Safety Benefits of Parcel Delivery Modes Using Geographically Weighted Negative Binominal Regression
Publication: International Conference on Transportation and Development 2024
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.
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REFERENCES
Gomes, M. J. T. L., F. Cunto, and A. R. da Silva. (2017). “Geographically weighted negative binomial regression applied to zonal level safety performance models.” Accident Analysis & Prevention 106: 254–261.
Hezaveh, A. M., R. Arvin, and C. R. Cherry. (2019). “A geographically weighted regression to estimate the comprehensive cost of traffic crashes at a zonal level.” Accident Analysis & Prevention 131: 15–24.
Moore, A. M. (2019). “Innovative scenarios for modeling intra-city freight delivery.” Transportation Research Interdisciplinary Perspectives 3: 100024.
Obelheiro, M. R., A. R. da Silva, C. T. Nodari, H. B. B. Cybis, and L. A. Lindau. (2020). “A new zone system to analyze the spatial relationships between the built environment and traffic safety.” Journal of transport geography 84: 102699.
Oluwajana, S. D., P. Y. Park, and T. Cavalho. (2022). “Macro-level collision prediction using geographically weighted negative binomial regression.” Journal of Transportation Safety & Security 14(7): 1085–1120.
Silva, A. D., and T. Rodrigues. (2016). “A SAS® macro for geographically weighted negative binomial regression.” http://support.sas.com/resources/papers/proceedings16/8000-2016. pdf>. Data de acesso 1(06): 2016.
Stinson, M., A. Enam, A. Moore, and J. Auld. (2019). Citywide impacts of E-commerce: Does parcel delivery travel outweigh household shopping travel reductions? Proceedings of the 2nd ACM/EIGSCC Symposium on Smart Cities and Communities.
Yocum, R. L., and V. V. Gayah. (2022). “County-level crash prediction models for Pennsylvania accounting for income characteristics.” Transportation research interdisciplinary perspectives 13: 100562.
Zhai, X., H. Huang, P. Xu, and N. Sze. (2019). “The influence of zonal configurations on macro-level crash modeling.” Transportmetrica A: transport science 15(2): 417–434.
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Published online: Jun 13, 2024
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