Python-based code for estimation of highway bottleneck probability using speed transition matrices.
Full paper with detailed explanation will be available soon!
Bottlenecks on the highways are caused by two types of congestion: 1) recurrent ones, which happens in the rush-hours due to a large number of commuters, and 2) non-recurrent ones that happen after some unexpected event like a traffic accident or sudden breaks. Bottlenecks are formally defined as a decrease in the capacity of the highway due to a congestion event.
This code implements the fuzzy method for estimation of the highway bottleneck probability occurrence using a novel traffic data model called Speed Transition Matrix (STM). The method is based on a Python package for fuzzy inference systems simpful and implemented class for STM computation.
The data is proveded in the file TrafData.pkl. It contains the routes of the vehicles on a highway simulated in the SUMO simulator.
- Install Python (3.8 recommended) Download link.
- Install required packages from
requirenments.txtusing virtual environment.
Medium article https://towardsdatascience.com/using-fuzzy-logic-for-road-traffic-congestion-index-estimation-b649f8ddede1.
Text:
Tišljarić, Leo, Filip Vrbanić, Edouard Ivanjko, and Tonči Carić. 2022. "Motorway Bottleneck Probability Estimation in Connected Vehicles Environment Using Speed Transition Matrices" Sensors 22, no. 7: 2807. https://doi.org/10.3390/s22072807
.bib:
@Article{s22072807,
AUTHOR = {Tišljarić, Leo and Vrbanić, Filip and Ivanjko, Edouard and Carić, Tonči},
TITLE = {Motorway Bottleneck Probability Estimation in Connected Vehicles Environment Using Speed Transition Matrices},
JOURNAL = {Sensors},
VOLUME = {22},
YEAR = {2022},
NUMBER = {7},
ARTICLE-NUMBER = {2807},
URL = {https://www.mdpi.com/1424-8220/22/7/2807},
PubMedID = {35408421},
ISSN = {1424-8220},
DOI = {10.3390/s22072807}
}