How big data enriches maritime research – a critical review of Automatic Identification System (AIS) data applications

ABSTRACT

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.     

Does the choice model method and/or the data matter?

The opportunity to have seven data sets associated with a stated choice experiment that are very similar in content and design is rare, and provides an opportunity to look in detail at the empirical evidence within and between each data set in the context of a range of discrete choice estimation methods, from multinomial logit to latent class to scale multinomial logit to mixed logit, and the most general model, generalized mixed multinomial logit that accounts for preference and scale heterogeneity. Given the problems associated with data from different countries and time periods, we estimate separate models for each data set, obtaining values of travel time savings that are then updated post estimation to a common dollar for comparative purposes. We also pooled all data sets for a scaled MNL model, treating each data set as a set of three separate utility expressions, but linked to the other data sets through scale heterogeneity. This is not behaviourally appropriate with MNL, latent class or mixed logit. The main question investigated is whether there exists greater synergy in the willingness to pay evidence within model form across data sets compared to across model forms within data sets. The evidence suggests that there is a relatively greater convergence of evidence across the choice models, with the exception of generalized mixed logit, after controlling for data set differences; and there is strong evidence to suggest that differences between data sets do matter.     

Estimation method for railway passengers’ train choice behavior with smart card transaction data

Smart card systems have become the predominant method of collecting public transport fares in Japan. Transaction data obtained through smart cards have resulted in a large amount of archived information on how passengers use public transportation. The data have the potential to be used for modeling passenger behavior and demand for public transportation. This study focused on train choices made by railway passengers. If each passenger’s train choice can be identified over a long period of time, this information would be useful for improving the customer relationship management of the railway company and for improving train timetables. The aim of this study was to develop a methodology for estimating which train is boarded by each smart card holder. This paper presents a methodology and an algorithm for estimation using long-term transaction data. To validate the computation time and accuracy of the estimation, an empirical analysis is carried out using actual transaction data provided by a railway company in Japan. The results show that the proposed method is capable of estimating passenger usage patterns from smart card transaction data collected over a long time period.     

Passengers’ response to transit fare change: an ex post appraisal using smart card data

Fare change is an effective tool for public transit demand management. An automatic fare collection system not only allows the implementation of complex fare policies, but also provides abundant data for impact analysis of fare change. This study proposes an assessment approach for analyzing the influence when substituting a flat-fare policy with a distance-based fare policy, using smart card data. The method can be used to analyze the impact of fare change on demand, riding distances, as well as price elasticity of demand at different time and distance intervals. Taking the fare change of Beijing Metro implemented in 2014 as a case study, we analyze the change of network demand at various levels, riding distances, and demand elasticity of different distances on weekdays and weekends, using the method established and the smart card data a week before and after the fare change. The policy implication of the fare change was also addressed. The results suggest that the fare change had a significant impact on overall demand, but not so much on riding distances. The greatest sensitivity to fare change is shown by weekend passengers, followed by passengers in the evening weekday peak time, while the morning weekday peak time passengers show little sensitivity. A great variety of passengers’ responses to fare change exists at station level because stations serve different types of land usage or generate trips with distinct purposes at different times. Rising fares can greatly increase revenue, and can shift trips to cycling and walking to a certain extent, but not so much as to mitigate overcrowding at morning peak times. The results are compared with those of the ex ante evaluation that used a stated preference survey, and the comparison illustrates that the price elasticity of demand extracted from the stated preference survey significantly exaggerates passengers’ responses to fare increase.     

Origin–destination missing data estimation for freight transportation planning: a gravity model-based regression approach

This paper develops a log-linear regression approach to estimate missing data in a sparse origin–destination (O–D) matrix assuming the sampled or observed O–D trips follow a good gravity pattern. The approach is tested with randomly selected samples from the known portions of 1997, 2002, and 2007 US Commodity Flow Survey (CFS) O–D value and tonnage matrices and validated with 2007 US O–D tonnage matrix at the state level. The missing data are also estimated for the 2007 CFS tonnage matrix with the best intercept and coefficients obtained using all known entries of the matrix. The concept of the approach can be extended beyond the gravity model to any strong mathematical pattern embedded in the known set of a sparse O–D matrix to estimate its missing cells.     

Making meaningful comparisons between road and rail – substituting average energy consumption data for rail with empirical analysis

Within the transport sector, modal shift towards more efficient and less polluting modes could be a key policy goal to help meet targets to reduce energy consumption and carbon emissions. However, making comparisons between modes is not necessarily straightforward. Average energy and emissions data are often relied upon, particularly for, rail, which may not be applicable to a given context. Some UK train operating companies have recently fitted electricity metres to their trains, from which energy consumption data have been obtained. This has enabled an understanding to be gained of how energy consumption and related emissions are affected by a number of factors, including train and service type. Comparisons are made with existing data for road and rail. It is noted that although more specific data can be useful in informing policy and making some decisions, average data continue to play an important role when considering the overall picture.     

Do new sources of traffic data make the application of Chaos Theory to traffic management a realistic possibility?

Current signal systems for managing road traffic in many urban areas around the world lack a coordinated approach to detecting the spatial and temporal evolution of congestion across control regions within city networks. This severely inhibits these systems’ ability to detect reliably, on a strategic level, the onset of congestion and implement effective preventative action. As traffic is a time-dependent and non-linear system, Chaos Theory is a prime candidate for application to Urban Traffic Control (UTC) to improve congestion and pollution management. Previous applications have been restricted to relatively uncomplicated motorway and inter-urban networks, arguably where the associated problems of congestion and vehicle emissions are less severe, due to a general unavailability of high-resolution temporal and spatial data that preserve the variability in short-term traffic patterns required for Chaos Theory to work to its full potential. This paper argues that this restriction can now be overcome due to the emergence of new sources of high-resolution data and large data storage capabilities. Consequently, this opens up the real possibility for a new generation of UTC systems that are better able to detect the dynamic states of traffic and therefore more effectively prevent the onset of traffic congestion in urban areas worldwide.     

Variation of households<Emphasis Type="Bold">’</Emphasis> car ownership across time: application of a panel data model

For economic and environmental policy formulation and with the effort of creating less car dependent societies, it is important to study the changing characteristics of car ownership in a household through time as well as factors responsible of these variations. There is a vast body of literature on empirical studies of car ownership and use. These studies have investigated the socio-economic background of the decision maker, the built environment and the perception associated with owning a car as determinant factors of car ownership and use. In most cases, these analyses have been carried out using cross-sectional data sets. However, the analysis of factors determining changes in travel behavior of an individual or household requires information on their behavior over time (longitudinal data set). In this study, the German Mobility Panel (1996–2006) is used to examine variation of car ownership through time and across households. The panel data modeling results showed that there are variations of car ownership between households whereas changes in car ownership of a given household over time (within household variations) are insignificant. The influence of other factors such as the households’ socio-economic background, the availability of public transportation and shopping/leisure facilities, perception on parking difficulties and satisfaction with existing public transportation services on the car owning characteristics of households is also presented and discussed in this paper.