Agent-Based Model of Navigable Inland Waterway Tow Operation Procedures

Transportation modeling within the context of extreme weather events induced by climate change is critical to understand and improve the resilience of transport systems as the world moves further into the 21st century. Among transportation modes, navigable inland waterways in particular face severe challenges to their future reliability as a result of extreme weather events. The economic implications of inland waterway operational efficiencies on commercial shipping have been studied in detail for several decades. Less well understood, however, are the effects of tow operation procedures enacted during adverse river conditions that have resulted from extreme weather events. This paper describes a model of a waterway segment that simulates stakeholder decision making and tow operator behavior to provide stakeholders with insights into the possible benefits of waterway action plans as operational guidance documents. Simulations run for a test area of the navigable inland waterway system indicated that operational procedures recommended in waterway action plans might have a significant impact on waterway operational efficiencies, which suggests that the model may be a useful decision-support tool for waterway stakeholders

Grain Shipments on the Mississippi River System: A Long-Term Projection

The costs of delays for shipping commodities on the Mississippi River are important and adversely impact growth in shipments. Lock and dam expansion requires substantial capital investment and an extended time period to complete. This study analyzes delay costs and the competitive position of grain shipments on the Mississippi River system. A spatial optimization model of the world grain trade was developed. Results indicated that without expansion in barge capacity, delay costs in 2020 would increase on each reach, with some up to $1.08/mt. Expansion results in reduced delay costs. Barge demand is also impacted by rail capacity. Finally, expanding the locks would result in a re-allocation of shipments among modes, reaches, and ports, notwithstanding minor adjustments in production.Agribusiness, International Relations/Trade,

Inland Waterway Operational Model & Simulation Along the Ohio River

The inland waterway system of the U.S. is a vital network for transporting key goods and commodities from the point of production to manufacturers and consumers. Shipping materials via the inland waterways is arguably the most economical and environmentally friendly option (compared to hauling freight by trains or railways). Despite the advantages the inland waterways enjoys over competing modes, key infrastructure – such as locks and dams, which help to control water levels on a number of rivers and make navigation possible – is declining. Limited funds have been allocated to make the necessary repairs to lock and dam facilities. Over the past 10 years Inland Waterways Trust Fund resources (which historically funded maintenance and improvement projects) has steadily declined. Locks and dams are of particular importance, because they assist in the maintenance of navigable depths on many of the major inland waterways (Ohio River, Upper Mississippi River, Tennessee River). To better understand the operation of the inland waterway system, this report examines a portion of the Ohio River (extending from Markland Locks and Dam to Lock 53). The specific focus is to determine what delays barge tows as they attempt to lock through these critical facilities. The Ohio River is a particularly important study area. In many ways it is representative of the conditions present throughout the inland waterways system. The average age of the lock and dam facilities exceed 50 years along our study segment. Most of these facilities are operating beyond their intended design life. As locks age, they increasingly demand more scheduled and unscheduled maintenance activities. Maintenance activities often require temporarily shuttering a lock chamber and diverting traffic through another onsite chamber (often of smaller capacity). All of the facilities included in the research area have two lock chambers ‐ thus, if one goes down for maintenance all vessels are diverted through the second chamber. In many cases this situation can produce extensive delays, which precludes cargo from reaching the destination in a timely manner. Recently, the aggregate number of hours that shippers and carriers lose due to delays has escalated. Although the U.S. Army Corps of Engineers – the agency responsible for the management and oversight of locks and dams – has worked to keep traffic flowing on the river, tightening budgets hamper efforts. For shippers and carriers to make informed decisions about when and where to deploy freight on the river, they require knowledge that illuminates factors that are most significant in affecting transit times. In particular this applies to certain conditions that are likely to create delays at lock and dam facilities. The purpose of this report is to 1) develop a comprehensive profile of the Ohio River that provides an overview of how it is integral to U.S. economic security 2) identify salient river characteristics or externally‐driven variables that influence the amount of water flowing through the main channel which consequently impacts vessels’ capacity to navigate 3) use this information (along with a 10‐year data set encompassing over 600,000 observations) to develop an Inland Waterways Operational Model (IWOM). The IWOM objective is to provide the U.S. Army Corps of Engineers, shippers, carriers, and other interested parties with access to8 a robust method that aids in the prediction of where and when conditions will arise on the river that have the potential to significantly impact lockage times and queue times (i.e. how long a vessel has to wait after it arrives at a facility to lock through). After qualitatively reviewing different features of the river system that affect vessel traffic, this report outlines two approaches to modeling inland waterway system behavior – a discrete event simulation (DES) model which uses proprietary software, and the IWOM. Although the DES produced robust findings that aligned with the historical data (because it relies upon proprietary software), it does not offer an ideal platform to distribute knowledge to stakeholders. Indeed, this is the major drawback of the DES given a critical objective of this project is to generate usable information for key stakeholders who are involved with inland waterway operations. Conversely, the IWOM is a preferable option given it relies on statistical analysis – in this sense, it is more of an open‐source solution. The IWOM uses linear regression to determine key variables affecting variation in lockage time. The final model accounts for over two‐thirds of the observed variation in lockage times from 2002‐2012, which is our study period. Practically, this means that the difference between predicted values and observed delay times is significantly less than how the delays vary around the composite average seen in the river system (R2 = 0.69). The IWOM confirms that variations in river conditions significantly affect vessel travel times. For example, river discharge ‐ the direction a vessel moves up or down a river ‐ meaningfully influences lockage times. The freight amount a vessel carries, which is represented by the amount of draft and newness of a vessel, influences lockage times. Larger vessels with more draft tend to wait longer and take longer to complete their lockage. The IWOM is less successful at predicting delay times. Because there is greater instability in this data only a modest amount of variation is explained by the model (R2 = 0.23). This, in turn, partly reflects in spillover from one vessel to the next that is difficult for the simulation to impose and account for therefore requiring additional logic. Once completed, the IWOM was used to parameterize a simulation model. This provided a graphical representation of vessels moving along the river. Users have the capability of adjusting the effects of different variables to anticipate how the system may react, and what changes in vessel traffic patterns emerge. This information will be of great use for stakeholders wanting to gain a better understanding of what conditions lockage times will increase or decrease, why delays emerge, and consequently how these impact traffic flows on the river. In programming a simulation model, users are able to visualize and intuit what causes vessel travel times to vary. Although the regression model accomplishes this, for many users this would prove unwieldy and difficult to grasp beyond a conceptual, abstract level. Matching up regression results with a visual counterpart lets users gain immediate and intimate knowledge of river and vessel behavior – this in turn can positively affect shipper and carrier modal choices. The report concludes with some recommendations for IWOM implementation and thoughts on future research needs. Also discussed are the implications results from the present study have for improving our ability to safely, securely, and swiftly move freight on the inland waterways network

Extreme weather disaster resilient port and waterway infrastructure for sustainable global supply chain

Global supply chain is largely dependent on seaports and marine terminals. Ports serve international cargo traffic for imports and exports. About 90% of the world’s goods are transported by sea and over 70% as containerized cargo ships. Coastal hurricanes/cyclones and rainfall flood disasters cause major disruptions for sea shipping traffic, disruptions of port infrastructure, and adverse impacts on coastal communities each year. Additionally, these weather related disasters threaten millions of people, damage infrastructure, and cost billions of dollars in global supply chain disruptions. Sustainable global supply chains, port infrastructure, and coastal community impact by these extreme weather disasters are the major motivation of this research. The objectives of this research are: (1) modeling shipping demand and level of service, (2) developing Landsat-8 satellite imagery based methodology for mapping surface types and landuse, and (3) assessing the impact of coastal disasters and climate related sea level rise. The Autoregressive Integrated Moving Average (ARIMA) model equations, the Artificial Neural Networks (ANN) models, and regression equations were developed using historical containerized cargo volumes to predict the future volumes for the Port of New Orleans and the Port of New York and New Jersey. The predictions by these models indicate that the ANN model achieves the most accurate predicted values, compared to reported volume. However, the ANN approach requires future values of independent variable inputs to calculate the forecast. Therefore, applying the ANN model was recommended for short-term prediction for these ports. The ARIMA model equation was applied for long-term prediction because it does not need other independent variable inputs. Results of cargo vessel volume analysis for ten selected international shipping navigation routes using Automatic Identification System (AIS) data show that the Europe Atlantic route to the East Coast of the U.S. has the largest cargo vessel volumes. A spatial map of cargo vessel demand for selected navigation routes was also created. A level of service (LOS) methodology for cargo vessel service was developed using AIS data for the Port of Miami to evaluate the operating conditions of a seaport. A mathematical function to estimate LOS level (A, B, C, D, E, F) was proposed based on delay time and waiting time of cargo vessels at the port and number of processed cargo vessels per total annual cargo vessels. A new methodology was developed to classify built and non-built surfaces using Landsat-8 satellite imagery. Groundtruth samples of the Landsat-8 pansharpened multispectral satellite imageries from six selected sites were sampled and used to develop the Landsat-8 Built-up Area and Natural Surface (L-BANS) auto-classification methodology. The L-BANS surface classification results for most sites using GeoMedia Pro geospatial analysis were within ±15% of the groundtruth. Based on analysis of variance (ANOVA) hypothesis testing results, the difference between the L-BANS results and the groundtruth was not statistically significant. The anthropogenic CO2 based global warming hypothesis was evaluated to undertand climate impacts. Measured global temperature and atmospheric carbon dioxide (CO2) data from 1958 to 2016 were analyzed. The final ARIMA time series seasonal model equation for monthly global temperature data had a high R value of 0.989 with only 2.25% difference compared to measured values. The final ARIMA model equation for monthly CO2 data provided reasonably accurate results for 2016 monthly measured CO2 data with high a R value of 0.999 with only 0.0025% difference compared to measured values. The results show that there is very poor crosscorrelation (0.08) between global temperature and CO2. Both IPCC and EPA models predict unreasonably high values of CO2 until 2050. This research shows that contrary to the IPCC claims, global warming is not caused by anthropogenic CO2. Rainfall flood simulations were conducted for five selected port cities using the one dimension (1-D) U.S. Army Hydrologic Engineering Center’s River Analysis System (HEC-RAS). Results of the rainfall flood simulations indicate that these selected port cities are at great risk to extreme floods, in which more than 37% of the land area of each port city is inundated by floodwater. This dissertation also presents the Center for Advanced Infrastructure Technology (CAIT) methodologies to evaluate the land submerged from 2 m sea level rise (SLR) related to climate impacts by the year 2100 and the impact of 2 m, 4 m, and 9 m tsunami wave peak heights (WPH) on the selected port cities. The results show that extreme rainfall flood, which can happen any year, is more disastrous to people and infrastructures compared to 2 m SLR and 2 m tsunami WPH. A resilience management plan was recommended to protect both people and infrastructure from coastal hazards. In response to SLR and tsunami, the seawall around port infrastructures should be improved and raised to 2 m height to protect life infrastructure and communities in the port cities. This research will benefit port authorities, maritime and waterway cargo shipping enterprises, and port cities in reducing impacts on communities and enhancing disaster resilience of port infrastructures, which are imperative for minimizing disruptions in the global supply chain and sustaining the world economy

Resilient Transportation Systems in a Post-Disaster Environment: A Case Study of Opportunities Realized and Missed in the Greater New Orleans Region, 2010

Based upon our research in Post-Katrina New Orleans, we define transportation resiliency as a system’s ability to function before, during and after major disruptions through reliance upon multiple mobility options. The importance of a resilient transportation system becomes more apparent during disasters where multiple options for mobility are necessary for both passenger and goods movement due to the potential loss of one or more modes. Post-Katrina New Orleans offers a unique opportunity to investigate pre-disaster planning and post-disaster recovery activities in a major metropolitan city where all modes of transportation were either severely damaged or completely destroyed. In response to Hurricane Katrina, the costliest disaster in U.S. history, new policies and programs have been adopted in New Orleans, in Louisiana, and at the federal level for disaster preparedness and post-disaster recovery. This paper addresses how transportation systems and policies in New Orleans have evolved in the wake of Hurricane Katrina (2005) to achieve a greater degree of resiliency and ultimately better serve the mobility needs of the community in future disaster situations

Commodity-based Freight Activity on Inland Waterways through the Fusion of Public Datasets for Multimodal Transportation Planning

Within the U.S., the 18.6 billion tons of goods currently moved along the multimodal transportation system are expected to grow 51% by 2045. Most of those goods are transported by roadways. However, several benefits can be realized by shippers and consumers by shifting freight to more efficient modes, such as inland waterways, or adopting a multimodal scheme. To support such freight growth sustainably and efficiently, federal legislation calls for the development of plans, methods, and tools to identify and prioritize future multimodal transportation infrastructure needs. However, given the historical mode-specific approach to freight data collection, analysis, and modeling, challenges remain to adopt a fully multimodal approach that integrates underrepresented modes, such as waterways, into multimodal forecasting tools to identify and prioritize transportation infrastructure needs. Examples of such challenges are data heterogeneity, confidentiality, limitations in terms of spatial and temporal coverage, high cost associated with data collection, subjectivity in surveys responses, etc. To overcome these challenges, this work fuses data across a variety of novel transportation sources to close existing gaps in freight data needed to support multimodal long-range freight planning. In particular, the objective of this work is to develop methods to allow integration of inland waterway transportation into commodity-based freight forecasting models, by leveraging Automatic Identification System (AIS) data. The following approaches are presented in this dissertation: i) Maritime Automatic Identification System (AIS) data is mapped to a detailed inland navigable waterway network, allowing for an improved representation of waterway modes into multimodal freight travel demand models which currently suffer from unbalanced representation of waterways. Validation results show the model correctly identifies 84% stops at inland waterway ports and 83.5% of trips crossing locks. ii) AIS and truck Global Positioning System (GPS) data are fused to a multimodal network to identify the area of impact of a freight investment, providing a single methodology and data source to compare and contrast diverse transportation infrastructure investments. This method identifies parallel truck and vessel flows indicating potential for modal shift. iii) Truck GPS and maritime Lock Performance Monitoring System (LPMS) data are fused via a multi-commodity assignment model to characterize and quantify annual commodity throughput at port terminals on inland waterways, generating new data from public datasets, to support estimation of commodity-based freight fluidity performance measures. Results show that 84% of ports had less than a 20% difference between estimated and observed truck volumes. iv) AIS, LPMS, and truck GPS datasets are fused to disaggregate estimated annual commodity port throughput to vessel trips on inland waterways. Vessel trips characterized by port of origin, destination, path, timestamp, and commodity carried, are mapped to a detailed inland waterway network, allowing for a detailed commodity flow analysis, previously unavailable in the public domain. The novel, repeatable, data-driven methods and models proposed in this work are applied to the 43 freight port terminals located on the Arkansas River. These models help to evaluate network performance, identify and prioritize multimodal freight transportation infrastructure needs, and introduce a unique focus on modal shift towards inland waterway transportation

Comparative assessment of the vulnerability and resilience of 10 deltas : work document

Background information about: Nile delta (Egypt), Incomati delta (Mozambique), Ganges-Brahmaputra-Meghna (Bangladesh), Yangtze (China), Ciliwung (Indonesia), Mekong (Vietnam), Rhine-Meuse (The Netherlands), Danube (Romania), California Bay-Delta, Mississippi River Delta (USA

What Can Water Managers Do About Global Warming?

Realistically, water managers (planners, designers,operators) can’t do much about preventing globalwarming, any more than they can affect contemporaryclimate. Historically, water management has been aprocess of continuous adaptation to the considerablevagaries of climate variability, and accommodations forany uncertainties associated with our lack ofunderstanding about climate cycles by introducingredundancies into engineering design. Adaptivemanagement (monitoring and learning from mistakes)has been the foundation of water resources managementsince the time of Noah. The key point is that societalresponse to both conditions, variability and change, isvirtually the same, i.e., to upgrade and intensifyintroduction of innovative and cost effective supply-sideand demand-side management measures, and continue tocreate institutions that are more flexible in adapting toboth social and physical changes. However, policyinitiatives that affect legal and institutional controls onwater management are likely to play a much larger rolein future adaptation to climate change than technical andengineering responses. Engineers can design and operatetheir systems more efficiently to increase robustness andresiliency and reduce vulnerability, but institutionalarrangements must be reconfigured to ensure that futurewater resources services can be provided in a sustainableand equitable manner under a wider range of circumstances.There are two tiers of adaptive management changes -policy mandates and agency/utility implementation.Many of the changes that will position society to betterdeal with future climate change uncertainty are alreadybeing debated and implemented in the context of policiesand institutional reforms to deal with an evermorecomplex host of issues, and include such matters as riverbasin compacts; defining new partnership roles betweenFederal, state, and local entities; nonstructural flooddamage reduction; the valuation of water both as aneconomic and environmental good; and the increasingrequirements for environmental protection and aquaticecosystem restoration. These are the strategic policychanges that will impose or influence future watermanagement goals, objectives, and responses on therespective water management agencies. The componentsof water resources management that are directly under thecontrol of or influenced by water managers includeadoption of improved methods of hydrologic analysiscoupled with risk analysis, improvement of forecastingmethods for system-wide analysis, and more integratedanalyses of multiple watershed needs and outcomes. Inaddition, fundamental criteria that affect projectinvestment analysis and the choice of moreenvironmentally benign alternatives are being modifiedso that future systems will be more robust and resilient toanticipated climate change, as well as to evolving societaldemands

Seafloor characterization using airborne hyperspectral co-registration procedures independent from attitude and positioning sensors

The advance of remote-sensing technology and data-storage capabilities has progressed in the last decade to commercial multi-sensor data collection. There is a constant need to characterize, quantify and monitor the coastal areas for habitat research and coastal management. In this paper, we present work on seafloor characterization that uses hyperspectral imagery (HSI). The HSI data allows the operator to extend seafloor characterization from multibeam backscatter towards land and thus creates a seamless ocean-to-land characterization of the littoral zone