scholarly journals An Interest-Based Approach for Reducing Network Contentions in Vehicular Transportation Systems

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2325 ◽  
Author(s):  
Allan M. de Souza ◽  
Guilherme Maia ◽  
Torsten Braun ◽  
Leandro A. Villas
Keyword(s):  
Traffic Management ◽  
State Of The Art ◽  
Transportation Systems ◽  
Traffic Information ◽  
Vehicular Traffic ◽  
Data Traffic ◽  
Traffic Demand ◽  
Traffic Efficiency ◽  
High Data ◽  
Content Sharing

Traffic management systems (TMS) are the key for dealing with mobility issues. Moreover, 5G and vehicular networking are expected to play an important role in supporting TMSs for providing a smarter, safer and faster transportation. In this way, several infrastructure-based TMSs have been proposed to improve vehicular traffic mobility. However, in massively connected and multi-service smart city scenarios, infrastructure-based systems can experience low delivery ratios and high latency due to packet congestion in backhaul links on ultra-dense cells with high data traffic demand. In this sense, we propose I am not interested in it (IAN3I), an interest-based approach for reducing network contention and even avoid infrastructure dependence in TMS. IAN3I enables a fully-distributed traffic management and an opportunistic content sharing approach in which vehicles are responsible for storing and delivering traffic information only to vehicles interested in it. Simulation results under a realistic scenario have shown that, when compared to state-of-the-art approaches, IAN3I decreases the number of transmitted messages, packet collisions and latency in up to 95 % , 98 % and 55 % respectively while dealing with traffic efficiency properly, not affecting traffic management performance at all.

scholarly journals Better safe than sorry: a vehicular traffic re-routing based on traffic conditions and public safety issues

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Allan M. de Souza ◽  
Torsten Braun ◽  
Leonardo C. Botega ◽  
Raquel Cabral ◽  
Islene C. Garcia ◽  
...  
Keyword(s):  
Traffic Management ◽  
State Of The Art ◽  
Context Aware ◽  
Vehicular Traffic ◽  
Transportation Management ◽  
Safety Issues ◽  
Traffic Efficiency ◽  
Traffic Conditions ◽  
Related Information ◽  
Simulation Results

Abstract Vehicular traffic re-routing is the key to provide better traffic mobility. However, taking into account just traffic-related information to recommend better routes for each vehicle is far from achieving the desired requirements of proper transportation management. In this way, context-aware and multi-objective re-routing approaches will play an important role in traffic management. Yet, most procedures are deterministic and cannot support the strict requirements of traffic management applications, since many vehicles potentially will take the same route, consequently degrading overall traffic efficiency. So, we propose an efficient algorithm named as Better Safe Than Sorry (BSTS), based on Pareto-efficiency. Simulation results have shown that our proposal provides a better trade-off between mobility and safety than state-of-the-art approaches and also avoids the problem of potentially creating different congestion spots.

Road Traffic Congestion (TraCo) Estimation Using Multi-Layer Continuous Virtual Loop (MCVL)

2021 ◽  
Vol 17 (2) ◽  
pp. 46-71
Author(s):  
Manipriya Sankaranarayanan ◽  
Mala C. ◽  
Samson Mathew
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Road Traffic ◽  
Vehicle Detection ◽  
Transportation Systems ◽  
Traffic Information ◽  
Traffic Characteristics ◽  
Traffic Video ◽  
Virtual Loop

Any road traffic management application of intelligent transportation systems (ITS) requires traffic characteristics data such as vehicle density, speed, etc. This paper proposes a robust and novel vehicle detection framework known as multi-layer continuous virtual loop (MCVL) that uses computer vision techniques on road traffic video to estimate traffic characteristics. Estimations of traffic data such as speed, area occupancy and an exclusive spatial feature named as corner detail value (CDV) acquired using MCVL are proposed. Further, the estimation of traffic congestion (TraCo) level using these parameters is also presented. The performances of the entire framework and TraCo estimation are evaluated using several benchmark traffic video datasets and the results are presented. The results show that the improved accuracy in vehicle detection process using MCVL subsequently improves the precision of TraCo estimation. This also means that the proposed framework is well suited to applications that need traffic characteristics to update their traffic information system in real time.

Based on Data Fusion Intelligent Traffic Information Analysis and Optimization

2014 ◽  
Vol 568-570 ◽  
pp. 831-834
Author(s):  
Yuan Zhang Lu ◽  
Bing Zhang
Keyword(s):  
Data Fusion ◽  
Intelligent Transportation Systems ◽  
Traffic Management ◽  
Scientific Basis ◽  
Transportation Systems ◽  
Traffic Information ◽  
Information Analysis ◽  
Existing Problems ◽  
Attribute Data ◽  
Intelligent Analysis

In this paper, we propose an analysis refine scheme based on data fusion towards some existing problems in data analysis of intelligent transportation systems .This method constructed the data into a plurality of time-series according to the characteristics of each attribute data. Providing an objective scientific basis for dynamic traffic management through intelligent analysis of traffic information based on the gray advantage analysis among data and system model of Intelligent Traffic Information decision support and auxiliary decision analysis.

scholarly journals Elevating the future of mobility UAV-enabled Intelligent Transportation Systems

2021 ◽  
Author(s):  
Abdul Saboor ◽  
Sander Coene ◽  
Evgenii Vinogradov ◽  
Emmeric Tanghe ◽  
Wout Joseph ◽  
...  
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Management ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Base Stations ◽  
Unmanned Aerial Systems ◽  
Traffic Efficiency ◽  
Autonomous Operation ◽  
Aerial Vehicles ◽  
The Future

Intelligent Transportation Systems (ITS) improve traffic efficiency, traffic management, driver’s comfort, and safety. They consist of a broad range of components, including vehicles, sensors, Base Stations, Road Side Units, and road infrastructure (i.e., traffic signals). ITS of the near future will need to support multi-modal transportation schemes (including ground and aerial vehicles, so-called Urban Air Mobility). ITS will have to be integrated with Unmanned Aerial Systems Traffic Management (UTM) and rely on 3 Dimensional (3D) connectivity provided by Integrated Aerial-Terrestrial 6G networks to achieve this support. In other words, various types of Unmanned Aerial Vehicles (UAVs) will become integral parts of future ITS due to their mobility, autonomous operation, and communication/processing capabilities. This article presents our view on the future integration of ITS and UTM systems, enabling wireless technologies and open research questions. We also present how UAVs can be used to enhance the performance of the currently available ITS.

Parallel CNN based big data visualization for traffic monitoring

2020 ◽  
Vol 39 (3) ◽  
pp. 2679-2691
Author(s):  
G. Madhukar Rao ◽  
Dharavath Ramesh
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Traffic Management ◽  
Temporal Dynamics ◽  
Traffic Monitoring ◽  
Transportation Systems ◽  
Traffic Prediction ◽  
Data Traffic ◽  
Traffic Condition

In a real-time application such as traffic monitoring, it is required to process the enormous amount of data. Traffic prediction is essential for intelligent transportation systems (ITSs), traffic management authorities, and travelers. Traffic prediction has become a challenging task due to various non-linear temporal dynamics at different locations, complicated underlying spatial dependencies, and more extended step forecasting. To accommodate these instances, efficient visualization and data mining techniques are required to predict and analyze the massive amount of traffic big data. This paper presents a deep learning-based parallel convolutional neural network (Parallel-CNN) methodology to predict the traffic conditions of a specific region. The methodology of deep learning contains multiple processing layers and performs various computational strategies, which is used to learn representations of data with multilevel abstraction. The data has captured from the department of transportation; thus, the size of data is vast, and it can be analyzed to get the behavior of the traffic condition. The purpose of this paper is to monitor traffic behavior, which enables the user to make decisions to build the traffic-free cities. Experimental results show that the proposed methodology outperforms other existing methods such as KNN, CNN, and FIMT-DD.

scholarly journals Collaborative and Infrastructure-less Vehicular Traffic Rerouting for Intelligent Transportation Systems

2020 ◽  
Author(s):  
Ademar Takeo Akabane ◽  
Edmundo Roberto Mauro Madeira ◽  
Leandro Aparecido Villas
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Management System ◽  
Cost Effective ◽  
Intelligent Transportation ◽  
Good Alternative ◽  
Transportation Systems ◽  
Vehicular Traffic ◽  
Traffic Management System

This extended abstract provides an at-a-glance view of the main contributions of my Ph.D. work. The work aims to investigate and develop cutting-edge an infrastructure-less vehicular traffic management system in order to minimize vehicular traffic congestion and advance the state-of-the-art in intelligent transportation systems. The proposed solutions were widely compared with other literature solutions on different performance evaluation metrics. The evaluation results show that the proposed vehicle traffic management system is efficient, scalable, and cost-effective, which may be a good alternative to mitigate urban mobility problems.

scholarly journals Wi-Fi a simple and efficient way for offloading data traffic in mobile networks

2013 ◽  
Vol 2 (1) ◽  
pp. 16-23
Author(s):  
Irena Malolli ◽  
Kozeta Sevrani
Keyword(s):  
Video Compression ◽  
Mobile Networks ◽  
Traffic Management ◽  
Business Models ◽  
Smart Devices ◽  
Technical Solution ◽  
Data Traffic ◽  
Management Tools ◽  
Mobile Data ◽  
High Data

Mobile data traffic is significantly increased year by year due to a number of factors including new smart devices, new applications such as M2M, the so-called “always-on” applications and services etc. In addition the recent studies tell us that the forecasts for mobile data traffic in near future will be tenfold higher, while the revenue for this market is expected to be increased only twofold. This trend raised a number of challenges for the mobile network operators (MNOs) in the world and in our region. Different technical and commercial solutions are discussed and developed and / or under developing. The first idea how to cope with high data traffic is to increase the network capacities. Even this is a direct traditional way as a technical solution it is too expensive and time consuming. Alternative ways to cope with data traffic in order to satisfy consumer demand and to keep key performance indicators are under developing. Some solutions in place are linked with traffic management tools such as data optimization, throttling, filtering, caching, video compression etc. In addition, new pricing policies and the adoption of the appropriate business models in new era of mobile data traffic are in the process. On top of the ways mentioned above or alternatively, Wi-Fi is considered as a simple way of data traffic off-load in mobile networks. In this article, we will identify the positive aspects of Wi-Fi offload versus other traffic management tools and draw some conclusions. We will give some recommendations how MNOs improve the situation for high data traffic through Wi-Fi offload solution, how Wi-Fi offload is related with other commercial aspects and quality of service in order to meet the customer satisfaction.

scholarly journals Elevating the future of mobility UAV-enabled Intelligent Transportation Systems

2021 ◽  
Author(s):  
Abdul Saboor ◽  
Sander Coene ◽  
Evgenii Vinogradov ◽  
Emmeric Tanghe ◽  
Wout Joseph ◽  
...  
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Management ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Base Stations ◽  
Unmanned Aerial Systems ◽  
Traffic Efficiency ◽  
Autonomous Operation ◽  
Aerial Vehicles ◽  
The Future

Intelligent Transportation Systems (ITS) improve traffic efficiency, traffic management, driver’s comfort, and safety. They consist of a broad range of components, including vehicles, sensors, Base Stations, Road Side Units, and road infrastructure (i.e., traffic signals). ITS of the near future will need to support multi-modal transportation schemes (including ground and aerial vehicles, so-called Urban Air Mobility). ITS will have to be integrated with Unmanned Aerial Systems Traffic Management (UTM) and rely on 3 Dimensional (3D) connectivity provided by Integrated Aerial-Terrestrial 6G networks to achieve this support. In other words, various types of Unmanned Aerial Vehicles (UAVs) will become integral parts of future ITS due to their mobility, autonomous operation, and communication/processing capabilities. This article presents our view on the future integration of ITS and UTM systems, enabling wireless technologies and open research questions. We also present how UAVs can be used to enhance the performance of the currently available ITS.

scholarly journals The Rolling Out Of International Project SMART E263/E77 For Advanced Traffic Management

2021 ◽  
Vol 1202 (1) ◽  
pp. 012043
Author(s):  
Boriss Jelisejevs ◽  
Kristjan Duubas
Keyword(s):  
Traffic Control ◽  
Intelligent Transportation Systems ◽  
Traffic Management ◽  
Transportation Systems ◽  
Added Value ◽  
Traffic Information ◽  
Road Maintenance ◽  
The European Union ◽  
Road Transportation ◽  
Project Proposal

Abstract Intelligent transportation systems (ITS) provide significant added value to road transportation, making the related investments distinctively effective and long-lasting. Moreover, some ITS activities may be eligible for financial support of the European union (EU). That was the way how Estonian Transport Administration and Latvian State Roads worked on the project proposal “Smart corridor Tallinn-Tartu-Luhamaa-Riga E263/E77” (acronym – SMART E263/E77), which was approved by EU program Interreg Central Baltics as CB891 project. The project started on June 1, 2020, and its implementation will last till the end of 2022 according to quite challenging schedule. Project activities primarily include numerous installations or road telemetry and telematics devices (especially, variable message signs) for advanced traffic management to be supported by cross-border traffic plans and improvements of traffic control centers. Project target is to provide general travel time savings at least by 0.88% across the whole corridor, however for the motorway-type sections it should reach more than 5.5%. Expected project results will establish new and improve existing functions on the E263 and E77 road transport corridors, namely: traffic management adaptive to variable road conditions; gathering and dissemination of traffic information; decision-making support for road maintenance operations (especially in winter). This report will summarize the information on project progress with emphasis on traffic management considerations.

5G Mobile Communication, Challenges: A Survey

2020 ◽  
Vol 2 (1) ◽  
pp. 35-42
Author(s):  
Hemalatha R ◽  
Rhesa M.J. ◽  
Revathi S
Keyword(s):  
Mobile Communication ◽  
Traffic Management ◽  
Radiation Effects ◽  
Critical Infrastructure ◽  
Security Threats ◽  
Technological Advancement ◽  
Data Traffic ◽  
Communication Challenges ◽  
5G Mobile Communication ◽  
Living Organisms

The hest for technological advancement in mobile communication is due to augmentation of wireless user. The deployment of 5G mobile communication is less than 4G mobile communication due to challenges in security like cyberwarfare, espionage, critical infrastructure threats. Nevertheless, critic of neurological discomforts, tissue damage in living organisms occur in the existence of EMF radiation. Also, physical scarcity for spectral efficiency arises due to ubiquitous data traffic. Inspite of these disputes data rate, low latency, device to device communication is also a challenge. In this paper we provide a survey on radiation effects, security threats, traffic management.

Sign in / Sign up

Export Citation Format

Share Document