Energy-Efficient Urban Traffic Management: A Microscopic Simulation-Based Approach

Abstract

M icroscopic urban traffic simulators embed the most detailed traveler behavior and network supply models. These simulators represent individual vehicles and can therefore account for vehicle-specific technologies. They can be coupled with instantaneous fuel consumption models to yield detailed network-wide fuel consumption estimates. Nonetheless, there is currently a lack of computationally efficient optimization techniques that enable the use of these complex integrated models to design sustainable transportation strategies. This paper proposes a methodology that combines a stochastic microscopic traffic simulation model with an instantaneous vehicular fuel consumption model. The combined models are embedded within a simulation-based optimization algorithm and used to address a signal control problem that accounts for both travel times and fuel consumption. The proposed technique couples detailed, stochastic, and computationally inefficient models, yet is an efficient optimization technique. Efficiency is achieved by combining simulated observations with analytical approximations of both travel time and fuel consumption. This methodology is applied to a network in the Swiss city of Lausanne. Within a tight computational budget, the proposed method identifies signal plans with improved travel time and fuel consumption metrics. It outperforms traditional methodologies, which use only simulated information or only analytical information. The case study illustrates the added value of combining simulated and analytical information when performance metrics with high variance, such as fuel consumption, are used. This method enables the use of disaggregate instantaneous vehicle-specific information to inform and improve traffic operations at the network-scale.

Analytical and Scalable Analysis of Transient Tandem Markovian Finite Capacity Queueing Networks

“…Additionally, the results of Section 4 show that the signal plans derived by the proposed transient model outperform the signal plans derived by the stationary model used in past work for simulation-based signal control (Osorio and Bierlaire 2013, Osorio and Chong 2013, Osorio and Nanduri 2013. This indicates the potential of the proposed model to enhance the performance of existing SO frameworks.…”

A Simulation-Based Traffic Signal Control for Congested Urban Traffic Networks

“…However, in most practical cases mesoscopic/microscopic simulators like AIM-SUN emulate the behavior of real networks with higher precision. Furthermore, mesoscopic/microscopic models offer the opportunity to check important features of a traffic network like position and velocity of single vehicles and emissions (Papageorgiou 1998;Osorio and Nanduri 2015).…”

Simulation-Based Optimization: Achieving Computational Efficiency Through the Use of Multiple Simulators

“…For both case studies of this paper, the values of the computational budget (i.e. the total number of simulation runs) and of the number of simulation replications per point are chosen such as to be consistent with past SO work (Osorio and Chong, 2014, Osorio and Nanduri, 2014, Osorio and Bierlaire, 2013, Chen et al, 2013.…”

“…Nonetheless, the computational cost of these models, along with their stochasticity and the, typically large number of simulation evaluations required by traditional optimization methods, makes their direct use for optimization computationally inefficient. In past work, the efficiency of SO algorithms that embed microscopic models has been achieved by combining information from the large-scale high-resolution inefficient simulator with information from analytical and highly efficient traffic models (Osorio and Chong, 2014, Osorio and Nanduri, 2014, Osorio and Bierlaire, 2013, Chen et al, 2013. The SO approach proposed in this paper achieves efficiency by combining information from the high-resolution large-scale, and hence inefficient, simulator with information from a highresolution small-scale, and hence more efficient, simulator.…”