Manoj Kanta Mainali

Optimal route of road networks by dynamic programming

This paper introduces an iterative Q value updating algorithm based on dynamic programming for searching the optimal route and its optimal traveling time for a given origin-destination (OD) pair of road networks. The proposed algorithm finds the optimal route based on the local traveling time information available at each adjacent intersection. For all the intersections of the road network, Q values are introduced for determining the optimal route. When the Q values converge, we can get the optimal route from multiple sources to single destination. If there exist multiple routes with the same traveling time, the proposed method can find all of it. When the traveling time of the road links change, an alternative optimal route is found starting with the already obtained Q values. The proposed method was applied to a grid like road network and the results show that the optimal route can be found in a small number of iterations.

Hierarchical efficient route planning in road networks

In this paper, a hierarchical route planning using Q value-based Dynamic Programming has been proposed. In the proposed method, a road network is divided into several subnetworks. A high-level road network is built with origin, destination and border intersections of subnetworks and the optimal traveling time between them are used to find the route from origin to destination using Q value-based Dynamic Programming. Additionally, a new re-routing method is proposed when the traveling times of sections are changed, where the border intersections in the route are kept fixed and only the route between them are re-optimized. The efficiency of the hierarchical method and accuracy of the re-routing method is evaluated using the Kitakyushu road network. The simulation results show that the hierarchical method is efficient to perform the search of a large number of routes. In addition, the results also show that the proposed re-routing method gives the route with the traveling time very near to the optimal traveling time.

Evolutionary approach for the traffic volume estimation of road sections

In recent years, a vast amount of real time traffic information is collected and provided to the travelers as a part of Intelligent Transportation Systems. These information is collected using sensors or detectors etc. set on the road sections and is utilized by car navigation devices to guide the travelers efficiently in the road network, or used to predict the future traffics. However, sometimes these information is not available for all the road sections in the road network. Generally speaking, road sections are classified into several different categories and currently real time traffic information is available only in road sections in major categories. In this paper, a genetic algorithm approach is proposed to estimate the traffic volume in road sections without the traffic information, where estimation is done using the known traffic volume information of the road sections. The proposed method is evaluated under static environments using a grid road network with various unknown rates of traffic volumes. Experimental results show that the proposed method can estimate the unknown traffic volume using only the known traffic volumes.

Class association rules mining with time series and its application to traffic prediction

An algorithm capable of finding important time related association rules and its application to classification systems have been described in this paper. We firstly describe a method of class association rule mining using genetic network programming (GNP) with time series processing mechanism in order to find time related sequence rules. Secondly, the classification system is applied to estimate to which class the current traffic data belong based on extracted association rules. Using this kinds of classification mechanism, the traffic prediction could be done since the rules extracted are based on time sequences. And, we also present experimental results using the traffic prediction problem.

Optimal route planning with restrictions for car navigation systems

The optimal route search in car navigation Systems is often considered to be a route search from the origin to destination. Many algorithms have been proposed to search for the optimal route from the origin to destination. However, in real situations several restrictions may need to be considered in the route search like some intersections must be included in the route while some should be excluded. The conventional optimal route search methods cannot consider such restrictions in the route search. In this paper, we propose a method to find the optimal route considering such restrictions, focusing on the restriction that some intermediate destinations must be visited before reaching the final destination. The proposed method is divided into three steps. In the first step, the optimal traveling times among the origin, intermediate destinations and final destination are calculated. In the second step, the optimal order of visiting intermediate destinations is optimized using RasID-D, a random search method for discrete optimization problems. Finally, in the third step, the optimal route from the origin to destination via intermediate destinations is determined. The paper also discusses the heuristic initialization to increase the efficiency of the optimal search. The proposed method was evaluated using a grid network with randomly generated intermediate intersections. Simulation results showed that the proposed method is more efficient than the genetic algorithm for optimizing the visiting order.

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.

Probabilistic model building Genetic Network Programming using multiple probability vectors

As an extension of GA and GP, a new evolutionary algorithm named Genetic Network Programming (GNP) has been proposed. GNP uses the directed graph structure to represent its solutions, which can express the dynamic environment efficiently. The reusable nodes of GNP can construct compact structures, leading to a good performance in complex problems. In addition, a probabilistic model building GNP named GNP with Estimation of Distribution Algorithm (GNP-EDA) has been proposed to improve the evolution efficiency. GNP-EDA outperforms the conventional GNP by constructing a probabilistic model by estimating the probability distribution from the selected elite individuals of the previous generation. In this paper, a probabilistic model building GNP with multiple probability vectors (PMBGNPM) is proposed. In the proposed algorithm, multiple probability vectors are used in order to escape from premature convergence, and genetic operations like crossover and mutation are carried out to the probability vectors to maintain the diversities of the populations. The proposed algorithm is applied to the controller of autonomous robots and its performance is evaluated.

Multiple ODs routing algorithm for traffic systems using GA

The multiple origins multiple destinations routing (MOMDR) problem becomes extremely complicated when considering the traffic volumes on road sections. When solving this kind of problem, only heuristic algorithms have practical values because it is a typical NP-Hard problem. This paper applies Genetic Algorithm (GA) to enhance Sorting-Randomizing-Adjusting-Updating (SRAU) algorithm [1]. The previous paper shows that different processing orders of the origin-destinations (ODs) result in different solutions with different performances. Therefore, an algorithm for finding the best processing order of ODs can optimize SRAU algorithm. In this paper, the processing order of ODs is transformed into a gene/chromosome of the individual of GA; then, the best gene can be found by evolution; finally, the best gene is transformed back to find the optimal solution of the problem. Sufficient simulations show that the proposed algorithm is more efficient than original SRAU algorithm. Comparisons also show that the proposed algorithm has higher performance and faster convergence speed than RAND algorithm which uses the random policy to find the proper processing order of ODs. Moreover, the consideration of the traffic volumes on the road sections enables the proposed algorithm to be applied to real traffic systems.

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.