Minimizing network-wide emissions by optimal routing through inner-city gating

Abstract In this paper, we propose a control and optimization framework to reduce network-wide emissions in an urban traffic network. The framework is comprised of two layers. The first layer (optimal green routing) predicts the optimal splitting coefficients for all Origin–Destination (OD) pairs crossing the city center or bypassing. As implementing optimal routing strategies on the field is almost impossible as we do not have direct control over the user’s decisions, we choose to balance travel time through gating at the city perimeter so that the usual Dynamic User Equilibrium (DUE) discipline matches the optimal splitting coefficients. The second layer then adjusts inflows at the city gates to the DUE solution based on the instantaneous travel times corresponding to the optimal routing strategy for each OD pair. The accumulation-based Macroscopic Fundamental Diagram (MFD) model of a single reservoir city with seven arterial routes and six bypass alternatives is developed. A Linear programming problem is formulated to determine the optimal splitting coefficients and a Nonlinear Model Predictive Control (NMPC)-based gating control strategy is designed to track the optimal splitting coefficients. The network-wide emission control framework is compared to three other gating strategies aiming to (i) optimize traffic conditions or (ii) minimize emissions, in the inner-city only, and (iii) optimize traffic conditions in the whole network. A comprehensive analysis conducted on all four approaches is presented. We compare the results of the controlled case with respect to the uncontrolled case and with respect to each other. The comparison results show that: (i) the proposed network-wide emission control strategy significantly outperforms the other two simpler control strategies focusing on inner-city only in reducing the total emission, (ii) but also improves the in total time spent and mean speed at the network-level.

[1]  Nikolas Geroliminis,et al.  Optimal Perimeter Control for Two Urban Regions With Macroscopic Fundamental Diagrams: A Model Predictive Approach , 2013, IEEE Transactions on Intelligent Transportation Systems.

[2]  Weimin Wu,et al.  A survey of model predictive control methods for traffic signal control , 2019, IEEE/CAA Journal of Automatica Sinica.

[3]  István Varga,et al.  Macroscopic modeling and control of emission in urban road traffic networks , 2015 .

[4]  Shangtai Jin,et al.  Model-Free Adaptive Predictive Control for an Urban Road Traffic Network via Perimeter Control , 2019, IEEE Access.

[5]  Jorge A. Laval,et al.  Macroscopic urban dynamics: Analytical and numerical comparisons of existing models , 2017 .

[6]  Simona Sacone,et al.  A multi-class model-based control scheme for reducing congestion and emissions in freeway networks by combining ramp metering and route guidance , 2017 .

[7]  Andreas Hegyi,et al.  Hierarchical ramp metering in freeways: An aggregated modeling and control approach , 2020 .

[8]  Gang Hu,et al.  Hierarchical perimeter control with guaranteed stability for dynamically coupled heterogeneous urban traffic , 2017 .

[9]  Stefan Hausberger,et al.  Road vehicle emission factors development: A review , 2013 .

[10]  Markos Papageorgiou,et al.  Sustainable Model-Predictive Control in Urban Traffic Networks: Efficient Solution Based on General Smoothening Methods , 2018, IEEE Transactions on Control Systems Technology.

[11]  Fang Guo,et al.  Traffic guidance–perimeter control coupled method for the congestion in a macro network , 2017 .

[12]  Nikolas Geroliminis,et al.  Hierarchical control of heterogeneous large-scale urban road networks via path assignment and regional route guidance , 2018, Transportation Research Part B: Methodological.

[13]  Ludovic Leclercq,et al.  Nonlinear Model Predictive Control to Reduce Network-Wide Traffic Emission , 2019 .

[14]  Alberto Bemporad,et al.  Predictive Control for Linear and Hybrid Systems , 2017 .

[15]  Carlos F. Daganzo,et al.  Urban Gridlock: Macroscopic Modeling and Mitigation Approaches , 2007 .

[16]  Wei Ni,et al.  City-wide traffic control: Modeling impacts of cordon queues , 2020, Transportation Research Part C: Emerging Technologies.

[17]  Nikolas Geroliminis,et al.  Optimal Hybrid Perimeter and Switching Plans Control for Urban Traffic Networks , 2015, IEEE Transactions on Control Systems Technology.

[18]  Martin Litzenberger,et al.  Impact of Traffic Management on Black Carbon Emissions: a Microsimulation Study , 2017 .

[19]  David Q. Mayne,et al.  Model predictive control: Recent developments and future promise , 2014, Autom..

[20]  Monica Menendez,et al.  Multi-scale perimeter control approach in a connected-vehicle environment , 2016, Transportation Research Part C: Emerging Technologies.

[21]  Nikolas Geroliminis,et al.  A linear formulation for model predictive perimeter traffic control in cities , 2017 .

[22]  Hai Yang,et al.  Managing congestion and emissions in road networks with tolls and rebates , 2012 .

[23]  Ludovic Leclercq,et al.  Flow exchanges in multi-reservoir systems with spillbacks , 2019, Transportation Research Part B: Methodological.

[24]  Hwasoo Yeo,et al.  Distributed Model Predictive Approach for Large-Scale Road Network Perimeter Control , 2019, Transportation Research Record: Journal of the Transportation Research Board.

[25]  F. Cavallaro,et al.  The potential of road pricing schemes to reduce carbon emissions , 2016, Transport Policy.

[26]  Bart De Schutter,et al.  Model predictive control for freeway traffic using discrete speed limit signals , 2013, 2013 European Control Conference (ECC).

[27]  Andreas Hegyi,et al.  An Extended Linear Quadratic Model Predictive Control Approach for Multi-Destination Urban Traffic Networks , 2019, IEEE Transactions on Intelligent Transportation Systems.

[28]  István Varga,et al.  A hybrid model predictive control for traffic flow stabilization and pollution reduction of freeways , 2018 .

[29]  Nikolas Geroliminis,et al.  Economic Model Predictive Control of Large-Scale Urban Road Networks via Perimeter Control and Regional Route Guidance , 2018, IEEE Transactions on Intelligent Transportation Systems.

[30]  Leonidas Ntziachristos,et al.  Enhancing average speed emission models to account for congestion impacts in traffic network link-based simulations , 2019, Transportation Research Part D: Transport and Environment.

[31]  Hani S. Mahmassani,et al.  INVESTIGATION OF NETWORK-LEVEL TRAFFIC FLOW RELATIONSHIPS: SOME SIMULATION RESULTS , 1984 .

[32]  Nikolaos Geroliminis,et al.  Enhancing model-based feedback perimeter control with data-driven online adaptive optimization , 2017 .

[33]  Bart De Schutter,et al.  Integrated urban traffic control for the reduction of travel delays and emissions , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[34]  Qi Guo,et al.  Freeway Traffic Congestion Reduction and Environment Regulation via Model Predictive Control , 2019, Algorithms.

[35]  Ali Zockaie,et al.  A Framework for Incorporating the Network-Wide Fundamental Diagram into Large-Scale Emission Estimation , 2018 .

[36]  Nikolas Geroliminis,et al.  Doubly dynamics for multi-modal networks with park-and-ride and adaptive pricing , 2017 .

[37]  W. Y. Szeto,et al.  Dynamic traffic assignment: A review of the methodological advances for environmentally sustainable road transportation applications , 2018 .

[38]  Lihua Luo,et al.  Real-time route diversion control in a model predictive control framework with multiple objectives: Traffic efficiency, emission reduction and fuel economy , 2016 .

[39]  Jack Haddad,et al.  Robust perimeter control design for an urban region , 2014 .

[40]  Allison DenBleyker,et al.  Comparison of the MOVES2010a, MOBILE6.2, and EMFAC2007 mobile source emission models with on-road traffic tunnel and remote sensing measurements , 2012, Journal of the Air & Waste Management Association.

[41]  W. Y. Szeto,et al.  Congestion and environmental toll schemes for the morning commute with heterogeneous users and parallel routes , 2019, Transportation Research Part B: Methodological.

[42]  Bart De Schutter,et al.  Traffic Management for Automated Highway Systems Using Model-Based Predictive Control , 2012, IEEE Transactions on Intelligent Transportation Systems.

[43]  Antonella Ferrara,et al.  Freeway Traffic Modelling and Control , 2018 .

[44]  N. Geroliminis,et al.  Existence of urban-scale macroscopic fundamental diagrams: Some experimental findings - eScholarship , 2007 .

[45]  Carolina Osorio,et al.  Urban transportation emissions mitigation: Coupling high-resolution vehicular emissions and traffic models for traffic signal optimization , 2015 .

[46]  Bart De Schutter,et al.  Integrated Model Predictive Traffic and Emission Control Using a Piecewise-Affine Approach , 2013, IEEE Transactions on Intelligent Transportation Systems.

[47]  Leonidas Ntziachristos,et al.  COPERT: A European road transport emission inventory model , 2009, ITEE.

[48]  S. Gokhale,et al.  The impact of traffic-flow patterns on air quality in urban street canyons. , 2016, Environmental pollution.

[49]  Nikolas Geroliminis,et al.  Cooperative traffic control of a mixed network with two urban regions and a freeway , 2013 .

[50]  Nikolas Geroliminis,et al.  Model predictive control of large-scale urban networks via perimeter control and route guidance actuation , 2016, 2016 IEEE 55th Conference on Decision and Control (CDC).

[51]  Ludovic Leclercq,et al.  Perimeter gating control and citywide dynamic user equilibrium: A macroscopic modeling framework , 2020 .

[52]  Sharad Gokhale,et al.  Urban real-world driving traffic emissions during interruption and congestion , 2016 .

[53]  Simona Sacone,et al.  Two-class freeway traffic regulation to reduce congestion and emissions via nonlinear optimal control , 2015 .

[54]  Nikolaos Geroliminis,et al.  Cruising-for-parking in congested cities with an MFD representation , 2015 .

[55]  A. Can,et al.  Accounting for traffic speed dynamics when calculating COPERT and PHEM pollutant emissions at the urban scale , 2018, Transportation Research Part D: Transport and Environment.