of sessions 1 full day session

Table 2. Core description of Bijlmer Junction Game

controllers was crucial, as studied earlier by Albrecht (2009).

frequency planning will mean in practice for their tracks.

Lessons Learned from Modeling Six Games for the Dutch Infrastructure Management 283

Purpose Testing and validating a control concept for high frequency

interfere with train driver behavior. Simulated world Detailed infrastructure between Amsterdam and Utrecht,

different from real-world abstraction.

Consequences Data generated in the game yielded insights in key materials

ProRail had assigned a project team to come to new control and steering procedures that suite the future reality of high-frequency passenger trains. The challenge of this project team was to come up with new concepts that would both be supported by train traffic controllers and network controllers, and would yield a stable, controllable control and routing operation when put into place. The question was raised: how to test new control and steering concepts when there is no option to test in real life? The Bijlmer Junction Game was targeted at this. In the game the interaction of train drivers, traffic controllers and network

The gaming simulation session yielded insights in key materials and resources needed for implementation of the control concept, and high frequency planning in general. The importance of buffer areas with sufficient space to side-track a train without disturbing other services, platforms asides the entire train for passenger exit, and alternative departure options for all passengers within reasonable time is a clear outcome for ProRail. Furthermore, train traffic controllers do not yet seem to realize what the projected high-

As described in Meijer et al (2009), this game was not a break-through success. We learned that involving the operational people in the organization in a game that modeled the infrastructure and timetabling as detailed as they are used to, requires interfaces that connect to the situation awareness capabilities of these operators. Simple said: even though we checked our approach upfront with the operators, they were not able to do what they

Difficult for train traffic controllers; see discussion in Meijer

and resources needed for implementation of the control concept, and high-frequency planning in general.

Immersion Very fast and deep for train drivers and network controllers.

Data presentation Highly detailed through computer interface. Interface

train transport.

controller (5) # of players 10 plus 2 facilitators and 2 experts.

Roles Train driver (2), Train traffic controller (3), Network

Own/real/fictitious role Own role, participant selected by their team leaders Scenarios 3 Scenarios, gradually testing more complexity. Intervention range Facilitators could start, stop and pause scenarios and

detailed timetable.

et al (2009).

Type of data generated Quantitative (failed) and qualitative.


Table 1. Core description of Rail Cargo Market Game

With respect to the three modeling issues we learnt that it is very hard to have operational level people in an abstract simulation when they have to work on infrastructure and timetabling they do not recognize. The usual flexibility that is commonly found in gaming with groups of higher education is not working here. To overcome this, we automated some of their tasks in the game into a computer model for train path reservations. This worked flawless for the more management-like question of this game.

### **5.2 Bijlmer junction game**

This subproject introduced ProRail to a computer-based gaming simulation developed on ProRail's own MATRICS simulator (Van Luipen and Meijer, 2009). This simulation pushed the envelope in terms of utilizing the technical specifications of MATRICS. This type of game was described as a multi-player process simulation due to its detailed reflection of real-life operational processes. The participants play a pre-defined role that is 100% identical to their job description, to carry out their real-life duties in a simulated game environment. Table 2 lists the core description of this game. For a full description we refer to Meijer et al (2009).

Purpose Studying the potential value of various market mechanisms

Roles Clients with demand for transport, Rail Cargo Transporters,

Own/real/fictitious role Real role, but selected for knowledge for instance from previous job position.

Scenarios 3 – 4 scenarios per session. First scenarios that explored the

Intervention range Facilitator could start and stop the scenario and dissolve disputes only on the process steps.

Simulated world Stylized train path market, stylized transport demand Immersion Fast, once roles were clear and adopted. Lively play including

Data presentation Simplified to stylized network, simplified timetable and

that was similar conceptually. # of sessions 3 subsequent games each with 1 session during 1 full day. Type of data generated Quantitative and qualitative, testing hypotheses about

Consequences Policy formulated but put out of scope for 2010/2011, possible

With respect to the three modeling issues we learnt that it is very hard to have operational level people in an abstract simulation when they have to work on infrastructure and timetabling they do not recognize. The usual flexibility that is commonly found in gaming with groups of higher education is not working here. To overcome this, we automated some of their tasks in the game into a computer model for train path reservations. This worked

This subproject introduced ProRail to a computer-based gaming simulation developed on ProRail's own MATRICS simulator (Van Luipen and Meijer, 2009). This simulation pushed the envelope in terms of utilizing the technical specifications of MATRICS. This type of game was described as a multi-player process simulation due to its detailed reflection of real-life operational processes. The participants play a pre-defined role that is 100% identical to their job description, to carry out their real-life duties in a simulated game environment. Table 2 lists

the core description of this game. For a full description we refer to Meijer et al (2009).

application in 2012. Politically very sensitive.

capacity allocation.

validate the successful configurations.

getting insight in their track system.

Management
