Shoichiro Nakayama

Reliable Network Design Problem: Case with Uncertain Demand and Total Travel Time Reliability

In the reliable network design problem (RNDP) the main sources of uncertainty are variable demand and route choice. The objective is to maximize network total travel time reliability (TTR), which is defined as the probability that the network total travel time will be less than a threshold. A framework is presented for a stochastic network model with Poisson-distributed demand and uncertain route choice. The travelers are assumed to choose their routes to minimize their perceived expected travel cost following the probit stochastic user equilibrium condition. An analytical method is presented for approximation of the first and second moments of the total travel time. These moments are then fitted with a log-normal distribution. Then the design problem is tackled in which the analytical derivative of the TTR is derived with the sensitivity analysis of the equilibrated path choice probability. This derivative is then supplied to a gradient-based optimization algorithm to solve the RNDP. The algorithm is tes...

ROUTE CHOICE MODEL WITH INDUCTIVE LEARNING

In this study drivers are assumed to reason and learn inductively based on the theory of cognitive psychology. The model system is basically a production system, a compilation of if-then rules in which the rules are revised by applying genetic algorithms. The behavior of drivers and network flow through Monte Carlo simulation using the model system is examined. The intention of this research is to shed light on the behavior of a driver-network system from a new standpoint, one different from that of equilibrium analysis. This research views drivers’ behaviors as psychological and heterogeneous rather than economical and homogeneous. The results of the numerical experiments can be summarized as follows: (1) network flow does not necessarily converge to the user equilibrium; (2) drivers form a delusion, an extremely biased perception of travel time as a result of experiencing excessive travel times on early parts of the simulation in which little experience had been gained; (3) the delusion is dissolved by switching routes capriciously; and (4) without caprice drivers continue to travel on the same route because of their delusions and develop the habit of choosing the same route, thus freezing their behaviors. These results indicate that system behavior is much more complex and dynamic than implied by equilibrium analysis.

DRIVERS' ROUTE CHOICE RULES AND NETWORK BEHAVIOR: DO DRIVERS BECOME RATIONAL AND HOMOGENEOUS THROUGH LEARNING?

Most analyses of driver-network transportation systems rest on the presence of network equilibrium. Equilibrium analyses presuppose that the driver is rational and homogeneous and has perfect information. A more realistic view, however, is that individuals’ rationality is bounded because of their cognitive limitations. A driver is assumed to adopt simple rules when he chooses a route. A model system in which the driver’s learning is simulated with multiple rules is applied to investigate the validity of the hypotheses underlying network equilibrium. The results of simulation analyses can be summarized as follows. Drivers do not become homogeneous and rational, as equilibrium analyses presuppose; rather, there are fewer rational drivers even after a long process of learning, and heterogeneous drivers make up the system. Drivers’ attitudes toward and perceptions of each route do not become homogeneous either but become bipolar. The results point to the need for a critical appraisal of the foundation of the equilibrium analysis of network flow.

Drivers' Learning and Network Behavior: Dynamic Analysis of the Driver-Network System as a Complex System

A model system of drivers’ cognition, learning, and route choice is formulated, taking into account the limitations in drivers’ cognitive capabilities, and is applied to examine the dynamic nature of a driver-network system through microsimulation. Network equilibrium is not assumed a priori; rather, finding how an equilibrium is reached, or not reached, is the objective. Although equilibrium analysis, in general, focuses on unique and static equilibrium by treating drivers’ behavior as simply as possible, drivers’ behavior is treated more realistically to enhance understanding of the day-to-day dynamics of the driver-network system. Results of microsimulation analyses indicate that the network flow does not necessarily converge to user equilibrium; instead, it may reach “deluded equilibrium,” which is caused by drivers’ false perceptions of travel times, and have “path dependence.” Results, especially the complex behavior such as path dependence shown in the simulation, indicate that the driver-network system is a complex system.

Simulation analysis for the management of an electric vehicle-sharing system: Case of the Kyoto Public-Car System

A multistation electric vehicle (EV)–sharing system has been in operation on an experimental basis in Kyoto, Japan. Members of the system can check out EVs at a station and return them at any station. To reduce the cost of system operation, EVs are not reallocated to stations by the operator. This feature, at the same time, may delimit the efficiency of the system because of the mismatch between the spatial distribution of the demand and the distribution of EVs among the stations. A simulation model of system operation is constructed and the optimal management of the system configured using genetic algorithms, with the number of checkouts per vehicle set as the objective function to be maximized. The number of vehicles, parking capacity at each station, and number of members are among the decision variables. The results suggest that the optimal number of EVs is about half the total number of parking stalls. This implies that the efficiency of the system does not necessarily improve as more EVs are introduced. Also, the results suggest that even if a new station is introduced into the system, the efficiency does not necessarily improve.

Bayesian Learning, Day-to-day Adjustment Process, and Stability of Wardrop Equilibrium

In this study, we assume that drivers under day-to-day dynamic transportation circumstances choose routes based on Bayesian learning and develop a day-to-day dynamic model of network flow. This model reveals that a driver using Bayesian learning chooses the route that frequently takes the minimum travel time. Furthermore, we find that the equilibrium point of the day-to-day dynamic model is identical to Wardrop’s equilibrium. Under complete information (when information about which route takes the minimum travel time is given after the trips), Wardrop’s equilibrium is globally asymptotically stable and the day-to-day dynamic system converges to Wardrop’s equilibrium if initial recognition among drivers is distributed widely. Under incomplete information, Wardrop’s equilibrium is always globally asymptotically stable regardless of what the drivers’ initial recognition is. Paradoxically, the condition for stable equilibrium under incomplete information is more relaxed than that under complete information.

A quasi-dynamic assignment model that guarantees unique network equilibrium

This article formulates a discrete-time dynamic traffic assignment (DTA) model and, under certain conditions, shows the existence and uniqueness of network equilibrium. Several theoretical issues need to be tackled. The inflow to a link in a particular discrete (time) period does not necessarily exit within the same period. We consider how flow is passed from one link and period to the next, and the corresponding costs. Under the proposed model, flow departing within a discrete period may experience different link travel times in different discrete periods, even if the flow chooses a single route. Route travel time must then be defined so that route and OD costs are meaningful. To this end, quasi-real route travel time is defined. Based on this definition, a quasi-equilibrium condition for DTA is proposed; a semi-dynamic analogue of user equilibrium. The existence and uniqueness of this equilibrium solution are proven.

Can Travel Time Information Influence Network Flow?: Implications of the Minority Game

Drawing from the literature on the minority game, this study proposes that predictive travel time information provided to drivers to assist with their route choices may not influence network flow; under the condition that route choices are made repeatedly by the same group of drivers, the drivers self-organize to reach the same network equilibrium regardless of the contents of the predictive travel time information given. A numerical example supports this conjecture by showing that improvements in network flow are negligibly small even when precise information on travel time is provided. If no predictive information is present, the results of agent-based route choice simulation studies indicate that higher levels of the cognitive capabilities of drivers tend to produce more self-organization and stable network flows and that information on those routes a driver did not take is important for this self-organization.

Estimation of Parameters of Network Equilibrium Models: A Maximum Likelihood Method and Statistical Properties of Network Flow

Estimation of the parameters in network equilibrium models, including OD matrix elements, is essential when applying the models to real-world networks. Link flow data are convenient for estimating parameters because it is relatively easy for us to obtain them. In this study, we propose a maximum likelihood method for estimating parameters of network equilibrium models using link flow data, and derive first and second derivatives of the likelihood function under the equilibrium constraint. Using the likelihood function and its derivatives, t-values and other statistical indices are provided to examine the confidence interval of estimated parameters and the model’s goodness-of-fit. Also, we examine which conditions are needed for consistency, asymptotic efficiency, and asymptotic normality for the maximum likelihood estimators with non-I.I.D. link flow data. In order to investigate the validity and applicability, the proposed ML method is applied to a simple network and the road network in Kanazawa City, Japan.

Ecotravel Coordinator Program: Effects on Travel Behavior and Environmental Attitude

This study introduces an ecotravel coordinator program to reduce car use. In this system, ecotravel coordinators are put in charge of several people. They organize ecotravel meetings and are responsible for taking the initiative. During the meetings participants recognize and deepen their understanding of their travel behaviors and devise their own plans for reducing car use. The aim of this study is to examine the validity of the ecotravel coordinator program through an experimental method. One result of the experiments was that the ecocoordinators and the other participants reduced their car travel mileage 54% and 48%, respectively. Nonparticipants, however, almost never reduced their travel mileage (1.4% reduction). Thus, experiment results indicate that the system is fairly effective for reducing car use.