Behavior of the cell transmission model and effectiveness of ramp metering

Gomes, Guilherme Ferreira; Horowitz, Roberto; Kurzhanskiy, Alex A.; Varaiya, Pravin; Kwon, Jaimyoung

doi:10.1016/j.trc.2007.10.005

Cited by 150 publications

(121 citation statements)

References 16 publications

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“…This section summarises the model and the traffic behavior predicted from the analysis of this model [4]. Figure 1 shows the freeway divided into N sections or cells, each with a maximum of one on-and one off-ramp.…”

Section: Cell Transmission Modelmentioning

confidence: 99%

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TOPL: Tools for Operational Planning of Transportation Networks

Chow

Dadok

Dervisoglu

et al. 2008

ASME 2008 Dynamic Systems and Control Conference, Parts a and B

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Section: Cell Transmission Modelmentioning

confidence: 99%

“…Subsequently, the California Cell Transmission model used for freeway corridor simulation. It also highlights main results advocating the use of ramp metering to alleviate excessive demand [4]. Section 3 explains the procedure adopted in TOPL for freeway network calibration, imputation, analysis and simulation.…”

Section: Introductionmentioning

confidence: 99%

TOPL: Tools for Operational Planning of Transportation Networks

Chow

Dadok

Dervisoglu

et al. 2008

ASME 2008 Dynamic Systems and Control Conference, Parts a and B

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“…Despite the limitation of LWR-type models such as assuming that the average speed adapts instantaneously to traffic density without considering any delay (Hoogendoorn and Bovy 2001) they are commonly used in a number of fields such as traffic control optimisation on motorways (e.g. Gomes et al 2008;Hadiuzzaman and Qiu 2013;and Li et al 2015), traffic signal optimisation on urban roads (e.g. Feldman and Maher 2002), and evacuation planning (e.g.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Propagation from the Cell Transmission Traffic Flow Model to Emission Predictions: A Data-Driven Approach

Sayegh¹,

Connors²,

Tate³

2018

Transportation Science

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Road traffic exhaust emission predictions are used to inform transport policy and investment decisions aimed at reducing emissions and achieving sustainable mobility. Emission predictions are also used as inputs when modelling air quality and human exposure to traffic-related air pollutants.To be effective, such policies and/or integration must be based on robust models that not only provide point-based predictions, but also inform these with an interval of confidence that properly accounts for the propagation of uncertainties through the complex chain of models involved. This paper develops a data-driven methodological framework which enables calculating the uncertainty in average speed-based emission predictions induced by uncertainty in its traffic data inputs which are most often predictions (or outputs) of traffic flow models. An ensemble-based optimisation approach is used to estimate both calibration and validation errors arising from uncertainty in the structure and parameterisation of the Cell Transmission Model (CTM); a discretised first-order macroscopic traffic flow model that is often integrated with average speed-based emission models. A Monte Carlo sampling approach is proposed to propagate the uncertainty in traffic flow inputs to emission predictions. To ensure transferability of findings, this methodology has been tested using multiple real data sets on three motorway road networks, one of which operates under Variable Speed Limits (VSL).

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“…It was proven in [12] that a traffic freeway system described by the Cell Transmission Model is asymptotically stable and consequently, for every fixed input it converges to some equilibrium state. Therefore, we can design desired density equilibria by finding the corresponding input flows.…”

Section: Introductionmentioning

confidence: 99%

Optimal balancing of road traffic density distributions for the Cell Transmission Model

Pisarski

Canudas‐de‐Wit

2012

2012 IEEE 51st IEEE Conference on Decision and Control (CDC)

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Abstract-In this paper, we study the problem of optimal balancing of traffic density distributions. The optimization is carried out over the sets of equilibrium points for the Cell Transmission Traffic Model. The goal is to find the optimal balanced density distribution that maximizes the Total Travel Distance. The optimization is executed in two steps. At the first step, we consider a nonlinear problem to find a uniform density distribution that maximizes the Total Travel Distance. The second step is to solve the constrained quadratic problem to find the near balanced optimal equilibrium point. At both steps, we use decomposition methods. The quadratic optimization problem is solved by using the Dual Problem. The computational algorithms associated to such a problem are given.

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Behavior of the cell transmission model and effectiveness of ramp metering

Cited by 150 publications

References 16 publications

TOPL: Tools for Operational Planning of Transportation Networks

TOPL: Tools for Operational Planning of Transportation Networks

Uncertainty Propagation from the Cell Transmission Traffic Flow Model to Emission Predictions: A Data-Driven Approach

Optimal balancing of road traffic density distributions for the Cell Transmission Model

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