• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.137-149
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• 2019

Traffic emissions are the main cause of environmental pollution in cities and respiratory problems amongst people. This study developed a model based on an integration of support vector regression (SVR) algorithm and geographic information system (GIS) to map traffic carbon monoxide (CO) concentrations and produce prediction maps from micro level to macro level at a particular time gap in a day in a very densely populated area (Utara-Selatan Expressway-NKVE, Kuala Lumpur, Malaysia). The proposed model comprised two models: the first model was implemented to estimate traffic CO concentrations using the SVR model, and the second model was applied to create prediction maps at different times a day using the GIS approach. The parameters for analysis were collected from field survey and remote sensing data sources such as very-high-resolution aerial photos and light detection and ranging point clouds. The correlation coefficient was 0.97, the mean absolute error was 1.401 ppm and the root mean square error was 2.45 ppm. The proposed models can be effectively implemented as decision-making tools to find a suitable solution for mitigating traffic jams near tollgates, highways and road networks.

• Geomechanics and Engineering
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• v.32 no.1
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• pp.35-47
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• 2023

Deformability of road pavements in the form of ruts represent a safety risk for road users. In the procedures for dimensioning the pavement structure, the requirement that such deformations do not occur is imperatively included, which results in the appropriate selection of elements (material, geometry) of the pavement structure. Deformability and functionality, will depend of the correct design of pavement structure during exploitation period. Nevertheless, there are many examples where deformations are observed on the pavement structure, in the form of rutting at parts of the road with relatively short length, realised in the same climatic and the same geoenvironmental conditions. The performed analysis of deformability led to the conclusion that the level of deformation is a function of the speed of traffic. This effect is observed on city roads, but also outside of urban areas at roads with speed limits are significant, due to the traffic management, traffic jams (intersections, etc.). Still, the lower speed cause greater deformations. The authors tried to describe the deformability of flexible pavement structures, from the aspects of geotechnical problems, as a function of driving speed. Outcome of the analysis is a traffic load correction coefficient, in terms of using the existing methods of flexible pavement structures design.

• Journal of Advanced Navigation Technology
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• v.17 no.1
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• pp.90-97
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• 2013

Recently, intelligent transportation system based on image processing has been developed. In this paper, we propose a collision detection algorithm based on the analysis of motion vectors and the staying periods of vehicles in intersections. Objects in the region of interest are extracted from the subtraction image between background images based on Gaussian mixture model and input images. Collisions and traffic jams are detected by analysing measured motion vectors of vehicles and their staying periods in intersections. Experiments are performed on video sequences actually recoded at intersections. Correct detection rate and false alarm rate are 85.7% and 7.7%, respectively.

• Journal of Advanced Navigation Technology
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• v.3 no.1
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• pp.60-68
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• 1999

In this paper, we propose a new background generation method for image detector for traffic information collection. Conventional methods result in bad performance when there are frequent traffic jams due to heavy traffic. To improve on this, we use high level information from vehicle detection. Only part of the image that is not considered as vehicle is used in background generation. The proposed method finds background more robustly than that of the conventional methods even in the presence of heavy traffic.

• Smart Structures and Systems
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• v.24 no.5
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• pp.617-630
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• 2019

Currently most of the vision-based structural identification research focus either on structural input (vehicle location) estimation or on structural output (structural displacement and strain responses) estimation. The structural condition assessment at global level just with the vision-based structural output cannot give a normalized response irrespective of the type and/or load configurations of the vehicles. Combining the vision-based structural input and the structural output from non-contact sensors overcomes the disadvantage given above, while reducing cost, time, labor force including cable wiring work. In conventional traffic monitoring, sometimes traffic closure is essential for bridge structures, which may cause other severe problems such as traffic jams and accidents. In this study, a completely non-contact structural identification system is proposed, and the system mainly targets the identification of bridge unit influence line (UIL) under operational traffic. Both the structural input (vehicle location information) and output (displacement responses) are obtained by only using cameras and computer vision techniques. Multiple cameras are synchronized by audio signal pattern recognition. The proposed system is verified with a laboratory experiment on a scaled bridge model under a small moving truck load and a field application on a footbridge on campus under a moving golf cart load. The UILs are successfully identified in both bridge cases. The pedestrian loads are also estimated with the extracted UIL and the predicted weights of pedestrians are observed to be in acceptable ranges.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.2
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• pp.321-327
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• 2023

With the development of artificial intelligence, the prediction system has become one of the essential technologies in our lives. Despite the growth of these technologies, traffic congestion at intersections in the 21st century has continued to be a problem. This paper proposes a system that predicts intersection traffic jams using a Convolutional LSTM (Conv-LSTM) algorithm. The proposed system models data obtained by learning traffic information by time zone at the intersection where traffic congestion occurs. Traffic congestion is predicted with traffic volume data recorded over time. Based on the predicted result, the intersection traffic signal is controlled and maintained at a constant traffic volume. Road congestion data was defined using VDS sensors, and each intersection was configured with a Conv-LSTM algorithm-based network system to facilitate traffic.

• Journal of Environmental Science International
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• v.16 no.12
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• pp.1393-1399
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• 2007

This research helps you understand the road traffic noise levels by using a noise map. We have observed the change of the road traffic noise levels around $07:00{\sim}08:30\;and\;22:00{\sim}23:00$ using the noise map in the city. The road traffic noise level is very high both at noon and at night around a beltway and an interchange that is linked with a highway. It seems that the main route of so many vehicles, which are at neighboring cities such as N city and D and H districts and which avoid traffic jams in the city, is the beltway and interchange. The road traffic noise level of a nearby express bus terminal, railroad station, and airport is more than 75 dB at noon and 65 dB at night. The road traffic noise level of G city at night is observed to be more than 55 dB. The noise levels of a residence area and a university are higher than a road with high noise levels when the commuters drive to work. The end of the day exceeds 11 o'clock because of a culture level of development that arouses spare time, eating out, adults' drinking culture, nightlife of the youth, etc. Therefore, the road traffic noise level is high during late night hours, and it exceeds regulatory guidelines(55 dB(A)). It also damages the residence area that is located near the road.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.6
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• pp.430-439
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• 2016

Estimation and analysis of traffic jams plays a vital role in an intelligent transportation system and advances safety in the transportation system as well as mobility and optimization of environmental impact. For these reasons, many researchers currently mainly focus on the brilliant machine learning-based prediction approaches for traffic prediction systems. This paper primarily addresses the analysis and comparison of prediction accuracy between two machine learning algorithms: Naïve Bayes and K-Nearest Neighbor (K-NN). Based on the fact that optimized estimation accuracy of these methods mainly depends on a large amount of recounted data and that they require much time to compute the same function heuristically for each action, we propose an approach that applies multi-threading to these heuristic methods. It is obvious that the greater the amount of historical data, the more processing time is necessary. For a real-time system, operational response time is vital, and the proposed system also focuses on the time complexity cost as well as computational complexity. It is experimentally confirmed that K-NN does much better than Naïve Bayes, not only in prediction accuracy but also in processing time. Multi-threading-based K-NN could compute four times faster than classical K-NN, whereas multi-threading-based Naïve Bayes could process only twice as fast as classical Bayes.

• Journal of Korean Society of Transportation
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• v.25 no.6
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• pp.89-97
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• 2007

The car-dependent traffic system based on highly advanced industrialization and economic growth causes various urban problems including traffic jams, energy consumption, air pollution, noise, car accidents and other issues. Particularly in urban areas, air pollution from motor vehicles is worse than pollution from past industrialization. In this study, therefore, the authors grasped car exhaust reduction effects by using microsimulation and those traffic policies that could make cars flow smoothly, reducing the air pollution in urban areas through analysis on profitability. As a result, the weekday-based car using system has been found most effective as it does not need investment cost. However, this system may be socially unacceptable, as it requires the government to change driver behavior. Therefore, the government needs to first reach a consensus with the citizens regarding this system. This system will also be effective with other alternatives. As a follow-up study, the authors will research citizens' perceived impacts of car exhaust on air pollution through a study on preference and grasp the possibility of applying these study results to real traffic policies.

• Journal of Environmental Science International
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• v.26 no.4
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• pp.467-481
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• 2017

This study sought to determine the changes in weather conditions in urban streets, along with conditions of traffic and roads in urban areas. The variations in weather conditions depending on traffic differed according to distance. First, the temperature difference measured by traffic results is as follows: T1 point $1.03^{\circ}C$, T2 point $1.04^{\circ}C$, T3 point $0.9^{\circ}C$, T4 point $1.01^{\circ}C$, and T5 point $0.31^{\circ}C$. The average difference between the measured temperatures by the point of measurement was $0.86^{\circ}C$. The changes in wind velocity according to traffic volume results of the measurements is T1 point 1.32 m/s, T2 point 0.80 m/s, T3 point 0.29 m/s, T4 point 0.04 m/s, and T5 point 0.09 m/s. The difference between the average wind speeds was 0.51 m/s and traffic jams caused substantial differences in distance. The relative humidity tended to be inversely proportional to temperature. The measurements results ares T1 point 2.29%, T2 point 2.67%, T3 point 2.47%, T4 point 2.16%, and T5 point 0.91% The difference between the average relative humidity was 7.3%. In case of independent sampling T test according to traffic volume, changes in wind velocity and temperature were directly proportional to the level of statistical significance(p<0.01). On the other hand, relative humidity tended to be inversely proportional; however, there was no statistical significance.