## 6 Results
### ML-TZSA Query Response Results
The results are presented as relative values, where values below 1 indicate that ML-TZSA finds better objective values for particular criteria compared to RAPTOR on average. When only walking and public transport are available, RAPTOR finds slightly faster and less strenuous (in terms of calorie expenditure) journeys on average. This perhaps is not so surprising considering that ML-TZSA's RAPTOR component is relatively constrained by the maximum transfers and transfer-zone sequence imposed by journey profiles. This reinforces the idea that ML-TZSA is best considered an option for augmenting the offerings of MaaS apps rather than being a replacement for their existing ones. However, in all other cases, ML-TZSA finds better best solutions than RAPTOR on average. This is true for all criteria when the available modes are walking, public transport and e-scooters, and for the criteria of walking and waiting time, cost and CO\({}_{2}\) emissions, when the available modes are walking and public transport. That is to say, ML-TZSA offers the possibility to directly address a wider range of objectives within a query response time suitable for real-time applications. Figure 10 displays a sample of routes generated by ML-TZSA (500), as well as RAPTOR-ES's two non-dominated solutions (top-right) and a pure taxi route (top-middle). The bar charts indicate the overall relative quality of each solution with respect to the objectives of cost, time, CO\({}_{2}\), walking and waiting time and calories, respectively, with higher bars indicating better quality (lower objective values). ML-TZSA can provide numerous solutions with very similar characteristics to RAPTOR's but with variations on the modes used to access and leave the main train segment of RAPTOR's solutions. ML-TZSA's solutions include those where e-scooters are used at both the start and end of the journey (ML-TZSA 10 and 13), just at the start of the journey (ML-TZSA 3 and 11), just at the end (ML-TZSA 1) or never. ML-TZSA also provides solutions from entirely different regions of the objective space. For instance, ML-TZSA 13 shows a solution where the maximum possible usage of e-scooters is made given the geofence constraint that e-scooters can only be used within cities and cannot be used to travel between them. Such a solution has low CO\({}_{2}\) emissions; it is significantly faster than walking but is also one of the most expensive. Nevertheless, it is a non-dominated solution for this query. Figure 10 also shows that ML-TZSA can also generate solutions which evenly balance the trade-offs between the various objectives via a more equal division of the  
Figure 9: (**Left**) Average best ML-TZSA objective values relative to those of RAPTOR for the case of walking and public transport being available. (**Right**) Average best ML-TZSA objective values relative to those of RAPTOR-ES for the case of walking, public transport and e-scooters being available. total trip distance between the different transport modes. For example, ML-TZSA 1 shows a solution with moderate values for each objective criteria, achieved by travelling approximately equal distances by walking, bus and e-sooter. ML-TZSA solutions also include bus-based alternatives to RAPTOR's train-based solution (ML-TZSA 14), as well as a solution involving the use of short ferry crossings (ML-TZSA 4), which provide a faster walking-based solution than the pure walking solution (ML-TZSA 7). While none of the solutions are faster than the pure taxi journey, many relatively fast alternatives are provided which vastly reduce cost and CO\({}_{2}\) emissions at a small cost in terms of convenience and calorie expenditure. Figure 10: A sample of the 252 non-dominated journeys generated by ML-TZSA (500) and RAPTOR-ES’s two non-dominated journeys, plus a taxi journey for an example query.