• Title/Summary/Keyword: Link travel time

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Progressive Iterative Forward and Backward (PIFAB) Search Method to Estimate Path-Travel Time on Freeways Using Toll Collection System Data (고속도로 경로통행시간 산출을 위한 전진반복 전후방탐색법(PIFAB)의 개발)

  • NamKoong, Seong
    • Journal of Korean Society of Transportation
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    • v.23 no.5 s.83
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    • pp.147-155
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    • 2005
  • The purpose of this paper is to develop a method for estimation of reliable path-travel time using data obtained from the toll collection system on freeways. The toll collection system records departure and arrival time stamps as well as the identification numbers of arrival and destination tollgates for all the individual vehicles traveling between tollgates on freeways. Two major issues reduce accuracy when estimating path-travel time between an origin and destination tollgate using transaction data collected by the toll collection system. First, travel time calculated by subtracting departure time from arrival time does not explain path-travel time from origin tollgate to destination tollgate when a variety of available paths exist between tollgates. Second, travel time may include extra time spent in service and/or rest areas. Moreover. ramp driving time is included because tollgates are installed before on-ramps and after off-ramps. This paper describes an algorithm that searches for arrival time when departure time is given between tollgates by a Progressive Iterative Forward and Backward (PIFAB) search method. The algorithm eventually produces actual path-travel times that exclude any time spent in service and/or rest areas as well as ramp driving time based on a link-based procedure.

A study on the imputation solution for missing speed data on UTIS by using adaptive k-NN algorithm (적응형 k-NN 기법을 이용한 UTIS 속도정보 결측값 보정처리에 관한 연구)

  • Kim, Eun-Jeong;Bae, Gwang-Soo;Ahn, Gye-Hyeong;Ki, Yong-Kul;Ahn, Yong-Ju
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.13 no.3
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    • pp.66-77
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    • 2014
  • UTIS(Urban Traffic Information System) directly collects link travel time in urban area by using probe vehicles. Therefore it can estimate more accurate link travel speed compared to other traffic detection systems. However, UTIS includes some missing data caused by the lack of probe vehicles and RSEs on road network, system failures, and other factors. In this study, we suggest a new model, based on k-NN algorithm, for imputing missing data to provide more accurate travel time information. New imputation model is an adaptive k-NN which can flexibly adjust the number of nearest neighbors(NN) depending on the distribution of candidate objects. The evaluation result indicates that the new model successfully imputed missing speed data and significantly reduced the imputation error as compared with other models(ARIMA and etc). We have a plan to use the new imputation model improving traffic information service by applying UTIS Central Traffic Information Center.

A Vehicle Reidentification Algorithm using Inductive Vehicle Signatures (루프검지기 자기신호 패턴분석을 통한 차량재인식 알고리즘)

  • Park, Jun-Hyeong;O, Cheol;NamGung, Seong
    • Journal of Korean Society of Transportation
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    • v.27 no.1
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    • pp.179-190
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    • 2009
  • Travel time is one of the most important traffic parameters to evaluate operational performance of freeways. A variety of methods have been proposed to estimate travel times. One feasible solution to estimating travel times is to utilize existing loop detector-based infrastructure since the loops are the most widely deployed detection system in the world. This study proposed a new approach to estimate travel times for freeways. Inductive vehicle signatures extracted from the loop detectors were used to match vehicles from upstream and downstream stations. Ground-truthing was also conducted to systematically evaluate the performance of the proposed algorithm by recognizing individual vehicles captured by video cameras placed at upstream and downstream detection stations. A lexicographic optimization method vehicle reidentification algorithm was developed. Vehicle features representing the characteristics of individual vehicles such as vehicle length and interpolations extracted from the signature were used as inputs of the algorithm. Parameters associated with the signature matching algorithm were calibrated in terms of maximizing correct matching rates. It is expected that the algorithm would be a useful method to estimate freeway link travel times.

Development of a n-path algorithm for providing travel information in general road network (일반가로망에서 교통정보제공을 위한 n-path 알고리듬의 개발)

  • Lim, Yong-Taek
    • Journal of Korean Society of Transportation
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    • v.22 no.4 s.75
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    • pp.135-146
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    • 2004
  • For improving the effectiveness of travel information, some rational paths are needed to provide them to users driving in real road network. To meet it, k-shortest path algorithms have been used in general. Although the k-shortest path algorithm can provide several alternative paths, it has inherent limit of heavy overlapping among derived paths, which nay lead to incorrect travel information to the users. In case of considering the network consisting of several turn prohibitions popularly adopted in real world network, it makes difficult for the traditional network optimization technique to deal with. Banned and penalized turns are not described appropriately for in the standard node/link method of network definition with intersections represented by nodes only. Such problem could be solved by expansion technique adding extra links and nodes to the network for describing turn penalties, but this method could not apply to large networks as well as dynamic case due to its overwhelming additional works. This paper proposes a link-based shortest path algorithm for the travel information in real road network where exists turn prohibitions. It enables to provide efficient alternative paths under consideration of overlaps among paths. The algorithm builds each path based on the degree of overlapping between each path and stops building new path when the degree of overlapping ratio exceeds its criterion. Because proposed algorithm builds the shortest path based on the link-end cost instead or node cost and constructs path between origin and destination by link connection, the network expansion does not require. Thus it is possible to save the time or network modification and of computer running. Some numerical examples are used for test of the model proposed in the paper.

A Link-Based Label Correcting Multi-Objective Shortest Paths Algorithm in Multi-Modal Transit Networks (복합대중교통망의 링크표지갱신 다목적 경로탐색)

  • Lee, Mee-Young;Kim, Hyung-Chul;Park, Dong-Joo;Shin, Seong-Il
    • Journal of Korean Society of Transportation
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    • v.26 no.1
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    • pp.127-135
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    • 2008
  • Generally, optimum shortest path algorithms adopt single attribute objective among several attributes such as travel time, travel cost, travel fare and travel distance. On the other hand, multi-objective shortest path algorithms find the shortest paths in consideration with multi-objectives. Up to recently, the most of all researches about multi-objective shortest paths are proceeded only in single transportation mode networks. Although, there are some papers about multi-objective shortest paths with multi-modal transportation networks, they did not consider transfer problems in the optimal solution level. In particular, dynamic programming method was not dealt in multi-objective shortest path problems in multi-modal transportation networks. In this study, we propose a multi-objective shortest path algorithm including dynamic programming in order to find optimal solution in multi-modal transportation networks. That algorithm is based on two-objective node-based label correcting algorithm proposed by Skriver and Andersen in 2000 and transfer can be reflected without network expansion in this paper. In addition, we use non-dominated paths and tree sets as labels in order to improve effectiveness of searching non-dominated paths. We also classifies path finding attributes into transfer and link travel attribute in limited transit networks. Lastly, the calculation process of proposed algorithm is checked by computer programming in a small-scaled multi-modal transportation network.

Study on Enhancement of TRANSGUIDE Outlier Filter Method under Unstable Traffic Flow for Reliable Travel Time Estimation -Focus on Dedicated Short Range Communications Probes- (불안정한 교통류상태에서 TRANSGUIDE 이상치 제거 기법 개선을 통한 교통 통행시간 예측 향상 연구 -DSRC 수집정보를 중심으로-)

  • Khedher, Moataz Bellah Ben;Yun, Duk Geun
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.18 no.3
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    • pp.249-257
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    • 2017
  • Filtering the data for travel time records obtained from DSRC probes is essential for a better estimation of the link travel time. This study addresses the major deficiency in the performance of TRANSGUIDE in removing anomalous data. This algorithm is unable to handle unstable traffic flow conditions for certain time intervals, where fluctuations are observed. In this regard, this study proposes an algorithm that is capable of overcoming the weaknesses of TRANSGUIDE. If TRANSGUIDE fails to validate sufficient number of observations inside one time interval, another process specifies a new validity range based on the median absolute deviation (MAD), a common statistical approach. The proposed algorithm suggests the parameters, ${\alpha}$ and ${\beta}$, to consider the maximum allowed outlier within a one-time interval to respond to certain traffic flow conditions. The parameter estimation relies on historical data because it needs to be updated frequently. To test the proposed algorithm, the DSRC probe travel time data were collected from a multilane highway road section. Calibration of the model was performed by statistical data analysis through using cumulative relative frequency. The qualitative evaluation shows satisfactory performance. The proposed model overcomes the deficiency associated with the rapid change in travel time.

A Study on the Estimate Real Time Delay Model using BIS Data (버스정보시스템(BIS) 운행데이터를 이용한 실시간 지체시간 산정모형 구축)

  • Lee, Young-Woo;Kwon, Hyuck-Jun
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.10 no.5
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    • pp.14-22
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    • 2011
  • This study is to estimate delay time model of signalized intersection by using travel data of Bus Information System. BIS, which applies the advanced information technology to an existing bus system, has been developing and operating in many cities. However, even though some useful traffic informations have been collected from BIS operation, utilization of real-time data to the traffic operation has not been promoted due to the inhomogeneity of modal speeds. Accordingly, in this study, a fundamental research is performed for traffic controls in urban areas and providing a traffic information throughout a methodology for estimating delay time using the data from BIS was developed. This delay time model setting bus travel time excluding service time of a bus stop as explanatory variables was constructed as a regression model, and the coefficient of determination of a linear regression model most highly appeared as 0.826. As a result of performing T-test with field survey values and model estimation values for verifying constructed models statistically, it was analyzed to be statistically significant in a confidence level of 95%.

Development of Vehicle Queue Length Estimation Model Using Deep Learning (딥러닝을 활용한 차량대기길이 추정모형 개발)

  • Lee, Yong-Ju;Hwang, Jae-Seong;Kim, Soo-Hee;Lee, Choul-Ki
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.17 no.2
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    • pp.39-57
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    • 2018
  • The purpose of this study was to construct an artificial intelligence model that learns and estimates the relationship between vehicle queue length and link travel time in urban areas. The vehicle queue length estimation model is modeled by three models. First of all, classify whether vehicle queue is a link overflow and estimate the vehicle queue length in the link overflow and non-overflow situations. Deep learning model is implemented as Tensorflow. All models are based DNN structure, and network structure which shows minimum error after learning and testing is selected by diversifying hidden layer and node number. The accuracy of the vehicle queue link overflow classification model was 98%, and the error of the vehicle queue estimation model in case of non-overflow and overflow situation was less than 15% and less than 5%, respectively. The average error per link was about 12%. Compared with the detecting data-based method, the error was reduced by about 39%.

Real-time Travel Time Estimation Model Using Point-based and Link-based Data (지점과 구간기반 자료를 활용한 실시간 통행시간 추정 모형)

  • Yu, Jeong-Whon
    • International Journal of Highway Engineering
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    • v.10 no.1
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    • pp.155-164
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    • 2008
  • It is critical to develop a core ITS technology such as real-time travel time estimation in order that the efficient use of the ITS implementation can be achieved as the ITS infrastructure and relevant facilities are broadly installed in recent years. The provision of travel time information in real-time allows travellers to make informed decisions and hence not only the traveller's travel utilities but also the road utilization can be maximized. In this paper, a hybrid model is proposed to combine VDS and AVI which have different characteristics in terms of space and time dimensions. The proposed model can incorporate the immediacy of VDS data and the reality of AVI data into one single framework simultaneously. In addition, the solution algorithm is made to have no significant computational burden so that the model can be deployable in real world. A set of real field data is used to analyze the reliability and applicability of the proposed model. The analysis results suggest that the proposed model is very efficient computationally and improves the accuracy of the information provided, which demonstrates the real-time applicability of the proposed model. In particular, the data fusion methodology developed in this paper is expected to be used more widely when a new type of traffic data becomes available.

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Fusion Strategy on Heterogeneous Information Sources for Improving the Accuracy of Real-Time Traffic Information (실시간 교통정보 정확도 향상을 위한 이질적 교통정보 융합 연구)

  • Kim, Jong-Jin;Chung, Younshik
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.42 no.1
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    • pp.67-74
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    • 2022
  • In recent, the number of real-time traffic information sources and providers has increased as increasing smartphone users and intelligent transportation system facilities installed at roadways including vehicle detection system (VDS), dedicated short-ranged communications (DSRC), and global positioning system (GPS) probe vehicle. The accuracy of such traffic information would vary with these heterogeneous information sources or spatiotemporal traffic conditions. Therefore, the purpose of this study is to propose an empirical strategy of heterogeneous information fusion to improve the accuracy of real-time traffic information. To carry out this purpose, travel speed data collection based on the floating car technique was conducted on 227 freeway links (or 892.2 km long) and 2,074 national highway links (or 937.0 km long). The average travel speed for 5 probe vehicles on a specific time period and a link was used as a ground truth measure to evaluate the accuracy of real-time heterogeneous traffic information for that time period and that link. From the statistical tests, it was found that the proposed fusion strategy improves the accuracy of real-time traffic information.