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Analyzing human driving data an approach motivated by data science methods

Wagner, Peter and Nippold, Ronald and Gabloner, Sebastian and Margreiter, Martin (2016) Analyzing human driving data an approach motivated by data science methods. Chaos, Solitons and Fractals, 90, pp. 37-45. Elsevier. doi: 10.1016/j.chaos.2016.02.008. ISSN 0960-0779.

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Official URL: http://www.sciencedirect.com/science/article/pii/S096007791630039X?utm_campaign=47461_AUTH_UP_GC&utm_campaignPK=227555295&utm_term=OP24101&utm_content=227555297&utm_source=30&BID=672741207&utm_medium=email&SIS_ID=0


By analyzing a large data-base of car-driving data in a generic way, a few elementary facts on car-following have been found out. The inferences stem from the application of the mutual information to detect correlations to the data. Arguably, the most interesting fact is that the acceleration of the following vehicle depends mostly on the speed-difference to the lead vehicle. This seems to be a causal relationship, since acceleration follows speed-difference with an average delay of 0.5 s. Furthermore, the car-following process organizes itself in such a manner that there is a strong relation between speed and distance to the vehicle in front. In most cases, this is the dominant relationship in car-following. Additionally, acceleration depends only weakly on distance, which may be surprising and is at odds to a number of simple models that state an exclusive dependency between acceleration and distance.

Item URL in elib:https://elib.dlr.de/110856/
Document Type:Article
Title:Analyzing human driving data an approach motivated by data science methods
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Wagner, PeterPeter.Wagner (at) dlr.dehttps://orcid.org/0000-0001-9097-8026
Nippold, RonaldRonald.Nippold (at) dlr.deUNSPECIFIED
Gabloner, Sebastiansebastian.gabloner (at) tum.deUNSPECIFIED
Margreiter, Martinmartin.margreiter (at) tum.deUNSPECIFIED
Date:September 2016
Journal or Publication Title:Chaos, Solitons and Fractals
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
DOI :10.1016/j.chaos.2016.02.008
Page Range:pp. 37-45
Keywords:Car-following; Big data analysis; Maximal information content
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Traffic Management (old)
DLR - Research area:Transport
DLR - Program:V VM - Verkehrsmanagement
DLR - Research theme (Project):V - I.MoVe (old)
Location: Berlin-Adlershof
Institutes and Institutions:Institute of Transportation Systems > Verkehrssimulation und -prognose
Deposited By: Wagner, Dr. Peter
Deposited On:25 Jan 2017 09:57
Last Modified:31 Jul 2019 20:08

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