scholarly journals Driving Behavior Analysis and Traffic Improvement using Onboard Sensor Data and Geographic Information

2021 ◽  
Author(s):  
Jun-Zhi Zhang ◽  
Huei-Yung Lin
Keyword(s):  
Behavior Analysis ◽  
Driving Behavior ◽  
Geographic Information ◽  
Sensor Data

scholarly journals Spatial Data Lake for Smart Cities: From Design to Implementation

2020 ◽  
Vol 1 ◽  
pp. 1-15
Author(s):  
Rodrique Kafando ◽  
Rémy Decoupes ◽  
Lucile Sautot ◽  
Maguelonne Teisseire
Keyword(s):  
Remote Sensing ◽  
Spatial Data ◽  
Smart Cities ◽  
Real Data ◽  
Heterogeneous Data ◽  
Geographic Information ◽  
Sensor Data ◽  
Remote Sensing Images ◽  
Thematic Information ◽  
General Data

Abstract. In this paper, we propose a methodology for designing data lake dedicated to Spatial Data and an implementation of this specific framework. Inspired from previous proposals on general data lake Design and based on the Geographic information – Metadata normalization (ISO 19115), the contribution presented in this paper integrates, with the same philosophy, the spatial and thematic dimensions of heterogeneous data (remote sensing images, textual documents and sensor data, etc). To support our proposal, the process has been implemented in a real data project in collaboration with Montpellier Métropole Méditerranée (3M), a metropolis in the South of France. This framework offers a uniform management of the spatial and thematic information embedded in the elements of the data lake.

scholarly journals Volunteered Geographic Information: Interpretation, Visualisation and Social Computing (VGIscience)

2018 ◽  
Vol 1 ◽  
pp. 1-5 ◽  
Author(s):  
Dirk Burghardt ◽  
Wolfgang Nejdl ◽  
Jochen Schiewe ◽  
Monika Sester
Keyword(s):  
Visual Analysis ◽  
Spatial Information ◽  
Volunteered Geographic Information ◽  
Scientific Basis ◽  
Geographic Information ◽  
Sensor Data ◽  
Short Paper ◽  
German Research Foundation ◽  
Knowledge Presentation ◽  
Large Heterogeneity

In the past years Volunteered Geographic Information (VGI) has emerged as a novel form of user-generated content, which involves active generation of geo-data for example in citizen science projects or during crisis mapping as well as the passive collection of data via the user’s location-enabled mobile devices. In addition there are more and more sensors available that detect our environment with ever greater detail and dynamics. These data can be used for a variety of applications, not only for the solution of societal tasks such as in environment, health or transport fields, but also for the development of commercial products and services. The interpretation, visualisation and usage of such multi-source data is challenging because of the large heterogeneity, the differences in quality, the high update frequencies, the varying spatial-temporal resolution, subjective characteristics and low semantic structuring.<br> Therefore the German Research Foundation has launched a priority programme for the next 3&amp;ndash;6 years which will support interdisciplinary research projects. This priority programme aims to provide a scientific basis for raising the potential of VGI- and sensor data. Research questions described more in detail in this short paper span from the extraction of spatial information, to the visual analysis and knowledge presentation, taking into account the social context while collecting and using VGI.

Driving Behavior Analysis and Speed Limit Optimization with the Driving Simulator

2021 ◽  
Author(s):  
Fan Wang ◽  
Fan Yang ◽  
Wei Yang ◽  
Huachun Tan ◽  
Bin Ran
Keyword(s):  
Behavior Analysis ◽  
Driving Simulator ◽  
Driving Behavior ◽  
Speed Limit

scholarly journals GIS Mapping of Driving Behavior Based on Naturalistic Driving Data

2019 ◽  
Vol 8 (5) ◽  
pp. 226 ◽  
Author(s):  
José Balsa-Barreiro ◽  
Pedro M. Valero-Mora ◽  
José L. Berné-Valero ◽  
Fco-Alberto Varela-García
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
Driving Behavior ◽  
Geographic Information ◽  
Limited Resources ◽  
Gis Mapping ◽  
Research Groups ◽  
Naturalistic Driving ◽  
Gis Software ◽  
Operational Capacity

Naturalistic driving can generate huge datasets with great potential for research. However, to analyze the collected data in naturalistic driving trials is quite complex and difficult, especially if we consider that these studies are commonly conducted by research groups with somewhat limited resources. It is quite common that these studies implement strategies for thinning and/or reducing the data volumes that have been initially collected. Thus, and unfortunately, the great potential of these datasets is significantly constrained to specific situations, events, and contexts. For this, to implement appropriate strategies for the visualization of these data is becoming increasingly necessary, at any scale. Mapping naturalistic driving data with Geographic Information Systems (GIS) allows for a deeper understanding of our driving behavior, achieving a smarter and broader perspective of the whole datasets. GIS mapping allows for many of the existing drawbacks of the traditional methodologies for the analysis of naturalistic driving data to be overcome. In this article, we analyze which are the main assets related to GIS mapping of such data. These assets are dominated by the powerful interface graphics and the great operational capacity of GIS software.

Emergency Steering Evasion Assistance Control Based on Driving Behavior Analysis

2019 ◽  
Vol 20 (2) ◽  
pp. 457-475 ◽  
Author(s):  
Zhiguo Zhao ◽  
Liangjie Zhou ◽  
Yugong Luo ◽  
Keqiang Li
Keyword(s):  
Behavior Analysis ◽  
Driving Behavior
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