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Passengers steering trains

Passengers steering trains. A Multi-Actor Approach for Operations and Control in the Netherlands Railways. Niels R. Faber * , René J. Jorna * Erwin Abbink † , Ramon Lentink † & Fred van Blommestein * * University of Groningen † Netherlands Railways. Outline. Introduction

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Passengers steering trains

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  1. Passengers steering trains A Multi-Actor Approach for Operations and Control in the Netherlands Railways Niels R. Faber*, René J. Jorna* Erwin Abbink†, Ramon Lentink† & Fred van Blommestein* * University of Groningen † Netherlands Railways

  2. Outline • Introduction • Research questions • Method • Case 1: initial MAS • Case 2: statistically simulated passengers • Discussion

  3. Helmhout, Gazendam, Jorna & Faber. MAS@NS: Transportbesturing door Smart Agents MAS projects at RUG/EB/IMS • Simulatie projectmanagement bij Ministerie OCW (1990): Gazendam • Computational Transaction Cost Economics (Tomas Klos, 2000) Jorna/Gazendam • Multi-Actor SOAR (Hans van den Broek, 2001): Gazendam/Jorna • The Social Cognitive Actor (Martin Helmhout, 2006): Gazendam/Jorna • Simulation of Crowd and Riot Control (Nanda Wijermans): Jorna/Jager • Transportbesturing door Smart Agents (MAS@NS) (2007)

  4. Introduction • Netherlands Railways • Train timetable specifies planned train movements • However:delays & disruptions occur

  5. Introduction • Handling delay & disruption • Dispatching task • Specific type of planning & control • Objective: • Restore train movements according to train timetable as quickly as possible

  6. Introduction • Dispatch task: • Solving a logistical puzzle • Available means: • Train level: • Speed of train (faster / slower) • Direction of train (reversing movement) • Action of train (activate / cancel train) • Between trains level: • Holding trains at station for transfer of passengers

  7. Introduction • Dispatch task organization: ProRail NSR 4 4 Network control Control center Central control 13 13 Train shift control Node coordination Local control

  8. Introduction • Dispatch task knowledge: • Railway network • Train timetable in controlled region • Contact information train drivers and ticket inspectors

  9. Introduction • Main foci of dispatch task • Restore train timetable • Balance moving material • Balance personnel

  10. Introduction • But what about passengers travelling by train? • Are they considered in dispatching? • How are demands and wishes of passengers included in dispatching solutions? • What does it mean to give passengers a voice in dispatching?

  11. Introduction • Types of control • Input oriented control • Dominant: budgets • Process oriented control • Dominant: production planning, utilization of capacity • Output oriented control • Dominant: costs and revenues per product, marketing

  12. Introduction • Organization of planning • Organizational units • Costs and revenues • Quality

  13. Introduction • Desire to incorporate passenger demands in dispatching • Currently, implicit consideration in handling delays / disruptions • No formal role in decision process of choosing dispatch solution • No coordination structure for incorporation in dispatch task

  14. Research questions • What is the intended role of passengers in dispatching? (informing / deciding) • What organizational structure supports the involvement of passengers in dispatching? • What knowledge about passengers needs to be included in dispatching? • What knowledge should passengers provide if they participate in dispatching?

  15. Method • Developments in thinking about planning and organization • System theory -> Cognition • Top-down -> Bottom-up • Logic -> Computational Mathematics • Fixed task distribution (DAI) -> learning systems (MAS)

  16. Method • Human cognition • Fayol as forerunner • Simon, March • DSS movement: The user at the driving wheel • KADS (Henk Gazendam, 2007)

  17. Method • Top-down -> bottom-up • Anthony: Hierarchical model planning • Lindblom: The science of muddling through • Planning cannot proceed until consensus is reached • Technically acceptable solutions -> Socially acceptable solutions • Mintzberg: The Rise and Fall of Strategic Planning • Emergent strategy (Henk Gazendam, 2007)

  18. Method • Logic • Closed worldview • Problems with processing of changes and time • Logic other than first order logic large computational complexity (Henk Gazendam, 2007)

  19. Method • Computational mathematics • Emergence (Holland) • Coherence (Thagard) • Production of complex systems (Wolfram) • Evolution (Gigerenzer, Dennett) (Henk Gazendam, 2007)

  20. Method • DAI -> MAS • From fixed task distribution (DAI) to learning systems (MAS) • DAI: Distributed Artificial Intelligence • Actors are not autonomous • Actors only able to execute specific task • Top-down, hierarchical coordination model (contract net protocol) • Communication limited to task execution • Fixed task distribution and fixed specialization of actors (Henk Gazendam, 2007)

  21. Method • Multi-Actor system (identified as promising technique) • Actor • Autonomous • Communicative ability • Ontologies • Protocols • Task execution ability • General problem solving methods • Recognition of task environment • Search in problem spaces (weak methods) • Learning ability • Exploration and imitation • Optimization (neural net) • Evolutionary learning (variation and selection)

  22. Method • Actors can be: • People • Active computer programmes meeting certain conditions (agents) • “An agent is a computer system, situated in some environment, that is capable of flexible autonomous action in order to meet its design objectives” (Wooldridge, 2002, p.15) • Organisational units represented by a human actor or computer agent

  23. Method • Characteristics of a MAS (Jennings et al., 1998) • Each agent has incomplete information, or capabilities for solving the problem, thus each agent has a limited viewpoint. • There is no global system control • Data is decentralized • Agents communicate through messages • Patterns of messages can be specified in protocols(FIPA: Foundation for Intelligent Physical Agents)

  24. Method • Agent task execution: • Tasks are executed through behaviours • Types of behaviours from simple to complex, composite • Various models to shape behaviour, e.g.: • Utility • BDI

  25. Method Social abilities Cooperative agents Cooperation Communication Interaction Multi-agentsystems Individual knowledge Social knowledge Control knowledge Solution plans World maps Behaviors resources Distributedproblem-solving Role Commitments, beliefs Protocols, primitives Distributedsystems Planning Navigation & obstacle avoidance Task solving Secure mechanisms, perception Autonomous agents Individual abilities (Glaser, 2002)

  26. Method • For Netherlands Railways: MAS used to explore organizational structuring when incorporating passengers in dispatching task • Human actors and software agents collaborate to solve problem

  27. Case 1: initial MAS • Objective: • develop a MAS prototype that enables passenger involvement in dispatching in situations of delays and disruptions of the train timetable • prototype is a useable simulation platform for future simulations

  28. Case 1: initial MAS • Exploration of; • organizing for retrieval of desires and demands from passengers • consequences of delay scenarios • Using real-time passenger agents

  29. Case 1: initial MAS • Methodology: • Prometheus Design Tool (http://www.cmis.rmit.edu.au/agents/pdt) • UML

  30. Case 1: initial MAS • PDT overview

  31. Case 1: initial MAS

  32. Case 1: initial MAS

  33. Case 1: initial MAS • Main agents: • Planner • communication with dispatcher • handling disruptions and delays • TravelManager • handles travelling information • message forwarding to/collection from passengers • CustomerTravelCoach • aids passengers with travel plan selection • TravelAssistent • communication with passengers • SecurityAssistent • handling subscription of passengers

  34. Case 1: initial MAS • MAS platform: Java Agent DEvelopment framework (JADE)

  35. Case 1: initial MAS • JADE characteristics • agents have behaviours • agents communicate through messages • protocols fix specific messages passing patterns • ontologies available to specify valid message content • communication between agents follows communication act theory • BDI possible through JADEX extension

  36. Case 1: initial MAS • Scenario: • Disruption: tracks between Haren and Zwolle • Cause: leaves on tracks

  37. Planner TravelManager Dispatcher …… TravelCoach TravelCoach …… Passenger n Passenger 1

  38. Case 1: initial MAS

  39. Case 1: initial MAS

  40. Case 1: initial MAS

  41. Case 1: initial MAS

  42. Case 2: statistically simulated passengers • Extension of initial MAS • Inclusion of statistical data to: • generate more realistic payloads of trains (passengers) • shape statistically based passenger agents with characteristics for: • travelling motive • ticket type • travelling frequency • departure-destination combinations • travel plan

  43. Case 2: statistically simulated passengers • Objectives: • extend MAS with passive passenger agents • integrate statistical data about passenger movements • prepare alternative disruption / delay scenarios • coordinate passenger responses • plan for empirical testing

  44. Case 2: statistically simulated passengers • Additional agents: • StatisticalPassenger • simulates one passenger in a train based on statistical data • StatisticalManager • handles statistical data • CommunicationManager • transforms information about delayed trains into StatisticalPassenger agents

  45. Planner TravelManager Dispatcher …… TravelCoach TravelCoach …… Passenger n Passenger 1

  46. TravelManager StatisticalManager CommunicationManager Statistical data StatisticalPassenger StatisticalPassenger

  47. Case 2: statistically simulated passengers • StatisticalPassenger • Station of departure • Station of destination • Ticket type, travel motive, travel frequency, • Travel plan (route and schedule) • Responds to messages about delays and disruptions from TravelManager

  48. Case 2: statistically simulated passengers • Coordination of reactions of passengers • TravelManager agent is enhanced • TravelManager needs to send one clear message to the dispatcher (through the Planner agent) • Collect responses from real-time passengers • Collect responses from StatisticalPassenger agents

  49. Case 2: statistically simulated passengers • Current status: • Statistical passengers are created • Behaviour of passengers needs to be implemented • Behaviour of TravelManager agent for collecting and handling responses from real-time and statistical passengers needs to be implemented

  50. Discussion • Focus on organization of passenger involvement in dispatching • Communication and collaboration between human actors and software agents • Cognition currently located in human actors

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