Correlations in Motion: A Simple Response-Based Analysis of Traffic Flow
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866918124613271552 |
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| author | Gartzke, Sebastian Wang, Shanshan Guhr, Thomas Schreckenberg, Michael |
| author_facet | Gartzke, Sebastian Wang, Shanshan Guhr, Thomas Schreckenberg, Michael |
| contents | Why does a traffic jams form out of nowhere, and why does it stretch for kilometers even after the initial cause is passed? This study examines how congestion moves and spreads across motorways using a surprisingly simple method: response functions. These functions are based purely on data and show how changes in traffic flow, density, and velocity are connected over time and space. Using real-world data from German motorways, we track how traffic reacts to earlier disturbances, capturing the waves of slowing and accelerating that drivers experience in stop-and-go traffic. The results demonstrate how congestion propagates and how its rhythm can be measured and predicted. Unlike complex traffic models, this approach requires no simulations or assumptions about driver behavior. It works directly from the information provided by the road. The goal is clear: to understand congestion better so that we can manage it more effectively and perhaps spend less time stuck in it. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_02307 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Correlations in Motion: A Simple Response-Based Analysis of Traffic Flow Gartzke, Sebastian Wang, Shanshan Guhr, Thomas Schreckenberg, Michael Physics and Society Why does a traffic jams form out of nowhere, and why does it stretch for kilometers even after the initial cause is passed? This study examines how congestion moves and spreads across motorways using a surprisingly simple method: response functions. These functions are based purely on data and show how changes in traffic flow, density, and velocity are connected over time and space. Using real-world data from German motorways, we track how traffic reacts to earlier disturbances, capturing the waves of slowing and accelerating that drivers experience in stop-and-go traffic. The results demonstrate how congestion propagates and how its rhythm can be measured and predicted. Unlike complex traffic models, this approach requires no simulations or assumptions about driver behavior. It works directly from the information provided by the road. The goal is clear: to understand congestion better so that we can manage it more effectively and perhaps spend less time stuck in it. |
| title | Correlations in Motion: A Simple Response-Based Analysis of Traffic Flow |
| topic | Physics and Society |
| url | https://arxiv.org/abs/2406.02307 |