Optimum transportation systems to serve the mineral industry north of the Yukon basin in Alaska

In 1972 the U. S . Bureau of Mines awarded a grant (No. G 01 22096) to the Mineral Industry Research Laboratory, University of Alaska, for a research project to determine optimum transportation systems to serve the mineral industry north of the Yukon River basin in Alaska. The study was conducted during the period May 1 - November 1, 1972. The study assesses the mineral potential of the region and selects two copper deposits: a known one at Bornite, and a potential one on the upper Koyukuk River. Two possible mining sites within the extensive coal bearing region north of the Brooks Range are also selected. A computer model was developed to perform an economic analysis of technically feasible transportation modes and routes from these four sites to Alaskan ports from which minerals could be shipped to markets. Transport modes considered are highway, rail, cargo aircraft, river barge, winter haul road and air cushion vehicles (A.C.V.). The computer program calculates the present worth of tax benefits from mining and transportation and revenues based on the value of minerals at the port, as well as the auxillary benefits derived from the anticipated use of the routes by the tourist industry. Annual and fixed costs of mining and transportation of minerals are calculated, and benefit-cost ratios determined for each combination of routes and modes serving the four mineral sites. The study concludes that the best systems in terms of a high benefit-cost ratio are those utilizing a minimum of new construction of conventional highways or railroads. The optimum system as derived from this study is one linking together existing transportation systems with aircraft or A.C.V. These modes are feasible only for the shipment of a high value product, namely blister copper produced by a smelter at the mining site, Of the several alternatives considered for the shipment of coal, only a slurry pipeline to an as yet undeveloped port on the Arctic coast showed significant promise. The study recommends that: 1. More government support should be given to mineral exploration in Alaska. 2. Potential mineral industry development should be considered in transportation planning at state and federal levels. 3. Additional research pertinent to mining and processing of minerals in the North should be conducted, and the feasibility of smelting minerals within Alaska explored. 4. Alternatives for providing power to Northwestern Alaska should be investigated

Truck Activity Pattern Classification Using Anonymous Mobile Sensor Data

To construct, operate, and maintain a transportation system that supports the efficient movement of freight, transportation agencies must understand economic drivers of freight flow. This is a challenge since freight movement data available to transportation agencies is typically void of commodity and industry information, factors that tie freight movements to underlying economic conditions. With recent advances in the resolution and availability of big data from Global Positioning Systems (GPS), it may be possible to fill this critical freight data gap. However, there is a need for methodological approaches to enable usage of this data for freight planning and operations. To address this methodological need, we use advanced machine-learning techniques and spatial analyses to classify trucks by industry based on activity patterns derived from large streams of truck GPS data. The major components are: (1) derivation of truck activity patterns from anonymous GPS traces, (2) development of a classification model to distinguish trucks by industry, and (3) estimation of a spatio-temporal regression model to capture rerouting behavior of trucks. First, we developed a K-means unsupervised clustering algorithm to find unique and representative daily activity patterns from GPS data. For a statewide GPS data sample, we are able to reduce over 300,000 daily patterns to a representative six patterns, thus enabling easier calibration and validation of the travel forecasting models that rely on detailed activity patterns. Next, we developed a Random Forest supervised machine learning model to classify truck daily activity patterns by industry served. The model predicts five distinct industry classes, i.e., farm products, manufacturing, chemicals, mining, and miscellaneous mixed, with 90% accuracy, filling a critical gap in our ability to tie truck movements to industry served. This ultimately allows us to build travel demand forecasting models with behavioral sensitivity. Finally, we developed a spatio-temporal model to capture truck rerouting behaviors due to weather events. The ability to model re-routing behaviors allows transportation agencies to identify operational and planning solutions that mitigate the impacts of weather on truck traffic. For freight industries, the prediction of weather impacts on truck driver’s route choices can inform a more accurate estimation of billable miles

A Globally Consistent Framework for Reliability-based Trade Statistics Reconciliation in the Presence of an Entrepôt

This paper develops a mathematicla programming model to reconcile trade statistics subject to a set of global consistency conditions in the presence of an entrepot. Initial data reliability serves a key function for governing the magnitude of adjustment. Through a two-stage optimization procedure, the adjusted trade statistics are achived as solutions to a system of simultaneous equations that minimize a quadratic penalty function. As an empirical illustration, the model is applied to reconcile the 2004 trade statistics reported by China, Hong Kong, and their major trading partners, initialized with detailed estimates of bilateral trade flows, re-export markups, cif/fob ratios and data reliability indexes.trade statistics reconciliation, entrepot trade, data reliability, global consistency

Competitive Positioning in International Logistics: Identifying a System of Attributes Through Neural Networks and Decision Trees

Firms involved in international logistics must develop a system of service attributes that give them a way to be profitable and to satisfy customers’ needs at the same time. How customers trade-off these various attributes in forming satisfaction with competing international logistics providers has not been explored well in the literature. This study explores the ocean freight shipping sector to identify the system of attributes that maximizes customers’ satisfaction. Data were collected from shipping managers in Singapore using personal interviews to identify the chief concerns in choosing and evaluating ocean freight services. The data were then examined using neural networks and decision trees, among other approaches to identify the system of attributes that is connected with customer satisfaction. The results illustrate the power of these methods in understanding how industrial customers with global operations process attributes to derive satisfaction. Implications are discussed

The Globalization of Steam Coal Markets and the Role of Logistics: An Empirical Analysis

In this paper, we provide a comprehensive multivariate cointegration analysis of three parts of the steam coal value chain - export, transport and import prices. The analysis is based on a rich dataset of international coal prices; in particular, we combine data on steam coal prices with freight rates, covering the period December 2001 until August 2009 at weekly frequency. We then test whether the demand and supply side components of steam coal trade are consistently integrated with one another. In addition, export and import prices as well as freight rates for individual trading routes, across regions and globally are combined. We find evidence of significant yet incomplete integration. We also find heterogeneous short-term dynamics of individual markets. Furthermore, we examine whether logistics enter coal price dynamics through transportation costs, which are mainly determined by oil prices. Our results suggest that this is generally not the case.Steam coal, market integration, multivariate cointegration

中国の開発計画と経済地理:交通インフラストラクチャーと自然資源

Tohoku University中島賢太郎課

Studying Regional and Cross Border Freight Movement Activities with Truck GPS Big Data

This dissertation utilizes an existing GPS data source to create and analyze a dataset of processed truck trips. The original data was generated for the purpose of fleet management by GPS transponders installed on Canadian owned trucks. These vehicles provide a critical service by fulfilling the economic need to move goods from one location to another. This thesis subsequently re-purposes the GPS pings as a form of opportunistic data to enrich the current state of knowledge regarding freight movement patterns. The first sections of this thesis are dedicated towards understanding the GPS data and devising processing methods needed to convert raw data into a suitable dataset of truck trips. Due to the nature of the topic, a geographic perspective was integral to this work to properly mine the data for useful information. For example, a new application of entropy based on the variety and distribution of carriers stopping at a location was created to assist with the classification of stop events. The data processing resulted in an approximate sample size of 245,000 trips per month from September 2012 to December 2014 and the month of March 2016. The volume of data and level of detail provides information that has not been available to date, which includes trip origins and destinations, associated industry, observed routes, and border crossing time/location if the trip was international. The processed trips derived from GPS data are applied towards a better understanding of inter-regional and cross-border truck movements. This area is underrepresented due to the difficulties in obtaining long-haul trip data where trucks move through multiple jurisdictions. These difficulties are compounded for international trips since the study area spans multiple nations. The processed truck trips are utilized to identify the spatial patterns of truck movements at specific border crossings between Canada and the U.S. including the Ambassador Bridge, Blue Water Bridge, and Peace Bridge. The choice of border crossing is also investigated using a specific case study of trucks travelling between Toronto, Ontario, and Chicago, Illinois. Finally, the observed trips from origin to destination allows for an analysis of delays at single locations (the border crossing) as well as their impact on the total trip. These applications represent a small part of the full potential that passive GPS data can provide after sufficient processing is applied. It is the hope of this author that these efforts can contribute towards the state of practice in transportation as GPS data becomes increasingly available to researchers. The work presented in this thesis illustrates how such GPS data can be used as a viable source to fill in gaps in knowledge. While traditional data collection techniques will remain a necessary facet of transportation research in the foreseeable future, information generated passively by users every day provides a new source of data that is characteristically large (in terms of volume and spatio-temporal coverage) and cost-effective