Intelligent Transportation Systems

Time-dependent shortest path problems arise in a variety of applications; e.g., dynamic traffic assignment (DTA), network control, automobile driver guidance, ship routing and airplane dispatching. In the majority of cases one seeks the cheapest (least generalized cost) or quickest route between an origin and a destination for a given time of departure. This is the "forward" shortest path problem. In some applications, however, e.g., when dispatching airplanes from airports and in DTA versions of the "morning commute problem", one seeks the cheapest or quickest routes for a given arrival...

The advancement of in-vehicle sensors provides abundant datasets to estimate parameters of car-following models that describe driver behaviors. The question of parameter identifiability of such models (i.e., whether it is possible to infer its unknown parameters from the experimental data) is a central system analysis question, and yet still remains open. This article presents both structural and practical parameter identifiability analysis on four common car-following models: i) the constant-time headway relative-velocity (CTH-RV) model, ii) the optimal velocity model (OV), iii) the...

Electric road systems (ERS), where power is delivered to the vehicles as they drive, are an intriguing option for road freight sector electrification. In order to analyse various aspects of their operation, such as their economical feasibility, or their influence on the power system, appropriate modelling approaches are needed. While microscopic, agent-based models have successfully been used for this purpose, their complexity makes them unsuitable for control design and implementation. In this work, we propose a macroscopic model, capturing the interaction between the ERS and Heavy-Duty...

As natural disasters become increasingly frequent, the need for efficient and equitable evacuation planning has become more critical. This paper proposes a data-driven, reinforcement learning (RL)-based framework to optimize public transit operations for bus-based evacuations in transportation networks with an emphasis on improving both efficiency and equity. We model the evacuation problem as a Markov Decision Process (MDP) solved by RL, using real-time transit data from General Transit Feed Specification (GTFS) and transportation networks extracted from OpenStreetMap (OSM). The RL agent...