Takeshi Nagae

An Evolutionary Game Model and MCMC Estimation for Analyzing Stochastic Properties of Traffic State on a Road Network

This paper proposes a novel methodology for evaluating the travel time reliability of an urban road network using latest advances in the evolutionary game theory and MCMC (Markov chain Monte Carlo). Because of the stochastic variation of congestion, the importance of travel time reliability on an urban road network is attractive for research. Moreover, analyzing the stochastic properties of travel time is essential for maintaining an urban road network reliable. Our proposed method has the following notable features: (i) it obtains a stationary distribution of traffic flow patterns which stems from a stochastic day-to-day dynamics of a population with rational users, (ii) this distribution is consistent with the traditional SUE (stochastic user equilibrium), and it is adaptable to an economical evaluation framework including cost-benefit analysis of road development projects that is standardized by government, and (iii) it can estimate the 95 percentile of minimum travel time of each OD pair for a practical size network. We first use an evolutionary game model proposed by Sandholm (2009) to describe the stochastic day-to-day dynamics of the traffic flow patterns generated by the perturbed best response of each user. This model is characterized by a stationary distribution of traffic flow patterns, and it is consistent with the SUE. We then develop a sampling method to generate sample traffic flow patterns from the stationary distribution by applying the MCMC re-sampling method proposed by Wei at el. (2012). Finally, we develop a numerical method for estimating the 95 percentile of minimum travel time efficiently applying the importance sampling and a randomized algorithm. Several numerical applications of the proposed method are demonstrated on a moderate size network.

A risk-cost minimization model for catastrophe averse shipment of hazardous materials

This article provides a quantitative method for estimating mixed routing strategies for catastrophe averse shipment of hazardous materials (hazmats), taking into account of both transportation cost and risk. The mixed routing problem is first formulate as a maximin problem and then is reduced to a single-level convex programming problem (CP). This CP reduction enables us to develop an efficient solution algorithm applicable to real-world large-scale networks.

Cost-Benefit Analysis of Road Network Reinforcement using Geographical Information Systems and User Equilibrium with Variable Demand

This study provides a framework for quantitative analysis for cost-benefit of anti-seismic reinforcement, using geographical information systems (GIS) and the user equilibrium with variable demand (UE/VD) traffic assignment model. In our framework, seismic intensity maps are estimated by using GIS, corresponding to 23 earthquake scenarios that are supposed around the Kobe urban network. Probabilities such that a road bridge is damaged by an earthquake are evaluated from the seismic intensity map and the fragility curve for each road bridge. The social benefit loss for each earthquake scenario is evaluated by using UE/VD applied to a maximum-likelihood damage network conditional to the scenario.