Telematics-Based Traffic Law Enforcement and Network Management System for Connected Vehicles

This paper introduces a telematics-based traffic law enforcement and management system (SLEM), which leverages connected vehicle (CV) and telematics technologies. The system assigns each driver a real-time score that measures her/his driving performance. Using these driver scores, SLEM then adopts a personalized route guidance strategy that favors high-performing drivers by guiding them to less congested routes at the expense of low-performing drivers whom are directed to alternative, slower routes. This routing strategy shifts the network traffic distribution pattern from the undesirable user equilibrium (UE) pattern to the system optimal (SO) pattern. Hence, SLEM not only incentivizes drivers to improve their driving performance, but it also provides a mechanism to manage network congestion. A bi-level mathematical program and an efficient solution methodology were developed to derive SLEM’s optimal routing strategy. A set of experiments that were conducted to evaluate the performance of SLEM under different operation scenarios showed that the adoption of SLEM’s routing strategy reduced travel time during recurrent congestion situations by about 5%.