Framework for Real-Time Traffic Management with Case Studies

Han, Ke

doi:10.3141/2658-05

“…3.4 each row corresponds to one sensor, and each column represents one single time step. The integer n is used to indicate the number of past time steps considered when making decisions at the current time step t; m is used to account for the fact that data collected in the most recent time intervals may not be immediately available for decision making due to limited capacities of data transmission and computation (Han 2017).…”

Section: Traffic Network State Variablesmentioning

confidence: 99%

“…The inherent stochasticity in the network states q can be handled in different ways such as using robust optimization and stochastic optimization, with varying degrees of conservatism and computational complexity; see Bertsimas et al (2011) and Liu et al (2015) and Han (2017) for more discussions. In this paper, due to the potentially expensive evaluation procedure, which is done through microscopic traffic and emission simulations, we propose a Monte-Carlo type evaluation method.…”

Section: Off-line Optimization Of the Ndrmentioning

confidence: 99%

Nonlinear Decision Rule Approach for Real-Time Traffic Signal Control for Congestion and Emission Mitigation

Song

¹

,

Hu

²

,

Han

³

et al. 2020

Self Cite

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We propose a real-time signal control framework based on a nonlinear decision rule (NDR), which defines a nonlinear mapping between network states and signal control parameters to actual signal controls based on prevailing traffic conditions, and such a mapping is optimized via off-line simulation. The NDR is instantiated with two neural networks: feedforward neural network (FFNN) and recurrent neural network (RNN), which have different ways of processing traffic information in the near past, and are compared in terms of their performances. The NDR is implemented within a microscopic traffic simulation (S-Paramics) for a real-world network in West Glasgow, where the off-line training of the NDR amounts to a simulation-based optimization aiming to reduce delay, CO 2 and black carbon emissions. The emission calculations are based on the high-fidelity vehicle dynamics generated by the simulation, and the AIRE instantaneous emission model. Extensive tests are performed to assess the NDR framework, not only in terms of its effectiveness in reducing the aforementioned objectives, but also in relation to local vs. global benefits, trade-off between delay and emissions, impact of sensor locations, and different levels of network saturation. The results suggest that the NDR is an effective, flexible and robust way of alleviating congestion and reducing traffic emissions.

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“…On the right hand side of (3.4) each row corresponds to one sensor, and each column represents one single time step. The integer n is used to indicate the number of past time steps considered when making decisions at the current time step t; m is used to account for the fact that data collected in the most recent time intervals may not be immediately available for decision making due to limited capacities of data transmission and computation (Han, 2017).…”

Section: Traffic Network State Variablesmentioning

confidence: 99%

“…The inherent stochasticity in the network states q can be handled in different ways such as using robust optimization and stochastic optimization, with varying degrees of conservatism and computational complexity; see Bertsimas et al (2011); Liu et al (2015) and Han (2017) for more discussions. In this paper, due to the potentially expensive evaluation procedure, which is done through microscopic traffic and emission simulations, we propose a Monte-Carlo type evaluation method.…”

Section: Off-line Optimization Of the Ndrmentioning

confidence: 99%

Nonlinear Decision Rule Approach for Real-Time Traffic Signal Control for Congestion and Emission Reductions

Song,

Hu,

Han

et al. 2018

Preprint

Self Cite

0

View full text Add to dashboard Cite

We propose a real-time signal control framework based on a nonlinear decision rule (NDR), which defines a nonlinear mapping between network states and signal control parameters to actual signal controls based on prevailing traffic conditions, and such a mapping is optimized via off-line simulation. The NDR is instantiated with two neural networks: feedforward neural network (FFNN) and recurrent neural network (RNN), which have different ways of processing traffic information in the near past, and are compared in terms of their performances. The NDR is implemented within a microscopic traffic simulation (S-Paramics) for a real-world network in West Glasgow, where the off-line training of the NDR amounts to a simulation-based optimization aiming to reduce delay, CO 2 and black carbon emissions. The emission calculations are based on the high-fidelity vehicle dynamics generated by the simulation, and the AIRE instantaneous emission model. Extensive tests are performed to assess the NDR framework, not only in terms of its effectiveness in reducing the aforementioned objectives, but also in relation to local vs. global benefits, trade-off between delay and emissions, impact of sensor locations, and different levels of network saturation. The results suggest that the NDR is an effective, flexible and robust way of alleviating congestion and reducing traffic emissions.

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Travel modeling under foreseeable communications-and-mobility technologies: Part I (short-term view)

Chan

¹

2019

Advances in Transport Policy and Planning

0

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Framework for Real-Time Traffic Management with Case Studies

Cited by 3 publications

References 7 publications

Nonlinear Decision Rule Approach for Real-Time Traffic Signal Control for Congestion and Emission Mitigation

Nonlinear Decision Rule Approach for Real-Time Traffic Signal Control for Congestion and Emission Mitigation

Nonlinear Decision Rule Approach for Real-Time Traffic Signal Control for Congestion and Emission Reductions

Travel modeling under foreseeable communications-and-mobility technologies: Part I (short-term view)

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