Traffic incident detection and modelling using Quantum Frequency Algorithm and AutoRegressive Integrated Moving Average models

Abstract

In this study, we used AutoRegressive Integrated Moving Average (ARIMA) to effectively represent expected normal traffic behavior of those weeks identified to be abnormal in the previous literature. Using the 2006 North Luzon Expressway North Bound (NLEX NB) Balintawak (Blk) segment's hourly traffic volume and time mean speed data sets provided by the National Center for Transportation Studies (NCTS), we processed the data to generate time series plots of the weekly densities, the normal range of traffic density, and the abnormal. We obtained these through Quantum Frequency Algorithm (QFA). We fit the ARIMA model to some weeks of Blk which have evident occurrences of incidents as detected and crosschecked with the incidents data provided by NCTS. We performed a forecast of the fit and generated a time series plot of the superimposed plots of the actual data and the forecast for each of the top incidents generated in the previous literature. These plots provided a simplistic time-domain 2D visualizations that successfully exposed the abnormal points where incidents happened. These also provided an estimate of the expected traffic density behavior if incidents did not happen.