Field Of Intelligent Transportation Systems Research Articles

5G-enabled Hierarchical architecture for software-defined intelligent transportation system

With the rapid advancement of technology, an increasing number of devices are being connected to the Internet and getting smart. Such advancement brings new challenges in the field of intelligent transportation system (ITS), including transferring of high data rates, providing rapid response system to users, addition of new devices, and their remote configuration. Thus, mobile information systems, along with intelligent multimodal mobility services, cope with such constraints and take significant benefit of the associated technology from emerging information and communication frameworks. Therefore, recent advancement in the field of telecommunication has witnessed increased interest in ITS, especially vehicular ad-hoc networks (VANETS). Software-defined networks (SDNs) can also bring advantages to ITS due their flexibility and programmability to the network via their logical and centralized control entity. However, the bandwidth and continuous connection between ITS and SDN is still a challenge owing to the highly mobile nature of VANETS. Therefore, to address this issue, this paper presents a novel concept for enhancing the capabilities of ITS via the newly proposed 5G-based SDN architecture for ITS. The proposed system architecture is based on the following three function layers: sensing layer, relay layer, and core network layer. Continuous accessibility, via flexible and programmable features, is achieved through SDN features. In addition, high data rates and bandwidth are provided by the proposed 5G architecture. The simulation results show that the proposed system architecture achieves better results than the ad-hoc on-demand distance vector routing protocol.

Water wave optimization-based routing protocol for vehicular adhoc networks

It is well known that Vehicular Adhoc Network (VANET) plays the major role in the field of Intelligent Transport System (ITS) to enhance road safety as well. The entire communication among vehicles is covered in VANETs. Since the number of researches is in progress for the enhancement of VANET model. Usually, the issues arising with this field is the achievement of multi-constrained Quality of Service (QoS) measures. To solve this issue, our paper intends to propose a cost model that considers certain network quality measures like travel, collision, congestion and the awareness about the QoS. Moreover, this paper adopts a renowned algorithm named Water Wave Optimization algorithm (WWO) to find the optimal route. The proposed method analyses its performance over other conventional methods like Lion Algorithm (LA), Genetic Algorithm (GA) and LA with Adaptive Mutation (LAAM) in terms of cost analysis, Convergence analysis and Computational time. Then the analytical results prove the superiority of proposed work in terms of decreased routing cost.

Deep Learning: Is it the Main Challenge Behind Autonomous Vehicles Deployment?

There has been a growing interest in the field of intelligent transportation systems (ITSs) to improve road safety [1] and traffic management issues. ITS is realized through social interaction among vehicles which offers plethora of applications and services ranging from safety to information and entertainment (collectively referred to as infotainment). These applications on one hand guarantee safe driving, and on the other hand add value to our driving experience. Significant efforts have been made in the last decade by researchers from both academia and industry to realize the ITS through different communication paradigms such as vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communications. ITS uses existing communication technologies such as Dedicated Short-Range Communication (DSRC), WiFi, 4G/LTE, Bluetooth, WiMax, and so on.

Analysis of selected elements of smart systems transportation in Szczecin

The aim of the article is to present solutions in the field of intelligent transport systems used in Szczecin. The city of Szczecin, due to its location and communication routes, is an important element of the national transport system. The city's task is to provide all users with efficient mobility, not forgetting to maintain the principles of sustainable development. The way to meet these goals is the implementation of intelligent transport systems. The subject of the research presented in the article are the selected subsystems of the intelligent transport system operating in Szczecin. The analysis was made by verifying information provided by Variable Message Signs.

Multiple Vehicle Detection and Segmentation in Malaysia Traffic Flow

Vision based system are widely used in the field of Intelligent Transportation System (ITS) to extract a large amount of information to analyze traffic scenes. By rapid number of vehicles on the road as well as significant increase on cameras dictated the need for traffic surveillance systems. This system can take over the burden some task was performed by human operator in traffic monitoring centre. The main technique proposed by this paper is concentrated on developing a multiple vehicle detection and segmentation focusing on monitoring through Closed Circuit Television (CCTV) video. The system is able to automatically segment vehicle extracted from heavy traffic scene by optical flow estimation alongside with blob analysis technique in order to detect the moving vehicle. Prior to segmentation, blob analysis technique will compute the area of interest region corresponding to moving vehicle which will be used to create bounding box on that particular vehicle. Experimental validation on the proposed system was performed and the algorithm is demonstrated on various set of traffic scene.

Comparative Analysis of V2V and A2A Technologies

Abstract In recent years, around the world, there has been work underway on systems, which will increase not only the comfort of traveling but, above all, the safety and reliability of the road traffic. The systems in this field, designed to replace human beings in the future, thus eliminating their mistakes on the road, already have their prototypes. However, these prototypes are still being improved and require a lot of work so they could operate fully and reliably. The subject of the publication is a compilation of two new concepts in the field of Intelligent Transport Systems. These concepts are V2V (Vehicle - to - Vehicle) and A2A (Autonomous vehicle - to - Autonomous vehicle). Their comparison was carried out in terms of functionality, communication, vehicle equipment, legal aspects and the anticipated date of their entry into the market. Also examples of first tests and implementations of vehicles with driver assistance systems, and semi-autonomous vehicles were presented.

International Automobile Scientific Forum (IASF-2017) Intelligent Transport Systems

International Automobile Scientific Forum (IASF-2017) “Intelligent Transport Systems”Prospects of the creation, production and operation of autonomous (driverless) vehicles and related infrastructure became the main topic of IASF-2017, which took place at FSUE “NAMI”. More than 350 people participated in the Forum, including representatives of the Ministry of Industry and Trade of the Russian Federation, Ministry of Transport of the Russian Federation, KAMAZ PTC, PJSC AVTOVAZ, GAZ GROUP, GLONASS NP, Yandex, FEV, VOLVO, Bosch, AVL, National Instruments, Cognitive Technologies, Mechanical Engineering Research Institute of the Russian Academy of Sciences, Joint Institute for High Temperatures of the Russian Academy of Sciences, Joint Institute of Mechanical Engineering of the National Academy of Sciences of Belarus, Yandex, Bauman Moscow State Technical University, Moscow Automobile and Road Construction State Technical University (MADI), Moscow Polytechnic University, South Ural State University and other institutions. Among the participants, there were young scientists and engineers developing technologies in the field of intelligent transport systems (ITS).Within IASF-2017, ITS development trends, creation and improvement of regulatory and legal framework in this field, development of processing equipment and ITS testing procedures, risks related to the implementation of such systems in everyday life were discussed.Alexander Morozov, Deputy Minister of Industry and Trade of the Russian Federation, has invited the audience to participate in the activities of AUTONET National Technological Initiative, which supports projects aimed at development of the related components, programs and systems.There was also an exhibition of R&D achievements in the field of autonomous vehicles and their components organized within the Forum. FSUE “NAMI” presented some projects, which are already well known to the public – SHATL driverless electric bus and electric driverless vehicle – as well as electronic control systems and vehicle components. KAMAZ together with FSUE “NAMI” presented the “Driverless KAMAZ” Project. GAZ GROUP presented a driverless bus based on a light electric platform and a new-generation frame bus, which completely meets the requirements of the “Accessible Environment” program. MADI showed the “Open Automobile Platform for Autonomous Vehicle Technology Testing” project.On the second day of the Forum, October 19, a joint session of young professionals, students and postgraduates’ section and “Vehicle Intelligent Systems” section was held.All postgraduates and students who presented their papers within the session were granted IASF-2017 Certificates of Participation.

Automatic Vehicle Accident Detection Based on GSM System

The evolution of technology has increased the rate of traffic accidents that occurs frequently causing loss of lifeand property. Therefore, the automatic traffic monitoring system gradually attracted the attention of researchers in improvingtraffic safety through the field of intelligent transport systems. In this paper, a cost-effective approach based on GSM systemfor automatic traffic incident detection is proposed. This paper provides an optimal solution to reduce the death rate by usingthe vibration sensor and GSM system; the implementation system is based on hardware (circuits) and software to build agraphical user interface (GUI) using LabView TM to process the data. Sensors are installed into the vehicles on each side ofthe vehicle. An SMS will be sent to the user after the accident. This system will assist in search and rescue vehicle involved inthe accident.

Automatic Vehicle Accident Detection Based on GSM System

The evolution of technology has increased the rate of traffic accidents that occurs frequently causing loss of life and property. Therefore, the automatic traffic monitoring system gradually attracted the attention of researchers in improving traffic safety through the field of intelligent transport systems. In this paper, a cost-effective approach based on GSM system for automatic traffic incident detection is proposed. This paper provides an optimal solution to reduce the death rate by using the vibration sensor and GSM system; the implementation system is based on hardware (circuits) and software to build a graphical user interface (GUI) using LabView TM to process the data. Sensors are installed into the vehicles on each side of the vehicle. An SMS will be sent to the user after the accident. This system will assist in search and rescue vehicle involved in the accident.

SYSTEM ANALYSIS OF MAJOR TRENDS IN DEVELOPMENT OF ADAPTIVE TRAFFIC FLOW MANAGEMENT METHODS

Adaptive algorithms, which current traffic systems are based on, exist for many decades. Information technologies have developed significantly over this period and it makes more relevant their application in the field of transport. This paper analyses modern trends in the development of adaptive traffic flow control methods. Reviewed the most perspective directions in the field of intelligent transport systems, such as high-speed wireless communication between vehicles and road infrastructure based on such technologies as DSRC and WAVE, traffic jams prediction having such features as traffic flow information, congestion, velocity of vehicles using machine learning, fuzzy logic rules and genetic algorithms, application of driver assistance systems to increase vehicle’s autonomy. Advantages of such technologies in safety, efficiency and usability of transport are shown. Described multi-agent approach, which uses V2I-communication between vehicles and intersection controller to improve efficiency of control due to more complete traffic flow information and possibility to give orders to separate vehicles. Presented number of algorithms which use such approach to create new generation of adaptive transport systems.

INTERACTIVITY OF THE MODERN AUTOMATED SYSTEMS OF THE HELP TO THE DRIVER

In this article the current technologies in the field of intelligent transportation systems are investigated. The latest systems on control of the safe movement on roads are considered. The analysis of the systems of the help to the driver implemented in cars is carried out. The system concept of the help to the driver of «System help» is offered. Algorithms of work of this system which is based on the principles of interactivity and interaction are investigated. By results of researches experiment on quality of work of system concept of «System help» is made.

Vulnerable Road User needs towards ITS

Due to fast development of new technologies in the field of Intelligent Transport Systems (ITS) a number of new research topics arise, especially in view of vulnerable road users (VRUs). While most advancements in the ITS sector are primarily targeting motorised transport with focus on safety and ecological aspects of transport, there is still a lack of both research and innovation considering VRUs. The vulnerable road users and ITS (VRUITS) project, co-funded by the European Commission, aims at actively integrating the "human" element into the ITS approach. The focus of this paper is on the mixed methods approach applied in the project to assess both actual user needs and expert opinions on perceived barriers and necessary adaptations on technological, societal and behavioural levels for a successful and sustainable deployment of new technologies.

Presenting a traffic management and control system in driver assistance form based on vehicular networks

Vehicular Networks is considered a major step in the field of Intelligent Transportation System (ITS). In this technology, some equipment will be installed on vehicles and special places at roadsides which will enable the wireless communication between vehicles with each other and will provide the communication between the vehicles and roadside equipment. One of the ITS application is Traffic monitoring system. Such system enables accessing traffic videos by traffic monitoring centers to make traffic decision. However, providing traffic video for the vehicles can be appealing. This paper addresses a new application in vehicular networks and ITS which can provide this videos for drivers in a city. Each driver request timely traffic video of a location from a web server and the web server forward this request to a stream management server. This server based on current location of the requester vehicle, its speed and its direction calculates appropriate video chunks for each RSU along vehicle destination. This study aims to present a system which can bring a high accessibility for content and can provide it with an appropriate bandwidth and quality for vehicles. Due to the scalability and bandwidth limitations for its content and streaming, vehicular networks are used in this system.

Real-Time Associative Memory–Based Rear-End Collision Warning System

In the intelligent transportation system field, there has been a growing interest in developing collision warning systems based on artificial neural network (ANN) techniques in an effort to address several issues associated with parametric approaches. Previous ANN-based collision warning algorithms were generally based on predetermined associative memories derived before driving. Because collision risk is highly related to the current traffic situation, such as traffic state transition from free flow to congestion, however, updating associative memory in real time should be considered. To improve further the performance of the warning system, a systemic architecture is proposed to implement the multilayer perceptron neural network–based rear-end collision warning system (MCWS), which updates the associative memory with the vehicle distance sensor and smartphone data in a cloud computing environment. For the practical use of the proposed MCWS, its collision warning accuracy is evaluated with respect to various time intervals for updating the associative memories and market penetration rates. Results show that the MCWS exhibits a steady improvement in its warning performance as the time interval decreases, whereas the MCWS works more efficiently as the sampling ratio increases overall. When the sampling ratio reaches 50%, the MCWS shows a particularly stable warning accuracy, regardless of the time interval. These findings suggest that the MCWS has great potential to provide an acceptable level of warning accuracy for practical use, as it can obtain the well-trained associative memories reflecting current traffic situations by using information from widespread smartphones.

Saxony as a Model Region in Germany for connected electric mobility

Saxony as a Model Region for electric mobility and intelligent transport systems has extensive experiences which ranges from the operation of hybrid and full electric buses including fast charging infrastructure, electric car fleets in companies and municipal institutions, eCarsharing and long distance mobility as well as in connected electric mobility. The paper describes the objectives and lessons learned of some of the noteworthy projects relating to electric public transport and multimodality as well as the perspective to establish Saxony as a pioneer in the field of intelligent transport systems and connected mobility.

A Hybrid Short-Term Traffic Flow Prediction Model Based on Singular Spectrum Analysis and Kernel Extreme Learning Machine

Short-term traffic flow prediction is one of the most important issues in the field of intelligent transport system (ITS). Because of the uncertainty and nonlinearity, short-term traffic flow prediction is a challenging task. In order to improve the accuracy of short-time traffic flow prediction, a hybrid model (SSA-KELM) is proposed based on singular spectrum analysis (SSA) and kernel extreme learning machine (KELM). SSA is used to filter out the noise of traffic flow time series. Then, the filtered traffic flow data is used to train KELM model, the optimal input form of the proposed model is determined by phase space reconstruction, and parameters of the model are optimized by gravitational search algorithm (GSA). Finally, case validation is carried out using the measured data of an expressway in Xiamen, China. And the SSA-KELM model is compared with several well-known prediction models, including support vector machine, extreme learning machine, and single KLEM model. The experimental results demonstrate that performance of the proposed model is superior to that of the comparison models. Apart from accuracy improvement, the proposed model is more robust.

A Bibliographic Analysis and Collaboration Patterns of IEEE <sc>Transactions on Intelligent Transportation Systems</sc> Between 2000 and 2015

Intelligent transportation systems (ITS) has been one of the most active research fields in recent years. This paper identifies most productive authors, institutions, and countries/regions in IEEE Transactions on Intelligent Transportation Systems from 2000 to 2015. The results of bibliographic analysis show that the USA is the most influential country in that it not only has the most papers but also has six out of the ten most-cited papers. Meanwhile, researchers from China and Europe have published nearly half of the papers in this field. In addition, we generate three networks (including coauthorship network, keyword co-occurrence network, and author co-keyword network) to analyze collaboration patterns among authors in the field of ITS. The active keywords are investigated, and the top three are vehicles , road vehicles , and road traffic . Finally, visual pictures are presented to show topological interactions of authors' collaboration.

Number Plate Detection with a Multi-Convolutional Neural Network Approach with Optical Character Recognition for Mobile Devices

In this paper, we propose a method to achieve improved number plate detection for mobile devices by applying a multiple convolutional neural network (CNN) approach. First, we processed supervised CNN-verified car detection and then we applied the detected car regions to the next supervised CNN-verifier for number plate detection. In the final step, the detected number plate regions were verified through optical character recognition by another CNN-verifier. Since mobile devices are limited in computation power, we are proposing a fast method to recognize number plates. We expect for it to be used in the field of intelligent transportation systems.

A Comparative Study on the Performance of Evolutionary Fuzzy and Crisp Rule Based Classification Methods in Congestion Prediction

Accurate estimation of the future state of the traffic is an attracting area for researchers in the field of Intelligent Transportation Systems (ITS). This kind of predictions can lead to traffic managers and drivers to act in consequence, reducing the economic and social impact of a possible congestion. Due to the inter-urban traffic information nature, the task of predicting the future state of the traffic requires, in most cases, a non-linear patterns search in the input data. In recent years, a wide variety of models has been used to solve this problem in the most accurate way. Due to that, models generated to provide information about the future state of the road are, usually, incomprehensible to a human operator, making impossible to give him/her an explanation about the causes of the prediction. Given the capacity of rule based systems to explain the reasoning followed to classify a new pattern, the advantages and disadvantages of such approaches are explored in this work.To conduct such task, datasets recorded from the California Department of Transportation are created. A 9-kilometer section of the I5 highway of Sacramento is used for this research. Two different types of datasets are built for the experimentation. One of them contains the entire information recorded. The other one contains with a simplified version of the information, considering only the first, middle and last monitored points of the road. Twelve prediction horizons, from 5 to 60minutes, were considered for prediction. An experimental comparative study involving 16 state of the art techniques is performed. Techniques tested include those that fall within the categories of Evolutionary Crisp Rule Learning (ECRL) and Evolutionary Fuzzy Rule Learning (EFRL). These methods were selected since they offer to the final user, not only a prediction, but also a legible model about the way in which the decision was taken. Techniques are compared in terms of accuracy and complexity of the models generated.

A methodology for structured ontology construction applied to intelligent transportation systems

The number of computers installed in urban and transport networks has grown tremendously in recent years, also the local processing capabilities and digital networking currently available. However, the heterogeneity of existing equipment in the field of ITS (Intelligent Transportation Systems) and the large volume of information they handle, greatly hinder the interoperability of the equipment and the design of cooperative applications between devices currently installed in urban networks. While the dynamic discovery of information, composition and invocation of services through intelligent agents are a potential solution to these problems, all these technologies require intelligent management of information flows. In particular, it is necessary to wean these information flows of the technologies used, enabling universal interoperability between computers, regardless of the context in which they are located. The main objective of this paper is to propose a systematic methodology to create ontologies, using methods such as a semantic clustering algorithms for retrieval and representation of information. Using the proposed methodology, an ontology will be developed in the ITS domain. This ontology will serve as the basis of semantic information to a SS (Semantic Service) that allows the connection of new equipment to an urban network. The SS uses the CORBA standard as distributed communication architecture.