Short-term travel time estimation: A case study

This paper presents a case study of traffic travel time estimation for the Deerfoot Trail highway in Calgary, AB, Canada. We analyzed the travel time for 12 hours per day for 5 consecutive months. The collected data are posted by Google maps, which are collected using hypertext transfer protocol requests. This paper provides a construction of the Holt-Winters forecasting model that streamlines the estimation of travel time for a specific hour of the day. The Holt-Winters model with exponential smoothing average has the best estimate of travel time. The model goodness of fit for the data collected at 07:00AM shows that the multiplicative Holt-Winters model has the best performance (mean square error 15.733 with R2 value of 0.8435). A fleet driver can use the results to decide to travel at a given hour. Moreover, decision makers can use the results to plan for road events.

[1]  Karim Djemame,et al.  A Macroscopic Forecasting Framework for Estimating Socioeconomic and Environmental Performance of Intelligent Transport Highways , 2014, IEEE Transactions on Intelligent Transportation Systems.

[2]  Rob J Hyndman,et al.  25 years of time series forecasting , 2006 .

[3]  Nancy L. Nihan,et al.  Use of the box and Jenkins time series technique in traffic forecasting , 1980 .

[4]  Fei-Yue Wang,et al.  Parallel Control and Management for Intelligent Transportation Systems: Concepts, Architectures, and Applications , 2010, IEEE Transactions on Intelligent Transportation Systems.

[5]  Ramkumar Venkatanarayana,et al.  CHARACTERIZATION OF FREEWAY CAPACITY REDUCTION RESULTING FROM TRAFFIC ACCIDENTS , 2003 .

[6]  C. Granger,et al.  Co-integration and error correction: representation, estimation and testing , 1987 .

[7]  A. R. Cook,et al.  ANALYSIS OF FREEWAY TRAFFIC TIME-SERIES DATA BY USING BOX-JENKINS TECHNIQUES , 1979 .

[8]  F Sa Su,et al.  Forecasting Time Series with Multiple Seasonal Patterns , 2007 .

[9]  Chih-Wei Yi,et al.  Mining overdispersed and autocorrelated vehicular traffic volume , 2013, 2013 5th International Conference on Computer Science and Information Technology.

[10]  H. Akaike.  A new look at the statistical model identification , 1974 .

[11]  Chris Chatfield,et al.  Holt‐Winters Forecasting: Some Practical Issues , 1988 .

[12]  U. Grenander,et al.  Statistical analysis of stationary time series , 1958 .

[13]  Christos Faloutsos,et al.  Data mining meets performance evaluation: fast algorithms for modeling bursty traffic , 2002, Proceedings 18th International Conference on Data Engineering.

[14]  Kaan Ozbay,et al.  Virtual Sensors , 2014 .

[15]  Baher Abdulhai,et al.  Short-Term Traffic Flow Prediction Using Neuro-Genetic Algorithms , 2002, J. Intell. Transp. Syst..

[16]  Billy M. Williams,et al.  Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results , 2003, Journal of Transportation Engineering.

[17]  Diwakar Krishnamurthy,et al.  Using Web Mining to Support Low Cost Historical Vehicle Traffic Analytics , 2014, SEKE.

[18]  Feng He,et al.  An Implementation of Artificial Transportation Systems based on JXTA , 2006, 2006 IEEE International Conference on Vehicular Electronics and Safety.

[19]  Fei-Yue Wang,et al.  Agent-Based Control for Networked Traffic Management Systems , 2005, IEEE Intell. Syst..