Travel Time Prediction on the Network Using Time Series (Case study: zone 1 in Mashhad)

Document Type : Original Article

Authors

1 M.Sc. Student, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

2 Associate Professor, Industrial Engineering Department, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

3 Assistant Professor, Civil Engineering Department, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

Abstract

In urban service systems, the lack of travel time between two different destinations could be felt. The main factor in many systems, which are based on routing vehicles, is travel time estimation. If a driver had travel time information before the trip was set about, he could make a good decision about his alternative routes. So, it needs to estimate travel times for routes, before routes traversed. According to traffic conditions at intersections and routes of the urban center, several methods are employed to obtain travel time. But among them, the role of spatial data to define the travel time has better performance compared to other ones. This paper intends to predict future traffic volume by using traffic volume pattern which obtained in the past and then, estimate travel time in the arc and delay in intersection with travel time-volume and Webster functions. This report is done for streets of zone 1 Mashhad including three types of retractable and arterial Grade 1 and 2. The results of the paired T-Test, by using the mentioned method, show that travel time estimation is acceptable.
 
 

Keywords

References

1-  

-    بهنگام­سازی مطالعات جامع حمل‌ونقل شهر مشهد، (1389)، "ساخت، پرداخت مدل­های تابع زمان سفر- حجم برای معابر اصلی"، سازمان حمل‌ونقل و ترافیک مشهد، گزارش شماره 38-300 ویرایش 01، اردیبهشت ماه 1389.
-  فروزنده، ر.، حکیم­پور، ف.، خادمی، ن.، (1394)، "محاسبه زمان سفر در کمان­های شریانی با استفاده از
داده­های خودروها کاوشگر"،علمی-ترویجی مهندسی نقشه­برداری و اطلاعات مکانی، دوره 6، شماره 4، آذرماه.
-    مطالعات جامع حمل­ونقل مشهد، "تابع زمان تأخیر در تقاطع­های چراغ­دار مشهد"، (1375)­، گزارش شماره 05-75، دانشگاه صنعتی شریف، دی ماه.
-    Ahmed MS, Cook AR, (1979), "Analysis of freeway traffic time-series data by using Box-Jenkins techniques", Transportation Research Record 722, pp.1-9.
-    Al-Deek HM, D'Angelo MP, Wang MC, (1998), "Travel time prediction with non-linear time series", In Fifth International Conference on Applications of Advanced Technologies in Transportation Engineering.
-    Aron M, Bhouri N, Guessous Y, (2014), "Estimating travel time distribution for reliability analysis", Transportation Research Arena, paper, 19638.
-    B Van Arem, MJM Van Der Vlist, MR Muste, Smulders SA, (1997), "Travel time estimation in the GERDIEN project", International Journal of Forecasting 13, pp.73–85.
-    CH Wu, JM Ho, DT Lee, (2004), "Travel-Time Prediction with support vector regression", Intelligent Transportation Systems, vol. 5, no. 4.
-    CN Kamga, KC Mouskos, RE Paaswel, (2011), "A methodology to estimate travel time using dynamic traffic assignment (DTA) under incident conditions", Transportation Research Part C 19, pp.1215–1224.
-    G.E. Box, M. Gwilym, G.C. Reinsel, (2008), "Time series analysis for casting and control", John Wiley & Sons, 4th.
-    J Kwon, B Coifman, P Bickel, (2000), "Day-to-day travel-time trends and travel time prediction from loop detector data", Transport Res. Rec, 1717, pp.120–129.
-    JS Wasson, JR Sturtevant, DM Bullock, (2008), "Real-time travel time estimates using media access control address matching", Institute of Transportation Engineers.
-    JZ Zhu, JX Cao, Y Zhu, (2014), "Traffic volume forecasting based on radial basis function neural network with the consideration of traffic flows at the adjacent intersections", Transportation Research Part C 47, pp.139–154.
-    Lee CJ, (2003), "Transportation information based on personal communication services base station data", master dissertation, Seoul National University. 
-    Li H, Guensler R, Ogle J, Wang J, (2004), "Using GPS data to understand the day-to-day dynamics of the morning commute behavior", 83th TRB Annual Meeting, Washington DC, USA.
-    Rice J, Van Zwet E, (2004), "A simple and effective method for predicting travel times on freeways", IEEE Transactions on Intelligent Transportation Systems, 5(3):200-7.
-    Rilett L, Park D, (2001), "Direct forecasting of freeway corridor travel times using spectral basis neural networks", Transportation Research Record: Journal of the Transportation Research Board, (1752): pp.140-7.
-    Spencer, Mark, L N Labell, A D May, (1989), "Detectors for Freeway Surveillance and Control", no.UCB-ITS-WP-89-9.
-    Taylor MA, Bonsall PW, Young W, (2000), "Data on travel times In, Understanding Traffic Systems: Data, Analysis and Presentation", Ashgate Publishing Ltd, pp.197-206.
-    Taylor, M. A. P., J. E. Woolley and R. Zito, (2000), "Integration of the Global Positioning System and Geographical Information Systems for Traffic Congestion Studies", Transportation Research Part C, Vol. 8, pp. 257-285.
-    Van Lint JW, (2006), "Reliable real-time framework for short-term freeway travel time prediction", Journal of transportation engineering, 132(12): pp.921-32.
-    X Zhang, JA Rice, (2003), "Short-term travel time prediction", Transportation Research Part C11, 187–210.
-    Y Guessous, M Aron, N Bhouri, S Cohen, (2014), "Estimating travel time distribution under different traffic conditions", Transportation Research
 
Procedia 3, pp.339 – 348.
 

Files

Share

How to cite

Statistics