Public Transport-Based Crowdshipping for Sustainable City Logistics: Assessing Economic and Environmental Impacts
Abstract
:1. Introduction
2. Background
3. Materials and Methods
4. Results and Discussions
4.1. Econometric Results
VB = β1 × Shipping FeesB + β2 × Shipping TimesB+β3 × Parcel TrackingB+β4 × Delivery PlanningB
Vno choice = β5 × Age + ASC
4.2. Economic and Environmental Impacts
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Features | Levels |
---|---|
Shipping fee (with respect to current national shipping companies) | ● Lower (+1) |
● Typical (−1) | |
Shipping time (with respect to current national shipping companies) | ● Lower (+1) |
● Typical (−1) | |
Parcel tracking | ● Available (+1) |
● Not available (−1) | |
Delivery date and Time schedule flexibility | ● Yes (+1) |
● No (−1) |
EURO Class | Vehicle Category (EURO) * | km/year ** | km Cumulative *** | |
---|---|---|---|---|
LCV–Petrol | LCV–Diesel | |||
0 | 0.8% | 11.7% | 4950.00 | 608,319.69 |
1 | 0.6% | 7.9% | 7920.00 | 368,979.34 |
2 | 1.0% | 14.3% | 14,548.00 | 272,132.62 |
3 | 1.4% | 19.0% | 19,000.00 | 223,531.25 |
4 | 1.4% | 19.8% | 28,726.00 | 190,067.89 |
5 | 1.1% | 15.3% | 30,193.00 | 138,635.23 |
6 | 0.4% | 5.4% | 36,000.00 | 36,125.92 |
MNL | |||||
---|---|---|---|---|---|
Coeff. (β) | Stand. Error | t-Test | p-Value | ||
Attributes | Age | 0.0905 | 0.0118 | 7.65 | 0.00 |
Shipping fees * [a] | |||||
Lower | 0.6750 | 0.0998 | 6.76 | 0.00 | |
Shipping time ** [a] | |||||
Lower | 0.5870 | 0.0882 | 6.65 | 0.00 | |
Parcel tracking *** | |||||
Present | 0.6980 | 0.0946 | 7.38 | 0.00 | |
Delivery date and Time schedule flexibility **** | |||||
Yes | 0.7860 | 0.0886 | 8.87 | 0.00 | |
“no choice” [ASC] | −5.2300 | 0.5880 | −8.90 | 0.00 | |
* base level: “Typical”; ** base level: “ Typical”; *** base level: “ Not available”; **** base level: “No”. [a] with respect to current national shipping companies | |||||
Model Fit Statistics | MNL | ||||
N. of observations (individuals) | 618 (206) | ||||
Null log-likelihood | −678.942 | ||||
Final log-likelihood | −469.842 | ||||
Likelihood ratio test | 418.201 | ||||
Rho-square | 0.308 | ||||
Adjusted rho-square | 0.299 | ||||
Simulation test | |||||
Sample reconstitution | 59.9% |
Demand SCENARIOS | Crowdshipping Service Adoption Probability | Potential Demand Current State (Orders/Day) | Potential Demand Year 2025 (Orders/Day) |
---|---|---|---|
Favorable | 66.10% | 14,100 | 29,540 |
Base | 59.70% | 12,730 | 26,680 |
Unfavorable | 16.40% | 3500 | 7330 |
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Gatta, V.; Marcucci, E.; Nigro, M.; Patella, S.M.; Serafini, S. Public Transport-Based Crowdshipping for Sustainable City Logistics: Assessing Economic and Environmental Impacts. Sustainability 2019, 11, 145. https://doi.org/10.3390/su11010145
Gatta V, Marcucci E, Nigro M, Patella SM, Serafini S. Public Transport-Based Crowdshipping for Sustainable City Logistics: Assessing Economic and Environmental Impacts. Sustainability. 2019; 11(1):145. https://doi.org/10.3390/su11010145
Chicago/Turabian StyleGatta, Valerio, Edoardo Marcucci, Marialisa Nigro, Sergio Maria Patella, and Simone Serafini. 2019. "Public Transport-Based Crowdshipping for Sustainable City Logistics: Assessing Economic and Environmental Impacts" Sustainability 11, no. 1: 145. https://doi.org/10.3390/su11010145