• The Journal of Information Technology and Database
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• v.7 no.1
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• pp.42-53
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• 2000

The major goal of this research is to develop an optimal route search algorithm for an intelligent route guidance system, one sub-area of ITS. ITS stands for intelligent Transportation System. ITS offers a fundamental solution to various issues concerning transportation and it will eventually help comfortable and swift moves of drivers by receiving and transmitting information on humans, roads and automobiles. Genetic algorithm, and fuzzy logic are utilized in order to implement the proposed algorithm. Using genetic algorithm, the proposed algorithm searches shortest routes in terms of travel time in consideration of stochastic traffic volume, diverse turn constraints, etc. Then using fuzzy logic, it selects driver-preference optimal route among the candidate routes searched by GA, taking into account various driver's preferences such as difficulty degree of driving and surrounding scenery of road, etc. In order to evaluate this algorithm, a virtual road-traffic network DB with various road attributes is simulated, where the suggested algorithm promptly produces the best route for a driver with reference to his or her preferences.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.24.2-24
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• 2001

In this paper, the authors present a row dynamic route planning by Q-learning. The proposed algorithm is executed in a cellular automation based traffic simulator, which is also newly created. In Vehicle Information and Communication System(VICS), which is an active field of Intelligent Transport System(ITS), information of traffic congestion is sent to each vehicle at real time. However, a centralized navigation system is not realistic to guide millions of vehicles in a megalopolis. Autonomous distributed systems should be more flexible and scalable, and also have a chance to focus on each vehicles demand. In such systems, each vehicle can search an own optimal route. We employ Q-learning of the reinforcement learning method to search an optimal or sub-optimal route, in which route drivers can avoid traffic congestions. We find some applications of the reinforcement learning in the "static" environment, but there are ...

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.1
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• pp.115-122
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• 2014

Mobile robot has been studied for long time due to its simple structure and easy modeling. Regarding path planning of the mobile robot, we suggest real-time hybrid path planning algorithm which is the combination of optimal path planning and real-time path planning in this paper. Real-time hybrid path planning algorithm modifies, finds best route, and saves calculating time. It firstly plan the route with real-time path planning then robot starts to move according to the planned route. While robot is moving, update the route as the best outcome which found by optimal path planning algorithm. Verifying the performance of the proposed method through the comparing real-time hybrid path planning with optimal path planning will be done.

• Journal of Korean Society of Transportation
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• v.23 no.7 s.85
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• pp.99-110
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• 2005

Total travel cost of route which connects origin with destination (O-D) is consist of the total sum of link travel cost and route perception cost. If the link perception cost is different according to the origin and destination, optimal route search has limitation to reflect the actual condition by route enumeration problem. The purpose of this study is to propose optimal route searching formulation and algorithm which is enable to reflect different link perception cost by each route, not only avoid the enumeration problem between origin and destination. This method defines minimum unit of route as a link and finally compares routes using link unit costs. The proposed method considers the perception travel cost at both origin and destination in optimal route searching process, while conventional models refect the perception cost only at origin. However this two-way searching algorithm is still not able to guarantee optimum solution. To overcome this problem, this study proposed an orign based optimal route searching method which was developed based on destination based optimal perception route tree. This study investigates whether proposed numerical formulas and algorithms are able to reflect route perception behavior reflected the feature of origin and destination in a real traffic network by the example research including the diversity of route information for the surrounding area and the perception cost for the road hierarchy.

• Journal of Navigation and Port Research
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• v.43 no.6
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• pp.413-419
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• 2019

Traffic routes typically have heavy traffic. Especially, the entrance of the route has a high risk of accidents occurring because of ships entering and exiting the port. However, almost of studies have focused on the distribution of traffic on the route. Thus, studies on the distribution between ships for passing through the route are insufficient. The purpose of this study was to analysis the traffic in the Busan north port No.1 route for one week. Based on present traffic conditions, one gate line was settled on the route with an analysis of traffic conditions. Based on the analysis data, each optimal time probability distribution between ships was divided into inbound/outbound and traffic volume. An analysis of the optimal probability distribution, was applied to 31 probability distributions divided into bounded, unbounded, non-negative, and advanced probability distribution. The KS test was applied for identifying three major optimal time probability distributions. According to the KS test results, the Wakeby distribution is the best optimal time probability distribution on the designated route. Although the optimal time probability distribution for other transportation studies such as on vehicles on highways is a non-negative probability distribution, this distribution is an advanced probability distribution. Thus, the application of major probability distribution for using other transportation studies is not applicable to this study Additionally, the distance between ships in actual traffic surveys and the distance estimated by the optimal probability distribution were compared. As a result of the comparison, those distances were fairly similar. However, this study was conducted in only one major port. Thus, it is necessary to investigate the time between ships and calculate a traffic volume on varying routes in future studies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.647-669
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• 2016

Multiple attribute for decision making including user preference will increase the complexity of route selection process. Various approaches have been proposed to solve the optimal route selection problem. In this paper, multi attribute decision making (MADM) algorithms such as Simple Additive Weighting (SAW), Weighted Product Method (WPM), Analytic Hierarchy Process (AHP) method and Total Order Preference by Similarity to the Ideal Solution (TOPSIS) methods have been proposed for acoustic signature based optimal route selection to facilitate user with better quality of service. The traffic density state conditions (very low, low, below medium, medium, above medium, high and very high) on the road segment is the occurrence and mixture weightings of traffic noise signals (Tyre, Engine, Air Turbulence, Exhaust, and Honks etc) is considered as one of the attribute in decision making process. The short-term spectral envelope features of the cumulative acoustic signals are extracted using Mel-Frequency Cepstral Coefficients (MFCC) and Adaptive Neuro-Fuzzy Classifier (ANFC) is used to model seven traffic density states. Simple point method and AHP has been used for calculation of weights of decision parameters. Numerical results show that WPM, AHP and TOPSIS provide similar performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.400-403
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• 2021

Algorithms for predicting the optimal route of vehicles are being actively sudied with the recent development of autonomous driving technology. Companies such as SK, Kakao, and Naver provide services that navigate the optimal route. They predicts the optimal path with information from the users in real time. However, they can predict the optimal route, but not optimal lane route. We proposes a system that navigates the optimal lane path with coordinates data from vehicles using Lidar sensor. The proposed method is a system that guides smooth lanes by acquiring time series coordinate data of a vehicle after performing the Lidar-based object detection method. we demonstrates the performance using actual acquired data from the experimental results.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.33-40
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• 2021

The traveling salesman problem (TSP) is one of the well-known and extensively studied NPC problems in combinatorial optimization. To solve it effectively and efficiently, various optimization algorithms have been developed by scientists and researchers. However, most optimization algorithms are designed based on the concept of improving route in the iterative improvement process so that the optimal solution can be finally found. In contrast, there have been relatively few algorithms to find the optimal solution using route construction mechanism. In this paper, we propose a route construction optimization algorithm to solve the symmetric TSP with the help of ratio value. The proposed algorithm starts with a set of sub-routes consisting of three cities, and then each good sub-route is enhanced step by step on both ends until feasible routes are formed. Before each subsequent expansion, a ratio value is adopted such that the good routes are retained. The experiments are conducted on a collection of benchmark symmetric TSP datasets to evaluate the algorithm. The experimental results demonstrate that the proposed algorithm produces the best-known optimal results in some cases, and performs better than some other route construction optimization algorithms in many symmetric TSP datasets.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2019.05a
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• pp.38-39
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• 2019

Optimal route planning is the route planning to minimize voyage time or fuel consumption in a given ocean environment. Unlike the previous studies on weather routing, this study proposes an optimization method for the route planning to avoid the grounding risk in the coast. The route way-points were searched using Dijkstra algorithm, and then the optimization was performed to minimize fuel consumption by setting the optimization design parameter to the engine rpm. To set the engine rpm, a method to use the fixed rpm from the departure point to the destination point, and a method to use the rpm for each section by dividing the route were used. The ocean environmental factors considered for route planning were wind, wave, and current, and the depth information was utilized to compute grounding risk. The proposed method was applied to the ship passing between Mokpo and Jeju, and then it was confirmed that fuel consumption was reduced by comparing the optimum route and the past navigated route.

• Journal of Information Processing Systems
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• v.19 no.1
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• pp.46-54
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• 2023

To improve the railway transportation capacity and maximize the benefits of railway transportation, a method for layout optimization of railway transportation route based on deep convolution neural network is proposed in this study. Considering the transportation cost of railway transportation and other factors, the layout model of railway transportation route is constructed. Based on improved ant colony algorithm, the layout model of railway transportation route was optimized, and multiple candidate railway transportation routes were output. Taking into account external information such as regional information, weather conditions and actual information of railway transportation routes, optimization of the candidate railway transportation routes obtained by the improved ant colony algorithm was performed based on deep convolution neural network, and the optimal railway transportation routes were output, and finally layout optimization of railway transportation routes was realized. The experimental results show that the proposed method can obtain the optimal railway transportation route, the shortest transportation length, and the least transportation time, maximizing the interests of railway transportation enterprises.