• International Journal of Highway Engineering
• /
• v.15 no.4
• /
• pp.155-165
• /
• 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
• /
• v.26 no.1
• /
• pp.97-108
• /
• 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.

• International Journal of Highway Engineering
• /
• v.14 no.6
• /
• pp.139-148
• /
• 2012

PURPOSES : The accuracy of travel time information is a key measure of effectiveness and reliability of advanced traveler information systems. This study aims at investigating drivers' perception on the acceptable level of information accuracy and their corresponding valuations. METHODS : A questionnaire survey was executed for collecting driver perception data to capture the expectation level of travel time information provided and their willingness to pay for the information. A Tobit model was adopted for exploring the relationship among the acceptable level, driver socioeconomic characteristics and travel attributes. Since drivers' willingness to pay for accurate travel time information can be different according to their travel lengths, a piecewise linear regression model was developed to capture the sensitivity of values of travel time information to travel length. RESULTS : The analysis results suggest that trip purpose and travel time are two dominant factors to determine drivers' acceptable level of travel time information. For business and short trips, drivers want more accurate information than for non-business and long trips. Drivers' willingness to pay for travel time information also varies depending on their incomes, trip purposes and travel lengths. The results also show that drivers' valuation of travel time information provided is sensitive to their travel length. For longer trips, drivers are less sensitive to travel time information and then put less value on the information provided. CONCLUSIONS : Censored nonlinear regression models are developed to estimate drivers' acceptable accuracy for travel time information and their valuation using questionnaire survey data. The findings on drivers perception to the required accuracy of travel time information and their corresponding willingness to pay can be used in the design and deployment of advanced traveler information system to improve its effectiveness and usefulness through high compliance.

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

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2000.04a
• /
• pp.5-8
• /
• 2000

Vehicle travel time (empty travel time pius loaded travel time) is a key parameter for designing AGV-based material handling systems. Especially, the determination of empty vehicle travel time is difficult because of the stochastic nature of the empty vehicle locations. This paper presents a method to estimate vehicle travel times for AGV-based material transport systems. The model considers probabilistic aspects for the travel time and vehicle location under random vehicle selection rule and nearest vehicle selection rule. The estimation of empty travel time is of major effort. Simulation experiments are used to verify the proposed travel time model, and the simulation results show that the presented model provides reasonable travel time estimations.

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

• International Journal of Highway Engineering
• /
• v.16 no.5
• /
• pp.99-108
• /
• 2014

PURPOSES : The travel times of expressway buses have been estimated using the travel time data between entrance tollgates and exit tollgates, which are produced by the Toll Collections System (TCS). However, the travel time data from TCS has a few critical problems. For example, the travel time data include the travel times of trucks as well as those of buses. Therefore, the travel time estimation of expressway buses using TCS data may be implicitly and explicitly incorrect. The goal of this study is to improve the accuracy of the expressway bus travel time estimation using DSRC-based travel time by identifying the appropriate analysis period of input data. METHODS : All expressway buses are equipped with the Hi-Pass transponders so that the travel times of only expressway buses can be extracted now using DSRC. Thus, this study analyzed the operational characteristics as well as travel time patterns of the expressway buses operating between Seoul and Dajeon. And then, this study determined the most appropriate analysis period of input data for the expressway bus travel time estimation model in order to improve the accuracy of the model. RESULTS : As a result of feasibility analysis according to the analysis period, overall MAPE values were found to be similar. However, the MAPE values of the cases using similar volume patterns outperformed other cases. CONCLUSIONS : The best input period was that of the case which uses the travel time pattern of the days whose total expressway traffic volumes are similar to that of one day before the day during which the travel times of expressway buses must be estimated.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.8 no.5
• /
• pp.1-11
• /
• 2009

TCS Data imply characteristics of traffic conditions. However, there are outliers in TCS data, which can not represent the travel time of the pertinent section, if these outliers are not eliminated, travel time may be distorted owing to these outliers. Various travel time can be distributed under the same section and time because the variation of the travel time is increase as the section distance is increase, which make difficult to calculate the representative of travel time. Accordingly, it is important to grasp travel time characteristics in order to compute the representative of travel time using TCS Data. In this study, after analyzing the variation ratio of the travel time according to the link distance and the level of congestion, the outlier elimination model and the smoothing model for TCS data were proposed. 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 variation 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.

• Journal of Korean Institute of Industrial Engineers
• /
• v.32 no.1
• /
• pp.61-73
• /
• 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
• /
• v.29 no.3
• /
• pp.83-91
• /
• 2011

This paper discusses models for estimating dynamic travel times based on probability theory. The dynamic travel time models proposed in the paper are formulated assuming that the travel time of a vehicle depends on the distribution of the traffic stream condition with respect to the location along a road when the subject vehicle enters the starting point of a travel distance or with respect to the time at the starting point of a travel distance. The models also assume that the dynamic traffic flow can be represented as an exponential distribution function among other types of probability density functions.