Max-pressure traffic controller based on travel times: An experimental analysis

Mercader, Patricia; Uwayid, Wasim; Haddad, Jack

doi:10.1016/j.trc.2019.10.002

Cited by 68 publications

(30 citation statements)

References 35 publications

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“…Another concern with the fourth property is the requirement that accurate queue length information is available. Considering the difficulties in queue length estimation at signalized intersections in today's road networks, Mercader et al [7,8] put forward a travel time based BP. Instead of using queue lengths as the input for calculating pressure, they used the travel times because (1) travel times are easier to obtain or estimate than queue lengths, and (2) travel times tend to diverge as queue sizes approach the spatial capacity of roads; hence, it is inherently capacity aware.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

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Backpressure control with estimated queue lengths for urban network traffic

Okoth²,

Jabari

2021

IET Intelligent Trans Sys

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Backpressure (BP) control was originally used for packet routing in communications networks. Since its first application to network traffic control, it has undergone different modifications to tailor it to traffic problems with promising results. Most of these BP variants are based on an assumption of perfect knowledge of traffic conditions throughout the network at all times, specifically the queue lengths (more accurately, the traffic volumes). However, it has been well established that accurate queue length information at signalized intersections is never available except in fully connected environments. Although connected vehicle technologies are developing quickly, a fully connected environment in the real world is still far. This paper tests the effectiveness of BP control when incomplete or imperfect knowledge about traffic conditions is available. BP control is combined with a speed/density field estimation module suitable for a partially connected environment. The proposed system is referred to as a BP with estimated queue lengths (BP-EQ). The robustness of BP-EQ is tested to varying levels of connected vehicle penetration, and BP-EQ is compared with the original BP (i.e. assuming accurate knowledge of traffic conditions), a real-world adaptive signal controller, and optimized fixed timing control using microscopic traffic simulation with field calibrated data. These results show that with a connected vehicle penetration rate as little as 10%, BP-EQ can outperform the adaptive controller and the fixed timing controller in terms of average delay, throughput, and maximum stopped queue lengths under high demand scenarios.

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Section: Background and Literature Reviewmentioning

confidence: 99%

“…where i is the traffic density of cell i and d i is the length of cell i. Substituting 7and (8) into (6), we have…”

Section: Bp-eq Controlmentioning

confidence: 99%

Backpressure control with estimated queue lengths for urban network traffic

Okoth²,

Jabari

2021

IET Intelligent Trans Sys

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“…The results from an experiment consisting of a real implementation of this approach in a signalized intersection from Jerusalem demonstrated its validity and brought many advantages. The benefits of this technology compared to video-based systems arose from the easier installation and maintenance processes and the availability of measurements of turning ratios [ 6 ]. On the same direction of Bluetooth technology usage, researchers propose in [ 7 ] a real-time road traffic monitoring system based on the purchased data from private companies.…”

Section: Literature Overviewmentioning

confidence: 99%

“…Considering that ζ i (t), i = {1, 3, s} are the measurement errors for the real traffic parameters, the output equations of the calibration system are computed by using relation (6).…”

Section: Kalman Filter For Online Calibrationmentioning

confidence: 99%

Hybrid Solution Combining Kalman Filtering with Takagi–Sugeno Fuzzy Inference System for Online Car-Following Model Calibration

Pop

Proştean

David

et al. 2020

Sensors

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Nowadays, the intelligent transportation concept has become one of the most important research fields. All of us depend on mobility, even when we talk about people, provide services, or move goods. Researchers have tried to create and test different transportation models that can optimize traffic flow through road networks and, implicitly, reduce travel times. To validate these new models, the necessity of having a calibration process defined has emerged. Calibration is mandatory in the modeling process because it ensures the achievement of a model closer to the real system. The purpose of this paper is to propose a new multidisciplinary approach combining microscopic traffic modeling theory with intelligent control systems concepts like fuzzy inference in the traffic model calibration. The chosen Takagi–Sugeno fuzzy inference system proves its adaptive capacity for real-time systems. This concept will be applied to the specific microscopic car-following model parameters in combination with a Kalman filter. The results will demonstrate how the microscopic traffic model parameters can adapt based on real data to prove the model validity.

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“…Although signal control systems have evolved over the last decades [1][2][3], poorly-timed (i.e., not responsive to traffic demand) signal controls cause significant user delays. In addition, the installation of traffic lights is a costly process and therefore needs to be planned carefully.…”

Section: Introductionmentioning

confidence: 99%

A Pairing Algorithm for Conflict-Free Crossings of Automated Vehicles at Lightless Intersections

2021

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This paper studies the planning of conflict-free and efficient crossings of antagonistic vehicles’ movements at lightless intersections. A fully automated infrastructure environment is considered, where all vehicles that enter the intersection area are connected and automated (CAVs), i.e., they are equipped with advanced communication and automation technologies. In such a futuristic environment, traffic lights that regulate the right-of-way of different traffic streams are obsolete because of vehicle communication capabilities. The connectivity is utilized to derive vehicle trajectories such that a safe and efficient crossing of lightless intersections is possible. So far, published studies lack the application to complex intersection layouts. To fill this gap, we introduce a control method for CAV pairing allowing for the safe, collision-free crossing of the intersecting area and optimize traffic conditions, i.e., total delays of the system. Simulation results demonstrate the feasibility and applicability of the presented approach, given that all the technical specifications (e.g., communications, velocity actuators) are present. Finally, we conduct a sensitivity analysis for the algorithm’s main parameters, which provides practical insights for the studied experimental scenarios and other existing algorithms in the literature that tackle this problem.

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Max-pressure traffic controller based on travel times: An experimental analysis

Cited by 68 publications

References 35 publications

Backpressure control with estimated queue lengths for urban network traffic

Backpressure control with estimated queue lengths for urban network traffic

Hybrid Solution Combining Kalman Filtering with Takagi–Sugeno Fuzzy Inference System for Online Car-Following Model Calibration

A Pairing Algorithm for Conflict-Free Crossings of Automated Vehicles at Lightless Intersections

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