Analysis of fixed-time control

Muralidharan, Ajith; Pedarsani, Ramtin; Varaiya, Pravin

doi:10.1016/j.trb.2014.12.002

Cited by 53 publications

(35 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This distributed control law, which was originally applied to production processes and communication networks and has lately gained a lot of attention in traffic control, acts locally in coupled intersections and has been proven (under certain conditions) to stabilize the queues of the network. In the same direction, Muralidharan et al (2015) studied the network stability under fixed-time control; this analysis provides useful insights about simple network traffic instances (i.e. the demand is assumed to be "feasible" -can be accommodated by the signals) and can potentially lead to analytical derivations of performance measures (i.e.…”

Section: Introductionmentioning

confidence: 99%

Enhancing model-based feedback perimeter control with data-driven online adaptive optimization

Kouvelas

Saeedmanesh

Geroliminis

2017

Transportation Research Part B: Methodological

188

View full text Add to dashboard Cite

Most feedback perimeter control approaches that are based on the Macroscopic Fundamental Diagram (MFD) and are tested in detailed network structures restrict inflow from the external boundary of the network. Although such a measure is beneficial for the network performance, it creates virtual queues that do not interact with the rest of the traffic and assumes small unrestricted flow (i.e. almost zero disturbance). In reality, these queues can have a negative impact to traffic conditions upstream of the protected network which is not modelled. In this work an adaptive optimization scheme for perimeter control of heterogeneous transportation networks is developed and the aforementioned boundary control limitation is dropped. A nonlinear model is introduced that describes the evolution of the multi-region system over time, assuming the existence of well-defined MFDs. Multiple linear approximations of the model (for different set-points) are used for designing optimal multivariable integral feedback regulators. Since the resulting regulators are derived from approximations of the nonlinear dynamics, they are further enhanced in realtime with online learning/adaptive optimization, according to performance measurements. An iterative data-driven technique is integrated with the model-based design and its objective is to optimize the gain matrices and setpoints of the multivariable perimeter controller based on real-time observations.

show abstract

Section: Introductionmentioning

confidence: 99%

Enhancing model-based feedback perimeter control with data-driven online adaptive optimization

Kouvelas

Saeedmanesh

Geroliminis

2017

Transportation Research Part B: Methodological

188

View full text Add to dashboard Cite

show abstract

“…To avoid collision, each signal switches among activation patterns of non-conflicting links according to a signal control sequence. All intersections are assumed to operated under fixed time control [24] with common cycle. This means that the signal control sequence of each intersection has a fixed periodic cycle, and all intersections have a common cycle time T = 1 time unit.…”

Section: A Traffic Network Modelmentioning

confidence: 99%

Large-Scale Traffic Signal Offset Optimization

Ouyang¹,

Zhang

Lavaei

et al. 2020

IEEE Trans. Control Netw. Syst.

Self Cite

View full text Add to dashboard Cite

The offset optimization problem seeks to coordinate and synchronize the timing of traffic signals throughout a network in order to enhance traffic flow and reduce stops and delays. Recently, offset optimization was formulated into a continuous optimization problem without integer variables by modeling traffic flow as sinusoidal. In this paper, we present a novel algorithm to solve this new formulation to near-global optimality on a large-scale. Specifically, we solve a convex relaxation of the nonconvex problem using a tree decomposition reduction, and use randomized rounding to recover a near-global solution. We prove that the algorithm always delivers solutions of expected value at least 0.785 times the globally optimal value. Moreover, assuming that the topology of the traffic network is "tree-like", we prove that the algorithm has near-linear time complexity with respect to the number of intersections. These theoretical guarantees are experimentally validated on the Berkeley, Manhattan, and Los Angeles traffic networks. In our numerical results, the empirical time complexity of the algorithm is linear, and the solutions have objectives within 0.99 times the globally optimal value.

show abstract

“…The existing signal control strategies can be divided into three categories, namely fixed time, actuated, and adaptive [3]. Fixed-time-based traffic control utilizes the historical traffic volume at an intersection to calculate the parameters for signal timing (e.g., cycle length, green time split, and offset) [4,5]. Fixed-time-based traffic control may not be able to adapt to demand fluctuation caused by the stochastic nature of traffic arrivals, which may degrade its benefits of signal control.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Optimization of Vehicle Arrival Time and Signal Timings within a Connected Vehicle Environment

Ling

2019

Sensors

View full text Add to dashboard Cite

Most existing signal timing plans are optimized given vehicles' arrival time (i.e., the time for the upcoming vehicles to arrive at the stop line) as exogenous input. In this paper, based on the connected vehicle (CV) technique, vehicles can be regarded as moving sensors, and their arrival time can be dynamically adjusted by speed guidance according to the current signal status and traffic conditions. Therefore, an integrated traffic control model is proposed in this study to optimize vehicle arrival time (or travel speed) and signal timing simultaneously. "Speed guidance model at a red light" and "speed guidance model at a green light" are presented to model the influences between travel speed and signal timing. Then, the methods to model the vehicle arrival time, vehicle delay, and number of stops are proposed. The total delay, which includes the control delay, queuing delay, and signal delay, is used as the objective of the proposed model. The decision variables consist of vehicle arrival time, starting time of green, and duration of green for each phase. The sliding time window is adopted to dynamically tackle the problems. Compared with the results optimized by the classical actuated signal control method and the fixed-time-based speed guidance model, the proposed model can significantly decrease travel delays as well as improve the flexibility and mobility of traffic control. The sensitivity analysis with the communication distance, the market penetration of connected vehicles, and the compliance rate of speed guidance further demonstrates the potential of the proposed model to be applied in various traffic conditions.

show abstract

Analysis of fixed-time control

Cited by 53 publications

References 13 publications

Enhancing model-based feedback perimeter control with data-driven online adaptive optimization

Enhancing model-based feedback perimeter control with data-driven online adaptive optimization

Large-Scale Traffic Signal Offset Optimization

Simultaneous Optimization of Vehicle Arrival Time and Signal Timings within a Connected Vehicle Environment

Contact Info

Product

Resources

About