BMT Simulation

September 8, 2007 5:43 RPS mtec07_new

Agent Based Risk Management & Operational Modelling of Ports

R. D. Colwill and S. L. Yeung

BMT Asia Pacific, 18/F Chun Wo Commercial Centre, 23-29 Wing Wo Street,Central, Hong Kong

The continuous development of world-wide shipping, both in terms of volume and indi-vidual ship size places increasing pressure on navigation safety in port approaches andon landside infrastructure. Within this complex environment sits the pilots, operators, anddrivers who are so crucial to the safe and efficient operation of port systems.

The ability to identify and predict the distribution of risk and identify operational efficiencyis a key component of a projects development. Increasing mechanical reliability ensures thathuman factors are now the most significant elements of risk within these systems, yet arefrequently the most poorly replicated.

This paper describes the analysis of port systems through an agent based simulationapproach that allows the key issues of humandecisionmaking in response to the environmentand perceived threats to be better represented, and provides a platform for more accurateplanning. The paper reviews the key architecture of such a model and illustrates the value of3D presentation and the capability of agent simulations to represent complex environments.

1. Introduction

The worlds ports are the cornerstones to the development of the worlds tradingeconomy, and have in recent years been under increasing pressure as more andlarger vessels ply the oceans.Port stakeholders (harbourmasters, terminal operators and shipping companies),

all have a strategic interest in ensuring that navigational safety and port/maritimesecurity is enhanced to maintain adequate port capacity and safety in the faceof rising volumes and ship size. Most frequently port authorities must address aversion of the following question:

What is the present capacity of the ports fairways and terrestrial infrastructureand how can we predict and manage the impacts from future growth.

Focussing initially on the marine side of the port it is possible to identify thefollowing factors that must be addressed within any assessment designed to

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accurately address the impact of future facilities and marine traffic:

Geometry (draft, width & airdraft) of navigable channels Traffic Mix (size, speed, type of vessels & manoeuvrability) Metocean Environment (current, wind & wave) Control Regime (TSS, speed limits, VTS advisory) Operational Capability (Mariners familiarity and tolerance to vessel proximity)These variables are not static; and key drivers for change include:

Global & Regional Growth Port Development New Port Facilities Changes in Vessel SizeTo accurately address capacity and safety a model must to able to model thephysical geography of the water space, the volume, nature and capabilities of thevessels and the response of mariners to different control stimuli.

2. Agent Based Simulation

In reviewing the requirements outlined above it is clear that an extremely flexiblearchitecture is required to integrate the ships reaction to the physical environmentin which they navigate and the human factors issues of control and reaction tothe marine traffic environment.BMT have in recent years been researching just this issue in a bid to develop

the next generation of traffic models that meld together marine traffic issues, withthe increasingly significant human factors element to map congestion and risk.To achieve this goal BMT have entered into a partnership with Massive Software(www.massivesoftware.com) in order to focus their Academy Award winningautonomous agent animation software towards marine engineering applications.Massive is the premier 3D animation system for generating crowd-related

visual effects for film and television. Animators are able to develop characterswith a sophisticated set of reactions to their environment, where each agentcan be programmed to develop reactive behaviour for the most complex actions.When scaled up into the hundreds or hundreds of thousands the interactionwithin groups of agents (such as people, orcs or penguins!) that emerges fromthese individuals is highly realistic.The same processing architecture that has been so successfully applied for

film and video is equally applicable for marine applications. The key features ofMassive of interest for marine congestion and safety assessment are:

Open and scaleable logic structure for the creation of autonomous agentbrains.

Open script structure for constraint input and output data creation. Fuzzy logic programming to mimic mariner response to navigation.


32 R. D. Colwill and S. L. Yeung

Current and wind fields and lane guidance options. 3D graphic ability and sophisticated element and terrain modelling.

3. Marine Applications

Key marine focussed behaviours have been developed within the Massive archi-tecture building on the core logic of BMTs established Dynamic Marine Trafficsimulation software, DYMTIRI.1 Principal features include:

Near Field Collision Avoidance. Mid Field Grounding Avoidance. Far Field Track Following.An autonomous agent brain is assigned to each vessel navigating withinthe Study Area to allow it to make decisions according to a predefined ruleset. In Massive each brain stores a collection of rule-sets for decision makingand controlling the response of the agent. An agent executes a rule-set whencriteria associated with the rule are satisfied. The building blocks of Massivebrains include input, fuzzy, logical, defuzz and output nodes. When combined,these nodes evaluate the external environment, develop weighted outputs, andultimately decide on a course of action for the agent.Massive includes a simple and self-explanatory Graphical User Interface for

constructing rule-sets in a brain; nodes can be dragged and dropped within awindow and rule-sets constructed by connecting nodes in the aforementionedorder. A key value of developing decisions making within a fuzzy logic environ-ment is that complex situations can create situations impossible to predict andaccount for in a deterministic manner. A series of fuzzy rule sets will result inthe most true action being identified (as defined by the membership functions)that represents the agents most pressing need. This action will then be taken;in many cases within deterministic software implementations freezing of theprogramme would occur.Vessels within the model each transmit and receive data on their location; the

distance, orientation and relative bearing between a ship agent and the target shipagent can be automatically recognised. A heirachy has been established to identifyvessel types so that the correct application of the COLREG2 may be made.Figure 1 shows a simplified brain element of a ship agent for collision avoidance

due to the starboard crossing of a target ship. It represents the simple case whena mariner becomes aware of an approaching ship and decides if his own ship isgoing to collide with this vessel. The decision options include slowing down hisown vessel and/or steering to starboard to avoid collision.Once the manoeuvring action has been completed and the rule sets no longer

identify that an action is required the vessel regains its course.In real-life, there is a variation in different peoples response to stimuli caused

by perception of external factors, degree of response and so on. These can beimplemented in agent based simulations by adding variation to membership



Fig. 1. Brain function for starboard avoiding action.

Fig. 2. Membership functions for direction perception.

functions. It also provides a smooth transition from one action to another one, acharacteristic of human decision making. Figure 2 shows themembership functionfor defining port, ahead and starboard where the trueness of a vessels positionrelative to the observer is input into the brain.It is feasible to develop extremely complex behaviour by the bundling of rule

sets; and the Massive architecture allows the independent testing of key rule setsand their later consolidation. An example of an early development of the marinerbrain is illustrated in Fig. 3.Of course the key to any model is its accuracy and in order to develop and

calibrate the model it is necessary to identify, in quantitative terms, the conceptsfor near and far etc, as perceived by the mariners. To achieve this validation


34 R. D. Colwill and S. L. Yeung

Fig. 3. Basic COLREGS implementation.

Fig. 4. Constricted marine traffic environment.

has been conducted against a number of digital radar data sets of port activity pro-viding quantitative support to the logical reasoning that underpins the marinersresponse behaviour.Figure 4 illustrates an application of the model where barge movements within

a constrained river channel are assessed to review the risk associated with theaddition of more berths.The output flexibility available in Massive allows the user to develop specific

reports. In this case the frequency of vessel conflicts and collision potential wasoutput against berth utilisation.



Fig. 5. Example of junction analysis.

4. Terrestrial Applications

While BMTs focus has been on marine applications it is straightforward to applythe same techniques to any problem where agent (people or vehicle) interaction isa significant issue; within ports such problems exist at port gates and within thestacking yard. Figure 5 illustrates the models application to a complex junction.While many existing models readily address junction capacity, this particularexample featured the assessment of the impact of large-scale roadworks, and themodel illustrated the impact of the works on traffic flows and system capacity,and of particular significance any knock-on impacts.

5. Conclusion

The continuing development of the worlds ports creates increasing capacityconstraints on existing infrastructure. The ability to plan and optimise develop-ments is essential, and recognising that the logistics systems will have significantman-machine interfaces is critical for future success. Autonomous agent modelsprovide the ability to represent complex marine and terrestrial systems and giveport stakeholders the tools to enhance the safe and efficient operation of portsystems.

References

1. Dand I. W., Colwill R. D. (2003), Simulation of Traffic Flows using Dynamic Ship ModellingProceedings, International Conference on Marine Simulation and Ship Manoeuvrability,Kanazawa, Japan.

2. International Maritime Organisation (1972), Convention on the International Regulationsfor Preventing Collisions at Sea (COLREGs).