Content uploaded by Magdalena I. Asborno
Author content
All content in this area was uploaded by Magdalena I. Asborno on Dec 08, 2019
Content may be subject to copyright.
A preview of the PDF is not available
Locating Transload Facilities to Ease Highway Congestion and Safeguard the Environment
Figures
Figures - uploaded by Magdalena I. Asborno
Author content
All figure content in this area was uploaded by Magdalena I. Asborno
Content may be subject to copyright.
... Costs for equipment (i.e., crane, conveyor, hooper, forklift) and storage facilities (i.e., warehouse, storage tank, paved and unpaved storage) are obtained from (Braham et al., 2017). These costs include labor and materials, as well as general overhead. ...
... These costs include labor and materials, as well as general overhead. (Braham et al., 2017) selected these costs from a material, construction, and equipment cost database from RS Means (2014, 2017) and validated through interviews with industry representatives. All costs for our study are calculated based on the 2020 dollar value. ...
This paper investigates inland port infrastructure investment planning under uncertain commodity (such as coal, petroleum, manufactured products, nonmetallic minerals) demand conditions. A two-stage stochastic optimization is developed to model the impact of demand uncertainty on infrastructure planning and transportation decisions. The model minimizes expected total costs, including capacity expansion costs, associated with handling equipment and storage infrastructure, and the expected transportation costs. To solve the problem, an accelerated Benders decomposition algorithm is implemented. The use of a stochastic approach is justified by comparing the value of stochastic solution with its corresponding deterministic solution. For demonstration, the model is applied to the Arkansas section of the McClellan-Kerr Arkansas River Navigation System (MKARNS). Given data availability, the model is generalizable to other regions. Results show that as investment in port capacities (handling equipment and storage infrastructure) increases by 21 M per year. The model serves as a decision-making tool for optimal, distributed allocation of monetary investments, that encourages mode shift to inland waterways.
... Costs for equipment (i.e., crane, conveyor, hooper, forklift) and storage facilities (i.e., warehouse, storage tank, paved and unpaved storage) are obtained from (Braham et al., 2017). These costs include labor and materials, as well as general overhead. ...
... These costs include labor and materials, as well as general overhead. (Braham et al., 2017) selected these costs from a material, construction, and equipment cost database from RS Means (2014, 2017) and validated through interviews with industry representatives. All costs for our study are calculated based on the 2020 dollar value. ...
This paper investigates inland port infrastructure investment planning under uncertain commodity demand conditions. A two-stage stochastic optimization is developed to model the impact of demand uncertainty on infrastructure planning and transportation decisions. The two-stage stochastic model minimizes the total expected costs, including the capacity expansion investment costs associated with handling equipment and storage, and the expected transportation costs. To solve the problem, an accelerated Benders decomposition algorithm is implemented. The Arkansas section of the McCllean-Kerr Arkansas River Navigation System (MKARNS) is used as a testing ground for the model. Results show that commodity volume and, as expected, the percent of that volume that moves via waterways (in ton-miles) increases with increasing investment in port infrastructure. The model is able to identify a cluster of ports that should receive investment in port capacity under any investment scenario. The use of a stochastic approach is justified by calculating the value of the stochastic solution (VSS).
... In doing this, estimates of system usage by mode may provide a means to more accurately reflect costs and benefits of a project. Benefits, for example, may be associated with reduce roadway maintenance costs and/or emissions by shifting cargo from truck to barge (Braham et al. 2017). ...
To estimate impacts, support cost–benefit analyses, and enable project prioritization, it is necessary to identify the area of influence of a transportation infrastructure project. For freight related projects, like ports, state-of-the-practice methods to estimate such areas ignore complex interactions among multimodal supply chains and can be improved by examining the multimodal trips made to and from the facility. While travel demand models estimate multimodal trips, they may not contain robust depictions of water and rail, and do not provide direct observation. Project-specific data including local traffic counts and surveys can be expensive and subjective. This work develops a systematic, objective methodology to identify multimodal “freight-shed” (or “catchment” areas) for a facility from vehicle tracking data and demonstrates application with a case study involving diverse freight port terminals. Observed truck Global Positioning System and maritime Automatic Identification System data are subjected to robust pre-processing algorithms to handle noise, cluster stops, assign data points to the network (map-matching), and address spatial and temporal conflation. The method is applied to 43 port terminals on the Arkansas River to estimate vehicle miles and hours travelled, origin, destination, and pass-through zones, and areas of modal overlap within the catchment areas. Case studies show that the state-of-the-practice 100-mile diameter influence areas include between 15 and 34% of the multimodal freight-shed areas mined from vehicle tracking data, demonstrating that adoption of an arbitrary radial area for different ports would lead to inaccurate estimates of project benefits.
Inland waterways play a key role within the freight transportation system by connecting productive heartland areas to international gateways, while keeping costs competitive. Quantifying commodity flow is important because it affects cost-based supply chain decision-making. However, data on commodity movements to inform investment and planning decisions is elusive. Publicly available commodity data on U.S. inland waterways is limited in its spatial aggregation to the location of locks, which is insufficient to identify inter-port commodity flows. Automatic Identification System (AIS) data has the potential to disaggregate freight-flows to the port and river segment levels but it does not identify the commodity carried. This paper characterizes and quantifies vessel trips by port of origin-destination, timestamp, commodity carried, and path (mapped to an inland waterway network), allowing for disaggregated commodity flow analysis, previously unavailable in the public domain in the U.S.
This is accomplished through a multi-commodity assignment model which conflates AIS vessel movement data with commodity-specific port throughput. A stochastic approach is introduced to handle uncertainty in cargo-to-vessel ratios. Validation using data from the Arkansas River show agreement between model predictions and aggregated commodity volumes with differences lower than 1.82% by commodity and lock. Ubiquitous AIS data permit the transferability of the proposed work.
Economic impacts of freight movement to Maryland's economy were estimated by input-output analysis using the 2010 IMPLAN data. A freight economic output (FECO) index was also developed based on the historical payroll data and gross domestic product (GDP) between 2002 and 2010. This effort was motivated by the absence of defendable performance measures for the economic contribution of freight transportation services. It was found that the freight industry generates sizable ripple effects. While the trucking sector is the largest in terms of an absolute employment size, the spillovers of the freight water and port services are about seven times its employment size. The impact of government spending is also significant. The aggregate FECO index parallels the Maryland GDP and the national freight service index. Interestingly, the evidence of modal competition between truck and freight rail is observed. Their trends and the magnitude of the changes are generally moving in opposite directions. The findings and methodologies of the study will help decision makers understand the role that each freight mode plays in order to make more informed decisions. The economic indicators used in this study-jobs, income, and GDP-can be used for public outreach to mitigate the negative perception of freight movement. While travel time reduction and increased business productivity used in past impact studies are useful performance measures, jobs and income measures appeal to citizens.
With the discontinuation of the national Vehicle Inventory and Use Survey (VIUS) in the United States in 2002, insufficient data have been available for well more than a decade on commercial vehicle activity. The goal of this pilot survey effort was to develop a preliminary design for a proposed California Vehicle Inventory and Use Survey (Cal-VIUS) and to test it with a scaled-down sample to provide guidance on the full-scale survey design. The sample was drawn from vehicle records obtained from the California Department of Motor Vehicles and International Registration Plan data sets by using a stratified sampling technique to capture intrastate and Interstate commercial vehicle activity in California. Limitations identified in the 2002 VIUS were addressed in the Cal-VIUS pilot survey questionnaire, which was administered on an online survey platform (http://surveyanalytics.com). The questionnaire was designed to collect annual and trip-based activity data through two complementary surveys: a web-based fleet manager survey and a smartphone app-based driver survey (with web-based option). These surveys were conducted between December 29, 2014, and February 28, 2015, and between February 24 and February 26, 2015, respectively. Results from the web-based fleet manager survey showed that the stratification design was adequate to describe the heterogeneous characteristics of vehicle activities between strata with respect to vehicle miles traveled within California. The driver survey was not fully tested because of limited response. Results from the pilot survey are expected to provide valuable insights to those who are developing future truck-related survey studies.
In the aggregate freight demand modeling literature, temporal assignment (annual to daily flows) is often oversimplified or neglected altogether. Unlike passenger flows, freight flows over the course of a year are not uniform and can vary significantly as the result of trade-offs between inventory and transportation cost management. We introduce the first temporal assignment model that explicitly considers these trade-offs for aggregate freight forecasting. A two-stage model is proposed that first decomposes aggregate annual zonal flows to firm group annual flows using a supply chain network model, which are then temporally assigned by simulating purchase order transactions throughout supply chains. Lot sizes are estimated with an EOQ model and calibrated with monthly inventory data. The result is an aggregate-disaggregate-aggregate model that fits into aggregate freight forecast models but makes use of more disaggregate logistical data. The model is illustrated with a simple replicable example, followed by a case study conducted with California statewide data to break out the distributed zonal flows into average daily volumes for network assignment. Calibration results using 2007 IMPLAN data showed a median percentage difference of simulated annual flows from FAF3 data of 2.38%, and a median percentage difference of simulated inventories from IMPLAN data of 4.85%, which suggests an excellent fit. Empirical validation results show the model outperforms fixed factor approaches in mean value accuracy by 15% - 31%.
Rail-truck intermodal terminals can play a key role in making rail-truck intermodal transportation an effective transportation alternative to long-haul trucking. To measure this effectiveness, public and private stakeholders require some qualitative and quantitative methodologies. This paper is based on a study describing a general methodological framework for evaluation of the impacts of intermodal terminals on the transportation system and applying it to the highway system of Virginia. Apart from incorporating the needs of several agencies into the framework, preliminary models for site-specific evaluation of impacts on mobility, accessibility, safety, and economic activities were also developed, and were calibrated with commodity flow, socioeconomic, and other data for the Commonwealth of Virginia. Results of the application of the evaluation framework OD a case study terminal are presented in this paper. The study identified the key steps involved in the assessment of highway system impacts and regional planning of intermodal terminals in Virginia. It is recommended that further research be carried out on a detailed evaluation to provide decision support on the feasibility of public-private shared financing of intermodal terminals.
Kentucky, despite its relatively small area, has an extensive multimodal freight transportation network. Presented are findings of the statewide freight commodity flow analysis that relate to one of the multimodal transportation planning issues currently facing Kentucky-the relative role of various modes in freight transport and the potential for modal substitution. Issues affecting the type of data required for statewide freight planning studies also are discussed. Statewide issues, such as modal substitution questions, require freight commodity data by origin, destination, and mode. The aggregation of data from the Bureau of Transportation Statistics or other publicly available data was considered unfeasible and the study team was referred to Reebie Associates for detailed freight commodity flow information. The Reebie freight commodity flow data were analyzed by mode, commodity, and spatial zone within Kentucky to determine where the potential for modal substitution was greatest. Three areas of the state were found in which improvements for intermodal facilities for water and rail transportation might be considered. The data confirmed the extent of Kentucky's multimodal reliance, that is, the majority of freight (by weight and volume) traveling to and from Kentucky moves by nonhighway modes. However, it was also noted that rail and water connections between Kentucky and certain areas of the United States may need further consideration because almost all freight to and from these areas moves by truck. Several other projects within the state are ongoing with these data.
Intermodal rail terminals are an important part of an integrated freight transportation system. The location of intermodal terminals often has depended on where railroads had spare land adjacent to freight-rail lines. But many of these locations are less than ideal in terms of surrounding land uses and offer little scope for expansion as intermodal traffic grows. A study undertaken in Minnesota to evaluate the need for new or expanded intermodal terminal facilities in the Twin Cities metropolitan area is described. The process was funded by the metropolitan planning organization for the Twin Cities region, the state department of transportation, and three private railroad carriers. It involved a series of studies to determine the market for an intermodal facility, to locate a site and develop a proposal for a multi-user terminal, and to assess the terminal's economic benefits for the region. Several lessons were learned during this public-private partnership process; these are useful for other metropolitan areas considering freight needs. The public and private sectors bring different perspectives to the development process, and coordinating their decision making is a challenge. It is crucial to obtain and to maintain appropriate and timely access to decision makers. The competitive positions of carriers must be assessed and the project's impact on the relative market share among them will be critical. Given the lack of support by the railroads to develop a joint-use intermodal facility, as recommended in the study, neither the railroads nor the development community was willing to implement the project.