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.

A local positional system (LPS) is proposed, in which particles are launched at given velocities, and a sensor system measures the trajectory of the particles in the platform frame. These measurements allow us to restore the position and orientation of the platform in the frame of the rotating Earth, without solving navigation equations. When the platform velocity is known and if the platform orientation stays the same, the LPS technique allows a navigational accuracy of 100 $\mu$m per one hour to be achieved. In this case, the LPS technique is insensitive to the type of platform trajectory. If there are also velocimeters installed on the platform, then one can restore the platform velocity and angular rate of the platform rotation with respect to the Earth. Instead of navigational equations, it is necessary to obtain the classical trajectory of a particle in the field of a rotating gravity source. Taking into account the gravity-gradient, Coriolis, and centrifugal forces, the exact expression for this trajectory is derived, which can be widely used in atomic interferometry. A new iterative method for restoring the orientation of the platform without using gyroscopes is developed. The simulation allows us to determine the conditions under which the LPS navigation error per hour is approximately $10$ m.

When conducting accident analysis, the assessment of risk is one of the important links. Moreover, with regards to crew training, risk cognition is also an important training subject. However, most of the existing researches only rely on a single or a few data sources. It is necessary to fuse the collected multi-source data to obtain a more comprehensive risk evaluation model. There are few studies on the three-dimensional (3D) multi-modal data-fusion-based trajectory risk cognition. In this paper, a fuzzy logic-based trajectory risk cognition method is proposed based on multi-model spatial data fusion and accident data mining. First, the necessity of multi-model spatial data fusion is analysed and a data-fusion-based scene map is constructed. Second, a risk cognition model fused by multiple factors, multi-dimensional spatial calculations as well as data mining results is proposed, including a novel ship boundary calculation approach and newly constructed factors. Finally, a radar chart is used to illustrate the risk, and a risk cognition system is developed. Experiment results confirm the effectiveness of the method. It can be applied to train human operators of unmanned ship systems.

Inadequate human actions are still the main cause of all reported accidents and incidents on board ships. The aim of this study is to explore the influence of safety resource activities and other exploratory variables on seafarers’ safety behaviour. For this study, a series of open-ended interviews were conducted to gain insight into how shipping organisations struggle with this issue. Following the interviews, a questionnaire was prepared and distributed to professional seafarers. Two exploratory factor analyses were conducted to identify the underlying factor structure and five factors emerged. Furthermore, three hierarchical multiple regression analyses were conducted to examine the influence of the factors obtained, namely, Company safety orientation and Additional safety incentives on seafarers’ perceived Safety awareness, Job satisfaction, Risk acceptance and other exploratory variables. The study shows that the perceived resource-related activities of the organisation and the demographic characteristics of the respondents can influence the safety behaviour of seafarers.

The civilian Automatic Identification System (AIS) has no inherent protection against spoofing. Spoofed AIS messages have the potential to interfere with the safe navigation of a vessel by, amongst other approaches, spoofing maritime virtual aids to navigation and/or differential global navigation satellite system (DGNSS) correction data conveyed across it. Acting maliciously, a single transmitter may spoof thousands of AIS messages per minute with the potential to cause considerable nuisance; compromising information provided by AIS intended to enhance the mariner's situational awareness. This work describes an approach to authenticate AIS messages using public key cryptography (PKC) and thus provide unequivocal evidence that AIS messages originate from genuine sources and so can be trusted. Improvements to the proposed AIS authentication scheme are identified which address a security weakness and help avoid false positives to spoofing caused by changes to message syntax. A channel loading investigation concludes that sufficient bandwidth is available to routinely authenticate all AIS messages whilst retaining backwards compatibility by carrying PKC ‘digital signatures’ in a separate VHF Data Exchange System (VDES) side channel.

Applying non-accident critical events (NACEs) as an alternative measuring method to assess ship collision risk has become popular in recent years. NACEs has shown temporal features in different waters. This study uses the quaternion ship domain method to identify NACEs around the Yangtze Estuary and adjacent coastal waters from the Automatic Identification System (AIS) data in October 2019. The results indicate that NACEs show different temporal features in estuaries and coastal waters. The relationship between tides, channel types and ships is discussed. In addition, we established a statistical method for the occurrence time of NACEs and the state of tides according to the half-tide level and the nearest time. The outcomes of this study provide a direction for exploring the relationship between NACEs and environmental conditions, which is also instructive for the study of the causes of ship accidents.

Multi-rotor Unmanned Aerial Vehicles (UAVs), although originally designed and developed for defence and military purposes, in the last ten years have gained momentum, especially for civilian applications, such as search and rescue, surveying and mapping, and agricultural crops and monitoring. Thanks to their hovering and Vertical Take-Off and Landing (VTOL) capabilities and the capacity to carry out tasks with complete autonomy, they are now a standard platform for both research and industrial uses. However, while the flight control architecture is well established in the literature, there are still many challenges in designing autonomous guidance and navigation systems to make the UAV able to work in constrained and cluttered environments or also indoors. Therefore, the main motivation of this work is to provide a comprehensive and exhaustive literature review on the numerous methods and approaches to address path-planning problems for multi-rotor UAVs. In particular, the inclusion of a review of the related research in the context of Precision Agriculture (PA) provides a unified and accessible presentation for researchers who are initiating their endeavours in this subject.

Demonstrations and local tests of several maritime autonomous surface ships (MASS) have recently been carried out. From a technological standpoint, MASS are becoming able to handle actual operations in certain sea areas. Since 2017, the MSC (Maritime Safety Committee) of the IMO (International Maritime Organization) has been discussing legal problems with the Regulations for Preventing Collisions at Sea (COLREGs) with regard to MASS operation. The purpose of this paper is to clarify the rules from the perspective of seafarers, who need to interpret COLREGs when dealing with MASS in ship handling situations on the sea, and also to discuss possible required amendments to COLREGs. This paper attempts to clarify the extent of current interpretations while also taking into account the answers to questionnaires received from 130 pilots, ship captains and navigation officers concerning COLREGs for MASS operation. Given the four common principles of COLREGs, it is considered whether the principles need to be changed with the introduction of MASS from the viewpoint of seafarers.

Estimating the collision frequency of ships (F) is important for assessing collision risk on waterways. To date, F has been estimated as the product of the number of collision candidates $({N_{a }})$ and the causation probability $({P_c})$: $F = {N_{a}} \cdot {P_c}$, where ${N_{a }}$ represents the number of collisions that occur when related ships continue on course with no intervention, and ${P_c}$ is the probability that collision avoidance fails. Fujii developed a general method and Pedersen formulated it to estimate ${N_{a }}$ in an intersectional area. Their method is generally called ‘the geometric method’ because collision candidates are estimated only from the geometric relationship between two ships. The method has been used in many projects to estimate F in waterways; however, its use should be limited to intersection angles ranging from 10° to 170°. This paper presents a method, statistically verified by computer simulation, that can be used at all intersection angles to overcome this limitation. Moreover, it demonstrates strong agreement with Pedersen's method at intersection angles of 10° to 170°.

Location and navigation services based on global navigation satellite systems (GNSS) are needed for real-time high-precision positioning applications in relevant economic sectors, such as precision agriculture, transport, civil engineering or mapping. Real-time navigation users of GNSS networks have significantly increased all around the world, since the 1990s, and usage has exceeded initial expectations. Therefore, if the evolution of GNSS network users is monitored, the dynamics of market segments can be studied. The implementation of this hypothesis requires the treatment of big volumes of navigation data over several years and the continuous monitoring of customers. This paper is focused on the management of massive connection of GNSS users in an efficient way, in order to obtain analysis and statistics. Big data architecture and data analyses based on data mining algorithms have been implemented as the best way to approach the hypothesis. Results demonstrate the dynamic of users of different market segments, the increasing demand over the years and, specifically, conclusions are obtained about the trends, year-on-year correlation and business volume recovering after economic crisis periods.

Recognition of obstacle type based on visual sensors is important for navigation by unmanned surface vehicles (USV), including path planning, obstacle avoidance, and reactive control. Conventional detection techniques may fail to distinguish obstacles that are similar in visual appearance in a cluttered environment. This work proposes a novel obstacle type recognition approach that combines a dilated operator with the deep-level features map of ResNet50 for autonomous navigation. First, visual images are collected and annotated from various different scenarios for USV test navigation. Second, the deep learning model, based on a dilated convolutional neural network, is set and trained. Dilated convolution allows the whole network to learn deep features with increased receptive field and further improves the performance of obstacle type recognition. Third, a series of evaluation parameters are utilised to evaluate the obtained model, such as the mean average precision (mAP), missing rate and detection speed. Finally, some experiments are designed to verify the accuracy of the proposed approach using visual images in a cluttered environment. Experimental results demonstrate that the dilated convolutional neural network obtains better recognition performance than the other methods, with an mAP of 88%.

The complex sea ice conditions in Arctic waters has different impacts on the legs of the Arctic passage, and ships of specific ice classes face different navigation risks. Therefore, the quantitative analysis of the navigation risks faced in different legs has important practical significance. Based on the POLARIS introduced by IMO, the sea ice condition data from 2011 to 2020 was used to quantify the navigation risk of the Arctic Northeast passage. The risk index outcome (RIO) of the Arctic Northeast Passage were calculated. The navigable windows of the route for ice class 1A ships sailing independently under different sea ice conditions in the last decade were determined, with a navigable period of 91 days under normal sea ice conditions, approximately 175 days under light sea ice conditions and only week 40 close to navigation under severe sea ice conditions. The three critical waters affecting the safety of ships were identified. Combined with the navigable windows and critical waters, recommendations on ship's navigation and manipulation and recommendations for stakeholders were given. The method and results provided reference and support for the assessment of the navigation risk of ships in the Northeast Passage and safety navigation and operations of ships, and satisfied the needs of relevant countries and enterprises to rationally arrange shipment dates and sailing plans based on different ice classes of ships.

This paper proposes a novel autonomous navigation method for Mars-orbiting probes. Satellite-to-satellite tracking (SST) between two probes is generally deemed to involve autonomous measurements with no dependence on any external observation sites on the Earth. For the conventional two-body dynamic model, it is well known that the orbit states cannot be estimated by merely using such SST measurements. Considering the effects of third-body gravitation perturbation and the weak Mars tesseral harmonics perturbation, autonomous navigation with SST measurements alone becomes weakly observable and may be achieved by some nonlinear filtering techniques. Two significant improvements are made to mitigate the nonlinearity brought by the dynamic models. First, singularity-avoiding orbit elements are selected to represent the dynamic models in order to reduce the intensity of the nonlinearity which cannot be overcome by the traditional position–velocity state expression. Second, the unscented Kalman filter method is effectively utilised to avoid the linearised errors calculated by its extended Kalman filter counterpart which may exceed the tesseral harmonics perturbation. A constellation, consisting of one low-orbit probe and one high-orbit probe, is designed to realise the autonomous orbit determination of both participating Mars probes. A reliable navigation solution is successfully obtained by Monte Carlo simulation runs. It shows that the errors of the semimajor axes of the two Mars probes are less than 10 m and the position errors are less than 1 km.

In traditional satellite navigation receivers, the parameters of tracking loop such as loop bandwidth and integration time are usually set in the design of the receivers according to different scenarios. The signal tracking performance is limited in traditional receivers. In addition, when the tracking ability of weak signals is improved by extending the integration time, negative effect of residual frequency error becomes more and more serious with extension of the integration time. To solve these problems, this paper presents out research on receiver tracking algorithms and proposes an optimised tracking algorithm with inertial information. The receiver loop filter is designed based on Kalman filter, reducing the phase jitter caused by thermal noise in the weak signal environment and improving the signal tracking sensitivity. To confirm the feasibility of the proposed algorithm, simulation tests are conducted.