Modeling Safety Impacts of Off-Hour Delivery Programs in Urban Areas

Abstract

Truck travel on urban road networks during daytime can be a major contributor to traffic congestion. A possible approach to relieve traffic congestion in urban areas is shifting a portion of trucks from regular daytime hours to nighttime off-hours. Benefits of this off-hour delivery strategy can be noticeable, but safety impacts need to be investigated. Manhattan, the most densely populated borough of New York City, with a large demand for truck deliveries, was the study area. Truck crashes, traffic volumes, and geometric design features of 256 road segments in Manhattan were collected to develop safety evaluation models. For quantification of the safety impacts of off-hour deliveries, an improved modeling approach was proposed; it involved the use of the multivariate Poisson-lognormal model integrated with measurement errors in truck volumes. The proposed model could address the inherent correlation of specific truck crash types and correct the estimation bias for safety effects of daytime and nighttime truck volumes. A Bayesian approach was employed to estimate the parameters of the proposed model. From the Bayesian posterior distributions, daytime and nighttime truck volumes did not have significantly different effects on either minor or serious crashes. In addition, truck crash counts were estimated with the proposed model under scenarios with different proportions of truck traffic shifted to nighttime. Results showed that off-hour delivery programs were not expected to increase the overall risk of truck-involved crashes significantly. Study findings can give transportation planners and policy makers insight on safety implications and decision making on off-hour deliveries.