Vehicle velocity distributions are of utmost relevance for the efficiency, safety, and sustainability of road traffic. Yet, due to technical limitations, they are often empirically analyzed using spatiotemporal averages. Here, we instead study a novel set of microscopic traffic data from Dresden comprising 346 million data points with a resolution of one vehicle from 145 detector sites with a particular focus on extreme events and distribution tails. By fitting q-exponential and Generalized Extreme Value distributions to the right flank of the empirical velocity distributions, we establish that their tails universally exhibit a power-law behavior with similar decay exponents. We also find that q-exponentials are best suitable to model the vast extent to which speed limit violations in the data occur. Furthermore, combining velocity and time headway distributions, we obtain estimates for free flow velocities that always exceed average velocities and sometimes even significantly exceed speed limits. Likewise, congestion effects are found to play a very minor, almost negligible role in traffic flow at the detector sites. These results provide insights into the current state of traffic in Dresden, hinting toward potentially necessary policy amendments regarding road design, speed limits, and speeding prosecution. They also reveal the potentials and limitations of the data set at hand and thereby lay the groundwork for further, more detailed traffic analyses.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:82557 |
| Date | 06 December 2022 |
| Creators | Piepel, Moritz |
| Contributors | Timme, Marc, Keimer, Alexander, Hirrle, Angelika, Schröder, Malte, Technische Universität Dresden |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/updatedVersion, doc-type:bachelorThesis, info:eu-repo/semantics/bachelorThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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