Accurate estimation of travel times from single-loop detectors1 (original) (raw)

Accurate estimation of travel times from single-loop detectors

Transportation Research Part A: Policy and Practice, 1998

As advanced traveler information systems become increasingly prevalent the importance of accurately estimating link travel times grows. Unfortunately, the predominant source of highway tra c information comes from single-trap loop detectors which d o n o t directly measure vehicle speed. The conventional method of estimating speed, and hence travel time, from the single-trap data is to make a common vehicle length assumption and to use a resulting identity relating density, o w, and speed. Hall and Persaud Hall and Persaud, 1989 and Pushkar, Hall, and Acha-Daza Pushkar et al., 1994 show that these speed estimates are awed. In this paper we present a methodology to estimate link travel times directly from the single-trap loop detector ow and occupancy data without heavy reliance on the awed speed calculations. Our methods arise naturally from an intuitive stochastic model of tra c ow. We demonstrate by example on data collected on I-880 data Skabardonis et al., 1994 that when the loop detector data has a ne resolution about one second, the single-trap estimates of travel time can accurately track the true travel time through many degrees of congestion. Probe vehicle data and double-trap travel time estimates corroborate the accuracy of our methods in our examples.

Improving a Travel-Time Estimation Algorithm by Using Dual Loop Detectors

2003

This paper presents an algorithm for the off-line estimation of route-level travel times for uninterrupted traffic flow facilities, such as motorway corridors, based on time-series of traffic speed observations taken from the sections that constitute a route. The proposed method is an extension of an existing and widely used method known as the trajectory method. The novelty of the new method is the fact that trajectories are constructed based on the assumption of piecewise linear (and continuous at section boundaries) vehicle speeds rather than piecewise constant (and discontinuous at section boundaries) speeds.

Automated Travel Time Measurement Using Vehicle Lengths from Loop Detector Speed Traps

California Partners For Advanced Transit and Highways, 2000

This report presents a vehicle reidentification algorithm for consecutive detector stations on a freeway, whereby a vehicle measurement made at a downstream detector station is matched with the vehicle's corresponding measurement at an upstream station. The algorithm should improve freeway surveillance by measuring the actual vehicle travel times; these are simply the differences in the times that each (matched) vehicle arrives to the upstream and downstream stations. Thus, it will be possible to quantify conditions between widely spaced detector stations rather than assuming that the local conditions measured at a single station are representative of an extended link between stations.

TRAVEL TIME ESTIMATION: NEW THEORY DEVELOPMENT AND LARGE SCALE EVALUATION

ABSTRACT In this paper a new method is presented for estimating off-line travel times out of speed measurements along a route. The speed is being sampled at separated points and traffic flow is considered uninterrupted, like on a motorway for example. Different existing methods are compared to a new method and a validation is made on a 40km long highway road.* Keywords: Travel time estimation; Traffic flow theory; Dual loop detectors; Vehicle trajectories

Toward Understanding and Reducing Errors in Real-Time Estimation of Travel Times

Transportation Research Record: Journal of the Transportation Research Board, 2008

In recent years the increased deployment of intelligent transportation systems infrastructure has enabled the provision of real-time traveler information to the public. Many states as well as private contractors are providing real-time travel time estimates to commuters to help improve the quality and efficiency of their trips. Accuracy of travel time estimates is important, as inaccurate estimates can be detrimental. Improving the accuracy of real-time estimates involves identifying and understanding the sources of error. This paper reports on the errors found during the evaluation of real-time travel time estimates in Portland, Oregon and provides solutions for reducing estimation error. The midpoint algorithm used by the Oregon Department of Transportation was used to estimate travel times from speeds obtained from loop detectors. The estimates were assessed for accuracy by comparisons with ground truth probe vehicle runs. The findings from the study indicate that 85% of the travel time runs had errors below 20% and, further, that accuracy varied widely between segments. The evaluation of high-error runs revealed the main causes of errors as transition traffic conditions, failure of detectors and detector spacing. Potential solutions were identified for each source of error. In addition, a method was tested for evaluating the benefits of additional detectors by simulation of virtual detectors. The results indicate that additional detection helps in reducing the mean average percent error in most cases but the location of detectors is critical to error reduction.

The Effects Of Detector Spacing On Travel Time Prediction On Freeways

2010

Loop detectors report traffic characteristics in real time. They are at the core of traffic control process. Intuitively, one would expect that as density of detection increases, so would the quality of estimates derived from detector data. However, as detector deployment increases, the associated operating and maintenance cost increases. Thus, traffic agencies often need to decide where to add new detectors and which detectors should continue receiving maintenance, given their resource constraints. This paper evaluates the effect of detector spacing on freeway travel time estimation. A freeway section (Interstate-15) in Salt Lake City metropolitan region is examined. The research reveals that travel time accuracy does not necessarily deteriorate with increased detector spacing. Rather, the actual location of detectors has far greater influence on the quality of travel time estimates. The study presents an innovative computational approach that delivers optimal detector locations th...

Evaluation of speed estimates made with single-detector data from freeway traffic management systems

1989

Freeway management systems that rely on single-detector data acquisition generally use a simple equation to calculate speeds. In this paper, the validity of that equation is tested using data from two locations in Ontario, collected using paired-detector speed traps. The results show that the equation gives biased estimates of speeds over a major portion of the range of operating conditions. Discussion of possible causes demonstrates that at least two key assumptions underlying the equation are not met by actual traffic. This result has important implications not only for operation and design of freeway traffic management systems, but also for theoretical work, such as that on speed-flow relationships.

Guaranteed Bounds on Highway Travel Times Using Probe and Fixed Data

88th TRB Annual …, 2009

This article investigates the problem of incorporating mobile probe data collected from GPS equipped cell phones into estimation algorithms for travel time. We use kinematic wave theory to create a modeling framework capable of incorporating trajectory data into the model. The problem of including loop detector data in this model is performed using a standard approach available in the literature. The problem of fusing this data with probe data is formulated using the Moskowitz function, which results from kinematic wave theory. Using this formulation, two linear programs are posed to compute upper and lower bounds travel time through the corresponding section of highway. The method thus provides a guaranteed range for the average travel time experienced by vehicles on the highway. The method is illustrated with data collected during the Mobile Century experiment on February 8th, 2008, using 100 Nokia N95 phones traveling onboard cars driving loops on I880 in California. A sampling and penetration rate study shows that the method provides accurate travel time estimates for penetration rates as low as 0:1% and spatial sampling strategies on the order of 0.2 miles. The performance of the method is illustrated with several case studies, in which measurements gathered by a few vehicles are sufficient to significantly improve results obtained from sparse loop detectors.