Processing and Visualizing Floating Car Data for Human-Centered Traffic and Environment Applications: A Transdisciplinary Approach

Processing and Visualizing Floating Car Data for Human-Centered Traffic and Environment Applications: A Transdisciplinary Approach

Patrick Voland (University of Potsdam, Germany) and Hartmut Asche (University of Potsdam, Germany)
DOI: 10.4018/978-1-5225-5978-8.ch005
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In the era of the internet of things and big data, modern cars have become mobile electronic systems or computers on wheels. Car sensors record a multitude of car- and traffic-related data as well as environmental parameters outside the vehicle. The data recorded are spatio-temporal by nature (floating car data) and can thus be classified as geodata. Their geospatial potential is, however, not fully exploited so far. In this chapter, the authors present an approach to collect, process, and visualize floating car data for traffic- and environment-related applications. It is demonstrated that cartographic visualization, in particular, is an effective means to make the enormous stocks of machine-recorded data available to human perception, exploration, and analysis.
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In our wired world, vehicles have developed into complex electronic systems. As early as 2011 the Canadian Author Cory Doctorow characterized the current status as follows, “We don’t have cars anymore; we have computers we ride in” (Doctorow 2011). Cars are equipped with a large number of sensor devices essential for smooth technical operation (monitoring, e.g., vehicle speed or engine revolutions) and environmental monitoring (measuring, e.g., barometric air pressure, ambient air temperature). Such constantly acquired data are indispensable for a growing number of so-called Advanced Driver Assistance Systems (ADAS). Automotive sensor data are a prerequisite for semi-autonomous and autonomous driving systems currently in development and testing.

Existing technical solutions in communications and bandwidth coupled with a constant drop of transmission costs have paved the way for using automotive sensor data in a variety of uses not or loosely linked to car-related applications. The range of applications includes communication models, such as vehicle-to-vehicle (v2v), vehicle-to-infrastructure (v2i), or vehicle-to-x (v2x) communication. In this context, so-called Extended Floating Car Data (XFCD) expand the existing concept of Floating Car Data (FCD). In fact, that XFCD data will play a pivotal role in future data-driven automobile developments. Currently cars are being turned into interconnected sensors that collect and provide a vast amount of mobile data on vehicles, vehicle drivers, routes, traffic, transport infrastructure and the environment. The enormous value of this massive data stock (big data) has only recently been recognized by the industry. Driving ongoing developments such as autonomous cars, (extended) floating car data will have a significant impact on our everyday life.


In addition to semantic parameters, automobile sensor data record locational and temporal information. They can hence be considered geospatial data allowing for geographical analysis and visualization. Application of both methods make these machine data accessible to wider human use. Extending the original machine-to-machine data communication and processing, data-to-human communication and filtering opens up a wide and novel range of infrastructure, environmental and planning applications or analysis based on car sensor data. Moreover, this approach helps to extract and utilize relevant and meaningful information for traffic or environmental planning from the ever-growing massive stock of automobile sensor data.

The ongoing research1 discussed in this paper aims at identifying and utilizing the spatial and temporal components of automotive sensor data, in particular, to process, analyze and visualize them for applications in traffic monitoring, driver assistance and selected environmental issues.

Underlying this research is the basic assumption that a human vehicle operator is and remains an integral part of the automobile system and thus is the creator of his/her own spatial mobility. Using machine data for human information, planning and decision support, i.e. for car-to-man communication, we develop a new field of applications that complements the existing realm of machine-to-machine communication.

For that purpose, car sensor data were extracted via the On Board Diagnostics (OBD) interface, a mandatory component of a FCD system, by a smartphone-based procedure. In a preliminary study the data acquired have been processed and visualized for a defined use case (Voland, 2014). In the work presented here, we aim to make available automotive sensor data (XFCD in particular) as geodata facilitating spatio-temporal analysis and geovisualization to aid the analysis of individual driving styles, environmental behaviour, or environmental conditions in a traffic network. We develop a modular process to handle XFCD based on the visualization pipeline. We also investigate whether open-source software tools can be used developed to implement the process for geospatial applications.

In the research context outlined above there are a number of issues that require further investigation not covered here. How and what data can be recorded, processed, visualized, and displayed automatically? How can the data be combined, processed, visualized, analyzed, and interpreted in a logical and productive manner? Which visualization components and strategies of are most effective and how can they be defined, combined, and eventually applied?

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