Vehicular Fog Computing: Enabling Real-Time Traffic Management for Smart Cities

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.

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Road Traffic Congestion Management Based on a Search-Allocation Approach

Transport and Telecommunication Journal ◽

10.1515/ttj-2017-0003 ◽

2017 ◽

Vol 18 (1) ◽

pp. 25-33 ◽

Cited By ~ 4

Author(s):

Jamal Raiyn

Keyword(s):

Travel Time ◽

Real Time ◽

Traffic Congestion ◽

Traffic Management ◽

Road Traffic ◽

Smart Cities ◽

Self Regulation ◽

Congestion Management ◽

Shortest Route ◽

Road Traffic Congestion

Abstract This paper introduces a new scheme for road traffic management in smart cities, aimed at reducing road traffic congestion. The scheme is based on a combination of searching, updating, and allocation techniques (SUA). An SUA approach is proposed to reduce the processing time for forecasting the conditions of all road sections in real-time, which is typically considerable and complex. It searches for the shortest route based on historical observations, then computes travel time forecasts based on vehicular location in real-time. Using updated information, which includes travel time forecasts and accident forecasts, the vehicle is allocated the appropriate section. The novelty of the SUA scheme lies in its updating of vehicles in every time to reduce traffic congestion. Furthermore, the SUA approach supports autonomy and management by self-regulation, which recommends its use in smart cities that support internet of things (IoT) technologies.

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Mobile Road Traffic Management System Using Weighted Sensor

International Journal of Interactive Mobile Technologies (iJIM) ◽

10.3991/ijim.v11i5.6745 ◽

2017 ◽

Vol 11 (5) ◽

pp. 147 ◽

Cited By ~ 2

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

Vehicular traffic is continuously increasing around the world, especially in urban areas, and the resulting congestion has become 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 was simulated and the performance was assessed using response time, delay and throughput. Results showed that, mobile devices are capable of assisting road users’ in faster decision making by providing real-time traffic information and recommending alternative routes.

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ENHANCED VANET ROUTING PROTOCOLS FOR DYNAMIC MAPPING IN REAL TIME TRAFFIC

March 2019 - IRO Journal on Sustainable Wireless Systems ◽

10.36548/jsws.2019.3.001 ◽

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.

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

Advances in Computational Intelligence and Robotics - Decision Support Systems and Industrial IoT in Smart Grid, Factories, and Cities ◽

10.4018/978-1-7998-7468-3.ch004 ◽

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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