Probe Data Sampling Guidelines for Characterizing Arterial Travel Time

Abstract

Probe data are emerging as an important source for characterizing transportation systems. Travel time distributions have traditionally been characterized by the mean and standard deviation. These statistics work well to characterize uncongested freeway systems, which have travel time distributions that are approximately normal. When congested conditions or interrupted-flow facilities are encountered, the travel time distributions become more complex. Recently some additional travel time reliability indexes have been developed to quantify these travel time distribution characteristics. This study develops mathematical techniques for determining the sample size required for estimating the underlying travel time distributions that can be used for assessing changes in travel time distributions associated with operational changes of traffic signal controller offsets. The example provided shows that while gross changes in offsets require approximately 7 probe vehicle samples per study interval, subtle changes in offsets require approximately 80 probe vehicle data samples per study interval. Although these guidelines were developed for evaluating offset changes, the mathematical framework can be applied for evaluating the impact of other parameters, such as split times and cycle lengths. Further research on applying these mathematical techniques to a broader cross section of traffic conditions is warranted to assess their transferability to oversaturated conditions and freeways.

Application of advanced sampling for efficient probabilistic traffic modelling

“…average demand, average supply, average desired speed, average maximum acceleration, etc.) However it must be realized that traffic in reality is hardly ever 'average' (Ernst et al, 2012). Therefore modelling traffic as if it were always in a deterministic state is not realistic and will lead, in many cases, to biased outcomes (Calvert et al, 2012;van Lint et al, 2012).…”

Roadway System Assessment Using Bluetooth-Based Automatic Vehicle Identification Travel Time Data

“…Figure 5.17 shows a map of the test corridor, which is SR 37 in Noblesville, Indiana. The southern group of four intersections (5)(6)(7)(8) are the same as those presented in the previous chapter. Between May and July of 2010, several different signal timing optimization strategies were tested on this network, with travel time measurements collected from the Bluetooth cases as indicated on the map in Figure 5.…”

“…In a day-today operational context, it is also useful to understand the impact of incidents. This chapter presents a detailed case study 5 investigating the use of AVI data to measure the impact of winter weather on a 62-mile freeway section.…”

Estimating Required Probe Vehicle Re-identification Requirements for Characterizing Link Travel Times

“…Arterial travel time distributions have been shown to be significantly non-Gaussian [8]. A comparison framework is developed in [9], but only ID matching travel time estimates are discussed. Signature matching studies have the additional complication that erroneous travel time estimates are mixed with the true estimates in the estimated travel time histogram.…”