Agent-based day-to-day traffic network model with information percolation. Issue 1 (2nd January 2017)

• Record Type: Journal Article
• Authors: Shang, Wenlong; Han, Ke; Ochieng, Washington; Angeloudis, Panagiotis
• Is Part Of: Transportmetrica. Volume 13:Issue 1(2017)
• Page Start: 38
• Page End: 66
• Publication Date: 2017-01-02
• Subjects: Agent-based simulation -- day-to-day dynamics -- information percolation -- convergence -- traffic network; Transportation -- Periodicals; Transportation -- Research -- Periodicals; 388.072
• DOI: 10.1080/23249935.2016.1209254
• Languages: English
• ISSNs: 2324-9935

Abstract

ABSTRACT: We explore the impact of travel information sharing on road networks using a two-layer, agent-based, day-to-day traffic network model. The first layer (cyber layer) represents a conceptual communication network where travel information is shared among drivers. The second layer (physical layer) captures the day-to-day evolution of a traffic network. Instead of having perfect information, the drivers are assumed to form groups, among which travel information is shared and utilized for routing decisions. The formation of groups occurs in the cyber layer according to the notion of percolation, which describes the formation of connected clusters in a random graph. The notion of percolation captures the disaggregated and distributed nature of travel information sharing. We present numerical studies on the convergence of the traffic network with a range of percolation rates. The findings suggest a positive correlation between the percolation rate and the speed of convergence, which is validated through statistical analysis.