Freight demand modeling and logistics planning for assessment of freight systems' environmental impacts

Hwang, Tae Sung

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https://hdl.handle.net/2142/49430 Copy

Description

Title

Freight demand modeling and logistics planning for assessment of freight systems' environmental impacts

Author(s)

Hwang, Tae Sung

Issue Date

2014-05-30T16:43:36Z

Director of Research (if dissertation) or Advisor (if thesis)

Ouyang, Yanfeng

Doctoral Committee Chair(s)

Ouyang, Yanfeng

Committee Member(s)

• Barkan, Christopher P.L.
• Bond, Tami C.
• Lee, Bumsoo

Department of Study

Civil & Environmental Eng

Discipline

Civil Engineering

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• freight transportation
• freight demand modeling
• logistics systems planning
• network modeling
• optimization
• vehicle emissions

Abstract

This dissertation research aims at examining the U.S. freight transportation systems and the relationship between freight shipment activities and the related environmental issues such as air pollution and greenhouse gas emissions in the nation. This study develops freight demand models to forecast freight movements between and within the U.S. geographical regions via two major shipment modes, truck and rail. Freight flow is categorized into two types: inter-regional and intra-regional freight flow. For the inter-regional freight flow, the well-known four-step freight demand forecasting model is adopted which consists of trip generation, trip distribution, modal split, and traffic assignment. In case of the intra-regional freight movements, various network modeling and logistics systems optimization methodologies are applied to address a large-scale freight delivery problem in the U.S. freight zones and an individual truck routing problem on stochastic congested roadway networks. Following the four-step freight demand forecasting framework, we first propose a methodology to estimate future freight demand for all commodity types that begin and end in each geographical region in the U.S., and the amount of freight that moves between all origin-destination pairs. This procedure corresponds to trip generation and trip distribution for inter-regional freight demand. Using future economic growth factors, the amounts of freight production and attraction in each geographical region are forecasted and taken as given. Then, an efficient matrix balancing method, an RAS algorithm, is applied to distribute the estimated freight shipment demand for all origin-destination pairs. Various freight shipment modes have significantly different impacts on air quality and environmental sustainability, and this highlights the need for a better understanding of inter-regional freight shipment mode choices. This dissertation work develops a binomial logit market share model to predict the U.S. inter-regional freight modal share between truck and rail, as a function of freight and shipment characteristics. This step corresponds to modal split procedure in the four-step freight demand forecasting framework. A set of multi-year freight shipment and geographical information databases as well as crude oil price information were integrated to construct regression models for typical freight commodities. The atmospheric impact levels incurred by different freight mode choice decisions are analyzed to provide insights on the relationship among freight modal split, oil price change, and air quality. In addition to ‘mode choices,’ ‘route choices’ in freight deliveries can significantly affect national and regional air quality. Therefore, as the last step of the inter-regional freight flow modeling framework, truck and rail freight shipment assignment is conducted while network congestion effect is taken into consideration. Carriers’ route choices are assumed to follow a user equilibrium principle. A traditional convex combinations algorithm is used to solve for traffic routing equilibria for truck flow in the U.S. highway network. Link cost function is modified to consider traffic volume that already exists on the highway network. A customized network assignment model is proposed for rail freight shipment demand, where single- and double-track lines are represented by an equivalent directed graph with railroad-specific link traffic delay functions. An adapted convex combinations algorithm is developed to find shipment routing equilibrium. Our models are applied to an empirical case study for the U.S. highway and rail networks and solutions are found within a short computation time. For the intra-regional freight demand, we first focus on developing a methodology for freight distribution and collection within the U.S. geographical regions where a large number of spatially distributed freight demand and supply points need to be served. This problem is formulated as a large-scale vehicle routing problem and solved by an modified ring-sweep algorithm. A set of closed-form formulae is constructed to estimate the asymptotic total travel distance of a fleet of trucks. A case study is conducted to forecast regional freight delivery cost for the U.S. geographical regions that include major metropolitan areas. Numerical results under three urban development scenarios show that the proposed methodology can effectively estimate the total cost and the related emissions. Lastly, a microscopic urban freight truck routing problem on a stochastic network is addressed. Freight trucks are known as a major source of air pollutant and greenhouse gas emissions in the U.S. metropolitan areas. Therefore, emissions from freight trucks during their deliveries need to be considered by the trucking service sector when they make routing decisions. This study proposes a model that incorporates total delivery time, various emissions from freight truck activities, and a penalty for late or early arrival into the total cost objective of a stochastic shortest path problem. We focus on urban networks in which random congestion state on each link follows an independent probability distribution. Our model finds the best truck routing on a given network so as to minimize the expected total travel cost. This problem is formulated into a mathematical model and two solution methods including a dynamic programming approach and a deterministic shortest path heuristic are proposed. Numerical examples show that the proposed algorithms perform very well even for the large-size U.S. urban networks. This dissertation study will be useful for transportation planners and decision makers in public and private sectors to assess how freight mode and route choices on the national scale will affect air quality and eventually human health in a variety of future global economic growth and environmental policy scenarios. Also, the estimated freight shipment activities in the regional level can be used to infer the human exposures to emissions from freight delivery in large urban areas.

Graduation Semester

2014-05

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Copyright 2014 Tae Sung Hwang

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