Fusing Loop Detector and Probe Vehicle Data to Estimate Travel Time Statistics on Signalized Urban Networks

Bhaskar, Ashish; Chung, Edward; Dumont, André-Gilles

doi:10.1111/j.1467-8667.2010.00697.x

Cited by 81 publications

(42 citation statements)

References 30 publications

(33 reference statements)

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“…Existing data collection techniques could be roughly classified into two categories: fixed traffic detection systems; and, floating car systems [9,10]. The fixed traffic detection systems employ conventional stationary detectors, such as loop detectors, installed at specific locations Firstly, an effective method is proposed to estimate path travel time distributions based on low-frequency FCD.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Travel Time Distributions in Urban Road Networks Using Low-Frequency Floating Car Data

Shi

Chen

2017

IJGI

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Travel times in urban road networks are highly stochastic. However, most existing travel time estimation methods only estimate the mean travel times, while ignoring travel time variances. To this end, this paper proposes a robust travel time distribution estimation method to estimate both the mean and variance of travel times by using emerging low-frequency floating car data. Different from the existing studies, the path travel time distribution in this study is formulated as the sum of the deterministic link travel times and stochastic turning delays at intersections. Using this formulation, distinct travel time delays for different turning movements at the same intersection can be well captured. In this study, a speed estimation algorithm is developed to estimate the deterministic link travel times, and a distribution estimation algorithm is proposed to estimate the stochastic turning delays. Considering the low sampling rate of the floating car data, a weighted moving average algorithm is further developed for a robust estimation of the path travel time distribution. A real-world case study in Wuhan, China is carried out to validate the applicability of the proposed method. The results of the case study show that the proposed method can obtain a reliable and accurate estimation of path travel time distribution in congested urban road networks.

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Section: Introductionmentioning

confidence: 99%

Estimation of Travel Time Distributions in Urban Road Networks Using Low-Frequency Floating Car Data

Shi

Chen

2017

IJGI

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“…In recent years, a number of technologies have emerged enabling travel time information to be collected on public roads (72)(73)(74)(75)(76)(77)(78)(79)(80)(81)(82)(83)(84)(85)(86)(87)(88). The probe data market is not yet mature enough to be able to effectively measure minor movements and side street delay, except where volumes are exceptionally high.…”

Section: Travel Time Data Sourcesmentioning

confidence: 99%

“…This technique uses high-resolution event data at each intersection along an arterial to estimate probable vehicle trajectories along the corridor. This yields an estimate of the arterial travel time (85)(86)(87).…”

Section: Travel Time Data Sourcesmentioning

confidence: 99%

Performance Measures for Traffic Signal Systems: An Outcome-Oriented Approach

Day¹,

Bullock²,

Li³

et al. 2014

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PERFORMANCE MEASURES FOR TRAFFIC SIGNAL SYSTEMS:AN OUTCOME-ORIENTED APPROACH This monograph is a synthesis of research carried out on traffic signal performance measures based on highresolution controller event data, assembled into a methodology for performance evaluation of traffic signal systems. High-resolution data consist of a log of discrete events such as changes in detector and signal phase states. A discussion is provided on the collection and management of the signal event data and on the necessary infrastructure to collect these data. A portfolio of performance measures is then presented, focusing on several different topics under the umbrella of traffic signal systems operation. System maintenance and asset management is one focus. Another focus is signal operations, considered from the perspectives of vehicle capacity allocation and vehicle progression. Performance measures are also presented for nonvehicle modes, including pedestrians, and modes that require signal preemption and priority features. Finally, the use of travel time data is demonstrated for evaluating system operations and assessing the impact of signal retiming activities.

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“…As is turns out, the provision of travel time information may reduce traffic congestion significantly and improve the performance of the whole network system (Ben-Akiva et al, 1991). Because of its critical role in traffic monitoring, extensive research has been conducted on the estimation and prediction of travel times on both freeways and urban roadways (Bhaskar et al, 2011, Coifman, 2002, Coifman and Krishnamurthy, 2007, Du et al, 2012, Ndoye et al, 2011, Sun et al, 2008, van Lint et al, 2005, van Lint and van der Zijpp, 2003. The widely used sensors to collect travel time data are loop detectors, which have been found to suffer from high maintenance costs and poor reliability (Rajagopal and Varaiya, 2007).…”

Section: Introductionmentioning

confidence: 99%

Probabilistic travel time progression and its application to automatic vehicle identification data

Nantes

Ngoduy

Miska

et al. 2015

Transportation Research Part B: Methodological

Self Cite

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Travel time has been identified as an important variable to evaluate the performance of a transportation system. Based on the travel time prediction, road users can make their optimal decision in choosing route and departure time. In order to utilize adequately the advanced data collection methods that provide realtime different types of information, this paper is aimed at a novel approach to the estimation of long roadway travel times, using Automatic Vehicle Identification (AVI) technology. Since the long roads contain a large number of scanners, the AVI sample size tends to reduce and, as such, computing the distribution for the total road travel time becomes difficult. In this work, we introduce a probabilistic framework that extends the deterministic travel time progression method to dependent random variables and enables the off-line estimation of road travel time distributions. In the proposed method, the accuracy of the estimation does not depend on the size of the sample over the entire corridor, but only on the amount of historical data that is available for each link. In practice, the system is also robust to small link samples and can be used to detect outliers within the AVI data.

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Fusing Loop Detector and Probe Vehicle Data to Estimate Travel Time Statistics on Signalized Urban Networks

Cited by 81 publications

References 30 publications

Estimation of Travel Time Distributions in Urban Road Networks Using Low-Frequency Floating Car Data

Estimation of Travel Time Distributions in Urban Road Networks Using Low-Frequency Floating Car Data

Performance Measures for Traffic Signal Systems: An Outcome-Oriented Approach

Probabilistic travel time progression and its application to automatic vehicle identification data

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