Reduction of travel times and traffic emissions using model predictive control

Zegeye, S. K.; De Schutter, Bart; Hellendoorn, Hans; Breunesse, E. A. · 2009 · OpenAlex-citations

DOI: 10.1109/acc.2009.5159942

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Summary

This paper addresses the challenge of simultaneously reducing travel times and traffic emissions in congested transportation networks. The authors highlight that while intelligent transportation systems can improve traffic flow, strategies focused solely on minimizing total time spent (TTS) do not necessarily reduce emissions and may even increase them. Conversely, emission-focused controls may compromise traffic efficiency. Motivated by the need to balance these conflicting objectives, the study proposes a Model Predictive Control (MPC) framework that integrates microscopic traffic flow models with average-speed-based emission models to optimize speed limit controls. The methodology employs a microscopic car-following model, specifically the Gazis-Herman-Rothery stimuli-response model, to simulate individual vehicle kinematics and driver behavior. This is coupled with a dynamic version of the COPERT III average-speed-based emission model to estimate carbon monoxide, nitrogen oxides, and hydrocarbon emissions based on local vehicle speeds. The MPC controller predicts the evolution of traffic states and emissions over a future horizon, optimizing a sequence of speed limits to minimize a weighted objective function comprising TTS, total emissions, and control input variations. The optimization problem, which is nonlinear and nonconvex, is solved using multi-start sequential quadratic programming. The approach is validated through a simulation of an 8 km single-lane freeway with an initial congestion jam and varying traffic demand. The results demonstrate that uncontrolled scenarios yield a TTS of 383.1 veh·h and total emissions of 13.24 kg. When the MPC controller targets only TTS reduction, travel time decreases by 45.1%, but emissions increase by 12.68%. In contrast, targeting only emission reduction lowers emissions by 37.39% but achieves a smaller TTS reduction of 11.23%. By assigning equal weights to both TTS and emissions, the controller achieves a balanced trade-off, reducing TTS by 11.1% and emissions by 37.45% simultaneously. The significance of this work lies in demonstrating that traffic control strategies must explicitly account for both travel time and environmental impact to avoid unintended negative consequences. The study confirms that optimizing for one metric in isolation can degrade the other, particularly in congested conditions. By integrating microscopic traffic dynamics with emission modeling within an MPC framework, the authors provide a viable method for traffic management authorities to achieve balanced improvements in both mobility and air quality through dynamic speed limit control.

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