A Scalable Spatio-temporal Data Storage for Intelligent Transportation Systems Based on HBase

2015 ◽  
Author(s):  
Hong Van Le ◽  
Atsuhiro Takasu
Keyword(s):  
Data Storage ◽  
Intelligent Transportation Systems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Temporal Data ◽  
Spatio Temporal

scholarly journals Privacy Preserving Data Transmission in Malicious Vehicular Adhoc Networks

2021 ◽  
Vol 12 (3) ◽  
pp. 5757-5761
Author(s):  
Mrs.R.M.Rajeshwari Et. al.
Keyword(s):  
Data Storage ◽  
Data Transmission ◽  
Intelligent Transportation Systems ◽  
Privacy Preservation ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Third Party ◽  
Mobile Nodes ◽  
Legitimate User ◽  
On The Road

Vehicle Adhoc Network is deployed on the road, where vehicles constitute mobile nodes in which active security and intelligent transportation are important applications of VANET. VANETs are a key part of the intelligent transportation systems (ITS) framework. Sometimes, VANETs are referred as Intelligent Transportation Networks. However, authentication and privacy of users are still two vital issues in VANETs.  In the traditional mode, the transactional data storage provides no distributed and decentralized security, so that the third party initiates the dishonest behaviors possibly. VANET has  temporary participants , communication between vehicles are short-lived messages. Possible situation might happens , adversary may play as an legitimate user and able to perform malicious activity .To address these challenges this paper proposes timestamp based message between users to  perform secure data transmission and give the negligible probability of the attacker. With the help of Certificate Authority (CA) and the RoadSide Units (RSUs), our proposal attains the confidentiality and  trace the identity of the unauthenticated vehicle in the anonymous announcements as well. Finally, through the theoretical analysis and simulations, our scheme is able to implement a secure VANET framework with accountability and privacy preservation

Human Movement Analysis Using Heterogeneous Data Sources

2013 ◽  
Vol 4 (3) ◽  
pp. 98-117 ◽  
Author(s):  
João Peixoto ◽  
Adriano Moreira
Keyword(s):  
Intelligent Transportation Systems ◽  
Real Data ◽  
Human Movement ◽  
Heterogeneous Data ◽  
Transportation Systems ◽  
Temporal Data ◽  
Urban Mobility ◽  
Multiple Sources ◽  
Urban Dynamics ◽  
Spatio Temporal

The analysis of urban mobility has been attracting the interest of the research community recently. The research challenges in this domain are diverse and include data acquisition and representation, human movement modeling and the visualization of dynamic geo-referenced data. Some of the direct applications for these studies are urban planning, security, intelligent transportation systems and wireless networks optimization. One of the drivers for recent work in this area is the availability of large datasets representing many aspects of the urban dynamics. Quite often, the proposed approaches are highly dependent on the data type. However, the analysis of urban dynamics could benefit from the combined and simultaneous use of multiple sources of spatio-temporal data. This paper describes the definition of a set of basic concepts for the representation and processing of spatio-temporal data, sufficiently flexible to deal with various types of mobility data and to support multiple forms of processing and visualization of the urban mobility. For this purpose the authors define a set of concepts and describe how real data from heterogeneous sources is mapped into the proposed framework. Available results obtained by the integration of geometric and symbolic data reveal the adequacy of the proposed concepts, and uncover new possibilities for the fusion of heterogeneous datasets.

scholarly journals An Improved Stacked Denoise Autoencoder with Elu Activation Function for Traffic Data Imputation

2019 ◽  
Vol 8 (11) ◽  
pp. 3951-3954
Keyword(s):  
Deep Learning ◽  
Missing Data ◽  
Intelligent Transportation Systems ◽  
State Of The Art ◽  
Intelligent Transportation ◽  
Activation Function ◽  
Transportation Systems ◽  
Data Imputation ◽  
Traffic Data ◽  
Spatio Temporal

Traffic data plays a major role in transport related applications. The problem of missing data has greatly impact the performance of Intelligent transportation systems(ITS). In this work impute the missing traffic data with spatio-temporal exploitation for high precision result under various missing rates. Deep learning based stacked denoise autoencoder is proposed with efficient Elu activation function to remove noise and impute the missing value.This imputed value will be used in analyses and prediction of vehicle traffic. Results are discussed that the proposed method outperforms well in state of the art approaches.

Sustainable Energy Management in Intelligent Transportation

2022 ◽  
Author(s):  
Ying Gao ◽  
Tong Ren ◽  
Xia Zhao ◽  
Wentao Li
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
Data Storage ◽  
Intelligent Transportation Systems ◽  
Field Tests ◽  
Intelligent Transportation ◽  
Analysis Data ◽  
Primary Energy ◽  
Transportation Systems ◽  
Intelligent Transport System

Intelligent transportation systems (ITS) are a collection of technologies that can enhance transport networks and public transit and individual decision-making about various elements of travel. ITS technologies comprise cutting-edge wireless, electronic and automated technology intending to improve safety, efficiency and convenience in surface transit. In certain cases, reducing energy usage has proven to be an ITS advantage. In this report, the primary energy advantages of a range of ITS systems established through models, pilot projects/field tests and extensive use are examined and summarized. In worldwide driving, the Internet of Things (IoT) solutions play a vital role. A new age of communication leading to ITS will be the communication between cars via IoT. IoT is a mixture of data and data analysis data storage and processing to manage the traffic system efficiently.Energy management, which is seen as an efficient, innovative approach to highly efficient energy generation plants. It simultaneously takes care of optimizing traditional sources of the IoT based intelligent transport system, helps to automate railways, roads, airways and shipways, which improve customer experience in the process. Following an evaluation of the situation, a proposal named energy management in intelligent transportation (EMIT) improves energy efficiency and economic efficiency in transportation. It improves energy management to reduce economic and ecological waste by decreasing global transport energy consumption. The sustainable development ratio is 85.7%, accidents detection ratio is 85.3%, electric vehicle infrastructure ratio is 83.6%, intelligent vehicle parking system acceptance ratio is 82.15%, and reduction ratio of energy consumption is 91.4%.

scholarly journals A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation

2021 ◽  
Vol 6 (1) ◽  
pp. 63-85
Author(s):  
Haitao Yuan ◽  
Guoliang Li
Keyword(s):  
Data Storage ◽  
Data Preprocessing ◽  
Intelligent Transportation ◽  
Mobile Internet ◽  
Traffic Prediction ◽  
Temporal Data ◽  
Traffic Light ◽  
Challenges And Opportunities ◽  
Spatio Temporal ◽  
Prediction Problems

AbstractIntelligent transportation (e.g., intelligent traffic light) makes our travel more convenient and efficient. With the development of mobile Internet and position technologies, it is reasonable to collect spatio-temporal data and then leverage these data to achieve the goal of intelligent transportation, and here, traffic prediction plays an important role. In this paper, we provide a comprehensive survey on traffic prediction, which is from the spatio-temporal data layer to the intelligent transportation application layer. At first, we split the whole research scope into four parts from bottom to up, where the four parts are, respectively, spatio-temporal data, preprocessing, traffic prediction and traffic application. Later, we review existing work on the four parts. First, we summarize traffic data into five types according to their difference on spatial and temporal dimensions. Second, we focus on four significant data preprocessing techniques: map-matching, data cleaning, data storage and data compression. Third, we focus on three kinds of traffic prediction problems (i.e., classification, generation and estimation/forecasting). In particular, we summarize the challenges and discuss how existing methods address these challenges. Fourth, we list five typical traffic applications. Lastly, we provide emerging research challenges and opportunities. We believe that the survey can help the partitioners to understand existing traffic prediction problems and methods, which can further encourage them to solve their intelligent transportation applications.

Experience Implementing a User Service for Archived Intelligent Transportation Systems Data

2005 ◽  
Vol 1917 (1) ◽  
pp. 90-99 ◽  
Author(s):  
Robert L. Bertini ◽  
Steve Hansen ◽  
Andrew Byrd ◽  
Thareth Yin
Keyword(s):  
Data Storage ◽  
Intelligent Transportation Systems ◽  
Traffic Management ◽  
Travel Demand ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Successful Implementation ◽  
Data Archive ◽  
Data Archiving ◽  
Advanced Traffic Management System

In cooperation with the Oregon Department of Transportation (ODOT) and other regional partners, the Portland regional intelligent transportation systems (ITSs) data archive was recently inaugurated via a direct fiber-optic connection between ODOT and Portland State University (PSU). In July 2004, the Portland Regional Transportation Archive Listing was activated; it received 20-s data from the 436 inductive loop detectors composing the Portland area's advanced traffic management system. PSU is designated as the region's official data archiving entity, consistent with the ITS architecture being developed. This paper discusses the steps taken for successful implementation of the Portland region's functional ITS data archive and plans for development and expansion. Included is a discussion of the archive structure, data storage, data processing, and user interface. An experiment involving Metro, the Portland region's metropolitan planning organization, demonstrates that archived loop detector data can be used to improve travel demand forecasts for the Portland region. The data archive will expand to include transit data, freeway incident data, city traffic signal data, and truck weigh-in-motion data.

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