scholarly journals Role and Key Applications of Artificial Intelligence & Machine Learning in Transportation

2020 ◽  
Vol 4 (1) ◽  
pp. 47-59
Author(s):  
Memoona Shaheen ◽  
Mehreen Arshad ◽  
Owais Iqbal
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Learning Strategies ◽  
Traffic Control ◽  
Intelligent Transportation Systems ◽  
Transportation Systems ◽  
Main Field ◽  
Accident Prediction ◽  
Control Traffic ◽  
Transportation Applications

Purpose: The main target of this paper was to examine the significance of Artificial Intelligence and Machine Learning and their effect on the transportation business. Methodology: This hypothesis was a survey of the significant machine learning calculations and their applications in the field of big data. This paper try to attempt to exhibit the need to remove significant data from the huge measure of enormous information as traffic data available in this day and age and recorded diverse machine learning strategies that can be utilized to separate this information needed to encourage better dynamic for transportation applications. Findings: This paper present an investigation of the different Artificial Intelligence (AI) methods that have been actualized to improve Intelligent Transportation Systems (ITS). Specifically, this paper assembled them into three main territories relying upon the main field where they were applied: Vehicle control, Traffic control and prediction, and Road security and accident prediction. The aftereffects of this examination uncover that the mix of various AI methodologies is by all accounts promising, particularly to oversee and investigate the huge measure of data created in transportation

Research on Traffic Guidance and Control System Coordinated Agent Model

2011 ◽  
Vol 97-98 ◽  
pp. 872-876
Author(s):  
Zheng Hong Peng ◽  
Jin Jun Xu
Keyword(s):  
Artificial Intelligence ◽  
Control System ◽  
Traffic Control ◽  
Intelligent Transportation Systems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Agent Model ◽  
Guidance And Control ◽  
Traffic Guidance ◽  
And Control

Traffic problem is one of serious problem in our society. Put forward the concept of intelligent transportation systems, is seeking to address traffic problems, and gradually become a research hotspot in traffic areas. As two subsystems, traffic control and traffic guidance systems tend to work independently, this paper aims to establish a coordination with the integration of the two systems, while use the agent technologies which emergy in artificial intelligence to establish an integrated model.

scholarly journals A Review of Machine Learning and IoT in Smart Transportation

2019 ◽  
Vol 11 (4) ◽  
pp. 94 ◽  
Author(s):  
Fotios Zantalis ◽  
Grigorios Koulouras ◽  
Sotiris Karabetsos ◽  
Dionisis Kandris
Keyword(s):  
Machine Learning ◽  
Intelligent Transportation Systems ◽  
Transportation Systems ◽  
Route Optimization ◽  
Smart Transportation ◽  
Smart Lighting ◽  
Iot Applications ◽  
Smart Parking ◽  
Lighting Systems ◽  
Accident Detection

With the rise of the Internet of Things (IoT), applications have become smarter and connected devices give rise to their exploitation in all aspects of a modern city. As the volume of the collected data increases, Machine Learning (ML) techniques are applied to further enhance the intelligence and the capabilities of an application. The field of smart transportation has attracted many researchers and it has been approached with both ML and IoT techniques. In this review, smart transportation is considered to be an umbrella term that covers route optimization, parking, street lights, accident prevention/detection, road anomalies, and infrastructure applications. The purpose of this paper is to make a self-contained review of ML techniques and IoT applications in Intelligent Transportation Systems (ITS) and obtain a clear view of the trends in the aforementioned fields and spot possible coverage needs. From the reviewed articles it becomes profound that there is a possible lack of ML coverage for the Smart Lighting Systems and Smart Parking applications. Additionally, route optimization, parking, and accident/detection tend to be the most popular ITS applications among researchers.

scholarly journals Three Fog Computing Based Variants of Congestion Control in ITS

2021 ◽  
Vol 10 (1) ◽  
pp. 333-348
Author(s):  
Ananya Paul ◽  
Kiton Ghosh ◽  
Mitra Sulata
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Fog Computing ◽  
Substantial Reduction ◽  
Transportation Systems ◽  
Road Accident ◽  
Road Capacity ◽  
Direction Of Movement ◽  
Quantitative Performance ◽  
Control Traffic

The growth of vehicles and inadequate road capacity in the urban area trigger traffic congestion and raise the frequency of road accident. Therefore the need of drastically reducing traffic congestion is a significant concern. Advancement in the technology like fog computing, Internet of Things (IoT)in Intelligent Transportation Systems (ITS) aid in the more constructive management of traffic congestion. Three IoT basedFog computing oriented models are designed in the present work for mitigating traffic congestion. The first two schemes are vehicledependent as they control traffic congestion depending upon thenumber of vehicles and their direction of movement across the intersections. The third scheme is environment dependent as theagent senses the environment and controls the sequence of green signal at different routes dynamically. The performances of thethree schemes in ITS are analyzed along with the comparison ofstorage, communication and computation overhead. The efficacy of the schemes is studied theoretically and quantitatively. The quantitative performance of the three schemes is compared with five existing schemes. On the basis of the result of thecomparison, it can be concluded that the proposed schemes are capable of alleviating congestion more optimally than existing schemes due to the substantial reduction in vehicle waiting time.

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.

Fruitful Synergy Model of Artificial Intelligence and Internet of Thing for Smart Transportation System

2020 ◽  
Vol 4 (1) ◽  
pp. 43-57 ◽  
Author(s):  
Vikram Puri ◽  
Chung Van Le ◽  
Raghvendra Kumar ◽  
Sandeep Singh Jagdev
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Urban Transportation ◽  
Fall Detection ◽  
Transportation System ◽  
Transportation Systems ◽  
Support Vector ◽  
Smart Transportation ◽  
Artificial Neural Network Ann

In urban transportation systems, bicycle sharing systems are majorly deployed in major cities of both developed and developing countries. The recent boom of bicycle sharing system along with its upgraded technology have opened new opportunities towards urban transportation system. With the enlargement of intelligent transportation systems (ITS's), smart bicycle sharing schemes are more popular to smart cities as a green transportation mode. In this article, the Internet of Things (IoT) and artificial intelligence-based monitoring devices have been proposed for the bicycles. This system contains a harmful exhaust gas sensor, wireless module, and a GPS receiver and camera that are capable to send data with time and date stamping. In addition, sensor also integrated on the bicycle for the fall detection. An artificial neural network (ANN) and support vector machine (SVM) applied to the data collected at central server is designed to analyze the root mean square error (RMSE), and coefficient of correlation (R2). Result shows that ANN performance is better when compared to SVM.

Proactive Decision Making for ITS Communication

2020 ◽  
pp. 197-226 ◽  
Author(s):  
Rodrigo Silva ◽  
Christophe Couturier ◽  
Thierry Ernst ◽  
Jean-Marie Bonnin
Keyword(s):  
Decision Making ◽  
Traffic Control ◽  
Intelligent Transportation Systems ◽  
Transportation Systems ◽  
Network Resources ◽  
Communication Architecture ◽  
Cooperative Vehicles ◽  
Near Future ◽  
Cooperative Intelligent Transportation Systems ◽  
Exchange Data

Demand from different actors for extended connectivity where vehicles can exchange data with other vehicles, roadside infrastructure, and traffic control centers have pushed vehicle manufacturers to invest in embedded solutions, which paves the way towards cooperative intelligent transportation systems (C-ITS). Cooperative vehicles enable the development of an ecosystem of services around them. Due to the heterogeneousness of such services and their specific requirements, as well as the need for network resources optimization for ubiquitous connectivity, it is necessary to combine existing wireless technologies, providing applications with a communication architecture that hides such underlying access technologies specificities. Due to vehicles' high velocity, their connectivity context can change frequently. In such scenario, it is necessary to take into account the short-term prevision about network environment; enabling vehicles proactively manage their communications. This chapter discusses about the use of near future information to proactive decision-making process.

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