Yos Sunitiyoso

Multi-Agent Reinforcement Learning for Value Co-Creation of Collaborative Transportation Management (CTM)

CollaborativeTransportationManagement(CTM)isacollaborationmodelintransportationarea.The useofCTMintoday’sbusinessprocessistocreateefficiencyintransportationplanningandexecution processes.However,previousresearchpaidlittleattentiontodemonstratetheabilityforallagents inCTMtoco-createvalueinservices.Thepurposeofthispaperistoincreasetheunderstandingof valueco-creationinCTMareaandlearningprocessesinrealsystemsbasedonvalueco-creation ofCTM.Multiplecasestudieswereusedtoanalyzethevaluethatwasperceivedbyallagentsin CTMineachcollaborationstageandprovidedempiricalevidenceontheinteractionsamongagents. Model-freereinforcementlearningwasusedtopredicthowCTMcouldreducetransportationcost, increasevisibility,andimproveagility.Thesimulationresultsshowthattheinput,feedback,andthe experienceoftheagentsareusedtostructurethecollaborationprocessesanddeterminethestrategies. KeywoRdS Agent Based Modeling, Collaborative Transportation Management (CTM), Multi-Agent Model, Reinforcement Learning, Value Co-Creation

Social Interaction in Travel Behaviour: Insights for Developing Effective Travel Demand Management for Indonesia

This chapter discusses research findings related to the effects of social interaction on travel behaviour. Insights gained from the findings are then analysed in relation to strategies and policies to manage travel demand, by reducing or redistributing the demand in space or in time for Indonesian context.

Understanding the Learning Processes of Traveller Behavioural Choices Using Agent-Based Approach: A Conceptual Framework

This paper presents a conceptual framework of agent-based model to study the learning processes of traveller behavioural choices. Social interaction and social learning among travellers are taken into the model. The way an agent-based model can be combined with traffic microsimulation model is also presented.

Multi-agent Reinforcement Learning for Collaborative Transportation Management (CTM)

Collaborative Transportation Management (CTM) is a model collaboration in transportation area conducted through information and resources sharing. Planning and implementing CTM not only involve optimization of decisions for all collaborative agents but also involve the influence of different interactions among agents to achieve higher CTM benefits. This paper explores how agent-based modelling is used to model interaction and learning process in CTM in real systems. Agent-based model is used in this paper based on consideration that agent-based model can model the emergent decision patterns and unexpected changes of decision based on the decision-making structure. Model-free reinforcement learning is used to predict the consequences and optimize all agents’ action in CTM.