• Journal of Korean Society of Transportation
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• v.33 no.3
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• pp.315-321
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• 2015

ATMS calculates section travel time using two-way communication system called DSRC(Dedicated Short Range Communications) which collects data of RSE (Road Side Equipment) and Hi-pass OBU (On-board Unit). Travel time estimation in urban area involves uncertainty due to the interrupted flow. This study not only analyzed real-time data but also considered pattern data. Baek-Je-Ro street in Jeon-Ju city was selected as a test site. Existing algorithm was utilized for data filtering and pattern data building. Analysis results repoted that travel time estimation with 20% of real-time data and 80% of pattern data mixture gave minimum average difference of 37.5 seconds compare to the real travel time at the 5% significant level. Results of this study recommend usage of intermixture between real time data and pattern data to minimize error for travel time estimation in urban area.

• 한국ITS학회:학술대회논문집
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• v.2006 no.10
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• pp.166-170
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• 2006

• Journal of Korean Society of Transportation
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• v.23 no.6 s.84
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• pp.19-30
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• 2005

The objective of this research is to keep track of path travel time using methods of collecting traffic data. Users of traffic information are looking for extensive information on path travel time, which is referred to as the time taken for traveling from the origin to the destination. However, all the information available is the average path travel times, which is a simple sum of the average link travel times. The average path travel time services are not up to the expectation of traffic information consumers. To improve provide more accurate path travel time services, this research makes a number of different estimates of various path travel times on one path, assuming it will be under the same condition, and provides a range of estimates with their probabilities to the consumers, who are looking for detailed information. To estimate the distribution of the path travel times as a combination of link travel times. this research analyzes the relation between the link travel time and path travel time. Based on the result of the estimation. this research develops the algorithm that combines the distribution of link travel time and estimates the path travel time based on the link travel times. This algorithm was tested and proven to be highly reliable for estimating the path traffic time.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.3 no.1 s.4
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• pp.31-44
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• 2004

The study of Estimation model for the short-term travel time prediction. There is a different solution which has predicted the link travel time to solve this problem. By using this solution, the link travel time is predicted based on link conditions from time to time. The predicated link travel time is used to search the shortest path. Before providing a dynamic shortest path finding, the prediction model should be verified. To verify the prediction model, three models such as Kalman filtering, Stochastic Process, ARIMA. The ARIMA model should adjust optimal parameters according to the traffic conditions. It requires a frequent adjustment process of finding optimal parameters. As a result of these characteristics, It is difficult to use the ARIMA model as a prediction. Kalman Filtering model has a distinguished prediction capability. It is due to the modification of travel time predictive errors in the gaining matrix. As a result of these characteristics, the Kalman Filtering model is likely to have a non-accumulative errors in prediction. Stochastic Process model uses the historical patterns of travel time conditions on links. It if favorably comparable with the other models in the sense of the recurrent travel time condition prediction. As a result, for the travel time estimation, Kalman filtering model is the better estimation model for the short-term estimation, stochastic process is the better for the long-term estimation.

• International Journal of Highway Engineering
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• v.15 no.4
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• pp.155-165
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• 2013

PURPOSES : Benefits for improvement of travel time reliability obtained from construction of new highways should be considered as a major factor in the feasibility study for highway constructions. The purpose of this study is to develop a method of estimation for the value of travel time reliability. METHODS : Highway type (urban/rural highway) and traffic flow type(interrupted/uninterrupted) was considered to estimate he value of travel time reliability. And Double-bounded Dichotomous Choice among Contingent Valuation Method(CVM) was applied to survey the willingness-to-pay of drivers when travel time reliability is improved. Finally the value of travel time reliability was estimated using the results of survey and logit model. The value of travel time reliability was estimated considering travel objectives, time constraint travel and non-time constraint travel. RESULTS: The value of travel time reliability of business trip is higher than that of non-business trip. The value of travel time reliability of time constraint travel is higher than that of non-time constraint travel. The value of travel time reliability in urban area is higher than that in rural area. CONCLUSIONS: It was concluded that the proposed method in this study is more realistic and proper to estimate the value of travel time reliability because it reflects the situations of time constraint travel and non-time constraint travel.

• Journal of the Korean Operations Research and Management Science Society
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• v.26 no.1
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• pp.97-108
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• 2001

Design and evaluation of AGV-based material handling systems are very complicated due to the randomness and the large number of variables involved Vehicle travel time is a key parameter for designing and evaluating AGV systems. Although loaded travel time is relatively easy to estimate, determination of empty vehicle travel time is difficult due to the inherent randomness of material handling systems. Most previous studies assume that the empty vehicle travel time is the same as the loaded travel time or assume very specific environments. This paper presents new vehicle travel time models for AGV-based material transport systems. The research effort is focused on the estimation of empty vehicle travel time under various vehicle dispatching policies. Simulation experiments are used to verify the proposed travel time models.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.1-13
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• 2020

In the event of fire, it is necessary to put out the fire within a golden time to minimize personal and property damages. To this end, it is necessary for fire engines to arrive at the site quickly. This study established a fire engine travel time estimation model to secure the golden time by identifying road and environmental factors that influence fire engine travel time in the case of fire by examining data on fire occurrence with GIS DB. The study model for the estimation of fire engine travel time (model 1) covers variables by applying correlation analysis and regression analysis with dummy variables and predicts travel time for different types of places where fire may occur (models 2, 3, 4). Analysis results showed that 17 siginificant independent variables are derived in model 1 and the fire engine travel time differs depending on the types of places where fire occurs. Key variables(travel distance, number of lane, type of road) that are included commonly in the 4 models were identified. Variables identified in this study can be utilized as indicators for research related to travel time of emergency vehicles and contribute to securing the golden time for emergency vehicles.

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

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.4
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• pp.537-545
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• 2018

Determination of the time parameters such as the travel time in the design flood is very important. The travel time is mainly used for flood and river management, and the travel time of non flood season is used for maintenance flow and management of the river. Estimation of travel time for natural rivers is mainly based on the geomorphological factors of the basin. In addition to the topographical factors, the travel time is calculated by considering the factors of the runoff curve, velocity and rainfall intensity. However, there is no study on the estimation of travel time considering both the rainfall condition and the soil moisture accounting by the frequency period. Therefore, the travel time calculation is divided into the case of setting the Hwanggang Dam and the Imjin bridge water level station of Imjin river as the natural river considering rainfall condition by the frequency period and the soil moisture accounting, and the case of traveling the Imjin bridge water level station according to the condition of outflow of the Hwanggang Dam. For the sections set as natural rivers, the results were verified by comparing with the newly developed travel time calculation method. Based on the results, the travel times of the Hwanggang Dam outflow conditions were calculated. The time to travel in this study can be secured flood control of the Imjin river basin and time to prepare for danger when outflowing the the Hwanggang Dam.

• Journal of Korean Society of Transportation
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• v.21 no.5
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• pp.83-95
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• 2003

This study aims to develop travel time estimation and prediction models on the freeway using measurements from vehicle detectors. In this study, we established a travel time estimation model using traffic volume which is a principle factor of traffic flow changes by reviewing existing travel time estimation techniques. As a result of goodness of fit test. in the normal traffic condition over 70km/h, RMSEP(Root Mean Square Error Proportion) from travel speed is lower than the proposed model, but the proposed model produce more reliable travel times than the other one in the congestion. Therefore in cases of congestion the model uses the method of calculating the delay time from excess link volumes from the in- and outflow and the vehicle speeds from detectors in the traffic situation at a speed of over 70km/h. We also conducted short term prediction of Kalman Filtering to forecast traffic condition and more accurate travel times using statistical model The results of evaluation showed that the lag time occurred between predicted travel time and estimated travel time but the RMSEP values of predicted travel time to observations are as 1ow as that of estimation.