Intelligent Pervasive Middleware for Context-Aware Vehicle Services

2011 ◽  
Vol 2 (3) ◽  
pp. 7
Author(s):  
M. Madkour ◽  
A. Maach
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Intelligent Systems ◽  
Engine Performance ◽  
Transportation Systems ◽  
Vital Role ◽  
Context Aware ◽  
New Paradigm ◽  
Context Aware Service ◽  
Service Framework

Transportation of goods and people plays a vital role in the lives of everyone and in virtually all businesses on earth accordingly the demand on our overburdened transportation system is increasing every day . The traffic congestion multiplies the effects of individual variations in driving performance as determined by physical abilities, knowledge, experience and, indeed, personality. We lose control over our plans and schedules; we rush because we're late; we cause accidents through recklessness and bad temper...In other hand The Intelligent Car initiative is an attempt to move towards a new paradigm, one where cars dont crash anymore, and traffic congestion is drastically reduced, Intelligent systems can support drivers to avoid accidents, optimise engine performance, reduce travel times, enhance efficiency and confort, increase productivity, improve road safety, raise management perfomance ...For intelligent transportation systems to reach their true potential we need an environment in which innovative and flexible services can be developed and delivered cost- effectively, to drivers and vehicles.Today building context-aware services is a complex and time consuming task. We present a Vehicle Context-Aware Service Framework architecture for the building and rapid prototyping of context aware vehicle services.We propose a simple and dynamique data-model which supports context acquiring and processing, semantic representing, context reasoning and cooperating sensing( sharing knowledge, resolving sensors conflicts...).The Vehicle Context-Aware Service Framework objective is to support vehicles users with personalized services. Our framework offers mechanisms to deliver vehicle diagnostic and maintenance services, Location and time Based Services, and also it suggests other services based on context-aware information, It provides them in a personalized and an adaptive manner to the user.

scholarly journals Context-Aware Naming and Forwarding in NDN-Based VANETs

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4629
Author(s):  
Waseeq Ul Islam Zafar ◽  
Muhammad Atif Ur Rehman ◽  
Farhana Jabeen ◽  
Byung-Seo Kim ◽  
Zobia Rehman
Keyword(s):  
Intelligent Transportation Systems ◽  
Ad Hoc Network ◽  
Ad Hoc ◽  
Geographical Area ◽  
Transportation Systems ◽  
Vital Role ◽  
Context Aware ◽  
Vehicle Communication ◽  
Event Notification ◽  
Safety Applications

Vehicular ad-hoc network (VANET) is a technology that allows ubiquitous mobility to mobile users. Inter-vehicle communication is an integral component of intelligent transportation systems that enables a wide variety of applications where vehicles interact and cooperate with each other, from safety applications to non-safety applications. VANETs applications have different needs (e.g., latency, reliability, delivery priorities, etc.) in terms of delivery effectiveness. In the last decade, named data networking (NDN) gained the attention of the research community for effective content retrieval and dissemination in mobile environments such as VANETs. In NDN, the content’s name has a vital role in storing and retrieving the content effectively and efficiently. In NDN-based VANETs, adaptive content dissemination solutions must be introduced that can make decisions related to forwarding, cache management, etc., based on context information represented by a content name. In this context, our main contributions are two-fold: (i) we present the hierarchical context-aware content-naming (CACN) scheme for NDN-based VANETs that enables naming the safety and non-safety applications, and (ii) we present a decentralized context-aware notification (DCN) protocol that broadcasts event notification information for awareness within the application-based geographical area. Simulation results show that the proposed DCN protocol succeeds in achieving reduced transmissions, bandwidth, and energy compared to existing critical contents dissemination protocols.

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.

scholarly journals Applications of Internet of Things

2018 ◽  
Vol 7 (9) ◽  
pp. 334
Author(s):  
Chi-Hua Chen ◽  
Kuen-Rong Lo
Keyword(s):  
Internet Of Things ◽  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Location Privacy ◽  
High Efficiency ◽  
Transportation Systems ◽  
Location Based Services ◽  
Vehicle Operation ◽  
Vehicle Positioning ◽  
Coverage Estimation

This editorial introduces the special issue entitled “Applications of Internet of Things”, of ISPRS International Journal of Geo-Information. Topics covered in this issue include three main parts: (I) intelligent transportation systems (ITS), (II) location-based services (LBS), and (III) sensing techniques and applications. Three papers on ITS are as follows: (1) “Vehicle positioning and speed estimation based on cellular network signals for urban roads,” by Lai and Kuo; (2) “A method for traffic congestion clustering judgment based on grey relational analysis,” by Zhang et al.; and (3) “Smartphone-based pedestrian’s avoidance behavior recognition towards opportunistic road anomaly detection,” by Ishikawa and Fujinami. Three papers on LBS are as follows: (1) “A high-efficiency method of mobile positioning based on commercial vehicle operation data,” by Chen et al.; (2) “Efficient location privacy-preserving k-anonymity method based on the credible chain,” by Wang et al.; and (3) “Proximity-based asynchronous messaging platform for location-based Internet of things service,” by gon Jo et al. Two papers on sensing techniques and applications are as follows: (1) “Detection of electronic anklet wearers’ groupings throughout telematics monitoring,” by Machado et al.; and (2) “Camera coverage estimation based on multistage grid subdivision,” by Wang et al.

Smart Traffic Management for Collision Avoidance Using V2V

2021 ◽  
Vol 11 (1) ◽  
pp. 01-07
Author(s):  
V. Naren Thiruvalar ◽  
E. Vimal
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Emergency Situation ◽  
Transportation Systems ◽  
Pollution Monitoring ◽  
Inappropriate Use ◽  
V2v Communication ◽  
Use Of Resources ◽  
Smart Traffic

The main objective of this project is to connect the vehicles together and avoid accidents by using V2V Communication. The vehicles are to be connected together by means of DSRC algorithm which is used for transceiving alert messages among the connected vehicles, in case of any emergency situation such as accidents. The Vehicle-to-Vehicle (V2V) and Vehicle-to- Infrastructure (V2I) technologies are specific cases of IoT and key enablers for Intelligent Transportation Systems (ITS). V2V and V2I have been widely used to solve different problems associated with transportation in cities, in which the most important is traffic congestion. A high percentage of congestion is usually presented by the inappropriate use of resources in vehicular infrastructure. In addition, the integration of traffic congestion in decision making for vehicular traffic is a challenge due to its high dynamic behaviour. An increase in the infrastructure growth is a possible solution but turns out to be costly in terms of both time and effort. Various applications that target transport efficiency could make use of the vast information collected by vehicles: safety, traffic management, pollution monitoring, tourist information, etc.

scholarly journals Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2229 ◽  
Author(s):  
Sen Zhang ◽  
Yong Yao ◽  
Jie Hu ◽  
Yong Zhao ◽  
Shaobo Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Large Scale ◽  
Transportation Networks ◽  
Transportation Network ◽  
Transportation Systems ◽  
Neural Network Models ◽  
Computation Efficiency ◽  
Congestion Prediction

Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available online traffic service provider Washington State Department of Transportation. We then propose a deep autoencoder-based neural network model with symmetrical layers for the encoder and the decoder to learn temporal correlations of a transportation network and predicting traffic congestion. Our experimental results on the SATCS dataset show that the proposed DCPN model can efficiently and effectively learn temporal relationships of congestion levels of the transportation network for traffic congestion forecasting. Our method outperforms two other state-of-the-art neural network models in prediction performance, generalization capability, and computation efficiency.

scholarly journals Real-Time Adaptive Traffic Signals Using Rate-Monotonic Scheduling

2019 ◽  
Vol 29 ◽  
pp. 03002 ◽  
Author(s):  
Mãdãlin-Dorin Pop
Keyword(s):  
Real Time ◽  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Traffic Signals ◽  
Fixed Time ◽  
Transportation Systems ◽  
Real Time Scheduling ◽  
Traffic Lights ◽  
Rate Monotonic ◽  
Rate Monotonic Scheduling

The studies and real situations shown that the traffic congestion is one of nowadays highest problems. This problem wassolved in the past using roundabouts and traffic signals. Taking in account the number of cars that is increasing continuously, we can see that past approaches using traffic lights with fixed-time controller for traffic signals timing is obsolete. The present and the future is the using of Intelligent Transportation Systems. Traffic lights systems should be aware about realtime traffic parameters and should adapt accordingly to them. The purpose of this paper is to present a new approach to control traffic signals using rate-monotonic scheduling. Obtained results will be compared with the results obtained by using others real-time scheduling algorithms.

scholarly journals Networked Electronic Equipments Using the IEEE 1451 Standard—VisioWay: A Case Study in the ITS Area

2012 ◽  
Vol 8 (4) ◽  
pp. 467124 ◽  
Author(s):  
F. Barrero ◽  
S. L. Toral ◽  
M. Vargas ◽  
J. Becerra
Keyword(s):  
Video Processing ◽  
Intelligent Transportation Systems ◽  
Road Traffic ◽  
Traffic Monitoring ◽  
Transportation Systems ◽  
New Paradigm ◽  
Ieee 1451 ◽  
The Impact ◽  
Smart Transducer

The concept of Intelligent Transportation Systems (ITSs) has been recently introduced to define modern embedded systems with enhanced digital connectivity, combining people, vehicles, and public infrastructure. The smart transducer concept, on the other hand, has been established by the IEEE 1451 standard to simplify the scalability of networked electronic equipments. The synergy of both concepts will establish a new paradigm in the near future of the ITS area. The purpose of this paper is to analyze the integration of electronic equipments into intelligent road-traffic management systems by using the smart transducer concept. An automated video processing sensor for road-traffic monitoring applications is integrated into an ITS network as a case study. The impact of the IEEE 1451 standard in the development and performance of ITS equipments is analyzed through its application to this video-based system, commercialized under the name VisioWay.

scholarly journals IoT based Real Time Traffic Monitoring System Using M/G/1 Queuing Research

2019 ◽  
Vol 8 (6) ◽  
pp. 3818-3822
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Queuing Theory ◽  
Traffic Monitoring ◽  
Transportation Systems ◽  
Theory Approach ◽  
The Road ◽  
Traffic Conditions ◽  
Performance Framework ◽  
Real Time Traffic

Vehicular Traffic crowding is paramount worry in urban cities. The use of technologies like Intelligent Transportation systems and Internet of Things can solve the problem of traffic congestion to some extent. The paper analyses the traffic conditions on a particular urban highway using queuing theory approach. It researches on performance framework such as time for waiting and queue length. The results can provide significant analysis to predict traffic congestion during peak hours. A congestion controlling action can be generated to utilize the road capacity fully during peak hours by using these results

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.

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