## 7 Conclusions  
This work has presented a methodology for rapidly generating many efficient alternative multi-modal journey alternatives with respect to the objectives of cost, travel time, CO2 emissions, convenience and calorie expenditure. The aim of this methodology is to augment the offerings of MaaS apps with cheap, fast, green, convenient and energy efficient alternatives to private car journeys, which, given their uptake, can in turn help to reduce pollution and congestion on our roads. Our methodology side steps the issue of the large solution space of possible transfer locations between pairs of transport modes using a two-step approach. Firstly, a Pareto set of journey profiles is generated based on aggregated transfer zones. Secondly, integrated and adapted versions of existing shortest-path algorithms for open and public transport networks, with various speed-up techniques, are used to optimise the transfer points and paths between transfer zones in a procedure guided by neural machine-learning predictions. A novel hybrid k-means and [PERSON]'s algorithm was introduced for determining sets of transfer zones which accounts for transport network topology and local geography. In general terms, this work provides another example of how learning and optimisation can be beneficially integrated. The proposed methodology was then tested on a real large-scale multi-modal transport network. Pre-processing times for ML-TZSA were found to be linearly increasing with increasing transfer-zone sample size. In terms of query responses, Pareto set sizes increased with transfer-zone sample size without adversely affecting query times, which were never more than a few seconds. The time required to generate journey profile Pareto sets was always very small, while the time to optimise the transfer points, and the paths between them, decreases with increasing transfer-zone sample size, because this reduces the transfer point solutions space. The result is that the recommended transfer point sample size should be as high as possible before the pre-processing times and memory requirements become too large. However, meaningful results are still obtained for moderate sample sizes. ML-TZSA was contrasted with RAPTOR in order to illustrate how ML-TZSA can generate large diverse Pareto sets of multi-modal journey, addressing a wider variety of objective criteria in similar query times to RAPTOR. By looking at a sample of the routes generated by RAPTOR and ML-TZSA, it was shown that ML-TZSA can generate routes including those of equal quality and characteristics to those of RAPTOR's, but also that ML-TZSA can provide many solutions that minimise and balance different objectives. Based on four different Pareto set metrics, ML-TZSA consistently found high-quality and diverse Pareto sets. While our transfer point optimisation procedure is driven by time minimisation, constraining the transfers to limited size transfer zones provided the mechanism by which the objective criteria characteristics of the original journey profiles are preserved. Quickly generating truly optimal and full Pareto sets of multi-modal journeys for the criteria considered, without relying on pre-processing and transfer-zone sampling as this work does, currently remains an open area of research. However, the issue of presenting too many journey options to commuters warrants methods that limit the sizes of the Pareto sets generated. A related future research direction could involve trials into the influence that presenting different sets of journey alternatives has on travel behaviour for different demographics and personas. Another future direction is to investigate how shortest-path algorithms can incorporate preference criteria such as avoiding motorways, large numbers of turns and lane changes, and to use such algorithms in modular multi-modal journey-planning algorithms such as that presented here. Conceptualization, D.O., N.D. and G.F.; Methodology, C.B. ; Software, C.B. ; Formal analysis, D.O.