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Approximating incident occurrence time with a change-point latent variable framework

Authors

Abstract

We propose a methodology to approximate actual incident occurrence time by analyzing downstream volume sensor data. We model the time difference between actual occurrence time and reported time (or \emph{delay}) as a latent variable that becomes a parameter in a change-point time series model. We then apply a \emph{maximum a posteriori} (MAP) framework to infer the most probable delay. This MAP framework uses the time series model as the likelihood function and a bayesian prior based on field knowledge.

We applied our model on 5 months of traffic sensor data and accident reports from 3 Singapore expressways and corrected the accident start times for 1086 accidents in total. We compared the results with a manually constructed baseline and obtained a mean absolute error (MAE) between 5.7 and 7.4 minutes and a root mean squared error (RMSE) between
10 and 12.

Keywords

Incident analysis, change point detection, latent variable framework

Subject

Incident analysis, change point detection, latent variable framework

Conference

93rd Transport Research Board Annual Meeting (TRB 2014), January 2014

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