Where and when do drivers speed? A feasibility study of using probe vehicle data for speeding analysis
| Autor: | Veronika Valentová, Richard Turek, Jan Elgner, Jiří Ambros |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
050210 logistics & transportation
probe vehicle data Computer science 05 social sciences lcsh:Automation lcsh:TA1001-1280 Transportation speed traffic safety lcsh:TA1-2040 0502 economics and business Automotive Engineering 0501 psychology and cognitive sciences lcsh:Transportation engineering lcsh:T59.5 lcsh:Engineering (General). Civil engineering (General) 050107 human factors |
| Zdroj: | Archives of Transport, Vol 53, Iss 1, Pp 103-113 (2020) |
| ISSN: | 2300-8830 0866-9546 |
| Popis: | Speed is a critical transportation concept – it is one of the most important factors that road users consider in relation to route convenience and efficiency; at the same time speed has been recognized as the most influential risk factor. To improve speeding analyses, an emerging data source – probe vehicle data (also known as floating car data), may be used. This data enables obtaining information on vehicle speeds, without being limited in time and space. To prove the feasibility of using this data, a study was conducted on a sample of Prague expressway and collector roads. Firstly, probe data sample validity was checked through comparison to a traditional speed measurement technique – average speed control. Secondly, descriptive analysis of speeding was performed, focusing on speeding differences across homogeneous road segments in individual hour intervals. Thirdly, statistical models were also developed to explain which road parameters contribute to speeding. Analysis utilized cross-section and geometry parameters, which may potentially be related to speed choice and driving speed and speeding. In general, the applied concept proved as feasible: particularly night time was found more prone to speeding, and the rates were significantly different between segments. Statistical models indicated the statistically significant influence on speeding: lower speed limit, lower number of lanes, absence of roadside activities, or presence of horizontal curves. Information on these factors may be generalized and used for planning adequate speeding countermeasures. Final discussion also identified and described several challenges for future research, including free-flow speed estimation uncertainty, quality of speed-safety models, and potential multicollinearity of explanatory variables. |
| Databáze: | OpenAIRE |
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