## 6 Recommender System Scalability and Validation
### Scalability and Validation  
The correctness of the traffic infrastructure model and its primitive scalability are assessed by verifying the number of created software agents, which represent road and public transport segments. The agents' initiation logs and console printouts enable checking the conformity of the agents created with the actual number of road and public transport segments retrieved from the static maps of our trial cities, namely Nottingham (United Kingdom) and Sofia (Bulgaria). Overall, we modeled approximately 4900 road segments, 267 bus segments, and 15 tram segments in the city of Nottingham, and 8440 road segments, 401 bus segments, 118 trolleybus segments, 157 tram segments, and 18 metro segments in the city of Sofia. These segments exclude service and residential roads. The scalability of the proposed system is a critical factor for verifying system support for larger urban areas. Such scalability can be restricted by the computational (server) resources made available. In particular, the computational processing power and dedicated memory needed to initialize the system with the transportation infrastructure of the urban area, such as roads, pedestrian and cyclist paths, public transport, and their interconnections. Figure 8 depicts the performance of our multimodal route guidance system on three different machines in terms of the maximum number of agents and their initiation times. We tested the initiation of software agents in three different workstations, namely, machine A (with the following technical specifications: 2.7 GHz Opteron VM, 2 GB, Linux, 32-bit), machine B (with the following technical specifications: 2.8 GHz Intel Core i5, 2 GB, Linux, 32-bit), and machine C (with the following technical specifications: 3.1 GHz Intel Core i5, 4 GB, Win-7, 64-bit). Figure 8 shows that machine C creates more software agents (140,000 agents) in the environment than machine B (90,000 agents) and machine A (45,000 agents). This might be explained by the larger memory size of machine C. Moreover, machine C creates more agents than machine B and A for the same execution period. Within 2 s, machine C initiated approximately 56,000 agents, while machine B initiated 40,000 agents and machine A initiated 32,000 agents. This technical testing demonstrated the suitability of AGlobe in creating agent-based environments that are appropriate for modeling urban cities within reasonable times. The purpose of creating the agents was to (1) pinpoint the hardware requirements for running the multimodal recommender system and (2) confirm the scalability of the platform and its ability to model a big number of transport segments. Overall, it was shown that a workstation with 4 GB of RAM enables us to create and simulate up to 140,000 physical transport segments. Figure 8 shows that these software agents (i.e., 140,000 agents) are initiated within approximately 7 s. It can also be noted that increasing the memory capacity of the workstation helps to accommodate for more transport segments in a particular city. Ideally, an agent-based implementation framework that empowers the creation of more agents to represent the full infrastructure of the city simply means the ability to support flexible multimodal route planning. Figure 8: Number of software agents initiated on three distinct workstations. The multimodal route guidance tool implements a visual web interface for traffic management operators to view and monitor the status of city traffic. This module over-lays (1) the physical transport network and (2) traffic updates received from traffic congestion data estimator on the Open Street Map images. The visualization tool depicts a live view of the traffic of the city and empowers the operators to test the recommender system. Generally, the backend of the multimodal recommender system pushing transport data (i.e., map of the city to be visualized), traffic updates (density, flow, CO\({}_{2}\) emissions, and so on), and route solutions to the connected web clients.