Dynamic optimal route search algorithm for car navigation systems with preferences by dynamic programming

Mainali, Manoj Kanta; Mabu, Shingo; Yu, Shanqing; Eto, Shinji; Hirasawa, Kotaro

doi:10.1002/tee.20601

Cited by 14 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are two key factors that determine the effect of real-time navigation technology. One is the processing speed of real-time traffic flow, and the other one is the time complexity of path planning algorithms [40]. Details of our traffic flow prediction method can be found in Section 5.…”

Section: Low-carbon-emission-oriented Navigation Methodsmentioning

confidence: 99%

Constructing an Environmental Friendly Low-Carbon-Emission Intelligent Transportation System Based on Big Data and Machine Learning Methods

Yang

et al. 2020

Sustainability

View full text Add to dashboard Cite

The sustainable development of mankind is a matter of concern to the whole world. Environmental pollution and haze diffusion have greatly affected the sustainable development of mankind. According to previous research, vehicle exhaust emissions are an important source of environmental pollution and haze diffusion. The sharp increase in the number of cars has also made the supply of energy increasingly tight. In this paper, we have explored the use of intelligent navigation technology based on data analysis to reduce the overall carbon emissions of vehicles on road networks. We have implemented a traffic flow prediction method using a genetic algorithm and particle-swarm-optimization-enhanced support vector regression, constructed a model for predicting vehicle exhaust emissions based on predicted road conditions and vehicle fuel consumption, and built our low-carbon-emission-oriented navigation algorithm based on a spatially optimized dynamic path planning algorithm. The results show that our method could help to significantly reduce the overall carbon emissions of vehicles on the road network, which means that our method could contribute to the construction of low-carbon-emission intelligent transportation systems and smart cities.

show abstract

Section: Low-carbon-emission-oriented Navigation Methodsmentioning

confidence: 99%

Constructing an Environmental Friendly Low-Carbon-Emission Intelligent Transportation System Based on Big Data and Machine Learning Methods

Yang

et al. 2020

Sustainability

View full text Add to dashboard Cite

show abstract

“…In this paper, we use Q value-based Dynamic Programming (DP) [4], [5] because of its efficiency in the dynamic environments. In Q value-based DP approach, Q value is defined for the pair of adjacent intersections and the optimal route is determined by solving the following simultaneous nonlinear equation iteratively,…”

Section: Procedures 1 Of the Proposed Methodsmentioning

confidence: 99%

Algorithm for Route Planning with Multiple Intermediate Destinations

Mainali

Mabu

Hirasawa

2011

SICE Journal of Control, Measurement, and System Integration

Self Cite

View full text Add to dashboard Cite

: The optimal route recommendation in navigation systems is often considered to be the optimal route recommendation between two locations, i.e., an origin and a destination. However, in practical scenarios, traveling to several intermediate destinations before the final destination needs to be considered. Conventional route search algorithms cannot consider such restrictions in the route search. In this paper, a method to find the optimal route via several intermediate destinations is proposed. The proposed method is divided into three steps. In the first step, the conventional route search algorithm is used to find the optimal traveling time and optimal route among the origin, intermediate destinations and final destination. In the second step, the visiting order of the intermediate destination is optimized using the population based RasID-D (RasID-DP) to minimize the total traveling time. Finally, the optimal route from the origin to destination is determined based on the results of the previous steps. The proposed method is evaluated based on the efficiency of the optimization of the visiting order of intermediate destinations. Simulation results show that RasID-DP based optimization can find better solutions efficiently.

show abstract

“…The software was originally developed by the German Aerospace Center in 2000. Because of its perfect function and open source features, it has been widely used in the field of intelligent transportation research [23]. SUMO is well suited for traffic simulation and supports the analysis and evaluation of traffic related algorithms.…”

Section: Experiments Frameworkmentioning

confidence: 99%

Application of Angle Related Cost Function Optimization for Dynamic Path Planning Algorithm

et al. 2018

View full text Add to dashboard Cite

Abstract:In recent years, Intelligent Transportation Systems (ITS) have developed a lot. More and more sensors and communication technologies (e.g., cloud computing) are being integrated into cars, which opens up a new design space for vehicular-based applications. In this paper, we present the Spatial Optimized Dynamic Path Planning algorithm. Our contributions are, firstly, to enhance the effective of loading mechanism for road maps by dividing the connected sub-net, and building a spatial index; and secondly, to enhance the effect of the dynamic path planning by optimizing the search direction. We use the real road network and real-time traffic flow data of Karamay city to simulate the effect of our algorithm. Experiments show that our Spatial Optimized Dynamic Path Planning algorithm can significantly reduce the time complexity, and is better suited for use as a real-time navigation system. The algorithm can achieve superior real-time performance and obtain the optimal solution in dynamic path planning.

show abstract

Dynamic optimal route search algorithm for car navigation systems with preferences by dynamic programming

Cited by 14 publications

References 16 publications

Constructing an Environmental Friendly Low-Carbon-Emission Intelligent Transportation System Based on Big Data and Machine Learning Methods

Constructing an Environmental Friendly Low-Carbon-Emission Intelligent Transportation System Based on Big Data and Machine Learning Methods

Algorithm for Route Planning with Multiple Intermediate Destinations

Application of Angle Related Cost Function Optimization for Dynamic Path Planning Algorithm

Contact Info

Product

Resources

About