Decomposition and distributed optimization of real-time traffic management for large-scale railway 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.

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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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RealTE: Real-time Trajectory Estimation Over Large-Scale Road Networks

Journal of Web Engineering ◽

10.13052/jwe1540-9589.21210 ◽

2022 ◽

Author(s):

Qibin Zhou ◽

Qingang Su ◽

Dingyu Yang

Keyword(s):

Travel Time ◽

Real Time ◽

High Speed ◽

Road Network ◽

Large Scale ◽

Road Networks ◽

Trajectory Data ◽

Real Time Traffic ◽

Histogram Estimation ◽

Traffic Estimation

Real-time traffic estimation focuses on predicting the travel time of one travel path, which is capable of helping drivers selecting an appropriate or favor path. Statistical analysis or neural network approaches have been explored to predict the travel time on a massive volume of traffic data. These methods need to be updated when the traffic varies frequently, which incurs tremendous overhead. We build a system RealTER⁢e⁢a⁢l⁢T⁢E, implemented on a popular and open source streaming system StormS⁢t⁢o⁢r⁢m to quickly deal with high speed trajectory data. In RealTER⁢e⁢a⁢l⁢T⁢E, we propose a locality-sensitive partition and deployment algorithm for a large road network. A histogram estimation approach is adopted to predict the traffic. This approach is general and able to be incremental updated in parallel. Extensive experiments are conducted on six real road networks and the results illustrate RealTE achieves higher throughput and lower prediction error than existing methods. The runtime of a traffic estimation is less than 11 seconds over a large road network and it takes only 619619 microseconds for model updates.

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Accurate Real-Time Traffic Speed Estimation Using Infrastructure-Free Vehicular Networks

International Journal of Distributed Sensor Networks ◽

10.1155/2015/530194 ◽

2015 ◽

Vol 2015 ◽

pp. 1-19 ◽

Cited By ~ 2

Author(s):

Zongjian He ◽

Buyang Cao ◽

Yan Liu

Keyword(s):

Real Time ◽

Large Scale ◽

Vehicular Networks ◽

Route Planning ◽

Speed Estimation ◽

Traffic Condition ◽

Real Time Traffic ◽

Traffic Trace ◽

Traffic Speed ◽

Traffic Speed Estimation

Real-time traffic speed is indispensable for many ITS applications, such as traffic-aware route planning and eco-driving advisory system. Existing traffic speed estimation solutions assume vehicles travel along roads using constant speed. However, this assumption does not hold due to traffic dynamicity and can potentially lead to inaccurate estimation in real world. In this paper, we propose a novel in-network traffic speed estimation approach using infrastructure-free vehicular networks. The proposed solution utilizes macroscopic traffic flow model to estimate the traffic condition. The selected model only relies on vehicle density, which is less likely to be affected by the traffic dynamicity. In addition, we also demonstrate an application of the proposed solution in real-time route planning applications. Extensive evaluations using both traffic trace based large scale simulation and testbed based implementation have been performed. The results show that our solution outperforms some existing ones in terms of accuracy and efficiency in traffic-aware route planning applications.

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