The conversation_id is used in interactions for identifying messages that belong to a specific conversation. All messages of one interaction conversation_id String should share the same conversation_id. Unique ids can e.g. be generated via the method SFipa.createUniqueId(). The reply_by field can contain the latest time for a reply_by Date response message. For tracking, future analysis, and interesting visualizations of simulation dynamics, each FIPA message has its own POJO to be stored in message and message_type table (see extended Entity/Relationship). This is shown in Appendix N. Generating new indicators For the assesment of new case studies and different policy instruments (scenarios) we designed different local and system-wide indicators (KPI). In order to generate those outcomes, we extended both our relational data model as well as agent-based model (mainly simulation engine). The following table shows the different indicators used: passenger oriented, aircraft performance, eco-sustainability related indicators, and different metrics evaluating solution complexity. 2 - DCI-4HD2D Id Indicator Unit Category 1 Gate-to-gate Minutes Passengers passenger performances trip time 2 Door-to-door Minutes passenger trip estimation 3 Societal cost Euros estimation 4 Pax delay minutes 5 Missed Passenger connections 6 Flight Minutes Aircraft performances 7 Airlines cost Euros 8 Hub airline cost
conversation_id String The conversation_id is used in interactions for identifying messages that belong to a specific conversation. All messages of one interaction should share the same conversation_id. Unique ids can e.g. be generated via the method SFipa.createUniqueId(). reply_by Date The reply_by field can contain the latest time for a response message. For tracking, future analysis, and interesting visualizations of simulation dynamics, each FIPA message has its own POJO to be stored in message and message_type table (see extended Entity/Relationship). This is shown in Appendix N. Generating new indicators For the assesment of new case studies and different policy instruments (scenarios) we designed different local and system-wide indicators (KPI). In order to generate those outcomes, we extended both our relational data model as well as agent-based model (mainly simulation engine). The following table shows the different indicators used: passenger oriented, aircraft performance, eco-sustainability related indicators, and different metrics evaluating solution complexity. 2 - DCI-4HD2D Id Indicator Unit Category 1 Gate-to-gate passenger trip time Minutes Passengers performances 2 Door-to-door passenger trip estimation Minutes 3 Societal cost estimation Euros 4 Pax delay minutes 5 Missed connections Passenger 6 Flight Minutes Aircraft performances 7 Airlines cost Euros 8 Hub airline cost
E.02.14-D2.1-DCI-4HD2D-Platform%20extension%20report%20V1.docx