Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
97 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
5 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Data-Driven Intersection Management Solutions for Mixed Traffic of Human-Driven and Connected and Automated Vehicles (2012.05402v1)

Published 10 Dec 2020 in cs.LG, cs.SY, and eess.SY

Abstract: This dissertation proposes two solutions for urban traffic control in the presence of connected and automated vehicles. First a centralized platoon-based controller is proposed for the cooperative intersection management problem that takes advantage of the platooning systems and V2I communication to generate fast and smooth traffic flow at a single intersection. Second, a data-driven approach is proposed for adaptive signal control in the presence of connected vehicles. The proposed system relies on a data-driven method for optimal signal timing and a data-driven heuristic method for estimating routing decisions. It requires no additional sensors to be installed at the intersection, reducing the installation costs compared to typical settings of state-of-the-practice adaptive signal controllers. The proposed traffic controller contains an optimal signal timing module and a traffic state estimator. The signal timing module is a neural network model trained on microscopic simulation data to achieve optimal results according to a given performance metric such as vehicular delay or average queue length. The traffic state estimator relies on connected vehicles' information to estimate the traffic's routing decisions. A heuristic method is proposed to minimize the estimation error. With sufficient parameter tuning, the estimation error decreases as the market penetration rate (MPR) of connected vehicles grows. Estimation error is below 30% for an MPR of 10% and it shrinks below 20% when MPR grows larger than 30%. Simulations showed that the proposed traffic controller outperforms Highway Capacity Manual's methodology and given proper offline parameter tuning, it can decrease average vehicular delay by up to 25%.

User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (1)
  1. Masoud Bashiri (4 papers)
Citations (2)

Summary

We haven't generated a summary for this paper yet.