Investigating shortest distance and lowest congestion routing in an urban road traffic network using a microscopic and a macroscopic model

Abstract

Increasing road traffic [1] is costing time, money and increasing emissions[12-14]. Reducing congestion will reduce CO2 emissions [5]. We explore the use of dynamic navigation with real-time traffic information and its impact on congestion. A microscopic and a macroscopic model of an urban road traffic network is used to investigate the impact of static shortest distance routing and dynamic routing with local and global knowledge of congestion. An agent-based simulation model and a fluid dynamic model is built for different road networks. The macroscopic model approach, combining methodologies from Angstmann et al. [6] with the viscous Burgers’ equation and the numerical solution method from Wani and Thakar [7] simplifies having a partial differential equation model on a network structure based on a road system. As expected a decrease in travel time with dynamic routing where congestion could be avoided was observed on a large fabricated road network for the microscopic model. This suggests mobile navigation with real-time traffic information should improve congestion and travel times. Even though some of the same road networks were modelled in the microscopic and macroscopic models, the results are difficult to compare