2021

Sepideh Zohoori (szohoori@lamar.edu), Lamar University
Masood Jafari Kang, Lamar University
Maryam Hamidi, Lamar University
Brian Craig, Lamar University

Investigation on waterway traffic characteristics is essential both to understand vessel movements and to improve waterway traffic conditions. In this study, a novel AIS-based algorithm is developed to study the traffic performance of waterways through capturing traffic measures including travel speed, traffic density, traffic flow, trip attraction, trip generation, and Origin-Destination (O-D) matrices. The vectorization technique used in the structure of the algorithm combining with waterway segmentation and trip separation techniques demonstrates high processing speed and accuracy as well as universal applicability for different waterways and vessels. A new trip separation approach based on moving and stationary status of vessels consisting of inbound, outbound, and stop status, is developed to investigate vessel behavior in brown and blue waters. The algorithm can analyze traffic behavior for a whole channel and each channel’s segment simultaneously to provide comprehensive and meticulous results. The methodology examines on the Houston Ship Channel (HSC) as a real case study. The combined measures could reveal the channel traffic condition and provide valuable analytical insights for decision-makers to better manage waterway traffic.

Agustina Calatayud (mcalatayud@iadb.org), Inter-American Development Bank
Santiago Sanchez-Gonzalez, Inter-American Development Bank
Jose Maria Marquez, Inter-American Development Bank

Using big data generated by crowdsourced mobility platforms, policymakers can now better understand the relationship between cruise activities and traffic congestion, identify key areas in the road network that need traffic flow improvement, and design appropriate and dynamic measures to take advantage of cruise tourism while mitigating its effects on urban mobility. This pioneering study uses more than 130 million observations to analyze traffic congestion and cruise activity for four cruise destinations in South America and the Caribbean during the period 2018-2019. The paper suggests that cruise activity is associated with an 11% increase in urban congestion. Results provide an unprecedented, granular understanding of traffic behavior in the vicinity of cruise terminals. This will facilitate the design of customized interventions to ease congestion according to the dynamic time variation of traffic flows, thus leading towards more efficient traffic management.

Jingjing Yu, Universitat Hamburg
Stefan Voß, Universitat Hamburg
Philip Cammin, Universitat Hamburg

The uncertainty in the vessel arrival time is identified as one of the major factors to disrupt the operation plan of the berth allocation and quay crane assignment problem (BACAP). To tackle the uncertainty, we integrate the vessel arrival time prediction into the optimization process of the BACAP and put forward an improved BACAP-VATP model. The vessel arrival time prediction provides an approximated estimate of the deviation between the ETA and ATA, which is in the form of an interval with only the upper and lower bounds known. Thus, when integrated with the vessel arrival time prediction, the proposed BACAP-VATP model includes an interval uncertainty that needs to be solved. A robust optimization approach based on interval linear programming (ILP) is applied to transform the proposed BACAP-VATP model with interval uncertainty into a deterministic multi-objective optimization model solved by an improved genetic algorithm (NSGA-II). By a case study conducted for a container terminal of Ningbo-Zhoushan Port, China, the performance of the proposed BACAP-VATP model is assessed based on the price of robustness and the reliability value. Compared to the BACAP model with inserting buffer times to absorb the uncertainty in the vessel arrival time, the proposed BACAP-VATP model has a more reliable performance with the uncertainty in the vessel arrival time without significantly declining the operational efficiency of the baseline schedule.

Cassia Galvao, Texas A&M University, Galveston
Joan Mileski, Texas A&M University, Galveston
Jim Kruse, Texas A&M Transportation Institute
Juan Carlos Villa, Texas A&M Transportation Institute

This research investigates the foundations and the main applications of blockchain to the maritime business sectors that are part of servicing supply chains. Since it is new to the industry that is international, fragmented, and complex in nature, we explore several research questions arising from the application of blockchain to maritime supply chains and logistics. In an era of fierce competition where information represents a competitive advantage, it becomes critical to examine how much or to what level of detail companies are expected to share information. Blockchain promises to add value to this process by offering a distributed safe ledger of transactions and records to all respective parties involved. This research provides a review of the state-of-the-art of blockchain implementation in the maritime sector using the Houston Maritime cluster as our case study. First, the research identifies the main characteristics of blockchain and examines the state-of-the-art of blockchain applications and implementation aiming to map the benefits applicable to the maritime sector as well as the business’ drivers for the adoption of blockchain. Second, we study the implications for training and education considering the disruptive and transformative potential of blockchain. We employ a survey method asking both multiple-choice and open-ended questions to key stakeholders operating in the Greater Port of Houston area. Survey responses were analyzed using descriptive statistics.

Masood Jafari Kang, Lamar University
Sepideh Zohoori, Lamar University
Maryam Hamidi, Lamar University
Brian Craig, Lamar University

Using the automatic identification system (AIS) data, this article first has extended the definition of two widely used roadway congestion indices to maritime transportation systems (MTS). Next, a methodology is developed to measure the traffic speed index (TSI) and dwell time index (DTI) using the AIS data, and they have been applied to the AIS data of the Houston Ship Channel (HSC) to evaluate the applicability in real cases. The results show that monitoring the real-time values of TSI rather than their average will help us find when, where, and how severe traffic congestion occurred. However, considering the operation at narrow waterways where vessels tend to wait at moored status rather than stuck in queues along a channel, both average and real-time DTI can quantify traffic congestion and highlight severity in different waterway segments for different types of vessels. According to HSC DTI, most tankers experience long waiting times at the sea buoy and Galveston bay, while cargo vessels experience delays at Bayport and Barbour’s Cut terminals. This paper helps the decision-makers quantify congestion in different sections of a waterway and provides measures to compare traffic congestion for competing projects at various waterways.

Yimei Chang (changyimei@bwu.edu.cn), Beijing Wuzi University
Ali Haghani, University of Maryland, College Park
Masoud Hamedi, Iteris

Stowage plan, which is planning for a reasonable movement of containers, plays an important role in enhancing the efficiency and competitiveness of container terminals. This is because loading and unloading containers from/to containerships take time and this is a cost. The time required for handling containerships also depends on the operations of quay cranes, which are one of the most expensive equipment in container terminals. This paper integrates stowage planning and crane split problem to minimize the total berthing time at each visiting port over the voyage. A new mathematical model is developed, which covers a wide range of operational and structural constraints for both stowage plan and crane operation. A genetic algorithm based on a novel encoding mode of assignment strategy is introduced to solve the problem. Some computational experiments are implemented to validate the proposed approach and the performance of the proposed algorithm. The results show that the proposed algorithm is prior than precious approach in solving the integration problem. Consequently, our approach is effective and efficient in solving the integration problem and enhancing the efficiency of container terminals.

Javier Garrido, Center For Innovation in Transport - CENIT
Sergi Saurí, Center For Innovation in Transport - CENIT
Esther Raventós, Center For Innovation in Transport - CENIT
Carles Rúa, Port de Barcelona
Jordi Torrent, Port de Barcelona

Important trends are taking place, some from port and maritime sector and other from outside (technology, new sources of energy, etc), can have an important impact on the business model of ports. Since the port ecosystem is going to be considerably different in the following two decades, decision makers need to have a Port Vision of 2040 to prioritize investments and build a strategic plan. This paper seeks to analyze the current trends impacting on ports and analyze the changes on their roles. The impacts are identified by means of the Delphi method process within the Port Community of the Port of Barcelona. The results of this paper will contribute to the state of art of Ports of the Future by identifying the main trends impacting on ports in the long term (2040).

Daniel Smith, Tioga Group

Application of big data in the form of AIS vessel call records can greatly improve our understanding of container vessel dwell times, especially when coupled with information on port volumes and vessel schedules. Discussions of port productivity often use vessel time in port, referred to as dwell time, turnaround time, or berth time, as a primary metric. This emphasis implies a need to understand the factors that affect dwell time, especially in comparisons between ports. There have been multiple previous efforts at analyzing container vessel dwell time, largely through regression analysis of limited data samples. This analysis differs from most previous work by using a multi-year, multi-port database covering all relevant vessel calls at major U.S. container ports, and by including factors such as vessel schedules and seasonality. The analysis indicates a much stronger association of dwell time with expected cargo volume at each call than with vessel capacity, and the differences in expected cargo volume help explain dwell time differences between ports. The analysis also found that vessel schedules may be the primary determinants of dwell time, and that schedule adherence may be a more important metric than dwell time per se. Seasonality also affects container vessel dwell time, but that role may be multi-faceted as both weather conditions and cargo volumes likely affect the outcomes. The most promising avenues for future research lie in merging AIS vessel call records with other datasets that, unfortunately, may not yet exist or be accessible.

Ellen Schenk, University of Delaware
Edward Carr, Energy and Environmental Research Associates LLC
James Corbett (jcorbett@udel.edu), University of Delaware

This paper evaluates economic, employment, and environmental changes that may result from electrifying port cargo handling and providing shore power at berth. We examine four ports, Port of Baltimore, Port Everglades, Port of Houston, and the Port of Seattle, to look at the regional differences, driven by the size of ports, cost of fuel and electricity, as well as variation on cargo and ship throughput, in electrification of both shore power and cargo handling equipment. Using input-output modeling and estimates for fuel consumption, port communities may see benefits of increased economic activity and employment. Port electrification in the base case doubles state and county economic activity between 2020 and 2050; in the high trend cases, port electrification increases economic activity 3.5 to ~5 times between 2020 and 2050 compared with the economic output of diesel-powered port operations. Ports also see large emissions reductions and high cost per unit pollution abatement, suggesting that electrification has both fiscal and environmental public policy implications.