Simulation-Based Travel Time Reliable Signal Control

Xiao, Chi; Osorio, Carolina; Santos, Bruno F.

doi:10.1287/trsc.2017.0812

Cited by 23 publications

(17 citation statements)

References 51 publications

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“…The broad family of metamodels defined by (6) have been used to efficiently address large-scale urban traffic management problems while using inefficient yet detailed stochastic microscopic simulators (Osorio and Selvam, 2016, Osorio and Chong, 2015, Osorio and Nanduri, 2015a,b, Chen et al, 2012, Chong and Osorio, 2016. Metamodel approaches have also been used recently for addressing various transportation problems, such as in Chen et al (2016), where a pricing optimization problem is addressed based on a large-scale mesoscopic network model.…”

Section: Metamodel Simulation-based Optimization Methodsmentioning

confidence: 99%

Efficient calibration techniques for large-scale traffic simulators

Zhang

Osorio

Flötteröd

2017

Transportation Research Part B: Methodological

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Road transportation simulators are increasingly used by transportation stakeholders around the world for the analysis of intricate transportation systems. Model calibration is a crucial prerequisite for transportation simulators to reliably reproduce and predict traffic conditions. This paper considers the calibration of transportation simulators. The methodology is suitable for a broad family of simulators. Its use is illustrated with stochastic and computationally costly simulators. The calibration problem is formulated as a simulation-based optimization (SO) problem. We propose a metamodel approach. The analytical metamodel combines information from the simulator with information from an analytical differentiable and tractable network model that relates the calibration parameters to the simulation-based objective function. The proposed algorithm is validated by considering synthetic experiments on a toy network. It is then used to address a calibration problem with real data for a large-scale network: the Berlin metropolitan network with over 24300 links and 11300 nodes. The performance of the proposed approach is compared to a traditional benchmark method. The proposed approach significantly improves the computational efficiency of the calibration algorithm with an average reduction in simulation runtime until convergence of more than 80%. The results illustrate the scalability of the approach and its suitability for the calibration of large-scale computationally inefficient network simulators.

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Section: Metamodel Simulation-based Optimization Methodsmentioning

confidence: 99%

Efficient calibration techniques for large-scale traffic simulators

Zhang

Osorio

Flötteröd

2017

Transportation Research Part B: Methodological

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“…In Section 2.3, we formulate an analytical traffic model with all of the above properties for toll optimization problems. This general metamodel SO idea has been formulated and used to design efficient algorithms for various transportation problems, including various traffic signal control problems (Osorio and Chong 2015, Osorio and Nanduri 2015, Chong and Osorio 2018, Chen et al 2019, and more recently for model calibration problems (Zhang et al 2017).…”

Section: Metamodel So Algorithmmentioning

confidence: 99%

Efficient Simulation-Based Toll Optimization for Large-Scale Networks

Osorio

Atasoy

2021

Transportation Science

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This paper proposes a simulation-based optimization technique for high-dimensional toll optimization problems of large-scale road networks. We formulate a novel analytical network model. The latter is embedded within a metamodel simulation-based optimization (SO) algorithm. It provides analytical and differentiable structural information of the underlying problem to the SO algorithm. Hence, the algorithm no longer treats the simulator as a black box. The analytical model is formulated as a system of nonlinear equations that can be efficiently evaluated with standard solvers. The dimension of the system of equations scales linearly with network size. It scales independently of the dimension of the route choice set and of link attributes such as link length. Hence, it is a scalable formulation suitable for the optimization of large-scale networks. For instance, the model is used in the case study of the paper for toll optimization of a Singapore network with more than 4,050 OD (origin-destination) pairs and 18,200 feasible routes. The corresponding analytical model is implemented as a system of 860 nonlinear equations. The analytical network model is validated based on one-dimensional toy network problems. It captures the main trends of the simulation-based objective function and, more importantly, accurately locates the global optimum for all experiments. The proposed SO approach is then used to optimize a set of 16 tolls for the network of expressways and major arterials of Singapore. The proposed method is compared with a general-purpose algorithm. The proposed method identifies good quality solutions at the very first iteration. The benchmark method identifies solutions with similar performance after 2 days of computation or similarly after more than 30 points have been simulated. The case study indicates that the analytical structural information provided to the algorithm by the analytical network model enables it to (i) identify good quality solutions fast and (ii) become robust to both the quality of the initial points and to the stochasticity of the simulator. The final solutions identified by the proposed algorithm outperform those of the benchmark method by an average of 18%.

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“…References [41][42][43][44][45][46][47][48] employ a mathematical optimization method along with micro-simulation. The methods employed range from dynamic programming to backpressure to optimal control.…”

Section: Miscellaneous Approachesmentioning

confidence: 99%

“…To deal with the reliable TSC problem, Chen et al [45] presented an approach in which the higher-order distributional information that was derived from a stochastic microscopic simulator was used. The TSC problem was based on a linear combination of the expectation of total travel time and its standard deviation.…”

Section: Miscellaneous Approachesmentioning

confidence: 99%

State-of-art review of traffic signal control methods: challenges and opportunities

Qadri

Gökçe

Öner

2020

Eur. Transp. Res. Rev.

111

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Introduction Due to the menacing increase in the number of vehicles on a daily basis, abating road congestion is becoming a key challenge these years. To cope-up with the prevailing traffic scenarios and to meet the ever-increasing demand for traffic, the urban transportation system needs effective solution methodologies. Changes made in the urban infrastructure will take years, sometimes may not even be feasible. For this reason, traffic signal timing (TST) optimization is one of the fastest and most economical ways to curtail congestion at the intersections and improve traffic flow in the urban network. Purpose Researchers have been working on using a variety of approaches along with the exploitation of technology to improve TST. This article is intended to analyze the recent literature published between January 2015 and January 2020 for the computational intelligence (CI) based simulation approaches and CI-based approaches for optimizing TST and Traffic Signal Control (TSC) systems, provide insights, research gaps and possible directions for future work for researchers interested in the field. Methods In analyzing the complex dynamic behavior of traffic streams, simulation tools have a prominent place. Nowadays, microsimulation tools are frequently used in TST related researches. For this reason, a critical review of some of the widely used microsimulation packages is provided in this paper. Conclusion Our review also shows that approximately 77% of the papers included, utilizes a microsimulation tool in some form. Therefore, it seems useful to include a review, categorization, and comparison of the most commonly used microsimulation tools for future work. We conclude by providing insights into the future of research in these areas.

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Simulation-Based Travel Time Reliable Signal Control

Cited by 23 publications

References 51 publications

Efficient calibration techniques for large-scale traffic simulators

Efficient calibration techniques for large-scale traffic simulators

Efficient Simulation-Based Toll Optimization for Large-Scale Networks

State-of-art review of traffic signal control methods: challenges and opportunities

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