Platoon Fundamental Diagram estimation can be Markovian: evidence from human- and self-driven vehicle trajectories
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866909088020955136 |
|---|---|
| author | Makridis, Michail A. Kouvelas, Anastasios Laval, Jorge A. |
| author_facet | Makridis, Michail A. Kouvelas, Anastasios Laval, Jorge A. |
| contents | We propose a simple and effective method to derive the Fundamental Diagram (FD) from platoon vehicle trajectories. Average traffic state variables are computed using Edie's generalized definitions within time-dependent trapezoidal space-time areas. To obtain a clear FD, we employ a bivariate data aggregation technique to eliminate scatter. Our findings are as follows: (i) The proposed method demonstrates a remarkably consistent relation between the traffic variables and a clear triangular shape for autonomously-driven vehicles. (ii) The FDs are invariant to several factors of heterogeneity such as the platoon length, vehicle characteristics, road particularities, and data acquisition accuracy. (iii) ACC-driven vehicle platoons with minimum headway setting achieve much higher capacity, roughly 90\% than those with a large headway setting. (iv) Connectivity might increase capacity. (v) Human drivers have a wider near-capacity operation area, showing different behaviors at high speeds than low ones, and (vi) Safety concerns might arise due to high values of backward wave speed for ACC-driven vehicles. Comparative analysis with the state-of-the-art confirms the validity of our approach. The proposed method stands out due to its simplicity and accuracy, which paves the way for practical applications in real-time traffic flow monitoring and control within modern intelligent transportation systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_17065 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Platoon Fundamental Diagram estimation can be Markovian: evidence from human- and self-driven vehicle trajectories Makridis, Michail A. Kouvelas, Anastasios Laval, Jorge A. Systems and Control We propose a simple and effective method to derive the Fundamental Diagram (FD) from platoon vehicle trajectories. Average traffic state variables are computed using Edie's generalized definitions within time-dependent trapezoidal space-time areas. To obtain a clear FD, we employ a bivariate data aggregation technique to eliminate scatter. Our findings are as follows: (i) The proposed method demonstrates a remarkably consistent relation between the traffic variables and a clear triangular shape for autonomously-driven vehicles. (ii) The FDs are invariant to several factors of heterogeneity such as the platoon length, vehicle characteristics, road particularities, and data acquisition accuracy. (iii) ACC-driven vehicle platoons with minimum headway setting achieve much higher capacity, roughly 90\% than those with a large headway setting. (iv) Connectivity might increase capacity. (v) Human drivers have a wider near-capacity operation area, showing different behaviors at high speeds than low ones, and (vi) Safety concerns might arise due to high values of backward wave speed for ACC-driven vehicles. Comparative analysis with the state-of-the-art confirms the validity of our approach. The proposed method stands out due to its simplicity and accuracy, which paves the way for practical applications in real-time traffic flow monitoring and control within modern intelligent transportation systems. |
| title | Platoon Fundamental Diagram estimation can be Markovian: evidence from human- and self-driven vehicle trajectories |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2401.17065 |