• Title/Summary/Keyword: travel time estimation

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On-Line Departure time based link travel time estimation using Spatial Detection System (구간검지체계를 이용한 On-Line 출발시각기준 링크 통행시간 추정 (연속류를 중심으로))

  • Kim, Jae-Jin;No, Jeong-Hyeon;Park, Dong-Ju
    • 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.

Determining Optimal Aggregation Interval Size for Travel Time Estimation and Forecasting with Statistical Models (통행시간 산정 및 예측을 위한 최적 집계시간간격 결정에 관한 연구)

  • Park, Dong-Joo
    • Journal of Korean Society of Transportation
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    • v.18 no.3
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    • pp.55-76
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    • 2000
  • We propose a general solution methodology for identifying the optimal aggregation interval sizes as a function of the traffic dynamics and frequency of observations for four cases : i) link travel time estimation, ii) corridor/route travel time estimation, iii) link travel time forecasting. and iv) corridor/route travel time forecasting. We first develop statistical models which define Mean Square Error (MSE) for four different cases and interpret the models from a traffic flow perspective. The emphasis is on i) the tradeoff between the Precision and bias, 2) the difference between estimation and forecasting, and 3) the implication of the correlation between links on the corridor/route travel time estimation and forecasting, We then demonstrate the Proposed models to the real-world travel time data from Houston, Texas which were collected as Part of the Automatic Vehicle Identification (AVI) system of the Houston Transtar system. The best aggregation interval sizes for the link travel time estimation and forecasting were different and the function of the traffic dynamics. For the best aggregation interval sizes for the corridor/route travel time estimation and forecasting, the covariance between links had an important effect.

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AGV travel time estimation for an AGV-based transport system (AGV기반 운반체계에서의 차량이동시간에 관한 연구)

  • 구평회;장재진
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 2000.04a
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    • pp.5-8
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    • 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.

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The Development of Freeway Travel-Time Estimation and Prediction Models Using Neural Networks (신경망을 이용한 고속도로 여행시간 추정 및 예측모형 개발)

  • 김남선;이승환;오영태
    • Journal of Korean Society of Transportation
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    • v.18 no.1
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    • pp.47-59
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    • 2000
  • The purpose of this study is to develop travel-time estimation model using neural networks and prediction model using neural networks and kalman-filtering technique. The data used in this study are travel speed collected from inductive loop vehicle detection systems(VDS) and travel time collected from the toll collection system (TCS) between Seoul and Osan toll Plaza on the Seoul-Pusan Expressway. Two models, one for travel-time estimation and the other for travel-time Prediction were developed. Application cases of each model were divided into two cases, so-called, a single-region and a multiple-region. because of the different characteristics of travel behavior shown on each region. For the evaluation of the travel time estimation and Prediction models, two Parameters. i.e. mode and mean were compared using five-minute interval data sets. The test results show that mode was superior to mean in representing the relationship between speed and travel time. It is, however shown that mean value gives better results in case of insufficient data. It should be noted that the estimation and the Prediction of travel times based on the VDS data have been improved by using neural networks, because the waiting time at exit toll gates can be included for the estimation of travel time based on the VDS data by considering differences between VDS and TCS travel time Patterns in the models. In conclusion, the results show that the developed models decrease estimation and prediction errors. As a result of comparing the developed model with the existing model using the observed data, the equality coefficients of the developed model was average 88% and the existing model was average 68%. Thus, the developed model was improved minimum 17% and maximum 23% rather then existing model .

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Analysis Period of Input Data for Improving the Prediction Accuracy of Express-Bus Travel Times (고속버스 통행시간 예측의 정확도 제고를 위한 입력자료 분석기간 선정 연구)

  • Nam, Seung-Tae;Yun, Ilsoo;Lee, Choul-Ki;Oh, Young-Tae;Choi, Yun-Taik;Kwon, Kenan
    • International Journal of Highway Engineering
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    • v.16 no.5
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    • pp.99-108
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    • 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.

A Study on Spatial Aggregation Method for Path Travel Time Estimation using Hi-Pass DSRC System (하이패스 DSRC 기반의 경로통행시간 산정을 위한 공간적 집계방안 산정에 관한 연구)

  • Lee, Hwanpil;Shim, Sangwoo;Choi, Yuntaek;Kim, Dongin
    • 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.

Investigating Optimal Aggregation Interval Size of Loop Detector Data for Travel Time Estimation and Predicition (통행시간 추정 및 예측을 위한 루프검지기 자료의 최적 집계간격 결정)

  • Yoo, So-Young;Rho, Jeong-Hyun;Park, Dong-Joo
    • Journal of Korean Society of Transportation
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    • v.22 no.6
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    • pp.109-120
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    • 2004
  • Since the late of 1990, there have been number of studies on the required number of probe vehicles and/or optimal aggregation interval sizes for travel time estimation and forecasting. However, in general one to five minutes are used as aggregation intervals for the travel time estimation intervals for the travel time estimation and/or forecasting of loop detector system without a reasonable validation. The objective of this study is to deveop models for identifying optimal aggregation interval sizes of loop detector data for travel time estimation and prediction. This study developed Cross Valiated Mean Square Error (CVMSE) model for the link and route travel time forecasting, The developed models were applied to the loop detector data of Kyeongbu expressway. It was found that the optimal aggregation sizes for the travel time estimation and forecasting are three to five minutes and ten to twenty minutes, respectively.

A Path Travel Time Estimation Study on Expressways using TCS Link Travel Times (TCS 링크통행시간을 이용한 고속도로 경로통행시간 추정)

  • Lee, Hyeon-Seok;Jeon, Gyeong-Su
    • 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.

Establish for Link Travel Time Distribution Estimation Model Using Fuzzy (퍼지추론을 이용한 링크통행시간 분포비율 추정모형 구축)

  • Lee, Young Woo
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.26 no.2D
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    • pp.233-239
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    • 2006
  • Most research for until at now link travel time were research for mean link travel time calculate or estimate which uses the average of the individual vehicle. however, the link travel time distribution is divided caused by with the impact factor which is various traffic condition, signal operation condition and the road conditional etc. preceding study result for link travel time distribution characteristic showed that the patterns of going through traffic were divided up to 2 in the link travel times. therefore, it will be more accurate to divide up the link travel time into the one involving delay and the other without delay, rather than using the average link travel time in terms of assessing the traffic situation. this study is it analyzed transit hour distribution characteristic and a cause using examine to the variables which give an effect at link travel time distribute using simulation program and determinate link travel time distribute ratio estimation model. to assess the distribution of the link travel times, this research develops the regression model and the fuzzy model. the variables that have high level of correlations in both estimation models are the rest time of green ball and the delay vehicles. these variables were used to construct the methods in the estimation models. The comparison of the two estimation models-fuzzy and regression model- showed that fuzzy model out-competed the regression model in terms of reliability and applicability.

Development of Travel Time Estimation Algorithm for National Highway by using Self-Organizing Neural Networks (자기조직형 신경망 이론을 이용한 국도 통행시간 추정 알고리즘)

  • Do, Myungsik;Bae, Hyunesook
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.28 no.3D
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    • pp.307-315
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    • 2008
  • The aim of this study is to develop travel time estimation model by using Self-Organized Neural network(in brief, SON) algorithm. Travel time data based on vehicles equipped with GPS and number-plate matching collected from National road number 3 (between Jangji-IC and Gonjiam-IC), which is pilot section of National Highway Traffic Management System were employed. We found that the accuracies of travel time are related to location of detector, the length of road section and land-use properties. In this paper, we try to develop travel time estimation using SON to remedy defects of existing neural network method, which could not additional learning and efficient structure modification. Furthermore, we knew that the estimation accuracy of travel time is superior to optimum located detectors than based on existing located detectors. We can expect the results of this study will make use of location allocation of detectors in highway.