Amending the heston stochastic volatility model to forecast local motor vehicle crash rates: A case study of Washington, D.C. (March 2022)

• Record Type: Journal Article
• Authors: Shannon, Darren; Fountas, Grigorios
• Is Part Of: Transportation research interdisciplinary perspectives. Volume 13(2022)
• Publication Date: 2022-03
• Subjects: Stochastic volatility -- Motor vehicle crashes -- Transportation safety -- Crash rate forecasting -- COVID-19 -- Temporal instability; Transportation -- Periodicals; 388.05
• DOI: 10.1016/j.trip.2022.100576
• Languages: English
• ISSNs: 2590-1982

Abstract

Highlights: We present the case of using Stochastic Volatility modelling in Transportation Safety. We extend the Heston model to forecast non-seasonal crash rates in Washington, D.C. Our model outperforms Vasicek and ARIMA-GARCH models over the forecast period. Highly-accurate forecasts for 2015–2019 rates demonstrate the efficacy of our model. Structural breaks from the series (COVID-19) suggest further improvements are required. 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, D.C., 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, D.C. 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, D.C. 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.