Predicting Road Accident Risk in the City of San Pablo, Laguna: A Predictive Model Using Time Series Forecasting Analysis with Multiplicative Model

Abstract

This research used the mechanisms in time series analysis such as secular trend, irregular fluctuation, cyclical and seasonal patterns to conclude forecast using the multiplicative model. In the conduct of this study, time, day and location are the main focuses in predicting road accident patterns. By analyzing road accident patterns based on time series forecasting using a multiplicative model, this research will provide suggestions for the government to take effective measures to reduce accident impacts and improve traffic safety. This research analyzed the road accident data in San Pablo City, Laguna Philippines from the year 2014 – 2016 and forecast the possible prevalence of road accidents and its pattern. A total of 1229 road accidents were included in this study. As recorded based on the cumulative frequency, Barangay San Francisco has the greatest number of road accidents with an average of 101 cases per year. It can be attributed to the fact that the road is considered the busiest because it is the only gateway of provincial travelers from Metro Manila to Southern Provinces. While for the monthly pattern prediction, April is the most risky in road accidents with possible 33 cases in a year with 13.08% mean absolute error or 86.92% accuracy, probably because most of the community is in summer vacation. And in terms of the daily pattern, Sunday is the crucial day in terms of road accidents with 44 possible cases with 10.29% mean absolute error or 89.71% accuracy, and the majority of the possible road accident arises between 6:00 pm to 9:00 pm with 45 possible cases with 13.21% mean absolute error or 86.79% accuracy.