• International Journal of Highway Engineering
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• v.16 no.3
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• pp.119-129
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• 2014

PURPOSES : This investigational survey is to observe a proper spatial aggregation method for path travel time estimation using the hi-pass DSRC system. METHODS : The links which connect the nodes of section detectors location are used for path travel time estimation traditionally. It makes some problem such as increasing accumulation errors and processing times. In this background, the new links composition methods for spatial aggregation are considered by using some types of nodes as IC, JC, RSE combination. Path travel times estimated by new aggregation methods are compared with PBM travel times by MAE, MAPE and statistical hypothesis tests. RESULTS : The results of minimum sample size and missing rate for 5 minutes aggregation interval are satisfied except for JC link path travel time in Seoul TG~Kuemho JC. Thus, it was additionally observed for minimum sample size satisfaction. In 15, 30 minutes and 1 hour aggregation intervals, all conditions are satisfied by the minimum sample size criteria. For accuracy test and statistical hypothesis test, it has been proved that RSE, Conzone, IC, JC links have equivalent errors and statistical characteristics. CONCLUSIONS : There are some errors between the PBM and the LBM methods that come from dropping vehicles by rest areas. Consequently, this survey result means each of links compositions are available for the estimation of path travel time when PBM vehicles are missed.

• Journal of Korean Institute of Industrial Engineers
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• v.32 no.1
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• pp.61-73
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• 2006

This paper deals with traveling salesman problem(TSP) with the stochastic travel time. Practically, the travel time between demand points changes according to day and time zone because of traffic interference and jam. Since the almost pervious studies focus on TSP with the deterministic travel time, it is difficult to apply those results to logistics problem directly. But many logistics problems are strongly related with stochastic situation such as stochastic travel time. We need to develop the efficient solution method for the TSP with stochastic travel time. From the previous researches, we know that Q-learning technique gives us to deal with stochastic environment and neural network also enables us to calculate the Q-value of Q-learning algorithm. In this paper, we suggest an algorithm for TSP with the stochastic travel time integrating Q-learning and neural network. And we evaluate the validity of the algorithm through computational experiments. From the simulation results, we conclude that a new route obtained from the suggested algorithm gives relatively more reliable travel time in the logistics situation with stochastic travel time.

• Journal of Korean Society of Transportation
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• v.27 no.5
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• pp.209-221
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• 2009

Travel time estimation under given traffic conditions is important for providing drivers with travel time prediction information. But the present expressway travel time estimation process cannot calculate a reliable travel time. The objective of this study is to estimate the path travel time spent in a through lane between origin tollgates and destination tollgates on an expressway as a prerequisite result to offer reliable prediction information. Useful and abundant toll collection system (TCS) data were used. When estimating the path travel time, the path travel time is estimated combining the link travel time obtained through a preprocessing process. In the case of a lack of TCS data, the TCS travel time for previous intervals is referenced using the linear interpolation method after analyzing the increase pattern for the travel time. When the TCS data are absent over a long-term period, the dynamic travel time using the VDS time space diagram is estimated. The travel time estimated by the model proposed can be validated statistically when compared to the travel time obtained from vehicles traveling the path directly. The results show that the proposed model can be utilized for estimating a reliable travel time for a long-distance path in which there are a variaty of travel times from the same departure time, the intervals are large and the change in the representative travel time is irregular for a short period.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.5
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• pp.631-638
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• 2023

This paper is to introduce methodologies of travel time reliability evaluation using daily traffic volumes. The methodologies include desirable speed concept, the relation between averaged daily speeds and daily traffic volumes, the standardized way in integration of travel time deviations obtained from continuous highway sections. The study began with traffic data collection from a freeway line with long lasting congestion. And then, provided establishment of the relation between averaged daily speeds and daily traffic volumes, and usability of Point estimate method (PEM) to integrate travel time deviations of sections. Based on the relation between averaged daily speeds and daily traffic volumes, it is identified that travel time reliability begins to decrease around LOS (Level of Service) C even before LOS D or F. It may be concluded that travel time reliability could be evaluated based on daily traffic volumes in highway sections using standardized PEM. The methodologies introduced in the paper, could be useful in practicing evaluation of travel time reliability during the works of highway operation or highway planning.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.1D
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• pp.43-50
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• 2011

The monitoring system for link travel speed using taxi probe is one of key sub-systems of ITS. Link travel speed collected by taxi probe has been widely employed for both monitoring the traffic states of urban road network and providing real-time travel time information. When sample size of taxi probe is small and link travel time is longer than a length of time interval to collect travel speed data, and in turn the missing state is inevitable. Under this missing state, link travel speed data is real-timely not collected. This missing state changes from single to multiple time intervals. Existing single interval prediction techniques can not generate multiple future states. For this reason, it is necessary to replace multiple missing states with the estimations generated by multi-interval prediction method. In this study, a multi-interval prediction method to generate the speed estimations of single and multiple future time step is introduced overcoming the shortcomings of short-term techniques. The model is developed based on Non-Parametric Regression (NPR), and outperformed single-interval prediction methods in terms of prediction accuracy in spite of multi-interval prediction scheme.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.86.6-86
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• 2001

For discriminating between water and oil, a novel method is proposed in this paper. As a fundamental result, the temperature dependence of ultrasonic travel time of water and oil measured from 5$^{\circ}C$ to 40$^{\circ}C$ at a step of 5$^{\circ}C$ is found as that the ultrasonic travel time of oil increases with increasing temperature, whereas that of water decreases. The proposed method for discriminating between water and oil is based on the opposite temperature dependence of ultrasonic travel time of water and oil. Besides the advantages of non-invasion and on-line measurement, there are no requirements of measuring the temperature of liquid being detected and obtaining previously a large quantity of database, and furthermore, only two times of measurements are ...

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.1 no.1
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• pp.41-51
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• 2002

As Advanced Traveler Information System(ATIS) is the kernel of the Intelligent Transportation System, it is very important how to manage data from traffic information collectors on a road and have at borough grip of the travel time's change quickly and exactly for doing its part. Link travel time can be obtained by two method. One is measured by area detection systems and the other is estimated by point detection systems. Measured travel time by area detection systems has the limitation for real time information because it Is calculated by the probe which has already passed through the link. Estimated travel time by point detection systems is calculated by the data on the same time of each. section, this is, it use the characteristic of the various cars of each section to estimate travel time. For this reason, it has the difference with real travel time. In this study, Artificial Neural Networks is used for estimating link travel time concerned about the relationship with vehicle detector data and link travel time. The method of estimating link travel time are classified according to the kind of input data and the Absolute value of error between the estimated and the real are distributed within 5$\~$15minute over 90 percent with the result of testing the method using the vehicle detector data and AVI data of Namsan Tunnel $\#$1. It also reduces Time lag of the information offered time and draws late delay generation and dissolution.

• Journal of the Korean Society for Railway
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• v.16 no.5
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• pp.418-429
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• 2013

This study assesses the appropriate amounts and applicable ranges of travel time values for freight transport modes in Korea. The findings suggest that it is possible to determine appropriate sizes and applicable ranges of travel time values of goods set for road transport mode (i.e. truck), whereas those for railway, inland waterway, marine, and air transport modes are not as easily estimated. The adequate travel time value for trucks is 20,000won/vehicle hour, and the application range of travel time value of trucks is 17,000~23,000won/vehicle hour considering other variables. The most adequate estimation method of determining the travel time value for road transport is the wage rate method, whereas the marginal rate of substitution method is more rational for railway, inland waterway, marine, and air transport modes considering the application purpose and characteristics of the goods transported.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3C
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• pp.157-170
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• 2006

Downhole method is considered as giving a little unreliable Vs profile when the signal to noise ratio(S/N) is low and the travel time information is erroneous although it is economical and ease of operation. Direct method has been applied for obtaining adequate result in this case. But it is difficult to determine optimum result by using direct method which is subjective and considering straight ray path. Therefore, in this paper, Mean Refracted Ray Path Method(MRM) was proposed, which is automated and considering refracted ray path. Artificial travel time data adding some travel time error was generated by forward modeling based on Snell's Law and travel time data was also obtained from numerical signal traces using FEM modelling. Using these travel time data, reliability of MRM was verified in the manner of comparing the results determined by MRM with the model. Finally, proposed method was applied to the real field data and it was considered as improved method for obtaining the optimum result in downhole seismic method.

• Journal of the Korean earth science society
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• v.28 no.1
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• pp.105-111
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• 2007

Seismic and georadar prospecting methods have been used to detect deep seated small tunnel in Korea. The tunnel position interpretation of seismic method has been performed mainly by wave travel time and amplitude. But it was very unstable to interpret the exact tunnel position because of short interval of two measuring boreholes and picking mistake of first arrivals. To solve this problem, this study applied travel-time and amplitude data simulation methods to detect tunnel position.