• Journal of Korean Society of Transportation
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• v.24 no.2 s.88
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• pp.157-168
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• 2006

Spatial detection system such as AVI, GPS, and Beacon etc. can provide spatial travel time only after a vehicle Passes through a road section. In this context, majority of the existing studies on the link travel time estimation area has focused on the arrival time-based link travel time estimation. rather than departure time-based link travel time estimation. Even if some of the researches on this area have developed departure time-based link travel time estimation algorithms, they are limited in that they are not applicable in a real-time mode. The objective of this study is to develop an departure time-based link travel time estimation algorithm which is applicable in a real-tine mode. Firstly, this study discussed the tradeoff between accuracy and timeliness of the departure time-based on-line link travel time estimates. Secondly, this study developed an departure time-based on-line link travel time estimation algorithm which utilizes the Baysian inference logic. It was found that the proposed approach could estimate departure time-based link travel times in a real-time context with an acceptable accuracy and timeliness.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.1
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• pp.1-16
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• 2018

Changes in the life style might vary trip purposes, ultimately leading to the change in the travel behavior. Therefore, this study analyzed the factors affecting travel time change by using the time use survey data in Seoul, surveyed by the Statistics Korea in 1999~2014. We developed multiple linear regression models for travel time, considering individual, household and time-related variables as independent variables. The models were separately estimated weekday and weekend. the model results show that the household, individual, and time related variables have an significant effect on the travel time. In addition, travel time is more influenced by individual characteristics thn household ones. Each activity time positively affects the travel time, indicating that travel is derived demand. The variable that have the greatest influence on the travel time is the activity time for leisure.

• Proceedings of the KOR-KST Conference
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• 1998.09a
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• pp.160-167
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• 1998

Recent world-wide interest in activity-based travel behavior modeling has generated an entirely new perspective on how the profession views the travel demand process. This paper seeks to further promote the case of activity-based travel behavior models by providing some empirical evidence of relationship between travel time and activity duration decision for worker with transportation panel data. The travel time from home to work and from work to home, without activity involvement, is estimated by the Ordinary Least Squares (OLS) method. And, the travel time to and from the selected activity and the activity duration are modeled simultaneously by the Three Stage Least Squares (3SLS) method due to the endogenous relationship between travel time and activity duration. Two kinds of models, OLS and 3SLS, include selectivity bias corrections in a discrete/continuous framework, because of the inter-relationship between the choice of activity type/travel mode (discrete) and the travel time/activity duration (continuous). Estimation is undertaken using a sample of over 1300 household two-day trip diaries collected from the same travelers in the Seattle area in 1989. The behavioral consequences of these models provide interesting and provocative findings that should be of value to transportation policy formulation and analysis.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.6
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• pp.1-13
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• 2015

Travellers consider extra travel time to be arriving their destination because of uncertainty of travel. So it is important to make predictable highway by providing information of travel time variability to traveller so as to enhance level of service at highway. In order to make predictable highway, it is necessary to develope measures of travel time variability that travellers can easily understand. Recently advanced country including the United States, travel time variability index are actively studied. In earlier study, 95percentile of travel time is considered to be most important calculation index of travel time variability. In this study, is has focused on the propriety analysis of 95percentile of travel time in domestic transportation environment. Result of analysis, All of measures(80percentile of travel time, 90percentile of travel time, 95percentile of travel time) show the tendency to increase when case of weather factor occur compare to normal condition under LOS A~D. Especially 95percentile of travel time increased sensitively.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4D
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• pp.559-564
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• 2006

The purpose of this study is to minimize negative effect of VMS travel time information service by sensitivity analysis, which forecasts the change in link traffic volume. As a result, strategies for providing travel information that can change driving patterns for minimizing travel time were found. The framework for analysis is recently expanded with the application of game theory. According to the experiment, the algorithm generated for travel time information service reduces total travel time and yields travel patterns that is very close to the system optimization. Also, this study found that the route the travel time service information is provided about could play the important role.

• Journal of Korean Society of Transportation
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• v.26 no.5
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• pp.217-226
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• 2008

The term, travel-time reliability, refers to variations in journey time that travelers cannot predict. The purpose of this paper is to suggest a standard way to measure travel time reliability. A modified buffer time indicator is proposed. The index is represented by the difference between planned and actual travel times based on lognormal type travel time distribution. Using this framework, a constant function for railways and a negative parabola function for roads are discussed. The model developed is applied to the real data of Korean road and rail usages to empirically verify the methodology proposed. In this process, the unit value of travel time reliability for each group is estimated. The result of this research is expected to be helpful of conducting more cautious economic feasibility studies of transport.

• Journal of the Korean Institute of Navigation
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• v.18 no.4
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• pp.137-146
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• 1994

In this paper, a new algorithm to measure the ultrasonic travel time is proposed, which is fundamental to estimate distance depth and volume in several media. Pulse wave has been used to measure travel time of transmitted signal. However, due to the characteristic of transducer and propagation, the received signal is so distorted that it is difficult to measure travel time, which is propagation, the received signal is so distorted that it is difficult to measure travel time, which is to be time difference between transmitted and received signals. In this proposed method, transmitted and received signal are transformed respectively into norm phase newly designed by this paper and displayed on phase-time curve. And travel time is simply determined by the arithmetic numerical mean of time difference at the identical norm phase on the phase-time curves of transmitted and received signals. This method has several features; firstly, travel time is calculated analytically with high accuracy by least square error method, secondly, it is useful to compare the difference of signal magnitude for time information, thirdly, noise and discrete errors are relatively small, finally, the measurement accuracy is not influenced by D.C. bias. In particular, this method is useful and applicable to measuring very short distance and sound speed with high accuracy.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.1
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• pp.56-73
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• 2019

Probe-based systems have been gaining popularity in advanced traveler information systems. However, the high possibility of providing inaccurate travel-time information due to the inherent time-lag phenomenon is still an important issue to be resolved. To mitigate the time-lag problem, different prediction techniques have been applied, but the techniques are generally regarded as less effective for travel times with high variability. For this reason, current 5-min aggregated data have been commonly used for real-time travel-time provision on highways with high travel-time fluctuation. However, the 5-min aggregation interval itself can further increase the time-lags in the real-time travel-time information equivalent to 5 minutes. In this study, a new scheme that uses both individual probe and 5-min aggregated travel times is suggested to provide reliable real-time travel-time information. The scheme utilizes individual probe data under congested conditions and 5-min aggregated data under uncongested conditions, respectively. As a result of an evaluation with field data, the proposed scheme showed the best performance, with a maximum reduction in travel-time error of 18%.

• Journal of Korean Society of Transportation
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• v.13 no.4
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• pp.5-29
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• 1995

Advanced Traveler Inoformation Systems*ATIS) , as a subsystem of ITS influence the travel choices of dreivers by providing them with historical, real-time and predictive information to supprot travel decisions and consequently improves the speed and quality of travel. For thesuccessul accomplishment of ATIS, the time-dependent variations of traffic in a road network and travel times of vehicles during their journey must be predicted . The purpose of this study is to evaluate the past developments in the dynamic route choice models and to apply the instantaneous dynamic user optimal route choice model. recently formulated with flow propagation constraints by Ran, Boyce and LeBlanc, to the real transportation network of Seocho-Ku in Seoul. As input data for this application, the time-dependent travel rates are estimated and the link travel time function is derived. The modelis validated from three view points : the efficiency of model itself the ability to predict traffic volume and travel time on links, and the optimal traffic control.

• Journal of Korean Society of Transportation
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• v.35 no.4
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• pp.348-359
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• 2017

Recently, various public transportation activation policies are being implemented in order to mitigate traffic congestion in metropolitan areas. Especially in the metropolitan area, the bus information system has been introduced to provide information on the current location of the bus and the estimated arrival time. However, it is difficult to predict the travel time due to repetitive traffic congestion in buses passing through complex urban areas due to repetitive traffic congestion and bus bunching. The previous bus travel time study has difficulties in providing information on route travel time of bus users and information on long-term travel time due to short-term travel time prediction based on the data-driven method. In this study, the path based long-term bus travel time prediction methodology is studied. For this purpose, the training data is composed of 2015 bus travel information and the 2016 data are composed of verification data. We analyze bus travel information and factors affecting bus travel time were classified into departure time, day of week, and weather factors. These factors were used into clusters with similar patterns using self organizing map. Based on the derived clusters, the reference table for bus travel time by day and departure time for sunny and rainy days were constructed. The accuracy of bus travel time derived from this study was verified using the verification data. It is expected that the prediction algorithm of this paper could overcome the limitation of the existing intuitive and empirical approach, and it is possible to improve bus user satisfaction and to establish flexible public transportation policy by improving prediction accuracy.