trajectory data, it becomes increasingly possible to
capture time-varying and uncertain travel costs, eg, travel time, in a road network. The …

The availability of massive vehicle trajectory data enables the modeling of road-network
constrained movement as travel-cost distributions rather than just single-valued costs …

This paper investigates the reliable shortest path (RSP) problem in Gaussian process (GP)
regulated transportation networks. Specifically, the RSP problem that we are targeting at is …

This article studies reliable shortest path (RSP) problems in stochastic transportation
networks. The term reliability in the RSP literature has many definitions, eg, 1) maximal …

This article considers the stochastic on-time arrival problem in transit networks where both
the travel time and the waiting time for transit services are stochastic. A specific challenge of …

We present a new and more efficient technique for computing the route that maximizes the
probability of on-time arrival in stochastic networks, also known as the path-based stochastic …

The availability of massive vehicle trajectory data enables the modeling of road-network
constrained movement as travel-cost distributions rather than just single-valued costs …

This paper studies the stochastic on-time arrival (SOTA) problem in transportation networks
and introduces a novel reinforcement learning-based algorithm, namely sample efficient …

We propose in this article an adaptive algorithm for optimal and robust guidance for the
users of the road networks. The algorithm is based on the Stochastic On Time Arrival (SOTA) …

Resource constrained shortest path problems are usually solved thanks to a smart
enumeration of all the non-dominated paths. Recent improvements of these enumeration …