Modeling Investments in County and Local Roads to Support Agricultural Logistics

Investments in local roads in North Dakota to support agricultural logistics are estimated with a detailed model that predicts flows from 1,406 crop-producing zones to 317 elevators and plants, and forecasts improvements and maintenance costs for paved and unpaved roads. The study finds that (1) the average farm-to-market trip distance has increased from 12 miles in 1980 to 26 miles in 2009, (2) the estimated resurfacing cost per mile for agricultural distribution routes is 40% greater than for non-agricultural routes, and (3) the estimated cost to maintain acceptable service levels on county and local roads is roughly double historical funding levels

Elevator Trip Distribution for Inconsistent Passenger Input-Output Data

Accurate traffic data are the basis for group control of elevators and its performance evaluation by trace driven simulation. The present practice estimates a time series of inter-floor passenger traffic based on commonly available elevator sensor data. The method demands that the sensor data be transformed into sets of passenger input-output data which are consistent in the sense that the transportation preserves the number of passengers. Since observation involves various behavioral assumptions, which may actually be violated, as well as measurement errors, it has been necessary to apply data adjustment procedures to secure the consistency. This paper proposes an alternative algorithm which reconstructs elevator passenger origin-destination tables from inconsistent passenger input-output data sets, thus eliminating the ad hoc data adjustment

Comprehensive Statewide Transportation Model: Multimodal Investment Analysis Methodology Phase II, CTRE Project 02-126, 2001

The purposes of this report (Phase II of the project) are to specify in mathematical form the individual modules of the conceptual model developed in Phase I, to identify and evaluate sources of data for the model set, and to develop the transport networks necessary to support the models

Container Handling Algorithms and Outbound Heavy Truck Movement Modeling for Seaport Container Transshipment Terminals

This research is divided into four main parts. The first part considers the basic block relocation problem (BRP) in which a set of shipping containers is retrieved using the minimum number of moves by a single gantry crane that handles cargo in the storage area in a container terminal. For this purpose a new algorithm called the look ahead algorithm has been created and tested. The look ahead algorithm is applicable under limited and unlimited stacking height conditions. The look ahead algorithm is compared to the existing algorithms in the literature. The experimental results show that the look ahead algorithm is more efficient than any other algorithm in the literature. The second part of this research considers an extension of the BRP called the block relocation problem with weights (BRP-W). The main goal is to minimize the total fuel consumption of the crane to retrieve all the containers in a bay and to minimize the movements of the heavy containers. The trolleying, hoisting, and lowering movements of the containers are explicitly considered in this part. The twelve parameters to quantify various preferences when moving individual containers are defined. Near-optimal values of the twelve parameters for different bay configurations are found using a genetic algorithm. The third part introduces a shipping cost model that can estimate the cost of shipping specific commodity groups using one freight transportation mode-trucking- from any origin to any destination inside the United States. The model can also be used to estimate general shipping costs for different economic sectors, with significant ramifications for public policy. The last part mimics heavy truck movements for shipping different kinds of containerized commodities between a container terminal and different facilities. The highly detailed cost model from part three is used to evaluate the effect of public policies on truckers\u27 route choices. In particular, the influence of time, distance, and tolls on truckers\u27 route selection is investigated.

Optimization for Urban Mobility Systems

In the recent decades, new modes of transportation have been developed due to urbanization, highly dense population, and technological advancement. As a result, design and operation of urban transportation have become increasingly important to better utilize the resources and efficiently meet demand. This dissertation was motivated by two problems on optimizing design and control of urban transportation. In the first one, we consider a problem of dynamically matching heterogeneous market parcitipants so as to maximize the total number of matching, which was motivated by practices of ride-sharing platforms. In the other problem, we study efficient design of elevator zoning system in high-rises with uncertainty in customer batching.In Chapter 1, we consider a multiperiod stochastic optimization of a market that matches heterogeneous and impatient agents. The model was mainly motivated from carpooling products run by ride-sharing platforms such as Uber and Lyft, and kidney exchange market, where market participants are heterogeneous in terms of how likely they can be matched with others. In the case of a ride-sharing platform, one of the key operational decisions for carpooling is to efficiently match riders and clear the market in a timely manner. In doing so, the platform needs to take into account the heterogeneity of riders in terms of their trip types(e.g origin-destination pair) and different matching compatibility. For example, some customers may request rides within San Francisco, while others may request rides from San Francisco to outside the city. Since picking up and dropping off a customer within the city can be done within relatively short amount of time, those who want to travel within the city can be matched with any other riders for carpooling. However, the destinations of those who want to travel to outside the city may be very different, and in order to maintain customers' additional transit time due to carpooling, it is likely that they can be only matched with those who want to travel within the city. In the case of kidney exchange where market participants arrive in the form of patient-donor pair, pairs with donor who can donate her kidney to most of patients (for example, blood type O) and patient who can get kidney from most of donors (for example, blood type AB) can be easily matched to other pairs. The opposite case would be hard-to-match pair that is incompatible for matching with most of other pairs. Our model is an abstraction of these two motivating examples, and considers two types of agents: easy-to-match agents that can be matched with either type of agents, and hard-to-match agents that can be only matched with easy-to-match ones. We first formulate a dynamic program to solve for optimal matching decisions over infinite time horizon in a discrete time setting, and characterize structure of optimal stationary policies. Inspired by practices in kidney exchange where the market is cleared for every fixed time interval, we connect the discrete time model to a continuous time setting by investigating the effect of the length of matching intervals on the matching performance. Results from numerical experiments indicate certain patterns in the relationship between the length of matching intervals and the maximum number of matching achieved, and provides valuable insights for future direction of research. In Chapter 2, we consider a zoning problem for elevator dispatching systems in high-rises. In practice, zoning is frequently used to improve efficiency of elevator systems. The idea of zoning is to prevent different elevators from stopping at common floors, which may result in long service times of elevators and thus long waiting times of customers. Our goal is to provide a mathematical framework that can help a system planner decide optimal zoning design with some performance guarantee. To this end, we focus on uppeak traffic situation during morning rush hour, which is in general the heaviest traffic during the day. The performance in the uppeak traffic situation can be considered as the system's capacity, because if the system can handle uppeak traffic well, it can also serve other types of traffic with good performance. Thus, the performance measure in the uppeak traffic situation can be used as a metric to choose the optimal zoning configuration. One of the components that complicate the problem is customer batching, on which the system may not have a control. In view of this, we formulate an adversarial optimization problem that can measure the system performance of different zoning decisions. By considering the heaviest traffic situation of the day and using the adversarial framework, we provide a model that can be used for capacity planning of elevator systems. We formulate mixed-integer linear program(MILP)s to find the optimal zoning configuration. To solve the MILPs, we show that we can use simple greedy algorithms and solve smaller linear programs. We also provide a few illustrative examples as well as numerical experiments to verify the theoretical results and obtain insights for further analysis

Derived Demand for Grain Freight Transportation, Rail-Truck Competition, and Mode Choice and Allocative Efficiency

The demand for grain freight transportation is a derived demand; consequently changes in the grain supply chain in production and handling, and those in the transportation domain will affect the demand for grain transportation. The U.S. transportation industry (e.g. railroad and trucking), and the grain supply chain in general have witnessed structural changes over the years that have potential long-run implications for demand, intermodal competition, and grain shippers mode choices both nationally and regionally. Deregulation of the railroad and trucking industries initiated innovations (e.g. shuttle trains) that have revolutionized the way grain is marketed. These and other related trends in agriculture including bioenergy suggest a dynamic environment surrounding grain transportation and the need to revisit agricultural transportation demand and evaluate changes over time. A majority of freight demand studies are based on aggregate data (e.g. regional) due to lack of disaggregate data. Aggregation of shippers over large geographic regions leads to loss of information with potential erroneous elasticity estimates. This study develops a method to estimate transportation rates at the grain elevator level to estimate a shipper link specific cost function for barley, corn, durum, hard red spring wheat, and soybeans shippers. The aim of this study is to assess and characterize the nature of rail-truck competition for the transportation of five commodities over distance and time as well as to assess whether North Dakota grain shippers’ mode choices reflect an allocatively efficient mix assuming the choice of mode is based on shipping rates. Our findings indicate that in general, rail dominates most of the grain traffic, however, the degree of dominance is variable by commodity. Additional findings suggest that grain shippers utilize more rail than they would if they chose modes based on rates. This may suggest unmeasured service quality advantages of rail in comparison to truck.Upper Great Plains Transportation Institute (UGPTI)Upper Great Plains Transportation Institute (UGPTI