Real-Time Traffic Information Provision Using Individual Probe and Five-Minute Aggregated Data
0
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
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%.
Abstract
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%.
참고문헌 (23)
- Bajwa S, "Performance Evaluation of an Adaptive Travel Time Prediction Model", Proc. Intelligent Transportation Systems Conference, IEEE, pp.1000~1005 (2005)
- Billings D, "Application of the ARIMA Models to Urban Roadway Travel Time Prediction: A Case Study", Proc. IEEE International Conference on Systems, Man, and Cybernetics, 3 (2006)
- Boxel D. V, "An Innovative Real-Time Methodology for Detecting Travel Time Outliers on Interstate Highways and Urban Arterials", TRB 2011 Annual Meeting CD-ROM (2011)
- Boxel D. V, "An Innovative Real-Time Methodology for Detecting Travel Time Outliers on Interstate Highways and Urban Arterials", TRB 2011 Annual Meeting CD-ROM (2011)
- Chien S, "Dynamic Travel Time Prediction with Real-Time and Historic Data", Journal of Transportation Engineering, 129(6) (2003), ASCE
- Clark S. D, "Cleaning of Matched License Plate Data", Transportation Research Record, (1804) (2002)
- Dion F, "Estimating Dynamic Roadway Travel Times Using Automatic Vehicle Identification Data", Transportation Research Part B (2006), Elsevier
- 장진환, "Analysis of Time Headway Distribution on Suburban Arterial", KSCE Journal of Civil Engineering, 16(4), pp.644~649 (2012), 대한토목학회[DOI]
- Jang J, "Short-Term Travel Time Prediction Using the Kalman Filter Combined with a Variable Aggregation Interval Scheme", Journal of the Eastern Asia Society of Transportation Studies, 10 (2013)
- Jang J, "Data-Cleaning Technique for Reliable Real-Life Travel Time Estimation: Use of Dedicated Short-Range Communication Probes on Rural Highways", Transportation Research Record, (2593) (2016)
- Kim P, "Kalman Filter for Beginners: with MATLAB Examples" (2010), A-JIN Publishing
- Study on Improvement of System of Relay and Provision of Real-Life Traffic Information for Integrated Traffic Management, "Lim S", Korea Institute of Civil Engineering and Building Technology, ~ (2013)
- Ma X, "Estimation of the Automatic Vehicle Identification Based Spatial Travel Time Information Collected in Stockholm", IET Intelligent Transport Systems, 4(4) (2010)
- Ministry of Land, Infrastructure, and Transport, "ITS Performance Evaluation Guidelines", ~ (2010)
- Moghaddam S. S, "Algorithm for Detecting Outliers in Bluetooth Data in Real Time", Transportation Research Record, (2442) (2014)
- Myung J, "Travel Time Prediction Using k-Nearest Neighbor Method with Combined Data from Vehicle Detector System and Automatic Toll Collection System", Transportation Research Record, (2256) (2011)
- "NIST/SEMATECH e-Handbook of Statistical Methods" (2017)[URL]
- Robinson S, "Modeling Urban Link Travel Time with Inductive Loop Detectoz Data by Using the k-NN Method", Transportation Research Record, (1935) (2005)
- Southwest Research Institute, "Automatic Vehicle Identification Model Deployment Initiative-System Design Document" (1998), Texas Department of Transportation
- van Hinsbergen C. P. I, "Bayesian Training and Committees of State-Space Neural Networks for Online Travel Time Prediction", Transportation Research Record, (2105) (2009)
- van Lint J, "Accurate Freeway Travel Time Prediction with State-Space Neural Networks under Missing Data", Transportation Research Part C, 13(5, pp.6) (2005), ~
- Wismans L, "In-Car Advice to Reduce Negative Effects of Phantom Traffic Jams", Transportation Research Record, (2489) (2015)
- You J, "Development and Evaluation of a Hybrid Travel Time Forecasting Model", Transportation Research Part C, 8(1-6), pp.231~256 (2000)