Identifying optimal data aggregation interval sizes for link and corridor travel time estimation and forecasting

Dongjoo Park; Laurence R.Rilett; Byron J.Gajewski; Clifford H.Spiegelman; Changho Choi
January 2009
Transportation;Jan2009, Vol. 36 Issue 1, p77
Academic Journal
With the recent increase in the deployment of ITS technologies in urban areas throughout the world, traffic management centers have the ability to obtain and archive large amounts of data on the traffic system. These data can be used to estimate current conditions and predict future conditions on the roadway network. A general solution methodology for identifying the optimal aggregation interval sizes for four scenarios is proposed in this article: (1) link travel time estimation, (2) corridor/route travel time estimation, (3) link travel time forecasting, and (4) corridor/route travel time forecasting. The methodology explicitly considers traffic dynamics and frequency of observations. A formulation based on mean square error (MSE) is developed for each of the scenarios and interpreted from a traffic flow perspective. The methodology for estimating the optimal aggregation size is based on (1) the tradeoff between the estimated mean square error of prediction and the variance of the predictor, (2) the differences between estimation and forecasting, and (3) the direct consideration of the correlation between link travel time for corridor/route estimation and forecasting. The proposed methods are demonstrated using travel time data from Houston, Texas, that were collected as part of the automatic vehicle identification (AVI) system of the Houston Transtar system. It was found that the optimal aggregation size is a function of the application and traffic condition.


Related Articles

  • Alternative Conditions for a Well-Behaved Travel Time Model. Carey, Malachy; Ge, Y. E. // Transportation Science;Aug2005, Vol. 39 Issue 3, p417 

    The travel time Τ(t) on a link has often been treated in dynamic traffic assignment (DTA) as a function of the number of vehicles x(t) on the link, that is, Τ(t) =f(x(t)). In earlier papers, bounds on the gradient of this travel time function f(x) have been introduced to ensure that the...

  • Benchmarking travel time estimates. Margulici, J.D.; Ban, X. // IET Intelligent Transport System;Sep2008, Vol. 2 Issue 3, p228 

    Travel time estimates are widely regarded as the most practical information about traffic conditions available to individual drivers. Although there are numerous data collection and estimation methods in use today, few attempts have been made to evaluate them in a systematic manner. Even more...

  • An Empirical Study of Travel Time Variability and Travel Choice Behavior. Jackson, W. Burke; Jucker, James V. // Transportation Science;Nov82, Vol. 16 Issue 4, p460 

    Although reliability of travel modes has generally been found to be one of the most important attributes of transportation systems, few attempts have been made to make these findings operational. The impact of a specific measure of reliability, the variability of travel time, on the...

  • DATA DAZE.  // Planner;Oct2013, p50 

    This section presents the average minimum travel time across seven key services, which include work, primary school, hospital, and food store, for England in 2012.

  • The Distance Traveled to Visit N points with a Maximum of C Stops per Vehicle: An Analytic Model and an Application. Daganzo, Carlos F. // Transportation Science;Nov84, Vol. 18 Issue 4, p331 

    The purpose of this paper is to develop a simple formula to predict the distance traveled by fleets of vehicles in physical distribution problems involving a depot and its area of influence. Since the transportation cost of operating a break-bulk terminal (or a warehouse) is intimately related...

  • An Analysis of Travel Time in Multimodal Public Transport. Ismail, Amiruddin; Ganji, Mohammad; Hesam Hafezi, Mohammad; Shokri, Foad; Rahmat, Riza Atiq O. K. // Australian Journal of Basic & Applied Sciences;Nov2012, Vol. 6 Issue 12, p165 

    Nowadays, with the growth of population, road expanding and approachability to the diverse commuting option Multimodal Trips which means using two or more transportation modes for a trip between two nodes ; are increasingly recognized as a consequential factor in solving traffic congestion...

  • Commuters want public transport, not expensive motorways. FAIRFAX, KATHY // Green Left Weekly;4/16/2014, Issue 1005, p11 

    The author discusses the government policy regarding the construction of motorways to earn generate revenue and to prevent the congestion of public transport in Australia. Topics discussed include construction of several tunnels under the WestConnex infrastructure project, reduction in air...

  • Time Dependent Vehicle Routing Problem with Fuzzy Traveling Times Under Different Traffic Conditions. Demirel, Tufan; Demirel, Nihan Cetin; Tasdelen, Belgin // Journal of Multiple-Valued Logic & Soft Computing;2008, Vol. 14 Issue 3-5, p387 

    The basic vehicle routing problem model usually needs to be extended in order to solve real-world vehicle routing problems. Time dependent vehicle routing problem is a vehicle routing problem in which travel costs along the network are dependent upon the time of day during which travel is to be...

  • The sampling effect on the value of travel-time savings: estimation by discrete choice models on Tunisian data. Dhibi, M.; Belkacem, L. // Advances in Transportation Studies;2013, Issue 29, p59 

    This paper examines how the value of travel time savings changes with the individual socio-economic variables and the transportation system performance. The individuals' perception of the value of time depends on three key factors: age, sex, income. The Multinomial Logit model underestimates the...


Read the Article


Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics