Extraction of Naturalistic Driving Patterns with Geographic Information Systems
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Extraction of Naturalistic Driving Patterns with Geographic Information Systems José Balsa-Barreiro 1
&
Pedro M. Valero-Mora 2 & Mónica Menéndez 3 & Rashid Mehmood 4
Accepted: 13 September 2020 # The Author(s) 2020
Abstract A better understanding of Driving Patterns and their relationship with geographical driving areas could bring great benefits for smart cities, including the identification of good driving practices for saving fuel and reducing carbon emissions and accidents. The process of extracting driving patterns can be challenging due to issues such as the collection of valid data, clustering of population groups, and definition of similar behaviors. Naturalistic Driving methods provide a solution by allowing the collection of exhaustive datasets in quantitative and qualitative terms. However, exploiting and analyzing these datasets is complex and resource-intensive. Moreover, most of the previous studies, have constrained the great potential of naturalistic driving datasets to very specific situations, events, and/or road sections. In this paper, we propose a novel methodology for extracting driving patterns from naturalistic driving data, even from small population samples. We use Geographic Information Systems (GIS), so we can evaluate drivers’ behavior and reactions to certain events or road sections, and compare across situations using different spatial scales. To that end, we analyze some kinematic parameters such as speeds, acceleration, braking, and other forces that define a driving attitude. Our method favors an adequate mapping of complete datasets enabling us to achieve a comprehensive perspective of driving performance. Keywords Big data . Driving patterns . Driving behavior . Geographic information systems . Naturalistic driving . Smart cities
1 Introduction Smart cities and societies are driven by our ever-growing desires for continual innovations and improvements in every aspect of our life [1, 2]. Transportation, which is the backbone of modern societies, has also been undergoing this continual innovation and improvement process [3]. The environmental, economic, social, and health-related damages caused by transportation are well-known, and demand innovative solutions. Such solutions, in turn, require new methods for modeling and analyzing
* José Balsa-Barreiro [email protected] 1
Institute for Transport Planning and Systems (IVT), Swiss Federal Institute of Technology, 8093 Zürich, Switzerland
2
University Research Institute on Traffic and Road Safety (INTRAS), University of Valencia, 46022 Valencia, Spain
3
Division of Engineering, New York University Abu Dhabi (NYUAD), 129188 Abu Dhabi, United Arab Emirates
4
High Performance Computing Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia
various aspects of our transportation systems. Understanding driving behavior is one such area that could bring massive environmental, economic, and social improvements. Driving behavior can be parameterized by defining patterns. Analyzing the patterns allows us to establ
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