Deep Reinforcement Learning for Adaptive Traffic Signal Control

Tan, Kai Liang; Poddar, Subhadipto; Sharma, Abha; Sarkar, Soumik

doi:10.48550/arxiv.1911.06294

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, Python was used to write an external COM script that initiates a VISSIM object, controls the simulation, extracts information from the simulation, and sends the vehicle speed and position data to the external signal controllers. Several studies (24)(25)(26) have used COM to implement customized signal control logic in VISSIM.…”

Section: Vissim Component Object Model (Com)mentioning

confidence: 99%

Advanced Gap Seeking Logic for Actuated Signal Control Using Vehicle Trajectory Data: Proof of Concept

Shams

Day

2022

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

As detection systems improve, opportunities are emerging for using vehicle position and speed to drive signal control. This study explored how basic actuation processes might be improved by using vehicle position and speed. The position and speed of arriving vehicles were used to calculate their estimated time of arrival. Two variations on vehicle extension logic were developed that extended a green until there was a headway in the arriving traffic above a size that corresponded to a minimum flow rate, at which it was desired to terminate the phase. In the first method, the headway was measured from the point where the leading vehicle passed the stop bar, whereas the second method measured the headway from where the leading vehicle was unlikely to stop at the onset of yellow. This was compared against a control with a conventional stop bar and upstream detectors. A simulation study was carried out for an eight-phase intersection. The results suggest that at higher levels of demand, trajectory-based actuation could yield substantial reductions in delay. The trajectory-based methods were able to terminate green more efficiently, leading to reductions in delay in some cases, and reductions in emissions and fuel consumption, although there was a tradeoff in the number of split failures. These early results show promise for the development and fine-tuning of the methods.

show abstract

Section: Vissim Component Object Model (Com)mentioning

confidence: 99%