The existing influence of means of individual mobility on the speed indicators of urban traffic flow
DOI: 10.1051/e3sconf/202338905049
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Summary
This study investigates the impact of Means of Individual Mobility (SIM), such as electric scooters and skateboards, on the speed indicators of urban traffic flow. The research is motivated by the rapid increase in SIM usage and the resulting rise in road accidents, particularly conflicts between SIM users, pedestrians, and vehicles. With new traffic regulations in Russia allowing SIM movement on roadways under specific conditions, understanding how SIM density affects traffic speed is critical for road safety and infrastructure planning. The authors aim to establish dependencies between the number of SIMs on the road network and traffic flow speed during morning rush, evening rush, and inter-peak hours. The methodology employs simulation modeling using the Aimsun program, a tool widely used for traffic management and autonomous traffic design. The study focuses on a regulated intersection in Belgorod, Russia, specifically the Popova Street–Civil Avenue section, chosen due to its high concentration of SIM rental stations and heavy traffic. The model incorporates various transport types, including passenger vehicles, public transport, cyclists, and SIMs. To analyze the relationship between SIM count and traffic speed, the authors applied the Lagrange polynomial interpolation method. This mathematical approach allowed them to calculate intermediate values of traffic flow speed based on discrete data points, simulating SIM counts ranging from 10 to 1,000 units. The results demonstrate a clear negative correlation between the number of SIMs and the speed of traffic flow. As the number of SIMs increased, traffic speeds decreased across all time periods. For instance, with 10 SIMs, morning rush hour speeds were approximately 42.12 km/h, dropping to 23.89 km/h with 1,000 SIMs. Similar trends were observed in inter-peak and evening rush hours. The authors derived logarithmic equations to describe these dependencies, such as $V = -5.411\ln(N_{SIM}) + 64.703$ for morning rush hours, with high coefficients of determination ($R^2$ values between 0.8726 and 0.8829). These equations enable the prediction of traffic flow speed for any given number of SIMs. The significance of this work lies in its contribution to traffic safety and urban planning. By providing mathematical models that link SIM density to traffic speed, the study offers a tool for determining optimal SIM limits on road networks to avoid conflicts and maintain safe driving conditions. The findings support the development of strategies for comfortable interaction between vehicles and SIM users, aiding policymakers in managing the growing presence of individual mobility devices in urban environments.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-18 |
| archive | success | canonical_url | — | — | 1 | 2026-06-25 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-18 |
| chunk | success | chunk | — | — | 1 | 2026-06-18 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-18 |
| promote | success | — | — | — | 1 | 2026-06-18 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-18 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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