Towards a Personalized Multi-objective Vehicular Traffic Re-routing System

Mariano de Souza, Allan (2021). Towards a Personalized Multi-objective Vehicular Traffic Re-routing System. (Dissertation, Institut für Informatik, Universitat Bern, Faculty of Science)

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Vehicular traffic re-routing is the key to provide better vehicular mobility. However, considering just traffic-related information to recommend better routes for each vehicle is far from achieving the desired requirements of a good Traffic Management System (TMS), which intends to improve mobility, driving experience, and safety of drivers and passengers. In this scenario, context-aware and multi-objective re-routing approaches will play an important role in traffic management, considering different urban aspects that might affect path planning decisions such as mobility, distance, fuel consumption, scenery, and safety. There are at least three issues that need to be handled to provide an efficient TMS, including: (i) scalability; (ii) re-routing efficiency; and (iii) reliability. Scalability refers to the ability of the system to deliver the desired performance without carrying about the vehicles’ number or the scenario’s size. On the other hand, re-routing efficiency refers to how good is the traffic management of the solution. Finally, reliability determines how reliable the system computes the routes regarding future changes in the urban dynamics.

In this way, this thesis contributes to efficient and reliable solutions to meet future TMSs. The first contribution lies in developing a scalable architecture for traffic management based on distributed and cooperative algorithms for sensing the urban environment, estimating urban aspects, and re-routing vehicles in real-time. The second contribution relies on enabling an efficient multi-objective re-routing based on each user’s preferences. Thus, each user can determine which urban aspects will be chosen to plan its route. Unlike other multi-objective approaches, our solution is non-deterministic, which decreases the chance of creating additional congestion spots since vehicles with similar origin and destination potentially will be re-routed through different routes. This thesis’s last contribution lies in improving the reliability of the routes computed by the TMSs using a route planning algorithm that considers the future changes in the urban dynamics is proposed. The significant advantage of this solution regarding literature solutions is that the system predicts future urban dynamics (i.e., future changes in traffic conditions, safety risks, etc.). Thus, the system knows beforehand when some changes will happen and how long they will last, consequently computing more reliable routes.

The proposed solutions were widely compared with other related works on different performance evaluation metrics. The evaluation results show that the proposed solutions are efficient, scalable, and cost-effective, pushing forward state-of-the-art traffic management systems.

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