DOI: 10.25338/b82k67
title: Analytical Modeling Framework to Assess the Economic and Environmental Impacts of Residential Deliveries, and Evaluate Sustainable City Logistics Strategies

description: In the last decade, e‐commerce has grown substantially, increasing
 business‐to‐business, business‐to‐consumer, and consumer‐to‐consumer
 transactions. As a result, there has been a continuous growth in last mile
 operations, especially deliveries to residential areas, bringing along
 externalities such as congestion, air and noise pollution, and energy
 consumption. This project aims to develop an analytical framework to model
 last mile operations based on continuous approximation techniques. The
 model will help estimate the economic and environmental impacts of
 residential deliveries, from a growth perspective, and through comparative
 analyses between consumer decisions (e.g., trip complementarity and
 substitution, trip‐induced demand). The model will estimate impacts for
 freight operators (shipper, and carriers), and the community. Based on
 data from the National Household Travel Survey, and the American Time Use
 Survey, the researchers will conduct empirical analyses with the modeling
 framework. Moreover, to contend with the transportation issues, the team
 will evaluate a number of scenarios involving city logistics strategies
 such as the introduction of cargo consolidation facilities (CF),
 alternative delivery points, and the use of cargo bikes and zero emission
 vehicles for the last mile.
description: The uploaded dataset is an
 Analytical Tool (.xlsm format) to model last-mile delivery and different
 city logistics measures in the context of e-commcer delivery.
 The objective here is to
 build an economic model for a last mile delivery service provider, serving
 N customers in a service region of size A in n<sub>r</sub>
 periods of time-window of length T<sub>TW</sub> from a depot
 located at a distance of ρ<sub>x</sub> and
 ρ<sub>y</sub> from the center of the service region. This
 depot – an e-commerce fulfillment center is serviced from a larger
 regional fulfillment center located at a distance of
 ρ<sub>x</sub>', ρ<sub>y</sub>' from the
 center of the service region. In addition, let there be
 N<sub>F</sub> randomly and uniformly distributed facilities
 within the service region, of which N<sub>MH</sub> operate as
 micro-hubs (consolidation facilities) and N<sub>CP</sub> are
 collection point pick-up facilities, serving a market share of
 p<sub>MH</sub> and p<sub>CP</sub> respectively.
 The vehicles departing from depot serve the
 N(1-p<sub>MH</sub>-p<sub>CP</sub>) customers
 directly and service the facilities as well, and the vehicles departing
 from MHs serve the market, while customers drive to the CPs to pick-up
 their packages. This work employs Continuous Approximation (CA)
 techniques to model last-mile parcel delivery operations to better
 understand the impacts of different city logisitcs measures and last-mile
 strategies.
description: <strong>2016 ATUS Data</strong><br> This file
 containts ATUS data for 2016. Check out ATUS Activity CodeBook.csv for
 help. This dataset was employed to develop individual econometric
 shoppping behavior models. Some variables have been removed to comply with
 legal and ethical guidelines. However the entire ATUS data can be accessed
 from https://www.atusdata.org/atus/about_atus.shtml
 <strong>LA-city Census</strong><br> Census data
 for LA. This data was employed to expand on the individual shopping
 behaviors to develop local/regional (in this case for LA) e-commerce
 demand. Note this data does not contain individual information but
 aggregated demographics for LA city.
 <strong>Last-Mile Analytical Tool</strong><br>
 This file contains essential information to use the Last-Mile Analytical
 Tool. This tool models last-mile delivery and different city logistics
 measures in the context of e-commcer delivery. Based on
 the structure of last-mile distribution (described above) we have four
 types of tours - <br>     Tour Type I: Inbound movement between
 Fulfillment center to E-commerce Fulfillment Facility<br>     Tour
 Type II: Vehicle movement between E-commerce Fulfillment Center and
 service region<br>     Tour Type III: Vehicle movement between
 micro-hubs and customers<br>     Tour Type IV: Vehicle movement
 between customers and collection point The Analytical
 Tool is a .xlsm file named "Lasy-mile Analysis.xlsm". This excel
 sheet contains the following sheets - <br> 1. Input<br>   
  This is the main/starting page of the tool.The Input sheet comprises of
 all the required inputs for last-mile analysis that pertain to<br>
      - Service region characteristics: size of the service region, total
 e-commerce demand and more.<br>      - Demographics:Demographics in
 conjunction with the shopping behavior model (presented in "Demand
 Gen" sheet) are used to estimate the e-commerce demand \of the
 service region<br>      - Operator characteristics: type of
 distribution structure, type and number of facilities, planning horizon
 and more.<br>      - Vehicle characteristics: vehicle
 specifications<br>      - Tour-vehicle combination: Vehicle used
 corresponding to tour type.<br>      - Charging levels: EV charging
 levels power and price<br>      <br>     In addition the Input
 file contains tools that help setting inputs for different last-mile
 structure and tools to set inputs from presets used in the case
 study. 2. Results<br>     This sheet contains the
 salient outputs for each tour type. These results include<br>     -
 Tour length (time)<br>     - Tour length (distance)<br>     -
 Fleet size<br>     - Tours per vehicle<br>     - Customers per
 tour<br>     - Externalities (VMT and emissions)<br>     -
 Costs (Fixed, Operational, External, Total and per package)<br>   
  <br> 3. Output (Hidden)<br>     This sheet is where last-mile
 delivery operations are optimized. Values from this sheet are aggregated
 in the Results sheet.<br>     <br> 4. Calx (Hidden)<br>
     Pre-processing and calculations to make optimization easier.<br>
     <br> 5. Demand Gen (Hidden)<br>     Shopping behavior
 econometric model used to calculate e-commerce demand for the service
 region.<br>     <br> 6. Case Study<br>     A copy of
 inputs sheet with case study presets.<br>     <br> 7.
 Reference Table<br>     A table describing all the parameters and
 variables used in the analytical tool.