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Assigning a commodity dimension to AIS data: Disaggregated freight flow on an inland waterway network
Abstract
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.
... Several studies have been conducted on inland water transport (IWT) focusing on diverse issues (Young-Seo & Gi-Tae, 2023;Durajczyk & Drop, 2021;Asborno & Hernandez, 2021;Solomon, et al., 2021;Nachtmann, 2021;Jiang et al., 2021;Aiyegbajeje & Dienne, 2021;Akinbamijo, Ipingbemi & Bayode, 2016;Ezenwaji, 2010). The study by Aiyegbajeje & Dienne (2021) highlighted the major reasons for the low patronage of water transportation for the movement of passengers. ...
... Since there is no alternative, serious traffic breaks may occur reducing the cost-effectiveness and reliability of the water route. Asborno and Hernandez (2021) used a stochastic approach with data from the automatic identification system (AIS) and the quantity of transported goods in the harbour of the Arkansas River from 2016 to verify a new estimation model. The importance of this study is to showcase a useful method of identifying the volumes of goods transported on waterways. ...
The high dependence on land transportation for the movement of both passengers and freight within the Lagos metropolis is not sustainable. The advocacy for the use of Lagos waterways for freight movement must be encouraged. In achieving this, there is the need to document necessary information about water transportation. This study is therefore intended to identify the determining factors for the use of water to move freight on the inland waterways within the Lagos metropolis. Both primary and secondary data were adopted for the study. The primary data was sourced using a structured questionnaire targeting ownership/management of the marine craft (i.e., private, public, or public/private). The respondents included 47 registered members of Barge Operators Association of Nigeria (BOAN). The secondary data were sourced from the records of BOAN focusing on the details of boats and operators. Frequency distribution and charts were used for presenting and summarizing the data, whereas, the multinomial logistic regression model was employed to test the formulated hypothesis The multinomial logistic regression results show that the determinants of the choice of water route were freight availability (Odd Ratio: 3.257), freight (Odd Ratio: 2.350), and availability of transshipment facilities (Odd Ratio: 2.159). Availability of transshipment facilities had positive and statistically significant relationships with the choice of water route (r=0.86). This study, therefore, recommends that the state government should take necessary steps to improve the inland water transport system in Lagos state.
... The Commission has proactively undertaken measures to champion the advancement of inland waterway transport through the Navigation and Inland Waterway Action and Development in Europe (NAIADES) program. This strategic initiative is geared toward not only endorsing, but actively fostering the transition, thus mitigating the pressures and congestion associated with conventional modes of transportation [6]. What is more, the deployment of a single vessel, with the capacity to supplant more than 100 trucks, holds the potential to ameliorate congestion issues and reduce the incidence of road network accidents. ...
Inland waterway transportation plays a pivotal role in advancing economic development and nurturing sustainable progress. It serves as a vital conduit linking communities, industries, and markets, thereby facilitating the seamless flow of essential commodities and fostering regional integration. However, in today’s era, marked by a resolute commitment to environmental responsibility and sustainability, inland shipping confronts formidable challenges, particularly pertaining to emission pollution and the escalating costs of fuel. These challenges represent significant impediments to the pursuit of environmentally conscious and sustainable growth by shipping companies. This research endeavor is geared towards the creation of a mathematical model that takes into account various factors, including the types of waterways, temporal constraints, and punctual arrival at the port of discharge. The primary objective is to empower shipping companies to make informed decisions about optimal sailing speeds and the most opportune time windows for entering one-way waterway segments. This, in turn, leads to reductions in fuel costs and waiting times for shipping companies, all while achieving cost minimization and mitigating emissions issues in inland waterway transportation. Ultimately, this research advances the cause of green and sustainable development in the inland waterway shipping sector. Specifically, this study focuses on routes that involve the dynamic transition between one-way and two-way segments. To accomplish this, an integer programming (IP) model is proposed to meticulously analyze the ideal sailing speed for each segment of the route and determine the optimal windows for accessing single-direction channels, thus representing a multistage decision-making process. Meanwhile, the model’s reliability is substantiated through a rigorous comparative assessment against three benchmark strategies (EAS, LAS, and MAS). In our experiments, the optimization model yielded a total cost for the entire inland waterway amounting to 87,118.14 under the EAS strategy and $83,494.70 under the MAS strategy (the LAS strategy failed to meet the port of discharge deadline), thereby conclusively validating its ability to guide vessels to their port of discharge within prescribed schedules, all while reducing overall operational costs and promoting sustainable and environmentally responsible practices.
... Because freight fluidity measurement is a performance-based evaluation, decision-makers may easily identify and rectify the system's flaws. According to a study on a freight fluidity measuring system, vessel Automatic Identification System (AIS) data held by public authorities is quite restricted (Asborno and Hernandez, 2021). The authors presented a multi-commodity assignment model and a stochastic approach to anticipate and collect commodity data to provide an effective freight fluidity assessment system. ...
Developments in transportation systems, changes in consumerism trends, and conditions such as COVID-19 have increased both the demand and the load on freight transportation. Since various companies are transporting goods all over the world to evaluate the sustainability, speed, and resiliency of freight transportation systems, data and freight fluidity measurement systems are needed. In this study, an integrated decision-making model is proposed to advantage prioritize the freight fluidity measurement alternatives. The proposed model is composed of two main stages. In the first stage, the Dombi norms based Logarithmic Methodology of Additive Weights (LMAW) is used to find the weights of criteria. In the second phase, an extended Evaluation based on the Distance from Average Solution (EDAS) method with Dombi unction for aggregation is presented to determine the final ranking results of alternatives. Three freight fluidity measurement alternatives are proposed, namely doing nothing, integrating freight activities into Metaverse for measuring fluidity, and forming global governance of freight activities for measuring fluidity through available data. Thirteen criteria, which are grouped under four main aspects namely technology, governance, efficiency, and environmental sustainability, and a case study at which a ground framework is formed for the experts to evaluate the alternatives considering the criteria are used in the multi-criteria decision-making process. The results of the study indicate that integrating freight activities into Metaverse for measuring fluidity is the most advantageous alternative, whereas doing nothing is the least advantageous one.
... e algorithm compares the optimization problem to bird predation, and the optimal solution is the target food. Each bird is the particle in THE PSO algorithm, and all the particles move at a certain speed and track in space, and guide the behavior of the whole population to approach the target function through the function to be optimized [9]. Its essence is to use individual competition mechanism to generate local optimal solution, and then through information sharing and cooperation mechanism to generate global optimal solution. ...
In order to better predict the sailing information data of fishing boats, make accurate prediction and spacing budget for the sailing status of ships, achieve more accurate coordination and early warning in advance, and ensure the safety of fishing boats’ laneway, the essay combined the kinematics equation and artificial neural network model to adapt to the traffic situation of fishing boats in the far sea. A course and collision test technique based on ship AIS data is proposed, and the course collision detection method of fishing boats is studied by means of actual ship beacon collision accident data. Through the practical test, taking the navigation mark 4560.117 as an example, under the detection track of the navigation mark field corresponding to R = 70, the two ships have the same track, thus verifying the practicality and feasibility of the ship navigation mark collision detection method.
... To overcome the uncertainty in the volume of cargo carried by vessels on inland waterways, the authors have developed a stochastic approach based on the average number of barges carried by tug, as observed from LPMS data, and combined AIS with aggregated commodity databases [58]. (Asborno & Hernandez, 2021). ...
Travel demand models (TDMs) with freight forecasts estimate performance metrics for competing infrastructure investments and potential policy changes. Unfortunately, freight TDMs fail to represent non-truck modes with levels of detail adequate for multi-modal infrastructure and policy evaluation. Recent expansions in the availability of maritime movement data, i.e. Automatic Identification System (AIS), make it possible to expand and improve representation of maritime modes within freight TDMs. AIS may be used to track vessel locations as timestamped latitude–longitude points. For estimation, calibration and validation of freight TDMs, this work identifies vessel trips by applying network mapping (map-matching) heuristics to AIS data. The automated methods are evaluated on a 747-mile inland waterway network, with AIS data representing 88% of vessel activity. Inspection of 3820 AIS trajectories was used to train the heuristic parameters including stop time, duration and location. Validation shows 84⋅0% accuracy in detecting stops at ports and 83⋅5% accuracy in identifying trips crossing locks. The resulting map-matched vessel trips may be applied to generate origin–destination matrices, calculate time impedances, etc. The proposed methods are transferable to waterways or maritime port systems, as AIS continues to grow.
This study aimed to analyze the research trends of inland waterways in the field of logistics by compiling previous studies that contain keywords related to inland waterways. To investigate the research trend, this study used a keyword network among the SNA methodology and deduced results using the concepts of centrality, including degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. According to the results of the comprehensive analysis by each region based on the eigenvector centrality outcome, Europe had a core keyword of modal shift, which refers to the conversion to eco-friendly vehicles due to the exhaust problem of land carriage, as well as the continuously implemented government policy. The results of China showed that the country’s task to solve was on studies aimed at reducing traffic jams in inland waterways using AIS data. In the case of the US, the problems of ship delay remain unsolved owing to old locking facilities and equipment. This study provided academic implications that clarify the state-of-the-art in this research topic in three regions that have advanced inland waterways.
Travel demand models (TDMs) with freight forecasts estimate performance metrics for competing infrastructure investments and potential policy changes. Unfortunately, freight TDMs fail to represent non-truck modes with levels of detail adequate for multi-modal infrastructure and policy evaluation. Recent expansions in the availability of maritime movement data, i.e. Automatic Identification System (AIS), make it possible to expand and improve representation of maritime modes within freight TDMs. AIS may be used to track vessel locations as timestamped latitude–longitude points. For estimation, calibration and validation of freight TDMs, this work identifies vessel trips by applying network mapping (map-matching) heuristics to AIS data. The automated methods are evaluated on a 747-mile inland waterway network, with AIS data representing 88% of vessel activity. Inspection of 3820 AIS trajectories was used to train the heuristic parameters including stop time, duration and location. Validation shows 84⋅0% accuracy in detecting stops at ports and 83⋅5% accuracy in identifying trips crossing locks. The resulting map-matched vessel trips may be applied to generate origin–destination matrices, calculate time impedances, etc. The proposed methods are transferable to waterways or maritime port systems, as AIS continues to grow.
The use of maritime transport is associated with external costs such as those related to greenhouse gas emissions and air pollution. If corrective taxes, fees or regulations are levied on the production of maritime transport services, these costs become internal and decision-makers will take into account the full social cost of transport. Shipping is subject to less incentive-based regulation than e.g. road transport, but Sweden represents a relatively unique regulatory case with its national system of fairway dues, which are differentiated according the environmental performance of ships. The central question posed in this paper is whether Sweden's internalizing national transport policy measures are in proportion to the societal costs generated by maritime traffic in Swedish waters. Using a dataset comprising all vessel movements in Swedish territorial waters during a year, we estimate the marginal external and infrastructure costs of maritime traffic and find that, depending on the set of unit values applied, the degree of internalization ranges between around 53% and 90%. We also evaluate the effects of a recent re-appraisal of CO2 in the Swedish official guidelines for cost benefit analysis and show that under the new recommendation the degree of internalization is significantly lower: 23–28%. Because the degree of internalization varies significantly between different shipping segments, a uniform increase of the charges currently in place is not recommended. Instead, we highlight that other, more appropriate instruments for internalization may be needed.
Full text: https://rdcu.be/b2eha . Abstract:
Inland waterways ports are key elements of an efficient multimodal freight transportation system. Data on the capacity and throughput of inland waterway ports by commodity support effective long-term freight planning and travel demand modeling. More specifically, such data can be used to estimate multimodal commodity-based freight fluidity performance measures and to support location selection for freight transload facilities. State-of-the-practice means of obtaining commodity flows data, such as shipper/carrier surveys and vessel and vehicle movements, are limited in their ability to provide monthly or seasonal statistics on individual port operations; rather, they provide annualized statistics for river segments which may contain multiple ports. These limitations are addressed herein by developing a multicommodity assignment model to quantify commodity throughput at inland waterways ports. The model fuses waterborne lock performance monitoring system data, which provides the commodity dimension, and anonymous truck Global Positioning System (GPS) data, which allows for spatial disaggregation. A goal-programming approach minimizes the deviation between known and estimated truck flows at each port. The methodology was applied to the Arkansas River, a 308-mile navigable waterway served by 14 locks and 43 freight ports. Overall, 84% of ports showed less than 20% difference between observed and predicted truck flows. The model is applicable to any inland waterways system with aggregated commodity flow data and truck GPS coverage and fills a critical data gap by describing commodity throughput at inland waterway ports using publicly available data.
The information-rich vessel movement data provided by the Automatic Identification System (AIS) has gained much popularity over the past decade, during which the employment of satellite-based receivers has enabled wide coverage and improved data quality. The application of AIS data has developed from simply navigation-oriented research to now include trade flow estimation, emission accounting, and vessel performance monitoring. The AIS now provides high frequency, real-time positioning and sailing patterns for almost the whole world's commercial fleet, and therefore, in combination with supplementary databases and analyses, AIS data has arguably kickstarted the era of digitisation in the shipping industry. In this study, we conduct a comprehensive review of the literature regarding AIS applications by dividing it into three development stages, namely, basic application, extended application, and advanced application. Each stage contains two to three application fields, and in total we identified seven application fields, including (1) AIS data mining, (2) navigation safety, (3) ship behaviour analysis, (4) environmental evaluation, (5) trade analysis, (6) ship and port performance, and (7) Arctic shipping. We found that the original application of AIS data to navigation safety has, with the improvement of data accessibility, evolved into diverse applications in various directions. Moreover, we summarised the major methodologies in the literature into four categories, these being (1) data processing and mining, (2) index measurement, (3) causality analysis, and (4) operational research. Undoubtedly, the applications of AIS data will be further expanded in the foreseeable future. This will not only provide a more comprehensive understanding of voyage performance and allow researchers to examine shipping market dynamics from the micro level, but also the abundance of AIS data may also open up the rather opaque aspect of how shipping companies release information to external authorities, including the International Maritime Organization, port states, scientists and researchers. It is expected that more multi-disciplinary AIS studies will emerge in the coming years. We believe that this study will shed further light on the future development of AIS studies.
We model the problem of budgeting and selecting inland maintenance dredging projects to maximize the value of commodities that can be transported without disruption through the inland waterway system. We develop a heuristic to solve our stochastic optimization model and demonstrate that, by incorporating uncertainty in the amount of reactive (i.e., emergency) dredging, our model yields improved solutions as compared to related deterministic optimization models in the literature. We apply our model to provide a detailed analysis of U.S. waterway network.
The U.S. Army Corps of Engineers (USACE) is responsible for the maintenance of federally authorized navigation channels and associated infrastructure. As such, USACE requires objective performance measures for determining the level of service being provided by the hundreds of maintained navigation projects nationwide. To this end, the U.S. Army Engineer Research and Development Center partnered with Texas A&M Transportation Institute to develop a freight fluidity assessment framework for coastal ports. The goal was to use archival automatic identification system (AIS) data to develop and demonstrate how ports can be objectively compared in relation to fluidity, or the turnaround time reliability of oceangoing vessels. The framework allows USACE to evaluate maintained navigation project conditions alongside port system performance indices, thereby providing insight into questions of required maintained channel dimensions. The freight fluidity concept focuses on supply chain performance measures such as travel time reliability and end-to-end shipping costs. Although there are numerous research efforts underway to implement freight fluidity, this is the first known application to U.S. ports. This paper covers AIS data inputs, quality control, and performance measures development, and also provides a demonstration application of the methodology at the Port of Mobile, Alabama, highlighting travel time and travel time reliability operating statistics for the overall port area. This work provides foundational knowledge to practitioners and port stakeholders looking to improve supply chain performance and is also valuable for researchers interested in the development and application of multimodal freight fluidity performance measures.
AIS data plays an increasingly important role in collision avoidance, risk evaluation, and navigation behavior study. However, the raw AIS data contains noise that can result in wrong conclusions. We propose a multi-regime vessel trajectory reconstruction model through three-steps processing, including (i) outliers removal, (ii) ship navigational state estimation and (iii) vessel trajectory fitting. This model allows for vessel trajectory reconstruction in different navigation states, namely hoteling, maneuvering, and normal-speed sailing. The normal-speed navigation trajectory is estimated with a spline model, which can fit any types of the trajectory even with circles. Then, the proposed model is tested and compared with other three popular trajectory reconstruction models based on a large AIS dataset containing the movement of more than 500 ships in Singapore Port. The results show that the proposed model performs significantly better than the linear regression model, polynomial regression model, and weighted regression model. The proposed model can decrease the abnormal rate of speed, acceleration, jerk and ROT (Rate of Turn) from 43.42%, 10.65%, 59.25%, 50.33%–0.00%, 0.00%, 17.28% and 15.81%, respectively. More importantly, the navigational behavior, such as turning operation, could be clearly shown in the trajectory reconstructed by the proposed model.
Effective barge scheduling in the logistic domain requires advanced information on the availability of the port terminals and the maritime traffic in their vicinity. To enable a long-term prediction of vessel arrival times, we investigate how to use the publicly available automatic identification system (AIS) data to identify maritime patterns and transform them into a directed graph that can be used to estimate the potential trajectories and destination points. To tackle this problem, we use a genetic algorithm (GA) to cluster vessel position data. Then, we show how to enhance the process to allow fast computation of incremental data coming from the sensors, including the importance of adding a quad tree structure for data preprocessing. Focusing on a real case implementation, characterized by partially incomplete and noisy AIS data, we show how the algorithm can handle routes intersecting the regions with missing data and the repercussions this has on the route graph. Finally, postprocessing is explained that handles graph pruning and filtering. We validate the results produced by the GA by comparing resulting patterns with known inland water routes for two Dutch provinces followed by the simulation using synthetic data to highlight the strengths and weaknesses of this approach.
Since the 1950s, the number of natural and man-made disasters has increased exponentially and the facility location problem has become the preferred approach for dealing with emergency humanitarian logistical problems. To deal with this challenge, an exact algorithm and a heuristic algorithm have been combined as the main approach to solving this problem. Owing to the importance that an exact algorithm holds with regard to enhancing emergency humanitarian logistical facility location problems, this paper aims to conduct a survey on the facility location problems that are related to emergency humanitarian logistics based on both data modeling types and problem types and to examine the pre- and post-disaster situations with respect to facility location, such as the location of distribution centers, warehouses, shelters, debris removal sites and medical centers. The survey will examine the four main problems highlighted in the literature review: deterministic facility location problems, dynamic facility location problems, stochastic facility location problems, and robust facility location problems. For each problem, facility location type, data modeling type, disaster type, decisions, objectives, constraints, and solution methods will be evaluated and real-world applications and case studies will then be presented. Finally, research gaps will be identified and be addressed in further research studies to develop more effective disaster relief operations.
The fishing industry is heavily reliant on the use of fossil fuel and emits large quantities of greenhouse gases and other atmospheric pollutants. Methods used to calculate fishing vessel emissions inventories have traditionally utilised estimates of fuel efficiency per unit of catch. These methods have weaknesses because they do not easily allow temporal and geographical allocation of emissions. A large proportion of fishing and other small commercial vessels are also omitted from global shipping emissions inventories such as the International Maritime Organisation's Greenhouse Gas Studies. This paper demonstrates an activity-based methodology for the production of temporally- and spatially-resolved emissions inventories using data produced by Automatic Identification Systems (AIS). The methodology addresses the issue of how to use AIS data for fleets where not all vessels use AIS technology and how to assign engine load when vessels are towing trawling or dredging gear. The results of this are compared to a fuel-based methodology using publicly available European Commission fisheries data on fuel efficiency and annual catch. The results show relatively good agreement between the two methodologies, with an estimate of 295.7 kilotons of fuel used and 914.4 kilotons of carbon dioxide emitted between May 2012 and May 2013 using the activity-based methodology. Different methods of calculating speed using AIS data are also compared. The results indicate that using the speed data contained directly in the AIS data is preferable to calculating speed from the distance and time interval between consecutive AIS data points.
The recent US Commodity Flow Survey data suggest that transporting hazardous materials (HAZMAT) often involves multiple modes, especially for long-distance transportation. However, not much research has been conducted on HAZMAT location and routing on a multimodal transportation network. Most existing HAZMAT location and routing studies focus exclusively on single mode (either highways or railways). Motivated by the less research on multimodal HAZMAT location and routing and the fact that there is an increasing demand for it, this research proposes a multimodal HAZMAT model that simultaneously optimizes the locations of transfer yards and transportation routes. A 0-1decision variable is defined which could make the optimization results reflect HAZMAT flow routing which subjected to railway transport organization principles. The developed model is applied to the simplified local multimodal network in the Northeast China for example. Results show that ideal location and routing plan can be obtained.
Fiscal constraints at the federal level drive the need for more robust
and objective performance evaluation methodologies for use by the
U.S. Army Corps of Engineers as it carries out its civil works mission
in support of the nation’s water resources infrastructure. One specific
area of need concerns functional performance evaluation of dredged
navigation channels at the local level as well as performance evaluation
of systems of navigation projects. The nationwide automatic identification
system (NAIS) for vessels that operate in coastal waters was
instituted after the terrorist attacks of September 2001 and is maintained
by the U.S. Coast Guard. The system constitutes an enabling technology
to provide the data required for quantitative performance assessments
of corps-maintained navigation infrastructure. In this paper, several
applications are presented of archival NAIS data toward waterway
performance evaluation. An assessment and comparison also are presented
of several deep-draft coastal ports with respect to the sensitivity
of vessel transit timing to tidal elevations. The NAIS data archive is
applied toward a point-to-point transit time monitoring strategy at the
local and regional levels. The corps confronts an uncertain fiscal outlook
and constrained budgets for annual operations and maintenance activities.
These metrics, as well as other potential applications of archival NAIS
data, can play a valuable role in the provision of objective, quantitative
assessments of waterway performance.
The use of radar information for collision avoidance is common, however it is effective only for
constant values of ship motion parameters. As information delays or information errors occur, it
is reasonable to supplement the information derived fromradar with another information system.
An ideal systemshould operate automatically and continuously.Asystem that appears to be suitable
to provide this kind of information is theAutomatic Identification System (AIS),whichmay
be classified as a radio communication systemthat uses radiowaves to transmit datawith regard
to ship motion parameters. In this paper the topic of integrity and completeness of AIS information
is discussed and the research results for the completeness and integrity of dynamic information
are presented. In addition, the outcomes ofAIS information correctness fromthe Gulf of
Gdańsk were compared with studies carried out in the Baltic Sea, east of Bornholm, between
Trelleborg and Arkona. The results of research for AIS dynamic information with the highest
completeness (Position, Course over Ground and Speed over Ground) are presented. The research
outcomes presented in the paper lead to the conclusion that AIS could deliver useful supplementary
information in the process of collision avoidance.
The study reported in this paper aimed to analyze vessel traffic characteristics in the Singapore Strait. Real-time vessel automatic identification system (AIS) data, with about 4 million records, were collected from Lloyd's Marine Intelligence Unit database for analysis. The results showed that containerships made up the largest proportion (36.36%) of vessel traffic in the Singapore Strait, while roll-on roll-off and passenger ships made up only a small percentage (4.77%). The analysis of the vessel characteristics suggested that special attention should be paid to tankers, bulk carriers, and ships that transport liquefied natural gas and petroleum gas, because of their bigger gross tonnage and draught. It was also found that vessels in traffic headed eastbound usually sailed at slightly higher speeds than those in the westbound traffic. The spatial distribution of vessel traffic flow indicated that the area between longitudes 103 degrees 48'E and 104 degrees 05'E had a larger traffic flow, with the highest traffic density. The ship density of the westbound traffic was higher than it was for the eastbound traffic. The AIS data collected between longitudes 104 degrees 10'E and 104 degrees 35'E were incomplete so that the corresponding traffic flow was underestimated. The advantages and limitations of AIS data were considered in detail in this study.
Understanding maritime traffic patterns is key to Maritime Situational Awareness applications, in particular, to classify and predict activities. Facilitated by the recent build-up of terrestrial networks and satellite constellations of Automatic Identification System (AIS) receivers, ship movement information is becoming increasingly available, both in coastal areas and open waters. The resulting amount of information is increasingly overwhelming to human operators, requiring the aid of automatic processing to synthesize the behaviors of interest in a clear and effective way. Although AIS data are only legally required for larger vessels, their use is growing, and they can be effectively used to infer different levels of contextual information, from the characterization of ports and off-shore platforms to spatial and temporal distributions of routes. An unsupervised and incremental learning approach to the extraction of maritime movement patterns is presented here to convert from raw data to information supporting decisions. This is a basis for automatically detecting anomalies and projecting current trajectories and patterns into the future. The proposed methodology, called TREAD (Traffic Route Extraction and Anomaly Detection) was developed for different levels of intermittency (i.e., sensor coverage and performance), persistence (i.e., time lag between subsequent observations) and data sources (i.e., ground-based and space-based receivers).
Recent studies have concentrated on environmental and economic impacts of ships. In this regard, fuel and CO2 emission is considered as one of the important factors for such impacts. In particular, the sailing speed of the vessels affects the fuel consumption and therefore the emission directly. In this study, we consider a speed optimization problem in inland waterway, which is characterized by stochastic waiting times at the lock caused by uncertainty in lock processing time estimations of other vessels. The objective is to minimize fuel consumption of an approaching ship, such that it traverses the river segment in a set deadline. We introduce a mathematical model for this problem and evaluate the effectiveness and attractiveness of two solution approaches: an optimal solution and a simple heuristic. This creates intuitive guidelines for skippers based on information provision to select an appropriate speed decision approach to minimize the total expected fuel consumption and CO2 emission of inland waterway transportation.
This study proposes to develop a mathematical model that captures and appropriately optimizes a number of realistic features (e.g., barge/towboat assignments, maintenance, and availability decisions) for the design and management of an inland waterway transportation network under stochastic commodity supply and water level fluctuations scenarios. To efficiently solve this challenging NP-hard problem, we propose to develop a highly customized parallelized hybrid decomposition algorithm that combines Sample Average Approximation with an enhanced Progressive Hedging and Nested Decomposition algorithm. Computational results indicate that the proposed algorithm is capable of producing high quality solutions consistently within a reasonable amount of time. Finally, a real-life case study is constructed by utilizing the inland waterway transportation network along the Mississippi River. Through multiple experimentations, a number of managerial insights are drawn that magnifies the impact of different key input parameters on the overall inland waterway port operations.
Freight is transported by the use of different cargo types to increase efficiency. However, multimodal freight transport research does not include different commonly used cargo types in its analyses. The literature has illustrated that the cargo types used affects transport, storage and transshipment costs, and consequently cost-based decision making in the supply chain. Moreover, from a practical point of view, it can be seen that local market players have begun to successfully shift new cargo types to rail and inland waterways. This research presents the incorporation of five different commonly used cargo types into an agent-based freight transport model for Belgium. The MATSim freight extension is extended by considering the cargo type used and by adding mode choice to the optimization process. The results demonstrate that the potential for a modal shift varies greatly between the considered cargo types. Solid bulk, liquid bulk and containers show the highest shares for rail and inland waterway transport (35,8%, 48,9% and 29,6% respectively). Moreover, the sensitivity of the modal split towards changes in the transport costs of road transport (+10%) differs according to the cargo type used. Containers are not very sensitive (+1,2%), while pallets (+2,4%), solid bulk (+3,9%) and liquid bulk (+5,9%) show higher increases. Consequently, some cargo types are more favorable for a modal shift than other cargo types. Tthe idea of considering the cargo type as a variable that affects the modal choice is new.
In this article, we introduce two novel multi-commodity inventory-location models considering continuous and periodic review inventory control policies and modular stochastic capacity constraints. The models address a logistic problem in which a single plant supplies a set of commodities to warehouses where they serve a set of customers or retailers. The problem consists of determining which warehouses should be opened, which commodities are assigned, and which customers should be served by the located warehouses; as well as their reorder points and order sizes in order to minimize costs of the system while satisfying service level requirements. This problem can be formulated as a mixed-integer nonlinear programming model, which is non-convex in terms of modular stochastic capacity constraints and the objective function. A Lagrangian relaxation and the subgradient method solution approach is proposed. We consider the relaxation of three sets of constraints, including customer assignment, warehouse demand, and variance constraints. Thus, we develop a Lagrangian heuristic to determine a feasible integer solution at each iteration of the subgradient method. An experimental study shows that the proposed algorithm provides good quality gaps and near-optimal solutions in a short time. It also evinces significant impacts of the selected inventory control policy into total costs and network design, including risk pooling effects, when it is compared with different review period values and continuous review.
Machine learning (ML) offers a promising avenue for international freight transportation management (IFTM) given its capability to harness the power of data that have become increasingly available to freight transportation researchers and practitioners. This paper conducts a comprehensive investigation of the state-of-the-art in developing ML models for applications to different aspects of IFTM. We start by giving an overview of various fundamental ML methods. Then, how different ML methods have been employed, adapted, and applied to a multitude of subject areas in IFTM are discussed, including demand forecast, operation and asset maintenance, and vehicle trajectory and on-time performance prediction. The potential data sources that may be used to develop ML models are further examined. Subsequently, a synthesis of the exiting work is performed to identify the specific topics addressed in the existing research, ML methods used, the trends of research, and opportunities for further explorations. Four directions for future research are proposed in the end
Devastating effects of disasters and global crises on people increases the importance of humanitarian logistics studies for pre and post-disaster stages. Location planning of Temporary Medical Centers/field hospitals is one of the most important problems for disaster response. We aimed to determine the location and number of temporary medical centers in case of disasters by considering the locations of the existing hospitals, casualty classification (triage), capacities of medical centers and possibilities of damage to the roads and hospitals. Besides, we aimed to assign different casualty classes to these medical centers for emergency medical response by considering the distances between disaster areas and medical centers. For this purpose, a two-stage stochastic programming model was developed. The proposed model finds an optimal TMC location solution while minimizing the total setup cost of the TMCs and the expected total transportation cost by considering casualty types, demand, possibilities of damage to the roads and hospitals, and distance between the disaster areas and the medical centers. In the model, α-reliability constraints for the expected number of unassigned casualties were also used. Besides, the model was reformulated without triage, in order to understand the impact of casualty classification on the solution of the problem. We performed a real case study for the district of Kartal expected to be widely damaged in the possible Istanbul earthquake, and a sensitivity analysis was made. The analysis of the results offer some managerial insights associated with the number of temporary medical centers’ needed, their locations, and additional hospital capacity requirements.
Increased demand for truck parking resulting from hours-of-service regulations and growing truck volumes, coupled with limited supply of parking facilities, is concerning for transportation agencies and industry stakeholders. To monitor truck parking congestion, the Arkansas Department of Transportation (ARDOT) conducts an annual observational survey of truck parking facilities. As a result of survey methodology, it cannot capture patterns of diurnal and seasonal use, arrival times, and duration. Truck Global Positioning System (GPS) data provide an apt alternative for monitoring parking facility utilization. The issue is that most truck GPS datasets represent a sample of the truck population and the representativeness of that sample may differ by application. Currently no method exists to accurately expand a GPS sample to reflect population-level truck parking facility utilization. This paper leverages the ARDOT study to estimate GPS “expansion factors” by parking facility type and defines two expansion factors: (1) the ratio of trucks parked derived from the GPS sample to those observed during the Overnight Study, and (2) the ratio of truck volume derived from the GPS sample to total truck volume measured on the nearest roadway. Varied expansion factors are found for public, private commercial (e.g., restaurant, retail store, etc.), and private truck stop facilities. Comparatively, the expansion factor based on roadway truck volumes was at least twice as high as that derived from the Overnight Study. Considering this, the method to determine expansion factors has significant implications on the estimated magnitudes of parking facility congestion, and thus will have consequences for investment prioritization.
This research is motivated by the opportunities for retailers to reduce waste and increase profitability of perishables using pricing. This paper proposes a two-stage stochastic optimization model that selects suppliers, identifies a replenishment schedule for a periodic-review inventory system with non-stationary demand and supply, and determines the timing and size of a price markdown in order to maximize retailer's profits. In this model, the first-stage problem is bilinear since it captures the additive relationship between price and demand. Therefore, we develop a solution approach which extends the Benders decomposition algorithm via a piecewise linear approximation method to solve the first-stage problem. A case study is presented to validate the model. Numerical experiments suggest that supply chain profits are enhanced by integrating inventory management with pricing decisions.
The majority of freight is transported within the U.S. by road. However, the use of alternative modes, such as rail and barge, is associated with lower transportation and infrastructure maintenance costs, release of highway capacity, increased safety, and lower emissions. Thus, there is a latent opportunity for shippers and consumers to benefit from modal shift. In this context, strategically located freight-transfer facilities to improve rail and barge access is key. Moreover, for states with lower commodity tonnages and access to short-line rail and navigable waterways, transload facilities have significant potential to shift freight to underutilized modes. This paper develops a multi-criteria assessment framework to identify strategic locations for transload facilities at the state level. Using a statewide travel demand model (STDM) as the main data source, this framework provides a sketch-planning tool to support decision-making for state Departments of Transportation and economic development agencies. The multi-criteria quantify four measures of facility potential: (a) interaction with the transportation network, (b) amount of freight transported between major freight routes, (c) spatial aggregation, and (d) directionality aggregation. Each criterion is estimated and combined at the county level to produce a multi-criteria score, which defines a county’s potential to support transload movements. Using this score, counties are ranked, and facilities prioritized. The framework is applied to Arkansas and validated using the STDM for base (2010) and forecast (2040) years.
Data derived from the Automatic Identification System (AIS) plays a key role in water traffic data mining. However, there are various errors regarding time and space. To improve availability, AIS data quality dimensions are presented for detecting errors of AIS tracks including physical integrity, spatial logical integrity and time accuracy. After systematic summary and analysis, algorithms for error pre-processing are proposed. Track comparison maps and traffic density maps for different types of ships are derived to verify applicability based on the AIS data from the Chinese Zhoushan Islands from January to February 2015. The results indicate that the algorithms can effectively improve the quality of AIS trajectories.
Characterizing system performance under disruption is a growing area of research, particularly for describing a system's resilience to disruptive events. Within the framework of system resilience, this study approaches the minimization of a multiple-commodity system's vulnerability to multiple disruptions. The vulnerability of a system is defined by the degree to which commodities can no longer flow through the system to satisfy demand given a disruptive event. A multi-objective formulation is developed to find defense strategies at minimal cost that maintain a high level of demand satisfaction across all commodities. A solution method involving an estimation of the Pareto frontier via the Non-dominated Sorted Genetic Algorithm II (NSGA-II) is also proposed. A decision support environment is proposed and supported by application of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The proposed formulation and solution method are illustrated with an example generated from the multi-commodity Swedish rail network.
This paper addresses global route choices for dry bulk carriers, focusing on the competitive situation between the Suez Canal (SC) and its competitors, such as the Panama Canal (PC) and the route via the Cape of Good Hope. The authors first establish a methodology and estimate the actual route choices for dry bulk carriers based on a vessel movement database, focusing on the share of SC transit for each pair of regions where the cargo originates and is destined to. Second, an aggregated logit model is applied to predict the estimated shares by regional pair and is utilised in sensitivity analyses for simulations that consider the recent changes in the dry bulk shipping market such as the PC's expansion, the decline in bunker prices, and the suppression of Somali piracy risk. The results suggest that the proposed methodology is useful for estimating the share of SC transit by regional pair and that the model for describing route choice is validated through not only output indices such as prediction rate but also sensitivity analyses, a time transferability check, and a comparison with the results of a shortest-path model.
Commodity flow modeling studies rely on traditional data sources, such as the Commodity Flow Survey, the Freight Analysis Framework, Transearch, surveys, the U.S. census, county business patterns, and input–output models. The strengths and shortcomings of those data sources have been evaluated in the literature; the sources can be useful for modeling, but they do not necessarily support a supply chain approach or provide the level of detail or accuracy desired for modeling a particular commodity’s supply chain and flow on a city or state roadway network. This paper expands on the work of NCFRP Report 35: Implementing the Freight Transportation Data Architecture: Data Element Dictionary by finding existing data sources unique to specific commodities that identify key supply chain locations and industry relationships and that provide more detail about commodity quantity and movement to overcome the limitations of traditional freight data sources. The goal of the investigation was to find more data sets to use in commodity flow modeling. For each commodity, this paper describes data sources found, data attributes, and how those data were used to estimate flow from origins and destinations within supply chain links. The commodity-specific approach opens doors to sources of data not normally incorporated into transportation research.
This paper presents an optimization approach to solve the short-term hydropower unit commitment and loading problem with uncertain inflows. A scenario tree is built based on a forecasted fan of inflows, which is developed using the weather forecast and the historical weather realizations. The tree-building approach seeks to minimize the nested distance between the stochastic process of historical inflow data and the multistage stochastic process represented in the scenario tree. A two-phase multistage stochastic model is used to solve the problem. The proposed approach is tested on a 31 day rolling-horizon with daily forecasted inflows for three power plants situated in the province of Quebec, Canada, that belong to the company Rio Tinto.
Uncertainties inherent in the airport traffic may lead to the unavailability of gates for accommodating scheduled flights. Incorporating random disruptions is crucial in constructing effective flight-gate assignments. We consider the gate assignment problem under uncertainty in flight arrival and departure times and develop stochastic programming models incorporating robustness measures based on the number of conflicting flights, idle and buffer times. The proposed models are formulated as large-scale mixed-integer programming problems and tabu search algorithms are implemented to obtain assignments of reasonable quality. We conduct a computational study to analyze the proposed alternate models and show the effectiveness of the solution methods.
This paper focuses on a fuzzy logic based intelligent decision making system that aims to improve the safety of marine vessels
by avoiding collision situations. It can be implemented in a decision support system of an oceangoing vessel or included in
the process of autonomous ocean navigation. Although Autonomous Guidance and Navigation (AGN) is meant to be an important
part of future ocean navigation due to the associated cost reduction and improved maritime safety, intelligent decision making
capabilities should be an integrated part of the future AGN system in order to improve autonomous ocean navigational facilities.
In this study, the collision avoidance of the Target vessel with respect to the vessel domain of the Own vessel has been analyzed
and input, and output fuzzy membership functions have been derived. The if–then rule based decision making process and the
integrated novel fuzzy inference system are formulated and implemented on the MATLAB software platform. Simulation results
are presented regarding several critical collision conditions where the Target vessel fails to take appropriate actions, as
the “Give way” vessel to avoid collision situations. In these situations, the Own vessel is able to take critical actions
to avoid collisions, even when being the “Stand on” vessel. Furthermore, all decision rules are formulated in accordance with
the International Maritime Organization Convention on the International Regulations for Preventing Collisions at Sea (COLREGs),
1972, to avoid conflicts that might occur during ocean navigation.
KeywordsAutonomous Guidance and Navigation–Collision avoidance–IMO rules and regulations–COLREGs–Fuzzy logic–Intelligent systems–Decision making process–Crash stopping
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