Safety Benefits of Parcel Delivery Modes Using Geographically Weighted Negative Binominal Regression
Document Type
Article
Publication Date
2024
Digital Object Identifier (DOI)
https://doi.org/10.1061/9780784485514.020
Abstract
Emerging urban parcel delivery (UPD) modes are anticipated to decrease surface UPD truck trips and stops, thus leading to less exposure of UPD trucks on surface roads and reduced UPD crashes. This paper evaluated the safety impacts of innovative last-mile delivery strategies in urban areas. The geographically weighted negative binominal regression (GWNBR) model was developed at zone levels based on the roadway, traffic, and demographic data collected in Hillsborough County, Florida. Future UPD scenarios were projected for coming years (2030, 2040, and 2050) with different replacement rates (10%, 30%, and 50%) of UPD truck stops by emerging UPD modes. The developed GWNBR model was used to predict UPD crashes for future scenarios. The results indicate that emerging UPD technologies cause a decrease in delivery truck stops and reduce UPD crashes by 3%, 11%, and 20% for 2030, 2040, and 2050, respectively.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
International Conference on Transportation and Development 2024, p. 226-237
Scholar Commons Citation
Wang, Zhenyu; Lin, Pei-Sung; and Keita, Yaye, "Safety Benefits of Parcel Delivery Modes Using Geographically Weighted Negative Binominal Regression" (2024). CUTR Faculty Journal Publications. 168.
https://digitalcommons.usf.edu/cutr_facpub/168
