• Journal of Institute of Control, Robotics and Systems
• /
• v.8 no.6
• /
• pp.468-476
• /
• 2002

The confidence of information from image processing depends on the original image quality. Enhancing the confidence by an algorithm has an essential limitation. Especially, road images are exposed to lots of noisy sources, which makes image processing difficult. We, in this paper, propose a FNN (fuzzy-neural network) capable oi deciding the quality of a road image prior to extracting lane-related information. According to the decision by the FNN, road images are classified into good or bad to extract lane-related information. A CDF (cumulative distribution function), a function of edge histogram, is utilized to construct input parameters of the FNN, it is based on the fact that the shape of the CDF and the image quality has large correlation. Input pattern vector to the FNN consists of ten parameters in which nine parameters are from the CDF and the other one is from intensity distribution of raw image. Correlation analysis shows that each parameter represents the image quality well. According to the experimental results, the proposed FNN system was quite successful. We carried out simulations with real images taken by various lighting and weather conditions and achieved about 99% successful decision-making.

• Journal of Korean Society of Transportation
• /
• v.22 no.4 s.75
• /
• pp.135-146
• /
• 2004

For improving the effectiveness of travel information, some rational paths are needed to provide them to users driving in real road network. To meet it, k-shortest path algorithms have been used in general. Although the k-shortest path algorithm can provide several alternative paths, it has inherent limit of heavy overlapping among derived paths, which nay lead to incorrect travel information to the users. In case of considering the network consisting of several turn prohibitions popularly adopted in real world network, it makes difficult for the traditional network optimization technique to deal with. Banned and penalized turns are not described appropriately for in the standard node/link method of network definition with intersections represented by nodes only. Such problem could be solved by expansion technique adding extra links and nodes to the network for describing turn penalties, but this method could not apply to large networks as well as dynamic case due to its overwhelming additional works. This paper proposes a link-based shortest path algorithm for the travel information in real road network where exists turn prohibitions. It enables to provide efficient alternative paths under consideration of overlaps among paths. The algorithm builds each path based on the degree of overlapping between each path and stops building new path when the degree of overlapping ratio exceeds its criterion. Because proposed algorithm builds the shortest path based on the link-end cost instead or node cost and constructs path between origin and destination by link connection, the network expansion does not require. Thus it is possible to save the time or network modification and of computer running. Some numerical examples are used for test of the model proposed in the paper.

• The Transactions of the Korea Information Processing Society
• /
• v.13 no.6
• /
• pp.269-277
• /
• 2024

With the advancement of modern IT technologies, the operation of autonomous vehicles is becoming a reality, and route planning is essential for this. Generally, route planning involves proposing the shortest path to minimize travel distance and the quickest path to minimize travel time. However, the quality of these routes depends on the topological characteristics of the road network graph. If the connectivity structure of the road network is not rational, there are limits to the performance improvement that routing algorithms can achieve. Real drivers consider psychological factors such as the number of turns, surrounding environment, traffic congestion, and road quality when choosing routes, and they particularly prefer routes with fewer turns. This paper introduces a simple path algorithm that seeks routes with the fewest turns, in addition to the traditional shortest distance and quickest time routes, to evaluate the characteristics of road networks. Using this simple path algorithm, we compare and evaluate the connectivity characteristics of road networks in 20 major cities worldwide. By analyzing these road network characteristics, we can identify the strengths and weaknesses of urban road networks and develop more efficient and safer route planning algorithms. This paper comprehensively examines the quality of road networks and the efficiency of route planning by analyzing and comparing the road network characteristics of each city using the proposed simple path algorithm.

• Journal of KIISE:Databases
• /
• v.35 no.5
• /
• pp.447-458
• /
• 2008

In this paper, we address an efficient processing scheme for k-nearest neighbor queries to retrieve k static objects in road network databases. Existing methods cannot expect a query processing speed-up by index structures in road network databases, since it is impossible to build an index by the network distance, which cannot meet the triangular inequality requirement, essential for index creation, but only possible in a totally ordered set. Thus, these previous methods suffer from a serious performance degradation in query processing. Another method using pre-computed network distances also suffers from a serious storage overhead to maintain a huge amount of pre-computed network distances. To solve these performance and storage problems at the same time, this paper proposes a novel approach that creates an index for moving objects by approximating their network distances and efficiently processes k-nearest neighbor queries by means of the approximate index. For this approach, we proposed a systematic way of mapping each moving object on a road network into the corresponding absolute position in the m-dimensional space. To meet the triangular inequality this paper proposes a new notion of average network distance, and uses FastMap to map moving objects to their corresponding points in the m-dimensional space. After then, we present an approximate indexing algorithm to build an R*-tree, a multidimensional index, on the m-dimensional points of moving objects. The proposed scheme presents a query processing algorithm capable of efficiently evaluating k-nearest neighbor queries by finding k-nearest points (i.e., k-nearest moving objects) from the m-dimensional index. Finally, a variety of extensive experiments verifies the performance enhancement of the proposed approach by performing especially for the real-life road network databases.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.21 no.4
• /
• pp.331-339
• /
• 2003

It is difficult for the road management system to give a correct road information because road facilities are differently managed according to each object. In this study, we have solved this problem of road information management and, developed a system which is able to integrate various data of facilities and to maintain the latest property of data by introducing server-client network structure for managing road facilities more efficiently. And, we have shown the affairs of the road information management could be achieved scientifically, by the integration of graphic, attribute and photograph information relevant to road. This enabled the connection of graphic data and the stereo drawing composition, and enhanced the feeling of real world experience using the dynamic image data of the road.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.22 no.1
• /
• pp.265-275
• /
• 2023

The purpose of this paper is to devise an optimal route allocation algorithm for automated vehicle(AV) in monitoring quality of road infrastructure to support the road safety. The tasks of an AV in this paper include visiting node-links at least once during its operation and checking status of road infrastructure, and coming back to its depot.. In selecting optimal route, its priority goal is visiting the node-links with higher risks while reducing costs caused by operation. To deal with the problem, authors devised reward maximizing algorithm for AVs. To check its validity, the authors developed simple toy network that mimic node-link networks and assigned costs and rewards for each node-link. With the toy network, the reward maximizing algorithm worked well as it visited the node-link with higher risks earlier then chinese postman route algorithm (Eiselt, Gendreau, Laporte, 1995). For further research, the reward maximizing algorithm should be tested its validity in a more complex network that mimic the real-life.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.21 no.1
• /
• pp.273-286
• /
• 2022

An HD map is essential in the automated driving of level 4 and above to generate the vehicle's global path since it contains road information and each road's lane information. Therefore, all the road elements in the HD map must be correctly defined to construct the correct road network necessary to generate the global path. But unfortunately, it is not difficult to find various errors even in the most recent HD maps. Hence, a preprocessing algorithm has been developed to detect and correct errors in the HD map. This error detection and correction result in constructing the correct road network for use in global path planning. Furthermore, the algorithm was tested on real roads' HD maps, demonstrating its validity.

• Journal of Korean Society of Transportation
• /
• v.19 no.6
• /
• pp.171-182
• /
• 2001

Travel Time is an important characteristic of traffic conditions in a road network. Currently, there are so many road users to get a unsatisfactory traffic information that is provided by existing collection systems such as, Detector, Probe car, CCTV and Anecdotal Report. This paper presents the results achieved with Data Fusion Model, Hybrid Neuro Fuzzy System for on - line estimation of travel times using RTMS(Remote Traffic Microwave Sensor) and Probe Data in the signalized arterial road. Data Fusion is the most important process to compose the various of data which can present real value for traffic situation and is also the one of the major process part in the TIC(Traffic Information Center) for analyzing and processing data. On-line travel time estimation methods(FALEM) on the basis of detector data has been evaluated by real value under KangNam Test Area.

• Journal of Cadastre & Land InformatiX
• /
• v.51 no.1
• /
• pp.97-110
• /
• 2021

This study estimated the delay factor, which is the ratio of travel time at the speed limit and travel time at the actual speed using real-time traffic information in Seoul. The actual travel speed on the road was lower than the maximum speed of the road and the travel speed was the slowest during the rush hour. As a result of accessibility analysis based on travel speed during the rush hour, the travel time at the actual speed was 37.49 minutes on average. However, the travel time at the speed limit was 15.70 minutes on average. This result indicated that the travel time at the actual speed is 2.4 times longer than that at the speed limit. In addition, this study proposedly defined the delay factor as the ratio of accessibility by the speed limit and accessibility to actual travel speed. As a result of delay factor analysis, the delay factor of Seoul was 2.44. The results by the administrative district showed that the delay factor in the north part areas of the Han River is higher than her south part areas. Analysis results after applying the relationship between road density and traffic volume showed that as the traffic volume with road density increased, the delay factor decreased. These results indicated that it could not be said that heavy traffic caused longer travel time. Therefore, follow-up research is needed based on more detailed information such as road system shape, road width, and signal system for finding the exact cause of increased travel time.

• Journal of Korean Society of Transportation
• /
• v.27 no.6
• /
• pp.139-146
• /
• 2009

While air pollutants emission caused by the traffic is one of the major sources, few researches have done. This study investigated the extent to which traffic and road related characteristics such as traffic volumes, speeds and road weather data including wind speed, temperature and humidity, as well as the road geometry affect the air pollutant emission. We collected the real time air pollutant emission data from Seoul automatic stations and real time traffic volume counts as well as the road geometry. The regression air pollutant emission models were estimated. The results show followings. First, the more traffic volume increase, the more pollutant emission increase. The more vehicle speed increase, the more measurement quantity of pollutant decrease. Secondly, as the wind speed, temperature, and humidity increase, the amount of air pollutant is likely to decrease. Thirdly, the figure of intersections affects air pollutant emission. To verify the estimated models, we compared the estimates of the air pollutant emission with the real emission data. The result show the estimated results of Chunggae 4 station has the most reliable data compared with the others. This study is differentiated in the way the model used the real time air pollutant emission data and real time traffic data as well as the road geometry to explain the effects of the traffic and road characteristics on air quality.