Traffic signal control for connected and non-connected vehicles

Connected-vehicles network provides opportunities and conditions for improving traffic signal control, and macroscopic fundamental diagrams (MFD) can control the road network at the macrolevel effectively. This paper integrated proposed real-time access to the number of mobile vehicles and the maximum road queuing length in the Connected-vehicles network. Moreover, when implementing a simple control strategy to limit the boundary flow of a road network based on MFD, we determined whether the maximum queuing length of each boundary section exceeds the road-safety queuing length in real-time calculations and timely adjusted the road-network influx rate to avoid the overflow phenomenon in the boundary section. We established a road-network microtraffic simulation model in VISSIM software taking a district as the experimental area, determined MFD of the region based on the number of mobile vehicles, and weighted traffic volume of the road network. When the road network was tending to saturate, we implemented a simple control strategy and our algorithm limits the boundary flow. Finally, we compared the traffic signal control indicators with three strategies: (1) no control strategy, (2) boundary control, and (3) boundary control with limiting queue strategy. The results show that our proposed algorithm is better than the other two.

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A Scientometric-Based Review of Traffic Signal Control Methods and Experiments Based on Connected Vehicles and Floating Car Data (FCD)

Applied Sciences ◽

10.3390/app11125547 ◽

2021 ◽

Vol 11 (12) ◽

pp. 5547

Author(s):

Vittorio Astarita ◽

Vincenzo Pasquale Giofrè ◽

Giuseppe Guido ◽

Alessandro Vitale

Keyword(s):

New Technologies ◽

State Of The Art ◽

Traffic Signal ◽

The State ◽

Signal Control ◽

Traffic Signal Control ◽

Connected Vehicles ◽

City Management ◽

Control Methods ◽

Floating Car Data

This paper reviews the state of the art in traffic signal control methods that are based on data coming from onboard smartphones or connected vehicles. The review of the state of the art is carried out by applying analytical scientometric tools (topic visualization, co-citation analysis to establish influential journals and references, country analysis based on coauthorship, trending-topics analysis carried out by overlay visualization). The introduction of autonomous and connected vehicles will allow city management organizations to introduce new intersection management systems that rely on real-time positional data coming from instrumented vehicles. Traditional vehicles also could benefit from these new technologies by profiting from better-regulated intersections. This paper using a scientometric approach frames all the scientific contributions aimed at the field of traffic signal methods and experiments based on connected vehicles and floating car data. The applied scientometric approach reveals trending ideas and concepts and identifies the relevant documents that can be consulted in order for scientists and professionals to develop further this field with the implementation of new traffic signal control systems that can “give the green light” to drivers.

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A Real-time Traffic Signal Control Strategy Under Partially Connected Vehicle Environment

PROMET - Traffic&Transportation ◽

10.7307/ptt.v31i1.2832 ◽

2019 ◽

Vol 31 (1) ◽

pp. 61-73 ◽

Cited By ~ 2

Author(s):

Kancharla Kamal Keerthi Chandan ◽

Álvaro Jorge Maia Seco ◽

Ana Maria César Bastos Silva

Keyword(s):

Estimation Error ◽

Estimation Algorithm ◽

Traffic Signal ◽

Estimation Accuracy ◽

Signal Control ◽

Traffic Signal Control ◽

Connected Vehicles ◽

Connected Vehicle ◽

Traffic Demand ◽

Real Time Traffic

The performance of a traffic system tends to improve as the percentage of connected vehicles (CV) in total flow increases. However, due to low CV penetration in the current vehicle market, improving the traffic signal operation remains a challenging task. In an effort to improve the performance of CV applications at low penetration rates, the authors develop a new method to estimate the speeds and positions of non-connected vehicles (NCV) along a signalized intersection. The algorithm uses CV information and initial speeds and positions of the NCVs from loop detectors and estimates the forward movements of the NCVs using the Gipps’ car-following model. Calibration parameters of the Gipps’ model were determined using a solver optimization tool. The estimation algorithm was applied to a previously developed connected vehicle signal control (CVSC) strategy on two different isolated intersections. Simulations in VISSIM showed the estimation accuracy higher for the intersection with less lanes. Estimation error increased with the decrease in CV penetration and decreased with the decrease in traffic demand. The CVSC strategy with 40% and higher CV penetration (for Intersection 1) and with 20% and higher CV penetration (for Intersection 2) showed better performance in reducing travel time delay and number of stops than the EPICS adaptive control.

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Traffic Signal Control with Connected Vehicles

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/2381-08 ◽

2013 ◽

Vol 2381 (1) ◽

pp. 65-72 ◽

Cited By ~ 117

Author(s):

Noah J. Goodall ◽

Brian L. Smith ◽

Byungkyu (Brian) Park

Keyword(s):

Traffic Signal ◽

Signal Control ◽

Traffic Signal Control ◽

Connected Vehicles

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Floating Car Data Adaptive Traffic Signals: A Description of the First Real-Time Experiment with “Connected” Vehicles

Electronics ◽

10.3390/electronics9010114 ◽

2020 ◽

Vol 9 (1) ◽

pp. 114 ◽

Cited By ~ 1

Author(s):

Vittorio Astarita ◽

Vincenzo Giofré ◽

Demetrio Festa ◽

Giuseppe Guido ◽

Alessandro Vitale

Keyword(s):

Real Time ◽

Traffic Management ◽

Traffic Signals ◽

Traffic Signal ◽

Signal Control ◽

Traffic Signal Control ◽

Connected Vehicles ◽

Test Bed ◽

Floating Car Data ◽

Better Regulation

The future of traffic management will be based on “connected” and “autonomous” vehicles. With connected vehicles it is possible to gather real-time information. The main potential application of this information is in real-time adaptive traffic signal control. Despite the feasibility of using Floating Car Data (FCD), for signal control, there have been practically no real experiments with all “connected” vehicles to regulate traffic signals in real-time. Most of the research in this field has been carried out with simulations. The purpose of this study is to present a dedicated system that was implemented in the first experiment of an FCD-based adaptive traffic signal. For the first time in the history of traffic management, a traffic signal has been regulated in real time with real “connected” vehicles. This paper describes the entire path of software and system development that has allowed us to make the steps from just simulation test to a real on-field implementation. Results of the experiments carried out with the presented system prove the feasibility of FCD adaptive traffic signals with commonly-used technologies and also establishes a test-bed that may help others to develop better regulation algorithms for these kinds of new “connected” intersections.

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