Efficient Context-Aware Vehicular Traffic Re-Routing Based on Pareto-Optimality: A Safe-Fast Use Case

Abstract

Vehicular traffic re-routing is the key to provide better traffic 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, which intends to improve not only mobility, but also driving experience and safety of drivers and passengers. Context-aware and multi-objective re-routing approaches will play an important role in traffic management. Yet, most approaches are deterministic and can not support the strict requirements of traffic management applications, since many vehicles potentially will take the same route, consequently degrading overall traffic efficiency. In this way, this work introduces an efficient algorithm based on Pareto-optimality for dealing with such problem. In addition, we focus on the improvement of traffic mobility and public safety during the route recommendation. Thus, methods for building pieces of knowledge about ongoing traffic conditions and risky areas based on city-wide criminal activities are presented. Simulation results have shown that our proposal provides a better trade-off between mobility and safety than state-of-the-art approaches and also avoids the problem of potentially creating different congestion spots.