# Using Reinforcement Learning with Partial Vehicle Detection for Intelligent Traffic Signal Control

arXiv: https://arxiv.org/abs/1807.01628 · HTML: https://mlsanity.com/paper/1807.01628/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2018.

## Built on by (most cited first, 10 of 10)

- [Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning](https://mlsanity.com/paper/2104.09936/) (2021)
- [Learning to Control and Coordinate Mixed Traffic Through Robot Vehicles at Complex and Unsignalized Intersections](https://mlsanity.com/paper/2301.05294/) (2023)
- [CVLight: Decentralized Learning for Adaptive Traffic Signal Control with Connected Vehicles](https://mlsanity.com/paper/2104.10340/) (2021)
- [Reinforcement learning for traffic signal control in hybrid action space](https://mlsanity.com/paper/2211.12956/) (2022)
- [Improving the generalizability and robustness of large-scale traffic signal control](https://mlsanity.com/paper/2306.01925/) (2023)
- [Deep Reinforcement Q-Learning for Intelligent Traffic Signal Control with Partial Detection](https://mlsanity.com/paper/2109.14337/) (2021)
- [A Bibliometric Analysis and Review on Reinforcement Learning for Transportation Applications](https://mlsanity.com/paper/2210.14524/) (2022)
- [Large-Scale Mixed-Traffic and Intersection Control using Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2504.04691/) (2025)
- [Deep Reinforcement Learning for the Joint Control of Traffic Light Signaling and Vehicle Speed Advice](https://mlsanity.com/paper/2309.09881/) (2023)
- [Robustness of Reinforcement Learning-Based Traffic Signal Control under Incidents: A Comparative Study](https://mlsanity.com/paper/2506.13836/) (2025)

## Abstract (arXiv)

Intelligent Transportation Systems (ITS) have attracted the attention of researchers and the general public alike as a means to alleviate traffic congestion. Recently, the maturity of wireless technology has enabled a cost-efficient way to achieve ITS by detecting vehicles using Vehicle to Infrastructure (V2I) communications. Traditional ITS algorithms, in most cases, assume that every vehicle is observed, such as by a camera or a loop detector, but a V2I implementation would detect only those vehicles with wireless communications capability. We examine a family of transportation systems, which we will refer to as `Partially Detected Intelligent Transportation Systems'. An algorithm that can act well under a small detection rate is highly desirable due to gradual penetration rates of the underlying wireless technologies such as Dedicated Short Range Communications (DSRC) technology. Artificial Intelligence (AI) techniques for Reinforcement Learning (RL) are suitable tools for finding such an algorithm due to utilizing varied inputs and not requiring explicit analytic understanding or modeling of the underlying system dynamics. In this paper, we report a RL algorithm for partially observable ITS based on DSRC. The performance of this system is studied under different car flows, detection rates, and topologies of the road network. Our system is able to efficiently reduce the average waiting time of vehicles at an intersection, even with a low detection rate.