scholarly journals Real Time Traffic Prediction Based On Social Media Text Data Using Deep Learning

2021 ◽  
Author(s):  
B. Mounica ◽  
K. Lavanya
Keyword(s):  
Social Media ◽  
Big Data ◽  
Real Time ◽  
Traffic Management ◽  
Traffic Analysis ◽  
Traffic Prediction ◽  
Text Data ◽  
Real Time Traffic ◽  
Data Architecture ◽  
Social Media Text

Due to urbanization Traffic management is one of the major issues in contemporary civic management, considering this circumstance traffic analysis is turning into the need of the present world. Text data generated by Twitter, Facebook and other social media platforms can be used for traffic management. Big data helps in traffic prediction and traffic analysis of advancing metropolitan zones. Constant traffic investigation requires preparing of information streams that are produced persistently to increase fast experiences. To measures stream information at a fast rate advancements on high figuring limit is required. Social media text data can be processed by using batch processing and stream processing with big data architecture through Spark and Hadoop framework. In this paper big data architecture is proposed for real time traffic text data analysis. In architecture Spark and Kafka are used in combination. Kafka helps in pipelines text data used in conjunction with spark stream processing engine. Big data architecture using Spark, Kafka with ability for processing and preparing huge measure of information, have settled the serious issue of handling and putting away constantly streaming data. The traffic information from Twitter API is streamed. In The proposed model pointed toward ensemble neural network model to reduce the variance in results for better prediction foreseeing traffic stream text data by incorporating Spark and Kafka that will be of an extraordinary incentive to the public authority for traffic management and analysis.

scholarly journals Assessments of Real-Time Communications over TSN Automotive Networks

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 556
Author(s):  
Lucia Lo Bello ◽  
Gaetano Patti ◽  
Giancarlo Vasta
Keyword(s):  
Performance Evaluation ◽  
Real Time ◽  
Communication System ◽  
Traffic Management ◽  
Mac Layer ◽  
Automated Driving ◽  
Real Time Traffic ◽  
Management Scheme ◽  
Event Driven ◽  
Automotive Networks

The IEEE 802.1Q-2018 standard embeds in Ethernet bridges novel features that are very important for automated driving, such as the support for time-driven communications. However, cars move in a world where unpredictable events may occur and determine unforeseen situations. To properly react to such situations, the in-car communication system has to support event-driven transmissions with very low and bounded delays. This work provides the performance evaluation of EDSched, a traffic management scheme for IEEE 802.1Q bridges and end nodes that introduces explicit support for event-driven real-time traffic. EDSched works at the MAC layer and builds upon the mechanisms defined in the IEEE 802.1Q-2018 standard.

Telecommunications- and Information Technology–Inspired Analyses: Review of an Intelligent Transportation Systems Experience

2017 ◽  
Vol 2658 (1) ◽  
pp. 44-55 ◽  
Author(s):  
Yupo Chan
Keyword(s):  
Information Technology ◽  
Big Data ◽  
Real Time ◽  
Traffic Management ◽  
Transportation Systems ◽  
Lessons Learned ◽  
Connected Vehicles ◽  
Ongoing Research ◽  
Real Time Information ◽  
Time Information

This paper reviews both the author’s experience with managing highway network traffic on a real-time basis and the ongoing research into harnessing the potential of telecommunications and information technology (IT). On the basis of the lessons learned, this paper speculates about how telecommunications and IT capabilities can respond to current and future developments in traffic management. Issues arising from disruptive telecommunications technologies include the ready availability of real-time information, the crowdsourcing of information, the challenges of big data, and the need for information quality. Issues arising from transportation technologies include autonomous vehicles and connected vehicles and new taxi-like car- and bikesharing. Illustrations are drawn from the following core functions of a traffic management center: ( a) detecting and resolving an incident (possibly through crowdsourcing), ( b) monitoring and forecasting traffic (possibly through connected vehicles serving as sensors), ( c) advising motorists about routing alternatives (possibly through real-time information), and ( d) configuring traffic control strategies and tactics (possibly though big data). The conclusion drawn is that agility is the key to success in an ever-evolving technological scene. The solid guiding principle remains innovative and rigorous analytical procedures that build on the state of the art in the field, including both hard and soft technologies. The biggest modeling and simulation challenge remains the unknown, including such rapidly emerging trends as the Internet of things and the smart city.

scholarly journals Mobile Road Traffic Management System Using Weighted Sensor

2017 ◽  
Vol 11 (5) ◽  
pp. 147 ◽  
Author(s):  
Solomon Adegbenro Akinboro ◽  
Johnson A Adeyiga ◽  
Adebayo Omotosho ◽  
Akinwale O Akinwumi
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Urban Areas ◽  
Traffic Management ◽  
Management System ◽  
Road Traffic ◽  
Traffic Information ◽  
Traffic Light ◽  
Real Time Traffic ◽  
Traffic Management System

<p><strong>Vehicular traffic is continuously increasing around the world, especially in urban areas, and the resulting congestion ha</strong><strong>s</strong><strong> be</strong><strong>come</strong><strong> a major concern to automobile users. The popular static electric traffic light controlling system can no longer sufficiently manage the traffic volume in large cities where real time traffic control is paramount to deciding best route. The proposed mobile traffic management system provides users with traffic information on congested roads using weighted sensors. A prototype of the system was implemented using Java SE Development Kit 8 and Google map. The model </strong><strong>was</strong><strong> simulated and the performance was </strong><strong>assessed</strong><strong> using response time, delay and throughput. Results showed that</strong><strong>,</strong><strong> mobile devices are capable of assisting road users’ in faster decision making by providing real-time traffic information and recommending alternative routes.</strong></p>

scholarly journals ENHANCED VANET ROUTING PROTOCOLS FOR DYNAMIC MAPPING IN REAL TIME TRAFFIC

2019 ◽  
Vol 01 (03) ◽  
pp. 139-147
Author(s):  
Wang Haoxiang ◽  
Smys S
Keyword(s):  
Real Time ◽  
Routing Protocols ◽  
Traffic Management ◽  
Path Optimization ◽  
Delivery Ratio ◽  
Digital Map ◽  
Real Time Traffic ◽  
Dynamic Mapping ◽  
Adhoc Network ◽  
Frame Work

The developments in the means of transportation along with the communication advancements has made the automotives to step into its next level of innovation by providing a safe, convenient and well-timed transportation. This is made possible by the introduction of the frame work that is particularly designed to establish connectivity between vehicles on road without any previous structure to support with. This paradigm formed particularly in organizing communication between vehicles is the vehicular Adhoc network (VANET) that causes a vehicles to vehicle connection for proper managing of the traffic flow to make the travel more safe and comfortable. The paper proposes a dynamic mapping of real time traffic with the acquisition of digital map by crowd mapping with clustering to offer path optimization to minimize the delay in the responses, for having an efficient traffic managing. The evaluation of the proposed methodology ensures the minimization of the delay in the communication and the improved delivery ratio incurred, when compared with the carry-forward based routings methods that cause more delay resulting in imperfect traffic management.

GWAP as a Tool to Analyze, Design, and Test Geo-Social Systems

2013 ◽  
pp. 380-407 ◽  
Author(s):  
Martina Deplano ◽  
Giancarlo Ruffo
Keyword(s):  
Social Media ◽  
Information Systems ◽  
Real Time ◽  
Mobile Devices ◽  
General Framework ◽  
Social Dynamics ◽  
Social Systems ◽  
Traffic Information ◽  
Real Time Traffic ◽  
Social Events

In this chapter, the authors discuss the state-of-the-art of Geo-Social systems and Recommender systems, which are becoming extremely popular for users accessing social media trough mobile devices. Moreover, they introduce a general framework based on the interaction among those systems and the “Game With A Purpose” (GWAP) paradigm. The proposed framework/platform can help researchers to understand geo-social dynamics in order to design and test new services, such as recommenders of places of interest for tourists, real-time traffic information systems, personalized suggestions of social events, and so forth. To target the governance of such complexity, relevant data must be collected by the investigators, shared with the community, and analyzed to find dynamical patterns that correlate spatial-temporal information with the user’s preferences and objectives. The authors argue that the GWAP approach can be exploited to successfully satisfy many of these tasks.

Cloud-Based Big Data Analysis Tools and Techniques Towards Sustainable Smart City Services

2021 ◽  
pp. 63-90
Author(s):  
Suresh P. ◽  
Keerthika P. ◽  
Sathiyamoorthi V. ◽  
Logeswaran K. ◽  
Manjula Devi R. ◽  
...  
Keyword(s):  
Big Data ◽  
Real Time ◽  
Smart City ◽  
Traffic Management ◽  
Smart Cities ◽  
Big Data Analytics ◽  
Streaming Data ◽  
City Development ◽  
Time Data ◽  
Public And Private

Cloud computing and big data analytics are the key parts of smart city development that can create reliable, secure, healthier, more informed communities while producing tremendous data to the public and private sectors. Since the various sectors of smart cities generate enormous amounts of streaming data from sensors and other devices, storing and analyzing this huge real-time data typically entail significant computing capacity. Most smart city solutions use a combination of core technologies such as computing, storage, databases, data warehouses, and advanced technologies such as analytics on big data, real-time streaming data, artificial intelligence, machine learning, and the internet of things (IoT). This chapter presents a theoretical and experimental perspective on the smart city services such as smart healthcare, water management, education, transportation and traffic management, and smart grid that are offered using big data management and cloud-based analytics services.

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