# On the Use of the Multi-Agent Environment for Mobility Applications
## 2 Environment as a Spatio-Temporal Reference
### Space-Time Environment in Mobility Simulation
To be aware of only the events that concern them, the Connected traveller agents subscrib to the only arcs of the multi-agent spatiotemporal environment that form their route. When the travel time of an arc or the departure time of the vehicle changes, the information is broadcast to the subscribed connected travellers. The planning process is then launched with the new network state. Disruptions are modelled exclusively by modifying the space-time arcs (according to the vehicle schedules). Indeed, a vehicle's delay is injected into the model by dynamically adjusting the space-time arcs representing the corresponding vehicle schedule, setting the destination node time to the delayed arrival times. Breakdowns are also modelled by removing the arcs corresponding to the vehicle's mission from the space-time network. To model the failure of an entire line, the arcs representing the schedules of the remaining vehicles on the line are all deleted. As soon as a schedule is modified, based on the space-time network, the information is immediately detected by the relevant Connected agents, and only to them. Thus, when a timetable is modified, information about the delay or breakdown is sent only to connected travellers interested in these vehicles' missions. We executed the experiments with the data of the city of Toulouse in France. We chose this French city because we have detailed data about its network and a description of the travel patterns of the region [14]. The data came from Tisseo-SMTC, the public transportation authority of the Greater Toulouse. The public transportation network of Toulouse is composed of 80 lines, 359 itineraries and 3887 edges. Frequencies and edges costs are updated hourly. The multiagent system comprises 18,180 vehicles and from 5000 to 30,000 passengers. We define the number of ticks per simulation to 5000 for a journey from 6 am to 2 am. Every simulated tick corresponds to approximately 14 s. We chose the origins and destinations of passengers coherently with travel patterns of the region (the origins-destinations generation method is in [10]). We executed the simulations  
Figure 4: Architecture of the multi-agent system. on a PC under Windows 7 with a processor Intel Xeon CPU E5-2630 (12 cores at 2 Ghz) with 50 GB of memory. Previous works have shown that traveller information has little impact in case of minor disturbances. For this reason, we decided to use severe disorders instead in the form of complete disconnection of edges. In every simulation, we generated five random edge disconnections on the network during the whole simulation (one disconnection every 233 real-time minutes approximately). Every disconnection lasts 250 simulated ticks (slightly less than one hour in real-time). Due to edge disconnections, some passengers can no longer find an itinerary to their destination because edge disconnection impacts network connectivity. In the following results, these passengers were not considered (the ratio of passengers without an itinerary is stable, around 5% in all the simulations). Disturbances are random but concern only a certain number of edges that we consider significant to disconnect: the edges through which pass at least five different itineraries. We chose the five randomly disconnected edges between 21 candidate edges that satisfy this requirement. We considered six different information level scenarios and executed each one 25 times. The first scenario is \"the reference configuration\" (to which we compared all the others) where no up-to-date information is provided to the passengers, neither local nor personalised. They only have the static description of the network and timetables. In the second scenario, the system provides only local information. The new travel times are available for the only passengers present at the considered stop. We did not consider any connected passengers in this scenario. The system provides local information at the stops in the remaining scenarios (3, 4, 5 and 6), and personal information is only available for the connected passengers.