Design and Implementation of Intelligent Traffic-Management System for Smart Cities using Roaming Agent and Deep Neural Network (RAD2N)

In metropolitan areas, the exponential growth in quantity of vehicles has instigated gridlock, pollution, and delays in the transportation of freight. IoT is the modern revolution which pushes the world towards intelligent management systems and automated procedures. This makes a significant contribution to automation and intelligent societies. Traffic regulation and effective congestion management assist conserve many priceless resources. In order to recognize, collect and send data, autonomous vehicles are furnished with IoT powered Intelligent Traffic Management System (ITMS) having a set of sensors.  Moreover, machine learning (ML) algorithms can also be employed to enhance the transportation system.  Traffic jams, delays, and a high death rate are the results of the problems that the current transport management systems face.  In this paper, an active traffic control for VANET is proposed which merges Roaming Agents (RA) with deep neural networks (DNN). The effectiveness of the DNN with RA (RAD2N) routing method in VANETs is evaluated experimentally and compared with the traditional ML and other DL routing algorithms. Several traffic congestion indicators, including delay, packet delivery ratio (PDR) and throughput are used to validate RAD2N. The outcomes demonstrate that the proposed approach delivers lower latency and energy consumption

A Large-Scale SUMO-Based Emulation Platform

A hardware-in-the-loop simulation platform for emulating large-scale intelligent transportation systems is presented. The platform embeds a real vehicle into SUMO, a microscopic road traffic simulation package. Emulations, consisting of the real vehicle, and potentially thousands of simulated vehicles, are run in real time. The platform provides an opportunity for real drivers to gain a feel of being in a large-scale, connected vehicle scenario. Various applications of the platform are presented

Semantic Management of Urban Traffic Congestion

Urban traffic congestion is a problem which affects the world and is related to the massive urbanization and excessive number of cars on our streets. This causes a variety of problems, from economical/financial and health-related, to environmental warnings caused by high CO2 and NO2 emissions. This paper proposes a novel software engineering solution, which generates a software application aimed at individual drivers on urban roads, in order to help and ease overall congestion. The novelty is twofold. We target individual drivers in order to motivate them to re-think the purpose and goals of each journey they take. Consequently, the proposed software application enables reasoning upon various options an individual driver may have and helps in choosing the best possible solution for an individual. Our software application utilizes reasoning with SWRL enabled OWL ontologies, which can be hosted by any software application we run in our cars, ready to assist in driving, and implemented in Android / iOS environments

VANET Applications: Hot Use Cases

Current challenges of car manufacturers are to make roads safe, to achieve free flowing traffic with few congestions, and to reduce pollution by an effective fuel use. To reach these goals, many improvements are performed in-car, but more and more approaches rely on connected cars with communication capabilities between cars, with an infrastructure, or with IoT devices. Monitoring and coordinating vehicles allow then to compute intelligent ways of transportation. Connected cars have introduced a new way of thinking cars - not only as a mean for a driver to go from A to B, but as smart cars - a user extension like the smartphone today. In this report, we introduce concepts and specific vocabulary in order to classify current innovations or ideas on the emerging topic of smart car. We present a graphical categorization showing this evolution in function of the societal evolution. Different perspectives are adopted: a vehicle-centric view, a vehicle-network view, and a user-centric view; described by simple and complex use-cases and illustrated by a list of emerging and current projects from the academic and industrial worlds. We identified an empty space in innovation between the user and his car: paradoxically even if they are both in interaction, they are separated through different application uses. Future challenge is to interlace social concerns of the user within an intelligent and efficient driving

Reducing non-recurrent urban traffic congestion using vehicle re-routing

Recently, with the trend of world-wide urbanization, some of the accompanying problems are getting serious, including road traffic congestion. To deal with this problem, city planners now resort to the application of the latest information and communications technologies. One example is the adaptive traffic signal control system (e.g. SCATS, SCOOT). To increase the throughput of each main intersection, it dynamically adjusts the traffic light phases according to real-time traffic conditions collected by widely deployed induction loops and sensors. Another typical application is the on-board vehicle navigation system. It can provide drivers with a personalized route according to their preferences (e.g. shortest/fastest/easiest), utilizing comprehensive geo-map data and floating car data. Dynamic traffic assignment is also one of the key proposed methodologies, as it not only benefits the individual driver, but can also provide a route assignment solution for all vehicles with guaranteed minimum average travel time. However, the non-recurrent road traffic congestion problem is still not addressed properly. Unlike the recurrent traffic congestion, which is predictable by capturing the daily traffic pattern, unexpected road traffic congestion caused by unexpected en-route events (e.g. road maintenance, an unplanned parade, car crashes, etc.), often propagates to larger areas in very short time. Consequently, the congestion level of areas around the event location will be significantly degraded. Unfortunately, the three aforementioned methods cannot reduce this unexpected congestion in real time. The contribution of this thesis firstly lies in emphasizing the importance of the dynamic time constraint for vehicle rerouting. Secondly, a framework for evaluating the performance of vehicle route planning algorithms is proposed along with a case study on the simulated scenario of Cologne city. Thirdly, based on the multi-agent architecture of SCATS, the next road rerouting (NRR) system is introduced. Each agent in NRR can use the locally available information to provide the most promising next road guidance in the face of the unexpected urban traffic congestion. In the last contribution of this thesis, further performance improvement of NRR is achieved by the provision of high-resolution, high update frequency traffic information using vehicular ad hoc networks. Moreover, NRR includes an adaptation mechanism to dynamically determine the algorithmic (i.e. factors in the heuristic routing cost function) and operational (i.e. group of agents which must be enabled) parameters. The simulation results show that in the realistic urban scenario, compared to the existing solutions, NRR can significantly reduce the average travel time and improve the travel time reliability. The results also indicate that for both rerouted and non-rerouted vehicles, NRR does not bring any obvious unfairness issue where some vehicles overwhelmingly sacrifice their own travel time to obtain global benefits for other vehicles

Safe Intelligent Driver Assistance System in V2X Communication Environments based on IoT

In the modern world, power and speed of cars have increased steadily, as traffic continued to increase. At the same time highway-related fatalities and injuries due to road incidents are constantly growing and safety problems come first. Therefore, the development of Driver Assistance Systems (DAS) has become a major issue. Numerous innovations, systems and technologies have been developed in order to improve road transportation and safety. Modern computer vision algorithms enable cars to understand the road environment with low miss rates. A number of Intelligent Transportation Systems (ITSs), Vehicle Ad-Hoc Networks (VANETs) have been applied in the different cities over the world. Recently, a new global paradigm, known as the Internet of Things (IoT) brings new idea to update the existing solutions. Vehicle-to-Infrastructure communication based on IoT technologies would be a next step in intelligent transportation for the future Internet-of-Vehicles (IoV). The overall purpose of this research was to come up with a scalable IoT solution for driver assistance, which allows to combine safety relevant information for a driver from different types of in-vehicle sensors, in-vehicle DAS, vehicle networks and driver`s gadgets. This study brushed up on the evolution and state-of-the-art of Vehicle Systems. Existing ITSs, VANETs and DASs were evaluated in the research. The study proposed a design approach for the future development of transport systems applying IoT paradigm to the transport safety applications in order to enable driver assistance become part of Internet of Vehicles (IoV). The research proposed the architecture of the Safe Intelligent DAS (SiDAS) based on IoT V2X communications in order to combine different types of data from different available devices and vehicle systems. The research proposed IoT ARM structure for SiDAS, data flow diagrams, protocols. The study proposes several IoT system structures for the vehicle-pedestrian and vehicle-vehicle collision prediction as case studies for the flexible SiDAS framework architecture. The research has demonstrated the significant increase in driver situation awareness by using IoT SiDAS, especially in NLOS conditions. Moreover, the time analysis, taking into account IoT, Cloud, LTE and DSRS latency, has been provided for different collision scenarios, in order to evaluate the overall system latency and ensure applicability for real-time driver emergency notification. Experimental results demonstrate that the proposed SiDAS improves traffic safety

A WAVE based and Collaboration driven Framework for Reduced Traffic Congestion in Smart Cities

Abstract—The fast emergence of autonomous vehicles promises a drastic change on how road traffic congestion is detected, controlled, and mitigated. To this end, we believe that it is beneficial to explore different ways of collaboration between autonomous vehicles, with the aid of modern road infrastructure, to optimize the commuters’ travel time. We, therefore, propose in this paper a novel solution based on a multi-parties collaboration framework and built upon the WAVE standard to optimize the usage of the road network and lower commuters’ travel time. Our solution, which is based on a Belief-Desire-Intention architecture, enables autonomous vehicles to opt for selfish or collaborative behaviors depending on their goals and current situations. The results obtained from our preliminary prototype under three representative road maps demonstrate the effectiveness of our approach in dealing with traffic congestion

Enabling technologies for urban smart mobility: Recent trends, opportunities and challenges

The increasing population across the globe makes it essential to link smart and sustainable city planning with the logistics of transporting people and goods, which will significantly contribute to how societies will face mobility in the coming years. The concept of smart mobility emerged with the popularity of smart cities and is aligned with the sustainable development goals defined by the United Nations. A reduction in traffic congestion and new route optimizations with reduced ecological footprint are some of the essential factors of smart mobility; however, other aspects must also be taken into account, such as the promotion of active mobility and inclusive mobility, encour-aging the use of other types of environmentally friendly fuels and engagement with citizens. The Internet of Things (IoT), Artificial Intelligence (AI), Blockchain and Big Data technology will serve as the main entry points and fundamental pillars to promote the rise of new innovative solutions that will change the current paradigm for cities and their citizens. Mobility‐as‐a‐service, traffic flow optimization, the optimization of logistics and autonomous vehicles are some of the services and applications that will encompass several changes in the coming years with the transition of existing cities into smart cities. This paper provides an extensive review of the current trends and solutions presented in the scope of smart mobility and enabling technologies that support it. An overview of how smart mobility fits into smart cities is provided by characterizing its main attributes and the key benefits of using smart mobility in a smart city ecosystem. Further, this paper highlights other various opportunities and challenges related to smart mobility. Lastly, the major services and applications that are expected to arise in the coming years within smart mobility are explored with the prospective future trends and scope