Traffic congestion in road networks has been studied as the congestion game in game theory. In the existing work, the road usage by each agent was assumed to be static during the whole time horizon of the agent's travel, as in the classical congestion game. This assumption, however, should be reconsidered because each agent sequentially uses roads composing the route. In this paper, we propose a multi-agent distributed route selection scheme based on a gradient descent method considering the time-dependency among agents' road usage for vehicular networks. The proposed scheme first estimates the time-dependent flow on each road by considering the agents' probabilistic occupation under the first-in-first-out (FIFO) policy. Then, it calculates the optimal route choice probability of each route candidate using the gradient descent method and the estimated time-dependent flow. Each agent finally selects one route according to the optimal route choice probabilities. We first prove that the proposed scheme can exponentially converge to the steady-state at the convergence rate inversely proportional to the product of the number of agents and that of individual route candidates. Through simulations under a grid-like network and a real road network, we show that the proposed scheme can improve the actual travel time by 5.1% and 2.5% compared with the conventional static-flow based approach, respectively. In addition, we demonstrate that the proposed scheme is robust against incomplete information sharing among agents, which would be caused by its low penetration ratio or limited transmission range of wireless communications.

Users of wireless mobile devices need Internet access not only when they stay at home or office, but also when they travel. It may be desirable for such users to select a "longcut route" from their current location to his/her destination that has longer travel time than the shortest route, but provides a better mobile wireless environment. In this paper, we formulate the above situation as the optimization problem of “optimal longcut route selection”, which requires us to find the best route concerning the wireless environment subject to a travel time constraint. For this new problem, we show NP-hardness, propose two pseudo-polynomial time algorithms, and experimental evaluation of the algorithms.

The multiobjective route selection problem (m-RSP) is a key research topic in the car navigation system (CNS) for ITS (Intelligent Transportation System). In this paper, we propose an interactive multistage weight-based Dijkstra genetic algorithm (mwD-GA) to solve it. The purpose of the proposed approach is to create enough Pareto-optimal routes with good distribution for the car driver depending on his/her preference. At the same time, the routes can be recalculated according to the driver's preferences by the multistage framework proposed. In the solution approach proposed, the accurate route searching ability of the Dijkstra algorithm and the exploration ability of the Genetic algorithm (GA) are effectively combined together for solving the m-RSP problems. Solutions provided by the proposed approach are compared with the current research to show the effectiveness and practicability of the solution approach proposed.

This paper introduces a multilayer traffic network model and traffic network clustering method for solving the route selection problem (RSP) in car navigation system (CNS). The purpose of the proposed method is to reduce the computation time of route selection substantially with acceptable loss of accuracy by preprocessing the large size traffic network into new network form. The proposed approach further preprocesses the traffic network than the traditional hierarchical network method by clustering method. The traffic network clustering considers two criteria. We specify a genetic clustering algorithm for traffic network clustering and use NSGA-II for calculating the multiple objective Pareto optimal set. The proposed method can overcome the size limitations when solving route selection in CNS. Solutions provided by the proposed algorithm are compared with the optimal solutions to analyze and quantify the loss of accuracy.

In this study, we present a way to choose route selection metric while discovering a new route in ad hoc mobile networks. We have used link expiration time and busy rate to calculate the route cost. The route cost is compared to a threshold value to decide whether the traffic of the route is high or low. If it is high then the system chooses busy rate as a route selection metric to avoid traffic congestion and if it is low the link expiration time is used to select the longlasting route. We have examined the characteristics of the routing protocol by computer simulation and found that it over performs the conventional protocols.

The single path routing protocol, known as the Ad Hoc On-demand Distance Vector, has been widely studied for use in mobile ad hoc networks. AODV requires a new route discovery whenever a path breaks. Such frequent route discoveries cause a delay due to route discovery latency. To avoid such inefficiency, a multipath routing protocol has been proposed that attempts to find link-disjoint paths in a route discovery. However, when there are two or more common intermediate nodes on the path, the protocol can not find a pair of link-disjoint paths even if the paths actually exist. To reduce this route discovery latency, it is necessary to increase the opportunities for finding a pair of link-disjoint paths. In this paper, we focus on AODV and propose an AODV-based new multipath routing protocol for mobile ad hoc networks. The proposed routing protocol uses a new method to find a pair of link-disjoint paths by selecting a route having a small number of common intermediate nodes on its path. Using simulation models, we evaluate the proposed routing protocol and compare it with AODV and the existing multipath routing protocol. Results show that the proposed routing protocol achieves better performance in terms of delay than other protocols because it increases the number of cases where a pair of link-disjoint paths can be established.