Real-time and Secure Geospatial Data Warehouse for Intelligent Transportation Systems

Author(s):  
Sean Ryan S. Chan ◽  
Datuluna Ali G. Dilangalen ◽  
Wilson M. Tan
2014 ◽  
Vol 926-930 ◽  
pp. 1314-1317 ◽  
Author(s):  
Li Yang

To solve the demand of real-time event detection in the RFID-based Intelligent Transportation Systems , using Complex Event Processing technology to establish a rule model to detect events.The model allows users to customize the Basic Events and Complex Events, using the rule files describe the complex events modes, clearly expressed the timing and gradation relationships between RFID events, meeting the needs of real-time event detection in the Intelligent Transportation System ,achieving the appropriate rules engine,. Finally, test and verify the effectiveness of the rules file and the rules engine model by experiments.


2014 ◽  
Vol 624 ◽  
pp. 567-570
Author(s):  
Dan Ping Wang ◽  
Kun Yuan Hu

Intelligent Transportation System is the primary means of solving the city traffic problem. The information technology, the communication, the electronic control technology and the system integration technology and so on applies effectively in the transportation system by researching rationale model, thus establishes real-time, accurate, the highly effective traffic management system plays the role in the wide range. Traffic flow guidance system is one of cores of Intelligent Transportation Systems. It is based on modern technologies, such as computer, communication network, and so on. Supplying the most superior travel way and the real-time transportation information according to the beginning and ending point of the journey. The journey can promptly understand in the transportation status of road network according to the guidance system, then choosing the best route to reach destination.


2018 ◽  
Vol 7 (2.18) ◽  
pp. 7 ◽  
Author(s):  
Venkata Ramana N ◽  
Seravana Kumar P. V. M ◽  
Puvvada Nagesh

Big data is a term that describes the large volume of data – both structured and unstructuredthat includes a business on a day-to-day basis including Intelligent Transportation Systems (ITS). The emerging connected technologies created around ubiquitous digital devices have opened unique opportunities to enhance the performance of the ITS. However, magnitude and heterogeneity of the Big Data are beyond the capabilities of the existing approaches in ITS. Therefore, there is a crucial need to develop new tools and systems to keep pace with the Big Data proliferation. In this paper, we propose a comprehensive and flexible architecture based on distributed computing platform for real-time traffic control. The architecture is based on systematic analysis of the requirements of the existing traffic control systems. In it, the Big Data analytics engine informs the control logic. We have partly realized the architecture in a prototype platform that employs Kafka, a state-of-the-art Big Data tool for building data pipelines and stream processing. We demonstrate our approach on a case study of controlling the opening and closing of a freeway hard shoulder lane in microscopic traffic simulation. 


2012 ◽  
Vol 542-543 ◽  
pp. 1316-1319
Author(s):  
Wen Dong Zhao ◽  
Hui Qi ◽  
Hai Yan Zhou

The status of transportation industry development of China has shown the necessity and urgency of the development of intelligent transportation systems. This article proposed a segmentation algorithm of traffic prohibited region based on wavelet transform. Based on the de-noising and image enhancement, sharpening pretreatment of the traffic video image captured on real-time, the algorithm combines the method determining the quadrilateral based on the sample images manually with the image segmentation based on wavelet transform in order to get the segmentation of traffic prohibited region which will be used in the detection of vehicle pressing highway central line region. The experimental results show that in the algorithm not only meet the real-time requirement of foundations but also improves the successful rate of detection results.


Author(s):  
Nouha Rida ◽  
Mohammed Ouadoud ◽  
Abderrahim Hasbi

Traffic optimization at an intersection, using real-time traffic information, presents an important focus of research into intelligent transportation systems. Several studies have proposed adaptive traffic lights control, which concentrates on determining green light length and sequence of the phases for each cycle in accordance with the real-time traffic detected. In order to minimize the waiting time at the intersection, the authors propose an intelligent traffic light using the information collected by a wireless sensors network installed in the road. The proposed algorithm is essentially based on two parameters: the waiting time in each lane and the length of its queue. The simulations show that the algorithm applied at a network of intersections improves significantly the average waiting time, queue length, fuel consumption, and CO2 emissions.


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