Structural and dynamic models for complex road networks
2020-05-04T20:19:53Z (GMT) by
The interplay between network topology and traffic dynamics in road networks impacts various performance measures. There are extensive existing researches focusing on link-level fundamental diagrams, traffic assignments under route choice assumptions. However, the underlying coupling of structure and dynamic makes network-level traffic not fully investigated. In this thesis, we build structural and dynamic models to deal with three challenges: 1) describing road network topology and understanding the difference between cities; 2) quantifying network congestion considering both road network topology and traffic flow information; 3) allocating transportation management resources to optimize the road network connectivity.
The first part of the thesis focuses on structural models for complex road networks. Online road map data platforms, like OpenStreetMap, provide us with reliable road network data of the world. To solve the duplicate node problem, an O(n) time complexity node merging algorithm is designed to pre-process the raw road network with n nodes. Hereafter, we define unweighted and weighted node degree distribution for
road networks. Numerical experiments present the heterogeneity in node degree distribution for Beijing and Shanghai road network. Additionally, we find that the power law distribution fits the weighted road network under certain parameter settings, extending the current knowledge that degree distribution for the primal road network is not power law.
In the second part, we develop a road network congestion analysis and management framework. Different from previous methods, our framework incorporates both network structure and dynamics. Moreover, it relies on link speed data only, which is more accessible than previously used link density data. Specifically, we start from the existing traffic percolation theory and critical relative speed to describe network-level traffic congestion level. Based on traffic component curves, we construct Aij for two road segments i and j to quantify the necessity of considering the two road segments in the same traffic zone. Finally, we apply the Louvain algorithm on defined road segment networks to generate road network partition candidates. These candidate partitions will help transportation engineers to control regional traffic.
The last part formulates and solves a road network management resource allocation optimization. The objective is to maximize critical relative speed, which is defined from traffic component curves and is closely related to personal driving comfort. Budget upper bound serves as one of the constraints. To solve the simulation-based nonlinear optimization problem, we propose a simple allocation and a meta-heuristic method based on the genetic algorithm. Three applications demonstrate that the meta-heuristic method finds better solutions than simple allocation. The results will inform the optimal allocation of resources at each road segment in metropolitan cities to enhance the connectivity of road networks.