Schedule-driven intersection control

Xie, Xiao-Feng; Smith, Stephen F.; Lü, Liang; Barlow, Gregory J.

doi:10.1016/j.trc.2012.03.004

Cited by 91 publications

(62 citation statements)

References 26 publications

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“…He et al (2014) proposed a request-based MILP model to consider multiple modes of vehicles as well as pedestrians. Xie et al (2012) proposed adaptive signal control strategy based on the concept of single machine scheduling which shares some similarity with the RHODES system in predicting vehicle arrivals. Sun et al (2015) proposed a ''quasi-optimal" (i.e., sub-optimal) signal control strategy at oversaturated intersections based on queue length estimation and store-and-forward network modeling.…”

Section: Traffic Signal Optimization Modelsmentioning

confidence: 99%

Solving simultaneous route guidance and traffic signal optimization problem using space-phase-time hypernetwork

Mirchandani

Zhou

2015

Transportation Research Part B: Methodological

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a b s t r a c tThis paper addresses the problem of simultaneous route guidance and traffic signal optimization problem (RGTSO) where each vehicle in a traffic network is guided on a path and the traffic signals servicing these vehicles are set to minimize their travel times. The network is modeled as a space-phase-time (SPT) hyper-network to explicitly represent the traffic signal control phases and time-dependent vehicle paths. A Lagrangianrelaxation-based optimization framework is proposed to decouple the RGTSO problem into two subproblems: the Route Guidance (RG) problem for multiple vehicles with given origins and destinations and the Traffic Signal Optimization (TSO) problem. In the RG subproblem, the route of each vehicle is provided subject to time-dependent link capacities imposed by the solution of the TSO problem, while the traffic signal timings are optimized according to the respective link travel demands aggregated from the vehicle trajectories. The dual prices of the RG subproblem indicate search directions for optimization of the traffic signal phase sequences and durations in the TSO subproblem. Both RG and TSO subproblems can be solved using a computationally efficient finite-horizon dynamic programming framework, enhanced by parallel computing techniques. Two numerical experiments demonstrated that the system optimum of the RGTSO problem can be quickly reached with relatively small duality gap for medium-size urban networks.

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Section: Traffic Signal Optimization Modelsmentioning

confidence: 99%

Solving simultaneous route guidance and traffic signal optimization problem using space-phase-time hypernetwork

Mirchandani

Zhou

2015

Transportation Research Part B: Methodological

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“…Schedule-driven traffic control [5], [6] is a recently developed approach to real-time adaptive traffic signal control, designed specifically to accommodate urban road networks. It follows the same decentralized structure of earlier "online planning" approaches to signal control [2], but avoids the prohibitive computational expense of earlier approaches through use of a more efficient core problem formulation.…”

Section: Schedule-driven Traffic Controlmentioning

confidence: 99%

“…1) Intersection Level: At the individual intersection level, the ability to consider real-time variability of traffic flows is made tractable by a novel formulation of online planning as a single machine scheduling problem [5]. Key to this formulation is an aggregate representation of traffic flows as inflows, i.e., IF = (J 1 , · · · , J |I| ), where J i contains those vehicles with the right of way during phase i.…”

Section: Schedule-driven Traffic Controlmentioning

confidence: 99%

“…Recent work in schedule-driven control of traffic signals [5], [6] has demonstrated the strong potential of decentralized, real-time adaptive signal control in urban environments. In an initial urban deployment, improvements of over 26% reductions in travel times, over 40% reductions in idle time, and a projected reduction in emissions of over 21% were achieved [7].…”

Section: Introductionmentioning

confidence: 99%

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Real-time traffic control for sustainable urban living

Xie

Smith

Chen

et al. 2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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Traffic congestion significantly degrades the quality of life in urban environments. It results in lost time, wasted fuel resources and reduced air quality for urban residents. Recent work in real-time, schedule-driven control of traffic signal networks has introduced new possibilities for reducing congestion in urban environments. However so far, this work has focused mainly on achieving more efficient vehicle flows and on reducing emissions, and the broader mobility of other modes of traffic that are central to sustainable urban living -especially pedestrians -has not been emphasized in an integrated way. In this paper, we extend this decentralized, schedule-driven approach to traffic network control in the direction of this broader mobility objective. We focus specifically on accommodating pedestrians and optimizing the delay tradeoffs between vehicles and pedestrians. Three basic extensions are introduced and evaluated on a real-world road network. Simulation results are first presented which analyze the ability of the extended approach to achieve good delay tradeoffs under different pedestrian traffic conditions. Tests are then performed in the field at selected intersections of the target road network, to demonstrate the effectiveness of the approach in operation.

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“…Therefore, in many cases, it is a myopic control approach [2,3]. Since their settings are based on history traffic flow information, fixed time coordinated control strategies can only provide good control performance under the condition of minor fluctuations in traffic demand [4].…”

Section: Introductionmentioning

confidence: 99%

Stochastic Model Predictive Control for Urban Traffic Networks

Gao

et al. 2017

Applied Sciences

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This paper proposes a stochastic model predictive control (MPC) framework for traffic signal coordination and control in urban traffic networks. One of the important features of the proposed stochastic MPC model is that uncertain traffic demands and stochastic disturbances are taken into account. Aiming to effectively model the uncertainties and avoid queue spillback in traffic networks, we develop a stochastic expected value model with chance constraints for the objective function of the stochastic MPC model. The objective function is defined to minimize the queue length and the oscillation of green time between any two control steps. Furthermore, by embedding the stochastic simulation and neural networks into a genetic algorithm, we propose a hybrid intelligent algorithm to solve the stochastic MPC model. Finally, numerical results by means of simulation on a road network are presented, which illustrate the performance of the proposed approach.

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Schedule-driven intersection control

Cited by 91 publications

References 26 publications

Solving simultaneous route guidance and traffic signal optimization problem using space-phase-time hypernetwork

Solving simultaneous route guidance and traffic signal optimization problem using space-phase-time hypernetwork

Real-time traffic control for sustainable urban living

Stochastic Model Predictive Control for Urban Traffic Networks

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