Citation

Deliali A, Ziakopoulos A, Dragomanovits A, Handanos I, Karadimas C, Kostoulas G, Frantzola EK, Yannis G. Transp. Res. Proc. 2023; 72: 1357-1363.

Copyright

(Copyright © 2023, Elsevier Publications)

DOI

10.1016/j.trpro.2023.11.598

PMID

unavailable

Abstract

Surrogate safety analysis is an alternative approach to crash-based analysis for the assessment of traffic safety. Surrogate safety analysis relies in various data types, and it is well admitted that the advancement of technology has enabled the recording of various types of traffic data (e.g., vehicle trajectories) that can be used for safety analysis. However, it is first needed to ensure that these data are correlated with crash data and so, can be used as a proxy of crash data. This study develops crash prediction models for motorway segments using harsh braking and harsh acceleration, and speeding data recorded via a smartphone app. The models indicate that harsh acceleration events are a good predictor of average crash frequency.

Language: en

Keywords

crash prediction; crowd-sourced data; driver behaviour; harsh braking; speeding; surrogate safety