Traffic State Estimation Method for Arterial Street

Liu, Gang; Jiang, Guiyan; Cai, Zhengyi

doi:10.1061/40932(246)73

Cited by 4 publications

(5 citation statements)

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“…In comparison to other higher order traffic flow models, CTM has lesser number of output variables and input parameters which qualifies CTM as a suitable model for real-time applications. CTM has been used for traffic state estimation (Munoz et al 2003;Munoz et al 2006;Gang, Jiang, and Cai 2007;Tampere andImmers 2007, Long et al 2008;Long et al 2011) as well as for DTA applications of traffic network optimization (Lo 1999, Ziliaskopoulos 2000, Lo 2001, Gomes and Horowitz 2006, Liu, Lai and Gang 2006, Chiu et al 2007). …”

Section: Methodsmentioning

confidence: 99%

“…Park and Lee (2004) used a Bayesian technique to estimate travel speed for a link of an urban arterial using data from a dual loop detector. Gang, Jiang, and Cai (2007) presented a traffic state estimation scheme based on the Cell Transmission Model (CTM) and Kalman filter for a single urban arterial street under signal control. Liu et al (2012) proposed a travel time estimation approach for a long corridor with signalized intersections based on probe vehicle data.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of traveller information and route choice based on real-time estimated traffic state

Ahmed

Ngoduy

Watling

2015

Transportmetrica B: Transport Dynamics

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Prediction of traveller information and route choice based on real-time estimated traffic stateAccurate depiction of existing traffic states is essential to devise effective real-time traffic management strategies using Intelligent Transportation Systems (ITS). Existing applications of Dynamic Traffic Assignment (DTA) methods are mainly based on either the prediction from macroscopic traffic flow models or measurements from the sensors and do not take advantage of the traffic state estimation techniques, which produce estimate of the traffic states which has less uncertainty than the prediction or measurement alone. On the other hand, research studies which highlight estimation of real-time traffic state are focused only on traffic state estimation and have not utilized the estimated traffic state for DTA applications. In this paper we propose a framework which utilizes real-time traffic state estimate to optimize network performance during an incident through traveller information system. The estimate of real-time traffic states is obtained by combining the prediction of traffic density using Cell Transmission Model (CTM) and the measurements from the traffic sensors in Extended KalmanFilter (EKF) recursive algorithm. The estimated traffic state is used for predicting travel times on alternative routes in a small traffic network and the predicted travel times are communicated to the commuters by a variable message sign (VMS). In numerical experiments on a two-route network, the proposed estimation and information method is seen to significantly improve travel times and network performance during a traffic incident.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of traveller information and route choice based on real-time estimated traffic state

Ahmed

Ngoduy

Watling

2015

Transportmetrica B: Transport Dynamics

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“…In comparison with other higher-order traffic flow models, CTM has fewer output variables and input parameters, which qualifies CTM as a suitable model for real-time applications (11). The CTM has been used for real-time traffic-state estimation (16)(17)(18)(19)(20)45) as well as for optimization of traffic networks (23,30,(46)(47)(48)(49)(50)(51)(52).…”

Section: Prediction Of Traffic State Using Ctmmentioning

confidence: 99%

“…This paper adapts the CTM-EKF framework proposed by Ahmed et al ( 11 ) for real-time estimation of traffic state with some modifications. Ahmed et al ( 11 ) and other research studies ( 16 – 20 , 45 ) estimated traffic density recursively by taking prediction of traffic density from CTM and assuming real-time measurements of traffic occupancy (density) from sensors. This study estimates inflow to cells (flow rates) instead of traffic density (cell occupancy) as flow rates are one of the basic measurements from traffic sensors.…”

Section: Dynamic Traffic Estimation Control and Feedback Frameworkmentioning

confidence: 99%

“…Ahmed et al ( 11 ), Wang et al ( 12 ), Wang et al ( 13 ), Wang and Papageorgiou ( 14 ), and Meier and Wehlan ( 15 ) applied extended Kalman filter (EKF) for traffic-flow-based, real-time estimation of traffic state. Munoz et al ( 16 ), Gang et al ( 17 ), Tampere and Immers ( 18 ), Long et al ( 19 ) and Long et al ( 20 ) estimated real-time traffic state using Kalman filter approach based on the cell transmission model (CTM).…”

mentioning

confidence: 99%

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Real-Time Dynamic Traffic Control Based on Traffic-State Estimation

Ahmed

Naqvi

Watling

et al. 2019

Transportation Research Record: Journal of the Transportation R

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31 32 33 Word count: 7434 words text + 0 table x 250 words (each) = 7,434 words ABSTRACT 1 The accurate depiction of the existing traffic state on a road network is essential in reducing 2 congestion and delays at signalized intersections. The existing literature in the optimization of 3 signal timings either utilizes prediction of traffic state from traffic flow models or limited real-time 4 measurements available from sensors. Prediction of traffic state based on historic data cannot 5 represent the dynamics of change in traffic demand or network capacity. Similarly, data obtained 6 from limited point sensors in a network provides estimates which contain errors. A reliable 7 estimate of existing traffic state is, therefore, necessary to obtain signal timings which are based on 8 the existing condition of traffic on the network. 9 This research proposes a framework which utilizes estimates of traffic flows and travel times based 10 on real-time estimated traffic state for obtaining optimal signal timings. The prediction of traffic 11 state from the Cell Transmission Model (CTM) and measurements from traffic sensors are 12 combined in the recursive algorithm of Extended Kalman Filter (EKF) to obtain a reliable estimate 13 of existing traffic state. The estimate of traffic state obtained from the CTM-EKF model is utilized 14 in the optimization of signal timings using Genetic Algorithm (GA) in the proposed 15 CTM-EKF-GA framework. 16The proposed framework is applied to a synthetic signalized intersection and the results are 17 compared with a model-based optimal solution and simulated reality. The optimal delay estimated 18 by CTM-EKF-GA framework is only 0.6% higher than the perfect solution, whereas the delay 19 estimated by CTM-GA model is 12.9% higher than the perfect solution. 20 21 22 23

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