Application of Time Series Analysis to Traffic Accidents in Los Angeles

TL;DR

This paper applies ensemble time series analysis methods to model and forecast traffic accidents in Los Angeles from 2010 to 2019, highlighting the effectiveness of the OGD model for accurate predictions.

Contribution

It introduces an ensemble approach combining multiple models, demonstrating the superior performance of the Online Gradient Descent (OGD) model in traffic accident forecasting.

Findings

OGD model achieved the best fit among tested models.

Traffic accidents in LA show seasonal patterns and increasing trends.

Ensemble methods improve forecasting accuracy.

Abstract

With the improvements of Los Angeles in many aspects, people in mounting numbers tend to live or travel to the city. The primary objective of this paper is to apply a set of methods for the time series analysis of traffic accidents in Los Angeles in the past few years. The number of traffic accidents, collected from 2010 to 2019 monthly reveals that the traffic accident happens seasonally and increasing with fluctuation. This paper utilizes the ensemble methods to combine several different methods to model the data from various perspectives, which can lead to better forecasting accuracy. The IMA(1, 1), ETS(A, N, A), and two models with Fourier items are failed in independence assumption checking. However, the Online Gradient Descent (OGD) model generated by the ensemble method shows the perfect fit in the data modeling, which is the state-of-the-art model among our candidate models. Therefore, it can be easier to accurately forecast future traffic accidents based on previous data through our model, which can help designers to make better plans.