A joint model of mode and shipment size choice using the first generation of Commodity Flow Survey Public Use Microdata

Stinson, Monique; Pourabdollahi, Zahra; Livshits, Vladimir; Jeon, Kyunghwi; Nippani, Sreevatsa; Zhu, Huangtianzhi

doi:10.1016/j.ijtst.2017.08.002

Cited by 30 publications

(14 citation statements)

References 13 publications

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“…All the calibrated parameters were statistically significant and correct in signs. The value of time (VOT) for heavy goods vehicles was approximately 24 €/h (similar to the ones reported in Stinson et al [65] and Nuzzolo et al [66]), while the value of time for cars was approximately 10 €/h, similar to the one reported in [7]. The statistic ρ 2 is, in value, similar to those reported in the literature (e.g., see [13]).…”

Section: Modelssupporting

confidence: 88%

Estimating Path Choice Models through Floating Car Data

Comi

Polimeni

2022

Forecasting

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The path choice models play a key role in transportation engineering, especially when coupled with an assignment procedure allowing link flows to be obtained. Their implementation could be complex and resource-consuming. In particular, such a task consists of several stages, including (1) the collection of a large set of data from surveys to infer users’ path choices and (2) the definition of a model able to reproduce users’ choice behaviors. Nowadays, stage (1) can be improved using floating car data (FCD), which allow one to obtain a reliable dataset of paths. In relation to stage (2), different structures of models are available; however, a compromise has to be found between the model’s ability to reproduce the observed paths (including the ability to forecast the future path choices) and its applicability in real contexts (in addition to guaranteeing the robustness of the assignment procedure). Therefore, the aim of this paper is to explore the opportunities offered by FCD to calibrate a path/route choice model to be included in a general procedure for scenario assessment. The proposed methodology is applied to passenger and freight transport case studies. Significant results are obtained showing the opportunities offered by FCD in supporting path choice simulation. Moreover, the characteristics of the model make it easily applicable and exportable to other contexts.

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Section: Modelssupporting

confidence: 88%

Estimating Path Choice Models through Floating Car Data

Comi

Polimeni

2022

Forecasting

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“…This is consistent with the maximum cargo weight of the vehicle (703 kg for V125 and 1,533 kg for V350) and the carrying capacity of the robot (30 kg). [28][29][30][31] The package volume is estimated to range from 0.2 to 1.9 ft 3 as part of our spatial modeling shown in Supporting Information (SI).…”

Section: Functional Unitmentioning

confidence: 99%

Life Cycle Greenhouse Gas Emissions for Last-Mile Parcel Delivery by Automated Vehicles and Robots

Keoleian

et al. 2021

Environ. Sci. Technol.

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Increased E-commerce and demand for contactless delivery during the COVID-19 pandemic have fueled interest in robotic package delivery. We evaluate life cycle greenhouse gas (GHG) emissions for automated ground delivery systems consisting of a vehicle (last-mile) and a robot (final-50-feet) in a suburban setting. Small and large cargo vans (125 and 350 cubic feet; V125 and V350) with internal combustion engine (ICEV) and battery electric (BEV) powertrains were assessed for three delivery scenarios: (i) conventional, human-driven vehicle with human delivery; (ii) partially automated, human-driven vehicle with robot delivery; and (iii) fully automated: connected automated vehicle (CAV) with robot delivery. The robot's contribution to life cycle GHG emissions is small (2-6%). CAV auxiliary loads offset operational benefits from automated driving. Compared to the conventional scenario, full automation results in 7% lower GHG emissions for the V350-ICEV but 5% higher for the V125-BEV. Conventional delivery with a V125-BEV provides the lowest GHG emissions, 160 g CO2e/package, while partially automated delivery with a V350-ICEV generates the most at 450 g CO2e/package. Sensitivity analysis shows delivery density and fuel economy are key parameters determining GHG emissions for all scenarios, while CAV power requirements and efficiency benefits have a smaller impact on automated scenario emissions.iii ACKNOWLEDGEMENTS I want to thank Dr. Gregory Keoleian and Dr. Hyung Chul Kim for making this thesis project possible. Their wisdom, experience, and support ensured the technical rigor and accuracy of the analysis. Dr. Keoleian has been a great advisor and mentor that has supported and guided me through this challenging time of virtual semesters. I would also like to thank Dr. Xiaoyi He (postdoctoral researcher at the Center for Sustainable Systems) and the environmental science team members at Ford Motor Company, including Dr. Hyung Chul Kim, Dr. Timothy Walling ton, and Robert De Kleine. Their constant support as well as technical and communication expertise were instrumental to the success of the research. It has been an honor to work with all five of these experts. Additionally, I want to express gratitude to my colleague Nick Kemp for his technical, communication, and moral support; and James Gawron at Ford Motor Company for his helpful input and mentorhsip.

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“…The resulting changes in trade could also be fed back to the transport system, which would allow to assess direct effects according to the classical CBA definitions, as used in most countries. f The interested reader is referred to Reiche (2017), Stinson et al (2017), or Rich et al (2009).…”

Section: Freight Transport Modelsmentioning

confidence: 99%