DeepAI AI Chat
Log In Sign Up

Context Aware Dynamic Traffic Signal Optimization

by   Kandarp Khandwala, et al.

Conventional urban traffic control systems have been based on historical traffic data. Later advancements made use of detectors, which enabled the gathering of real time traffic data, in order to reorganize and calibrate traffic signalization programs. Further evolvement provided the ability to forecast traffic conditions, in order to develop traffic signalization programs and strategies precomputed and applied at the most appropriate time frame for the optimal control of the current traffic conditions. We, propose the next generation of traffic control systems based on principles of Artificial Intelligence and Context Awareness. Most of the existing algorithms use average waiting time or length of the queue to assess an algorithms performance. However, a low average waiting time may come at the cost of delaying other vehicles indefinitely. In our algorithm, besides the vehicle queue, we use fairness also as an important performance metric to assess an algorithms performance.


page 5

page 6


Grey Models for Short-Term Queue Length Predictions for Adaptive Traffic Signal Control

Traffic congestion at a signalized intersection greatly reduces the trav...

Lyapunov Function Consistent Adaptive Network Signal Control with Back Pressure and Reinforcement Learning

This research studies the network traffic signal control problem. It use...

Traffic Queue Length and Pressure Estimation for Road Networks with Geometric Deep Learning Algorithms

Due to urbanization and the increase of individual mobility, in most met...

Coping with Large Traffic Volumes in Schedule-Driven Traffic Signal Control

Recent work in decentralized, schedule-driven traffic control has demons...

A Novel Ramp Metering Approach Based on Machine Learning and Historical Data

The random nature of traffic conditions on freeways can cause excessive ...

Joint Estimation of Multi-phase Traffic Demands at Signalized Intersections Based on Connected Vehicle Trajectories

Accurate traffic demand estimation is critical for the dynamic evaluatio...