Abstract
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.
Highlights
Road traffic is one of the most widely discussed social topics
We analyze which are the main assets related to Geographic Information Systems (GIS) mapping of such data
This paper evaluates how driving behavior can be represented with Geographic Information Systems (GIS)
Summary
Road traffic is one of the most widely discussed social topics. The continuous growth in mobility is a direct consequence of the increasing number of vehicles, levels of urbanization, and new social trends. There is an extensive literature dealing with issues related to increasing traffic flows [1,2,3] such as pollution [4], congestion [5], the inefficiency of transportation in some regions [6], and even about how transport systems articulate territories [7], among others These problems present significant drawbacks with severe socio-economic and environmental costs [8,9]. All these phenomena can be observed to be global in nature, there are huge differences that depend on the different spatial scales These are more evident in urban areas, especially in larger ones [10,11], with some signs of diseconomies of agglomeration becoming apparent [12,13]. Research into intelligent and sustainable mobility systems to meet the challenges ahead acquires more relevance and urgency [14,15]
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