Inland waterway network mapping of AIS data for freight transportation planning

2022 ◽  
pp. 1-22
Author(s):  
Magdalena I. Asborno ◽  
Sarah Hernandez ◽  
Kenneth N. Mitchell ◽  
Manzi Yves
Keyword(s):  
Travel Demand ◽  
Performance Metrics ◽  
Automatic Identification ◽  
Identification System ◽  
Time Duration ◽  
Movement Data ◽  
Demand Models ◽  
Inland Waterway ◽  
Infrastructure Investments ◽  
Network Mapping

Abstract 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.

scholarly journals GIS-based identification and visualization of multimodal freight transportation catchment areas

2021 ◽  
Author(s):  
Magdalena I. Asborno ◽  
Sarah Hernandez ◽  
Manzi Yves
Keyword(s):  
Travel Demand ◽  
Cost Benefit ◽  
Vehicle Tracking ◽  
Automatic Identification ◽  
Identification System ◽  
Tracking Data ◽  
Demand Models ◽  
Catchment Areas ◽  
Area Of Influence ◽  
Support Cost

AbstractTo 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.

Freight Distribution Analysis and Modelling of Inland Waterway Transport along the Yangtze River Economic Belt using Big Data

2022 ◽  
pp. 1-36
Author(s):  
Guihua Deng ◽  
Ming Zhong ◽  
Mo Lei ◽  
John Douglas Hunt ◽  
Wanle Wang ◽  
...  
Keyword(s):  
Yangtze River ◽  
Length Distribution ◽  
Water Levels ◽  
Automatic Identification ◽  
Identification System ◽  
Distribution Analysis ◽  
The Yangtze River ◽  
Distribution Models ◽  
Inland Waterway ◽  
Different Types

The Yangtze River Economic Belt (YREB) serves as the main east-west axis of China to promote economic development and environmental protection along the Yangtze River. This paper analyses the factors that affect the freight distribution of major types of cargo transported through the Yangtze River, using data from the automatic identification system (AIS) and ship visa data. First, a set of freight impedance functions are developed for different types of links of the waterway network, by considering a number of factors such as cargo types, delays at ship locks, water levels and flows at different waterway segments and upstream and downstream shipping speeds. Both the distance- and time-based impedance matrices of different types of cargo are computed, respectively. After that, gravity model (GM) and intervening opportunity model (IOM) are estimated to simulate the distribution of different types of cargo based on the computed impedance matrices. Meanwhile, a trip length distribution (TLD) method is applied to validate the estimated distribution models. The results indicate that GM with a power term outperforms other models, and the time-based models are superior to the distance-based ones for the prediction of freight distributions over large geographies like the YREB. This work offers an in-depth understanding of the freight characteristics of inland waterways and therefore it should be helpful for relevant authorities in formulating their port and inland waterway plans and policies.

scholarly journals Ship AIS Trajectory Clustering: An HDBSCAN-Based Approach

2021 ◽  
Vol 9 (6) ◽  
pp. 566
Author(s):  
Lianhui Wang ◽  
Pengfei Chen ◽  
Linying Chen ◽  
Junmin Mou
Keyword(s):  
Traffic Management ◽  
Performance Metrics ◽  
Spatial Clustering ◽  
Maritime Transportation ◽  
Automatic Identification ◽  
Identification System ◽  
Clustering Methods ◽  
Trajectory Clustering ◽  
Clustering Method ◽  
Shape Characteristics

The Automatic Identification System (AIS) of ships provides massive data for maritime transportation management and related researches. Trajectory clustering has been widely used in recent years as a fundamental method of maritime traffic analysis to provide insightful knowledge for traffic management and operation optimization, etc. This paper proposes a ship AIS trajectory clustering method based on Hausdorff distance and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), which can adaptively cluster ship trajectories with their shape characteristics and has good clustering scalability. On this basis, a re-clustering method is proposed and comprehensive clustering performance metrics are introduced to optimize the clustering results. The AIS data of the estuary waters of the Yangtze River in China has been utilized to conduct a case study and compare the results with three popular clustering methods. Experimental results prove that this method has good clustering results on ship trajectories in complex waters.

scholarly journals Assessing the Impact of Disruptive Events on Port Performance and Choice: The Case of Gothenburg

2021 ◽  
Vol 9 (2) ◽  
pp. 145
Author(s):  
Martin Svanberg ◽  
Henrik Holm ◽  
Kevin Cullinane
Keyword(s):  
Performance Metrics ◽  
Automatic Identification ◽  
Identification System ◽  
Container Port ◽  
Medium Term ◽  
Short Term ◽  
Labour Dispute ◽  
Container Ports ◽  
The Impact ◽  
The Relationship

This paper assesses the impact of a major disruptive event at the port of Gothenburg, Scandinavia’s largest container port. Automatic Identification System (AIS) data is analyzed, in combination with official port statistics on container handling in the four main container ports in Sweden, from 2014–2018. Particular attention is paid to the relationship between container volumes handled and calculated performance metrics at the specific times of the intense labour dispute at the port of Gothenburg during the periods Q2 (2016) and Q4 (2016)–Q2 (2017). The paper concludes that the decline in container volumes handled at Gothenburg over the period is specifically due to fewer ships calling at the port following each of the intense periods of the labour dispute. It is also concluded that the effect on competitor ports in the region were significant in terms of both increased volumes of gateway container traffic and the resulting short-term and medium term impacts on both port user profiles and port efficiency levels.

Microcontroller and Android Based Automatic Identification System for Inland Waterway

2021 ◽  
Author(s):  
Md. Rokonuzzaman ◽  
Nazmus Shakib ◽  
Mashiur Rahman Shakil ◽  
Kausarul Islam ◽  
Md Reaz Hasan Khondoker ◽  
...  
Keyword(s):  
Automatic Identification ◽  
Identification System ◽  
Automatic Identification System ◽  
Inland Waterway

Study of Automatic Anomalous Behaviour Detection Techniques for Maritime Vessels

2017 ◽  
Vol 70 (4) ◽  
pp. 847-858 ◽  
Author(s):  
Abdoulaye Sidibé ◽  
Gao Shu
Keyword(s):  
Situational Awareness ◽  
State Of The Art ◽  
Automatic Identification ◽  
Identification System ◽  
Statistical Machine Learning ◽  
Maritime Safety ◽  
Detection Techniques ◽  
Movement Data ◽  
Important Concern ◽  
Made In

The maritime domain is the most utilised environment for bulk transportation, making maritime safety and security an important concern. A major aspect of maritime safety and security is maritime situational awareness. To achieve effective maritime situational awareness, recently many efforts have been made in automatic anomalous maritime vessel movement behaviour detection based on movement data provided by the Automatic Identification System (AIS). In this paper we present a review of state-of-the-art automatic anomalous maritime vessel behaviour detection techniques based on AIS movement data. First, we categorise some approaches proposed in the period 2011 to 2016 to automatically detect anomalous maritime vessel behaviour into distinct categories including statistical, machine learning and data mining, and provide an overview of them. Then we discuss some issues related to the proposed approaches and identify the trend in automatic detection of anomalous maritime vessel behaviour.

PENGEMBANGAN ”REAL TIME TRAFFIC INFORMATION SYSTEM” BAGI PENGGUNA JALAN

2013 ◽  
Vol 12 (3) ◽  
Author(s):  
Rusmadi Suyuti
Keyword(s):  
Information System ◽  
Real Time ◽  
Travel Demand ◽  
Urban Traffic ◽  
Road User ◽  
Traffic Information ◽  
Estimation Methods ◽  
Traffic Counts ◽  
Real Time Traffic ◽  
Demand Models

Traffic information condition is a very useful  information for road user because road user can choose his best route for each trip from his origin to his destination. The final goal for this research is to develop real time traffic information system for road user using real time traffic volume. Main input for developing real time traffic information system is an origin-destination (O-D) matrix to represent the travel pattern. However, O-D matrices obtained through a large scale survey such as home or road side interviews, tend to be costly, labour intensive and time disruptive to trip makers. Therefore, the alternative of using traffic counts to estimate O-D matrices is particularly attractive. Models of transport demand have been used for many years to synthesize O-D matrices in study areas. A typical example of the approach is the gravity model; its functional form, plus the appropriate values for the parameters involved, is employed to produce acceptable matrices representing trip making behaviour for many trip purposes and time periods. The work reported in this paper has combined the advantages of acceptable travel demand models with the low cost and availability of traffic counts. Two types of demand models have been used: gravity (GR) and gravity-opportunity (GO) models. Four estimation methods have been analysed and tested to calibrate the transport demand models from traffic counts, namely: Non-Linear-Least-Squares (NLLS), Maximum-Likelihood (ML), Maximum-Entropy (ME) and Bayes-Inference (BI). The Bandung’s Urban Traffic Movement survey has been used to test the developed method. Based on several statistical tests, the estimation methods are found to perform satisfactorily since each calibrated model reproduced the observed matrix fairly closely. The tests were carried out using two assignment techniques, all-or-nothing and equilibrium assignment.  

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