Data fusion algorithm improves travel time predictions

Lim, Sung Han; Lee, C K

doi:10.1049/iet-its.2011.0014

Cited by 24 publications

(7 citation statements)

References 9 publications

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“…The objective of data fusion in the field of advanced transportation information system (ATIS) is to develop better performed estimation on a system from kinds of independent data sources. In order to provide more reliable traffic information efficiently, fusing the stationary sensor data and mobile probe data is perceived as a well-adapted choice for satisfying the operational needs of traffic operators and traffic information centres [ 20 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…An optimal fusion method would be able to make full use of the useful information from these two types of data sources [ 20 ]. It is well known that there is a strong complementarity between these two types of data.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Data fusion approaches in link travel time estimation are proposed for various types of data where different uncertainty may be observed. Some fusion techniques for handing biases of various kinds are developed, through simulation [ 9 ] or experimental research [ 15 , 20 – 23 ].…”

Section: Literature Reviewmentioning

confidence: 99%

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Iterative Bayesian Estimation of Travel Times on Urban Arterials: Fusing Loop Detector and Probe Vehicle Data

et al. 2016

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On urban arterials, travel time estimation is challenging especially from various data sources. Typically, fusing loop detector data and probe vehicle data to estimate travel time is a troublesome issue while considering the data issue of uncertain, imprecise and even conflicting. In this paper, we propose an improved data fusing methodology for link travel time estimation. Link travel times are simultaneously pre-estimated using loop detector data and probe vehicle data, based on which Bayesian fusion is then applied to fuse the estimated travel times. Next, Iterative Bayesian estimation is proposed to improve Bayesian fusion by incorporating two strategies: 1) substitution strategy which replaces the lower accurate travel time estimation from one sensor with the current fused travel time; and 2) specially-designed conditions for convergence which restrict the estimated travel time in a reasonable range. The estimation results show that, the proposed method outperforms probe vehicle data based method, loop detector based method and single Bayesian fusion, and the mean absolute percentage error is reduced to 4.8%. Additionally, iterative Bayesian estimation performs better for lighter traffic flows when the variability of travel time is practically higher than other periods.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Iterative Bayesian Estimation of Travel Times on Urban Arterials: Fusing Loop Detector and Probe Vehicle Data

et al. 2016

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“…The biggest flaw in neural networks is the type of training required and their need to handle large volumes of data to train the network weights. Fuzzy [14], k-nearest neighbour (KNN) [15], support vector machine [16][17], Bayesian networks [18] and wavelet [19] are other parametric methods used to predict traffic flow.…”

Section: Literature Reviewmentioning

confidence: 99%

Traffic Flow Prediction Using MI Algorithm and Considering Noisy and Data Loss Conditions: An Application to Minnesota Traffic Flow Prediction

Hosseini

Moshiri

Rahimi-Kian

et al. 2014

PROMET

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“…Recent advances in information and communication technologies (ICTs) have produced a variety of spatiotemporal big data for travel time estimation [ 6 ]. Existing data collection techniques could be classified into point detection, interval detection, and floating car systems [ 7 , 8 ]. Point detectors (such as loop detectors and video image detectors) are generally deployed at specific road segment locations, to collect vehicle point speeds.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous Data Fusion Method to Estimate Travel Time Distributions in Congested Road Networks

Shi

Chen

Lam

et al. 2017

Sensors

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Travel times in congested urban road networks are highly stochastic. Provision of travel time distribution information, including both mean and variance, can be very useful for travelers to make reliable path choice decisions to ensure higher probability of on-time arrival. To this end, a heterogeneous data fusion method is proposed to estimate travel time distributions by fusing heterogeneous data from point and interval detectors. In the proposed method, link travel time distributions are first estimated from point detector observations. The travel time distributions of links without point detectors are imputed based on their spatial correlations with links that have point detectors. The estimated link travel time distributions are then fused with path travel time distributions obtained from the interval detectors using Dempster-Shafer evidence theory. Based on fused path travel time distribution, an optimization technique is further introduced to update link travel time distributions and their spatial correlations. A case study was performed using real-world data from Hong Kong and showed that the proposed method obtained accurate and robust estimations of link and path travel time distributions in congested road networks.

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Data fusion algorithm improves travel time predictions

Cited by 24 publications

References 9 publications

Iterative Bayesian Estimation of Travel Times on Urban Arterials: Fusing Loop Detector and Probe Vehicle Data

Iterative Bayesian Estimation of Travel Times on Urban Arterials: Fusing Loop Detector and Probe Vehicle Data

Traffic Flow Prediction Using MI Algorithm and Considering Noisy and Data Loss Conditions: An Application to Minnesota Traffic Flow Prediction

Heterogeneous Data Fusion Method to Estimate Travel Time Distributions in Congested Road Networks

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