Quantifying the effects of adverse weather conditions on road traffic is crucial for several reasons, among which monitoring traffic safety, managing vehicle fleets, and ensuring autonomous vehicle security and performance. This paper studies the incidence of adverse weather effects on road traffic safety. The aim is to assign a unified risk level to all spatial regions sharing the same context (e.g., the highly populated areas), which reflects the impact of adverse weather events on road accident occurrences. The proposed approach relies on an incomplete set of historical accident data, which report weather-related accident occurrences in specific risky areas. To estimate the percontext risk level we propose to analyze the weather element measurements acquired by meteorological stations spread over the analyzed area. The series of adverse weather events are embedded into a high-dimensional embedding space to enable the identification of temporal event patterns similar to those observed in risky areas. The experimental results show the effectiveness of the proposed approach in a real case study.

Estimating the incidence of adverse weather effects on road traffic safety using time series embeddings / Fior, Jacopo; Cagliero, Luca. - STAMPA. - 1:(2021), pp. 402-407. ((Intervento presentato al convegno 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC) tenutosi a Madrid (Spain) nel 12-16 July 2021 [10.1109/COMPSAC51774.2021.00063].

Estimating the incidence of adverse weather effects on road traffic safety using time series embeddings

Jacopo Fior;Luca Cagliero
2021

Abstract

Quantifying the effects of adverse weather conditions on road traffic is crucial for several reasons, among which monitoring traffic safety, managing vehicle fleets, and ensuring autonomous vehicle security and performance. This paper studies the incidence of adverse weather effects on road traffic safety. The aim is to assign a unified risk level to all spatial regions sharing the same context (e.g., the highly populated areas), which reflects the impact of adverse weather events on road accident occurrences. The proposed approach relies on an incomplete set of historical accident data, which report weather-related accident occurrences in specific risky areas. To estimate the percontext risk level we propose to analyze the weather element measurements acquired by meteorological stations spread over the analyzed area. The series of adverse weather events are embedded into a high-dimensional embedding space to enable the identification of temporal event patterns similar to those observed in risky areas. The experimental results show the effectiveness of the proposed approach in a real case study.
978-1-6654-2463-9
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2923114