Reinforcement learning: Introduction to theory and potential for transport applications

2003 ◽  
Vol 30 (6) ◽  
pp. 981-991 ◽  
Author(s):  
Baher Abdulhai ◽  
Lina Kattan
Keyword(s):  
Reinforcement Learning ◽  
Traffic Control ◽  
Intelligent Transportation Systems ◽  
Traffic Engineering ◽  
Transportation Systems ◽  
Transport Systems ◽  
Transportation Engineering ◽  
Research Subjects ◽  
Comprehensive Overview ◽  
Learning Machine

The aim of this paper is to develop insight into the potential of reinforcement learning (RL) agents and distributed reinforcement learning agents in the domain of transportation and traffic engineering and specifically in Intelligent Transport Systems (ITS). This paper provides a crystallized, comprehensive overview of the concept of RL and presents related successful applications in the field of traffic control and transportation engineering. It is divided into two parts: the first part provides a thorough overview of RL and its related methods and the second part reviews most recent applications of RL algorithms to the field of transportation engineering. Finally, it identifies many open research subjects in transportation in which the use of RL seems to be promising.Key words: reinforcement learning, machine learning, traffic control, artificial intelligence, intelligent transportation systems.

scholarly journals Multiagent Model of Rail-Road Intersection with Connected Vehicles

2021 ◽  
Vol 2 (1) ◽  
pp. 17-24
Author(s):  
Aleš Janota ◽  
Vojtech Šimák ◽  
Jozef Hrbček
Keyword(s):  
Traffic Control ◽  
Intelligent Transportation Systems ◽  
Collective Intelligence ◽  
Communication Technologies ◽  
Complex Problem ◽  
Transportation Systems ◽  
Comprehensive Overview ◽  
Vehicle Communication ◽  
Is Implementation ◽  
Cooperating Agents

The multiagent approach to modelling, traditionally dedicated for distributed systems, can be applied on any platform where there are more processes or control threads. The world of surface transport is a typical example of such a situation where high numbers of dynamic entities (agents) interacting with each other represent a complex problem to solve, analyse and visualise. The main focus of this paper is on functional description of the traffic control problem at the rail-road intersection. Unlike conventional approaches, this model assumes usage of modern (infrastructure-to-vehicle, vehicle-to-vehicle) communication technologies  as an essential base of cooperative intelligent transportation systems. The authors use the development toolkit NetLogo, explaining step-by-step the key programming details, to get a comprehensive overview of the operation of the entire system through simple definitions of a number of simple cooperating agents. The introduced model is implementation free and shows newly offered functionalities on the principal level, while a minimum theory of collective intelligence hidden in the background is needed.

A Comprehensive Study on Intelligent Transportation Systems

2018 ◽  
Vol 4 (10) ◽  
pp. 10
Author(s):  
Ankur Mishra ◽  
Aayushi Priya
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Transportation Systems ◽  
Environmental Conservation ◽  
Integrated System ◽  
Transport Systems ◽  
Transport Sector ◽  
Intelligent Transport System ◽  
Intelligent Transport ◽  
Transportation Sector

Transportation or transport sector is a legal source to take or carry things from one place to another. With the passage of time, transportation faces many issues like high accidents rate, traffic congestion, traffic & carbon emissions air pollution, etc. In some cases, transportation sector faced alleviating the brutality of crash related injuries in accident. Due to such complexity, researchers integrate virtual technologies with transportation which known as Intelligent Transport System. Intelligent Transport Systems (ITS) provide transport solutions by utilizing state-of-the-art information and telecommunications technologies. It is an integrated system of people, roads and vehicles, designed to significantly contribute to improve road safety, efficiency and comfort, as well as environmental conservation through realization of smoother traffic by relieving traffic congestion. This paper aims to elucidate various aspects of ITS - it's need, the various user applications, technologies utilized and concludes by emphasizing the case study of IBM ITS.

Recent Advances in Reinforcement Learning for Traffic Signal Control

2021 ◽  
Vol 22 (2) ◽  
pp. 12-18 ◽  
Author(s):  
Hua Wei ◽  
Guanjie Zheng ◽  
Vikash Gayah ◽  
Zhenhui Li
Keyword(s):  
Reinforcement Learning ◽  
Real World ◽  
Intelligent Transportation Systems ◽  
Transportation Systems ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Control Methods ◽  
Advantages And Disadvantages ◽  
Recent Advances

Traffic signal control is an important and challenging real-world problem that has recently received a large amount of interest from both transportation and computer science communities. In this survey, we focus on investigating the recent advances in using reinforcement learning (RL) techniques to solve the traffic signal control problem. We classify the known approaches based on the RL techniques they use and provide a review of existing models with analysis on their advantages and disadvantages. Moreover, we give an overview of the simulation environments and experimental settings that have been developed to evaluate the traffic signal control methods. Finally, we explore future directions in the area of RLbased traffic signal control methods. We hope this survey could provide insights to researchers dealing with real-world applications in intelligent transportation systems

Potential Applications of Intelligent Transportation Systems to Road Freight Transport in Mexico

2000 ◽  
Vol 1707 (1) ◽  
pp. 81-85
Author(s):  
Alberto Mendoza ◽  
Antonio García
Keyword(s):  
Intelligent Transportation Systems ◽  
Intelligent Transportation ◽  
Freight Transport ◽  
Transportation Systems ◽  
Transport Systems ◽  
Environment Friendly ◽  
Generic Term ◽  
Road Freight ◽  
Potential Applications ◽  
Made In

In the past few years, tools have been developed based on different communication means with the purpose of achieving a safer, more efficient, and environment-friendly operation of vehicular flows in the transport systems. Some of the early means generally involved a very strong human participation. In the course of time and with the rapid progress made in electronics, telecommunications, and computer systems, such processes have become automated until generating a series of technologies that currently are incorporated into the single generic term of intelligent transportation systems (ITS). This research has multiple purposes. First, some characteristics of road freight transport in Mexico are presented. Then, with such characteristics under consideration, the ITS technologies with the largest potential for application to that transportation type are described. A vision of future implementation is shown. Finally, some conclusions are presented.

scholarly journals Vehicular Ad hoc Network for Intelligent Transport System: A review

2018 ◽  
Vol 7 (4.36) ◽  
pp. 350
Author(s):  
Mohammed Saad Talib ◽  
Aslinda Hassan ◽  
Burairah Hussin ◽  
Ali Abdul-Jabbar Mohammed ◽  
Ali Abdulhussian Hassan ◽  
...  
Keyword(s):  
Traffic Safety ◽  
Intelligent Transportation Systems ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
High Efficiency ◽  
New Technologies ◽  
Negative Impact ◽  
Transportation Systems ◽  
Transport Systems ◽  
Intelligent Transport System

the numbers of accidents are increasing in an exponential manner with the growing of vehicles numbers on roads in recent years.  This huge number of vehicles increases the traffic congestion rates. Therefore, new technologies are so important to reduce the victims in the roads and improve the traffic safety. The Intelligent Transportation Systems (ITS) represents an emerging technology to improve the road's safety and traffic efficiency. ITS have various safety and not safety applications. Numerous methods are intended to develop the smart transport systems. The crucial form is the Vehicular Ad hoc Networks (VANET). VANET is becoming the most common network in ITS. It confirms human’s safety on streets by dissemination protection messages among vehicles. Optimizing the traffic management operations represent an urgent issue in this era a according to the massive growing in number of circulating vehicles, traffic congestions and road accidents. Street congestions can have significant negative impact on the life quality, passenger's safety, daily activities, economic and environmental for citizens and organizations. Current progresses in communication and computing paradigms fetched the improvement of inclusive intelligent devices equipped with wireless communication capability and high efficiency processors.  

scholarly journals Cooperative Multiagent Deep Deterministic Policy Gradient (CoMADDPG) for Intelligent Connected Transportation with Unsignalized Intersection

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Tianhao Wu ◽  
Mingzhi Jiang ◽  
Lin Zhang
Keyword(s):  
Reinforcement Learning ◽  
Intelligent Transportation Systems ◽  
Transportation Systems ◽  
Information Interaction ◽  
Multiagent Reinforcement Learning ◽  
Policy Gradient ◽  
Critical Issues ◽  
Multiple Vehicles ◽  
Dynamic Quantity ◽  
Intersection Control

Unsignalized intersection control is one of the most critical issues in intelligent transportation systems, which requires connected and automated vehicles to support more frequent information interaction and on-board computing. It is very promising to introduce reinforcement learning in the unsignalized intersection control. However, the existing multiagent reinforcement learning algorithms, such as multiagent deep deterministic policy gradient (MADDPG), hardly handle a dynamic number of vehicles, which cannot meet the need of the real road condition. Thus, this paper proposes a Cooperative MADDPG (CoMADDPG) for connected vehicles at unsignalized intersection to solve this problem. Firstly, the scenario of multiple vehicles passing through an unsignalized intersection is formulated as a multiagent reinforcement learning (RL) problem. Secondly, MADDPG is redefined to adapt to the dynamic quantity agents, where each vehicle selects reference vehicles to construct a partial stationary environment, which is necessary for RL. Thirdly, this paper incorporates a novel vehicle selection method, which projects the reference vehicles on a virtual lane and selects the largest impact vehicles to construct the environment. At last, an intersection simulation platform is developed to evaluate the proposed method. According to the simulation result, CoMADDPG can reduce average travel time by 39.28% compared with the other optimization-based methods, which indicates that CoMADDPG has an excellent prospect in dealing with the scenario of unsignalized intersection control.

scholarly journals A Bidirectional Searching Strategy to Improve Data Quality Based on K-Nearest Neighbor Approach

Symmetry ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 815 ◽  
Author(s):  
Minghui Ma ◽  
Shidong Liang ◽  
Yifei Qin
Keyword(s):  
Data Quality ◽  
Traffic Control ◽  
Intelligent Transportation Systems ◽  
Nearest Neighbor ◽  
Transportation Systems ◽  
Traffic Data ◽  
K Nearest Neighbor ◽  
Time Dimension ◽  
Nearest Neighbor Searching ◽  
Searching Strategy

Traffic data are the basis of traffic control, planning, management, and other implementations. Incomplete traffic data that are not conducive to all aspects of transport research and related activities can have adverse effects such as traffic status identification error and poor control performance. For intelligent transportation systems, the data recovery strategy has become increasingly important since the application of the traffic system relies on the traffic data quality. In this study, a bidirectional k-nearest neighbor searching strategy was constructed for effectively detecting and recovering abnormal data considering the symmetric time network and the correlation of the traffic data in time dimension. Moreover, the state vector of the proposed bidirectional searching strategy was designed based the bidirectional retrieval for enhancing the accuracy. In addition, the proposed bidirectional searching strategy shows significantly more accuracy compared to those of the previous methods.

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