Amending the Heston Stochastic Volatility Model to Forecast Local Motor Vehicle Crash Rates: A Case Study of Washington, D.C

TL;DR

This paper introduces a stochastic volatility model based on the Heston model to forecast crash rates in Washington, D.C., accounting for exogenous factors and local traffic patterns, with potential applications in public safety policy.

Contribution

The study adapts the Heston stochastic volatility model for urban crash rate forecasting, capturing local heterogeneity and temporal instability, improving upon traditional models.

Findings

Model outperforms conventional forecasting methods in Washington, D.C.

Struggles to accurately predict crash rates during COVID-19 pandemic.

Demonstrates potential for policy application in local road safety management.

Abstract

Modelling crash rates in an urban area requires a swathe of data regarding historical and prevailing traffic volumes and crash events and characteristics. Provided that the traffic volume of urban networks is largely defined by typical work and school commute patterns, crash rates can be determined with a reasonable degree of accuracy. However, this process becomes more complicated for an area that is frequently subject to peaks and troughs in traffic volume and crash events owing to exogenous events (for example, extreme weather) rather than typical commute patterns. One such area that is particularly exposed to exogenous events is Washington, DC, which has seen a large rise in crash events between 2009 and 2020. In this study, we adopt a forecasting model that embeds heterogeneity and temporal instability in its estimates in order to improve upon forecasting models currently used in transportation and road safety research. Specifically, we introduce a stochastic volatility model that aims to capture the nuances associated with crash rates in Washington, DC. We determine that this model can outperform conventional forecasting models, but it does not perform well in light of the unique travel patterns exhibited throughout the COVID-19 pandemic. Nevertheless, its adaptability to the idiosyncrasies of Washington, DC crash rates demonstrates its ability to accurately simulate localised crash rates processes, which can be further adapted in public policy contexts to form road safety targets.