Predicting Urban Arterial Travel Time with State-Space Neural Networks and Kalman Filters

Liu, Hao; Zuylen, Henk J van; Lint, Hans van; Salomons, Maria

doi:10.3141/1968-12

Cited by 55 publications

(19 citation statements)

References 15 publications

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“…Parametric approaches can achieve a good performance when traffic shows regular variations, but the forecast error is obvious when the traffic shows irregular variations. To address this problem, researchers also paid much attention to non-parametric approaches in the traffic flow forecasting field, such as nonparametric regression [17], neural network prediction [18], support vector machine (SVM) [19], Kaman filtering [20,21] and the combination of these algorithms [22][23][24][25][26][27]. Li and Liu proposed an improved prediction method based on a modified particle swarm optimisation algorithm [28].…”

Section: Introductionmentioning

confidence: 99%

LSTM network: a deep learning approach for short‐term traffic forecast

Zhao

Chen

et al. 2017

IET Intelligent Transport Systems

1,286

561

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Short-term traffic forecast is one of the essential issues in intelligent transportation system. Accurate forecast result enables commuters make appropriate travel modes, travel routes, and departure time, which is meaningful in traffic management. To promote the forecast accuracy, a feasible way is to develop a more effective approach for traffic data analysis. The availability of abundant traffic data and computation power emerge in recent years, which motivates us to improve the accuracy of short-term traffic forecast via deep learning approaches. A novel traffic forecast model based on long short-term memory (LSTM) network is proposed. Different from conventional forecast models, the proposed LSTM network considers temporal-spatial correlation in traffic system via a two-dimensional network which is composed of many memory units. A comparison with other representative forecast models validates that the proposed LSTM network can achieve a better performance.

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Section: Introductionmentioning

confidence: 99%

LSTM network: a deep learning approach for short‐term traffic forecast

Zhao

Chen

et al. 2017

IET Intelligent Transport Systems

1,286

561

View full text Add to dashboard Cite

show abstract

“…Significant research effort has been focusing on models for predicting speed, travel times, and departure/arrival times on both spatial and temporal domains for different transportation modes. A variety of prediction methods, such as the Box–Jenkins models , the KF models , regression models , artificial neural networks , and microscopic simulation models were developed for various applications. A wide range of factors are considered in these studies, including dynamic traffic characteristics (e.g., volume, speed, vehicle composition, and driving behavior) and external factors (e.g., incidents, traffic control devices, roadway geometry, and weather) that affect daily traffic operation.…”

Section: Literature Reviewmentioning

confidence: 99%

An adaptive model for highway travel time prediction

Liu

Chien

Chen

2012

J of Advced Transportation

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Traffic congestion caused by either insufficient road capacity or unexpected events has been a major problem in urban transportation networks. To disseminate accurate traveler information and reduce congestion impact, it is desirable to develop an adaptive model to predict travel time. The proposed model is practically implementable to capture dynamic traffic patterns under various conditions, which integrates the features of exponential smoothing and the Kalman filter by utilizing both real-time and historic data. The model is simple in formulation while robust in performance in terms of accuracy and stability. With a constraint or nonconstraint smoothing factor, the proposed model is tested with both real world and simulated data and demonstrated itself a sound model that outperforms others (e.g., Kalman filter and simple exponential smoothing) specifically under recurring and nonrecurring congestion. MARE, mean absolute relative error; SDAPE, standard deviation of absolute relative error; SES, simple exponential smoothing; ESES, enhanced SES; ESES-A, alternative ESES. 650 X. LIU ET AL.

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“…There has been limited research on arterial corridors. While some research has focused on arterial travel time estimation and measurement (Liu et al, 2005;Kwong et al, 2009;Liu et al, 2006b), other researchers have studied arterial performance and ranking corridors for the purpose of traffic signal retiming (Day et al, 2015;Lavrenz et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Although some of these studies focus on arterials, the majority put their attention towards freeway travel times. Of the arterial-related work, areas of focus include travel time prediction, (Polus, 1979;Sen et al, 1996;Liu et al, 2006a;Liu et al, 2006b); travel time estimation (H. X. Liu and Ma, 2009;Chan et al, 2009;Hans et al, 2015;Skabardonis and Geroliminis, 2005); and travel time distribution (Hans et al, 2015;Chen et al, 2017;Zheng et al, 2017;Ramezani and Geroliminis, 2012;Yang et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Understanding Factors Affecting Arterial Reliability Performance Metrics

Anderson¹,

Sirupa²,

Kothuri³

et al. 2019

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The contents of this report reflect the views of the authors, who are solely responsible for the facts and the accuracy of the material and information presented herein. This document is disseminated under the sponsorship of the U.S. Department of Transportation University Transportation Centers Program in the interest of information exchange. The U.S. Government assumes no liability for the contents or use thereof. The contents do not necessarily reflect the official views of the U.S. Government. This report does not constitute a standard, specification, or regulation.

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Predicting Urban Arterial Travel Time with State-Space Neural Networks and Kalman Filters

Cited by 55 publications

References 15 publications

LSTM network: a deep learning approach for short‐term traffic forecast

LSTM network: a deep learning approach for short‐term traffic forecast

An adaptive model for highway travel time prediction

Understanding Factors Affecting Arterial Reliability Performance Metrics

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