A Cell-Based Traffic Control Formulation: Strategies and Benefits of Dynamic Timing Plans

Lo, Hong Kam

doi:10.1287/trsc.35.2.148.10136

Cited by 211 publications

(87 citation statements)

References 13 publications

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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: Research Methodsologymentioning

confidence: 99%

“…In their method, they assumed availability of data from measurement sensors and only utilized these measurements, without using any kind of traffic flow model. Lo (2001) proposed a method for determining dynamic signal control timing plans based on system optimal principle using CTM based network model, which optimize network performance by keeping the density at an optimum level so as to ensure maximum flow on all links approaching a signalized intersection. The results indicated that green progression could reduce delays on the network.…”

Section: Introductionmentioning

confidence: 99%

“…Traffic state estimation can be considered equivalent to traffic flow prediction or traffic state reconstruction using observations from measurement sensors, as all these techniques aim to determine the state of the network (traffic flow, density, speed, travel times, etc.). Existing literature contains many studies combining traffic flow models and DTA, for example, Lo (1999), Ziliaskopoulos (2000), Lo (2001), Gomes and Horowitz (2006), Liu, Lai and Gang (2006), Chiu et al (2007). Similarly, measurements from traffic sensors have also been used for DTA and traffic management in real-time, e.g., Kachroo and Ozbay (1998), Mirchandani and Head (2001), Dotoli, Fanti, and Meloni (2006).…”

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: Research Methodsologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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“…Daganzo (1994) proposed a cell transmission model (CTM) that adopted a convergent approximation to the LWR model to evaluate the traffic on a highway network with a single entrance and exit. Afterwards, more cell types are applied in CTM to broaden its applicability (Daganzo 1995;Lo 1999;Lo 2001;Lo, Chang et al 2001). METANET (Messmer and Papageorgiou 1990) considered the situation of a motorway network based on the secondorder model.…”

Section: Introductionmentioning

confidence: 99%

A network traffic flow model for motorway and urban highways

Yang

Remenyte‐Prescott

Andrews

2014

Journal of the Operational Research Society

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The research reported in this paper develops a network level traffic flow model (NTFM) which is applicable for both motorway and urban roads. It forecasts the traffic flow rates, queue propagation at the junctions and travel delays through the network. NTFM uses sub-models associated with all road and junction types which comprise the highway. The flow at any one part of the network is obviously very dependent upon the flows at all other parts of the network. To predict the two-way traffic flow in NTFM, an iterative simulation method is executed to generate the evolution of dependent traffic flows and queues. To demonstrate the capability of the model it is applied to a small case study network and a local Loughborough-Nottingham highway network. The results indicate that NTFM is capable of identifying the relationship between traffic flows and capturing traffic phenomena such as queue dynamics. By introducing a reduced flow rate on links of the network then the effects of strategies employed to carry out roadworks can be mimicked. of pavements. The survey also reports that a further £10.65b is currently required to bring the UK's roads to the required standard. In addition to the expenditure on carrying out the work, the travel delay cost to road users caused by maintenance is significant and expected to substantially exceed the corresponding cost of maintenance. Consequently, with the purpose of examining and understanding the travel delays which occur when maintenance is performed, the NTFM network traffic flow model has been developed. It predicts the traffic flows and queues build up in a road network. When roadwork is performed it will cause a restriction in the flow rate capacity of parts of the network. By comparing the flow and delay characteristics resulting from different maintenance strategies the best way of keeping the highways in a good state of repair can be established.Typically, traffic flow models are categorised into two main groups: macroscopic models and microscopic models. Macroscopic traffic models are used to identify the aggregate behaviour of sets of vehicles, are generally easy to validate and ensure a good real-time quality, such as the 3 fluid-dynamic traffic models (Lighthill and Whitham 1955;Richards 1956). Microscopic models are applied to model the travel behaviour of an individual vehicle which is recognised as a function of the traffic conditions in its environment (Cremer and Ludwig 1986;Nagel and Schreckenberg 1992).As drivers' behaviour in real traffic is difficult to observe and measure, microscopic models are difficult to validate accurately (Daganzo 1994). In addition, the computational effort required by microscopic models is significantly higher than that for macroscopic models, and the data required by microscopic models are more difficult to record. Due to the reasons, described above, and the suitability of macroscopic models for network-level analysis, a macroscopic model has been developed in this paper.Many macroscopic traffic models have been constructed...

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“…(1) store-and-forward models (Hakimi, 1969;Singh and Tamura, 1974;D'Ans and Gazis, 1976), (2) disper sion-and-store models (Cremer and Schoof, 1989;Chang et al, 1994), and (3) kinematic wave models (Ste phanedes and Chang, 1993;Lo, 2001).…”

Section: Introductionmentioning

confidence: 99%

Stochastic adaptive control model for traffic signal systems

Recker

2006

Transportation Research Part C: Emerging Technologies

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An adaptive control model of a network of signalized intersections is proposed based on a discrete-time, stationary, Markov decision process. The model incorporates probabilistic forecasts of individual vehicle actuations at downstream inductance loop detectors that are derived from a macroscopic link transfer function. The model is tested both on a typical isolated traffic intersection and a simple network comprised of five four-legged signalized intersections, and compared to full-actuated control. Analyses of simulation results using this approach show significant improvement over traditional full-actuated control, especially for the case of high volume, but not saturated, traffic demand.

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A Cell-Based Traffic Control Formulation: Strategies and Benefits of Dynamic Timing Plans

Cited by 211 publications

References 13 publications

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

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

A network traffic flow model for motorway and urban highways

Stochastic adaptive control model for traffic signal systems

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