A Decentralized Signal Control for Non-Lane-Based Heterogeneous Traffic Under V2I Communication

Kumaravel, Sharmila Devi; Ayyagari, Ramakalyan

doi:10.1109/tits.2020.2970193

Cited by 9 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… Simulations reveal that the number of conflicts decreases with a decrease in speeds and volume, and vice versa. [ 145 ] VISSIM's environment demonstrates the efficacy of the propositioned algorithm. Signal control for Heterogeneous traffic under Vehicle-to-infrastructure (V2I) communication.…”

Section: Vissim Application Literature Reviewmentioning

confidence: 99%

“…Using VISSIM [ 144 ], investigated the assessment of unsignalized intersections using proximal safety indicators. Using a simulation scenario [ 145 ], demonstrated the efficacy of signal regulation for heterogeneous traffic under vehicle-to-infrastructure (V2I) communication. A microsimulation environment was presented in Ref.…”

Section: Vissim Application Assessment and Evaluationmentioning

confidence: 99%

See 1 more Smart Citation

Review of driving-behaviour simulation: VISSIM and artificial intelligence approach

Al-Msari,

Koting,

Ahmed

et al. 2024

Heliyon

View full text Add to dashboard Cite

Section: Vissim Application Literature Reviewmentioning

confidence: 99%

Section: Vissim Application Assessment and Evaluationmentioning

confidence: 99%

Review of driving-behaviour simulation: VISSIM and artificial intelligence approach

Al-Msari,

Koting,

Ahmed

et al. 2024

Heliyon

View full text Add to dashboard Cite

“…For completeness, we note that V2I control in non-lane based heterogeneous traffic scenarios has been studied in [75]; in contrast, we consider lane-based traffic. Bus holding time control has been examined in [76]; we focus on conventional automobile traffic.…”

Section: Related Workmentioning

confidence: 99%

Optimised Traffic Light Management Through Reinforcement Learning: Traffic State Agnostic Agent vs. Holistic Agent With Current V2I Traffic State Knowledge

Busch

Latzko

Reisslein

et al. 2020

IEEE Open J. Intell. Transp. Syst.

View full text Add to dashboard Cite

Traffic light control falls into two main categories: Agnostic systems that do not exploit knowledge of the current traffic state, e.g., the positions and velocities of vehicles approaching intersections, and holistic systems that exploit knowledge of the current traffic state. Emerging fifth generation (5G) wireless networks enable Vehicle-to-Infrastructure (V2I) communication to reliably and quickly collect the current traffic state. However, to the best of our knowledge, the optimized traffic light management without and with current traffic state information has not been compared in detail. This study fills this gap in the literature by designing representative Deep Reinforcement Learning (DRL) agents that learn the control of multiple traffic lights without and with current traffic state information. Our agnostic agent considers mainly the current phase of all traffic lights and the expired times since the last change. In addition, our holistic agent considers the positions and velocities of the vehicles approaching the intersections. We compare the agnostic and holistic agents for simulated traffic scenarios, including a road network from Barcelona, Spain. We find that the holistic system substantially increases average vehicle velocities and flow rates, while reducing CO2 emissions, average wait and trip times, as well as a driver stress metric. Index Terms-Deep reinforcement learning (DRL), intelligent transportation system (ITS), intersection control, vehicle-toinfrastructure communication (V2I).

show abstract

Review on Adaptive Traffic Signal Control Based on Intelligent Computing

Ranpura,

Gujar

2024

Lecture Notes in Civil Engineering

View full text Add to dashboard Cite

A Decentralized Signal Control for Non-Lane-Based Heterogeneous Traffic Under V2I Communication

Cited by 9 publications

References 22 publications

Review of driving-behaviour simulation: VISSIM and artificial intelligence approach

Review of driving-behaviour simulation: VISSIM and artificial intelligence approach

Optimised Traffic Light Management Through Reinforcement Learning: Traffic State Agnostic Agent vs. Holistic Agent With Current V2I Traffic State Knowledge

Review on Adaptive Traffic Signal Control Based on Intelligent Computing

Contact Info

Product

Resources

About