Snížení doby jízdy v městské dopravní síti pomocí lokálního směrování

Abstract

The thesis explores the concept of local level routing, where vehicles receive either travel times or route advice based on accurate short-term traffic conditions to reroute themselves throughout a network. We focus on developing a system to provide time-dependent road travel times considering information of signal timings, queue lengths, road closures and vehicle arrivals, in which traffic controllers model the movement of vehicles and share their knowledge about the traffic amongst themselves as a flexible centralized or distributed system. Our motivation is to reduce travel times, avoid congestion and balance the load of vehicles throughout the network. In order to achieve this, we first analyse how similar route guidance systems estimate travel times and define optimal routes. Then, we study possible solutions for the optimal route problem, specially shortest-path algorithms, and we defined the necessary inputs for the chosen algorithm type. After that, we research methods for estimation of discrete time-dependent travel times and decide to propose an event-based traffic theoretic model that models the driving behaviour of vehicles and cope with the restrictions of communication bandwidth between traffic controllers, the necessity of certain degree of prediction, and running time in larger networks, while feeding an deterministic queueing model that ensures estimations within a horizon of planning. We describe the functionality of our proposed system and evaluate it through simulations of a real-world scenario for several penetration rates, comparing it against the best, worst and current situations cases. Finally, we discuss the performance and future work for our local level routing system.

The thesis explores the concept of local level routing, where vehicles receive either travel times or route advice based on accurate short-term traffic conditions to reroute themselves throughout a network. We focus on developing a system to provide time-dependent road travel times considering information of signal timings, queue lengths, road closures and vehicle arrivals, in which traffic controllers model the movement of vehicles and share their knowledge about the traffic amongst themselves as a flexible centralized or distributed system. Our motivation is to reduce travel times, avoid congestion and balance the load of vehicles throughout the network. In order to achieve this, we first analyse how similar route guidance systems estimate travel times and define optimal routes. Then, we study possible solutions for the optimal route problem, specially shortest-path algorithms, and we defined the necessary inputs for the chosen algorithm type. After that, we research methods for estimation of discrete time-dependent travel times and decide to propose an event-based traffic theoretic model that models the driving behaviour of vehicles and cope with the restrictions of communication bandwidth between traffic controllers, the necessity of certain degree of prediction, and running time in larger networks, while feeding an deterministic queueing model that ensures estimations within a horizon of planning. We describe the functionality of our proposed system and evaluate it through simulations of a real-world scenario for several penetration rates, comparing it against the best, worst and current situations cases. Finally, we discuss the performance and future work for our local level routing system.