Agent-based day-to-day traffic network model with information percolation

Abstract

This paper explores 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 in a traffic network where individual drivers seek to minimize their own travel costs by making route choices. A key hypothesis in this model is that instead of having perfect information, the drivers form individual 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 (groups) in a random graph. We apply the novel notion of percolation to capture the disaggregated and distributed nature of travel information sharing. We present a numerical study on the convergence of the transport network, when a range of percolation rates are considered. The findings suggest a positive correlation between the percolation rate and the speed of convergence, which is validated through statistical analysis. A sensitivity analysis is also presented which shows a bifurcation phenomenon with regard to certain model parameters.

Resilience Analysis of Transport Networks by Combining Variable Message Signs With Agent-Based Day-to-Day Dynamic Learning

“…However, In reality, due to the imperfect information and behavioral inertia, drivers prefer to sticking to the original route if the difference between actual cost of current route and the minimal perception among all routes is below a threshold (tolerance); otherwise, they are reluctant to consider the route change. This phenomenon is termed as Bounded Rationality (BR), and has been extensively studied in the field of traffic assignment [37], [38], [40]. In this study, as a sub-model of route choice, BR is introduced to capture the nature of imperfect information and inertial behavior, and we employ the BR model presented in [36], as shown below:…”

“…It is worth nothing that ABDTD model has inherent stochasticity due to logit-based route choice model at atomic level, so quantifying convergence of the model is very difficult. Here the method of approximation convergence (equilibrium) is utilized to quantify the convergence, which can be referred to [40] for details.…”

“…To capture the inertial behavior of individual drivers and imperfect information of transport networks, bounded rationality (BR) is introduced to combine with the proposed ABDTD model. The BR user behavior can be incorporated into the DTD dynamics [37], [38], and they either focus on route choice [39], [40] or departure time choice [41]. In this study, in order to improve the resilience of the transport networks, VMS with different compliance rates (CR) is also incorporated into the proposed model.…”

Developing model of toll road traffic forecasting during ramp-up period

“…: is a set of routes in the network. Wei et al [11] and Shang et al [26] define utility as a travel time or travel cost.…”

“…The basic assumption of the deterministic model is that the route selection behavior of the traveler is perfect rationality, who always tends to switch to the route with the lowest travel costs [26] Under equilibrium conditions, travel costs on all routes available from the origin o to the destination d are equal and minimum. But in reality, travelers do not always choose the route that has the least cost of travel [28] due to various reasons such as lack of information and inertial behavior.…”