Shanqing Yu

A global routing strategy in dynamic traffic environments with a combination of Q value-based dynamic programming and boltzmann distribution

A simple traffic system is proposed in this paper, in which the global optimal route considering the traffic volume is selected as the guidance for the vehicles. In the proposed system, Q value-based dynamic programming is adopted to calculate the optimal traveling time to each destination from every intersection of the road network. And Boltzmann optimal route method is used to generate several route candidates, the usefulness of which is evaluated in terms of the total traveling time considering traffic volume. Furthermore, dynamically changing traffic volumes of all the given origin-destination pairs of road networks are constantly provided in the road simulation we used and the traveling time of each section is continuously updated according to its dynamic traffic volume. In this paper, the analysis and comparison between greedy strategy and Bolztmann strategy with various ldquotemperaturesrdquo are carried out. The simulation result showed the effectiveness of the proposed Boltzmann optimal route method.

A Q value-based Dynamic Programming algorithm with Boltzmann Distribution for optimizing the global traffic routing strategy

In this paper, we propose a heuristic method trying to find a good approximation to the global optimum route for origin-destination pairs through iterations until the total traveling time converges in static traffic systems. The overall idea of our method is to iteratively update the traveling time of each route section according to its corresponding traffic volume, and continuously generate a new global route by Q value-based dynamic programming combined with Boltzmann distribution. Finally, we can get the global optimum route considering the traffic volumes of the road sections. The new proposed method is compared with the conventional shortest-path method and the result demonstrates that the proposed method performs better than the conventional method in global perspective.

Multi-routes algorithm using temperature control of Boltzmann distribution in Q value-based dynamic programming

In this paper, we propose a heuristic method trying to improve the efficiency of traffic systems in the global perspective, where the optimal traveling time for each Origin-Destination (OD)pair is calculated by extended Q value-based Dynamic Programming and the global optimum routes are produced by adjusting the temperature parameter in Boltzmann distribution. The key point is that the temperature parameter for each section is not identical, but constantly changing with the traffic of the section, which enables the diversified routing strategy depending on the latest traffics. In addition, the simulation results show that comparing with the Greedy strategy and constant temperature parameter strategy, the proposed method, i.e., temperature parameter control strategy of the Q value-based Dynamic Programming with Boltzmann distribution, could reduce the traffic congestion effectively and minimize the negative impact of the information update interval by adopting suitable temperature parameter control strategy.

Q value-based Dynamic Programming with SARSA Learning for real time route guidance in large scale road networks

In this paper, a distributed dynamic traffic management model has been proposed to guide the vehicles, in order to minimize the computation time, make full use of real time traffic information and consequently improve the efficiency of the traffic system. For making the model work, we proposed a new dynamic route determination method, in which Q value-based Dynamic Programming and Sarsa Learning are combined to calculate the approximate optimal traveling time from each section to the destinations in the road networks. The proposed traffic management model is applied to the large scale microscopic simulator SOUND/4U based on the real world road network of Kurosaki, Kitakyushu in Japan. The simulation results show that the proposed method could reduce the traffic congestion and improve the efficiency of the traffic system effectively compared with the conventional method in the real world road network.

Dynamic traffic management model for real world road networks

In this paper, a dynamic traffic management model has been proposed to alleviate the traffic congestion and improve the efficiency of the traffic systems in global perspective. The proposed traffic management model is applied to the large scale microscopic simulator SOUND/4U based on the real world road network of Kurosaki, Kitakyushu in Japan. All the vehicles in the simulator follow the direction from the route guidance of the dynamic traffic management model, in which the extended Q value-based Dynamic Programming with Boltzmann Distribution and the time-varying traffic information are used to generate the routes from the origins to destinations. The simulation results show that the proposed Q value-based Dynamic Programming with Boltzmann Distribution could reduce the traffic congestion and improve the efficiency of the whole traffic system effectively compared with the greedy method in the real world road network.

Various temperature parameter control methods in Q value-based Dynamic Programming with Boltzmann Distribution

In order to alleviate the congestion in modern metropolises with over crowded traffics and improve the efficiency of Intelligent Transportation Systems, three temperature parameter control methods of Q value-based Dynamic Programming with Boltzmann Distribution have been proposed in this paper. The simulation result shows that each method has its own areas of expertise depending on its features and all of the methods could improve the efficiency of the traffic system comparing with the conventional Greedy Method.