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.

https://www.mdpi.com/1999-5903/11/4/94/pdf

A Review of Machine Learning and IoT in Smart Transportation

Unmanned aerial vehicles (UAVs) have recently attracted the attention of researchers due to their numerous potential civilian applications. However, current robot navigation technologies need furth...

Overview of Path-Planning and Obstacle Avoidance Algorithms for UAVs: A Comparative Study

ICARUS: Improvement of traffic Condition through an Alerting and Re-routing System

Nowadays, the research on vehicular computing enhanced a very huge amount of services and protocols, aimed to vehicles security and comfort. The investigation of the IEEE802.11p, Wireless Access in Vehicular Environments (WAVE) and Dedicated Short Range Communication (DSRC) standards gave to the scientific world the chance to integrate new services, protocols, algorithms and devices inside vehicles. This opportunity attracted the attention of private/public organizations, which spent lot of resources and money to promote vehicular technologies. In this paper, the attention is focused on the design of a new approach for vehicular environments able to gather information during mobile node trips, for advising dangerous or emergency situations by exploiting on-board sensors. It is assumed that each vehicle has an integrated on-board unit composed of several sensors and Global Position System (GPS) device, able to spread alerting messages around the network, regarding warning and dangerous situations/conditions. On-board units, based on the standard communication protocols, share the collected information with the surrounding road-side units, while the sensing platform is able to recognize the environment that vehicles are passing through (obstacles, accidents, emergencies, dangerous situations, etc.). Finally, through the use of the GPS receiver, the exact location of the caught event is determined and spread along the network. In this way, if an accident occurs, the arriving cars will, probably, avoid delay and danger situations.

Managing Emergency Situations in VANET Through Heterogeneous Technologies Cooperation.

Traffic congestion worldwide has led to loss of human lives due to failure in transporting accident victims, critical patients, medical equipment and medicines on time. With the unending growth in vehicular traffic everywhere, Internet of Things (IOT) and Vehicular Ad Hoc Network (VANET) have embarked as a promising platform for an Intelligent Traffic Management System (ITMS). Many researches have been done to clear emergency vehicles, but they have not only failed to meet the target travel time of an emergency vehicle, but also failed to provide solutions, when traffic signals are hacked. This paper proposes an intelligent traffic management system for a Smart City that is inspired by the concepts of IoT and VANET. In the proposed solution, emergency vehicles are scheduled to prioritize them for a smooth passage through the traffics based on incident type. It not only navigates ambulances in finding the shortest possible paths till their destination, but also presents a solution to detect and respond to hacking of traffic signals. To show the superiority of our proposed system, a simulated environment modeling actual roads and vehicle movements has been demonstrated using CupCarbon simulator. Comparative results exhibits superior performance of our proposed system over the other recently proposed ITS for emergency vehicles, in terms of both congestion avoidance, transmission delay, travel time and respond to hacking.

Intelligent Traffic Management System for Prioritizing Emergency Vehicles in a Smart City (TECHNICAL NOTE)

Most current navigation devices or apps, based on the global positioning system (GPS), only adopt static information for route planning. Although some are equipped with an RDS-TMC receiver to receive real-time traffic events, many of these real-time traffic events are irrelevant to the vehicle. This paper offers three major contributions. First, a vehicular-ad-hoc-network- (VANET-) based () route planning algorithm is proposed to calculate the route with the shortest travelling time or the lowest fuel consumption, depending on two real-time traffic information sources, which have not been used in traditional GPS navigation applications. The first traffic information source is the recorded traffic information of the road segment that the vehicle has passed through. It is further exchanged between vehicles through an IEEE 802.11p wireless link. The second traffic information is provided by Google Maps. A GPS navigation app is then implemented on the Android platform to realize . Finally, simulations for six route planning algorithms are executed by VANET simulator The ONE, in one congested and one noncongested time period, respectively. In summary, achieves significant reductions in both the average travelling time and fuel consumption of the planned route, as compared to traditional route planning algorithms.

Feng Han Yeh

Papers

A VANET-Based A* Route Planning Algorithm for Travelling Time- and Energy-Efficient GPS Navigation App: