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A large-scale analytical residential parcel delivery model evaluating greenhouse gas emissions,COVID-19 impact,and cargo bikes

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摘要 The e-commerce industry has experienced significant growth in the past decade,particu-larly post-COVID.To accommodate such growth,the parcel delivery sector has also grown rapidly.However,there is a lack of study that properly evaluates its social and environ-mental impacts at a large scale.A model is proposed to analyze such impacts.A parcel gen-eration process is presented to convert public data into parcel volumes and stops.A continuous approximation model is fitted to estimate the length of parcel service tours.A case study is conducted using New York City(NYC)data.The parcel generation is shown to be a valid fit.The continuous approximation model parameters have R2 values of 98%or higher.The model output is validated against UPS truck trips.Application of the model to 2021 suggests residential parcel deliveries contributed to 0.05%of total daily vehicle-kilometer-traveled(VKT)in NYC corresponding to 14.4 metric tons of carbon equivalent(MTCE)emissions per day.COVID-19 contributed to an increase in parcel deliveries that led to up to 1064.3 MTCE of annual greenhouse gas(GHG)emissions in NYC(which could power 532 standard US households for a year).The existing bike lane infrastructure can support the substitution of 17%of parcel deliveries by cargo bikes,which would reduce VKT by 11%.Adding 3 km of bike lanes to connect Amazon facilities can expand their cargo bike substitution benefit from a VKT reduction of 5%up to 30%.If 28 km of additional bike lanes are made,parcel delivery substitution citywide could increase from 17%to 34%via cargo bike and save an additional 2.3 MTCE per day.Cargo bike priorities can be set to reduce GHG emissions for lower-income neighborhoods including Harlem,Sunset Park,and Bushwick.
机构地区 C
出处 《International Journal of Transportation Science and Technology》 2024年第3期136-154,共19页 交通科学与技术(英文)
基金 support from C2SMART University Transportation Center(USDOT#69A3551747124).

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