## 5 Machine-Learning-Based Transfer Point Optimisation of Journey Profiles (Online)
### Transfer Point Optimisation  
Using an example journey profile including the transfer-zone sequence \(1,2,3\) and corresponding mode sequence \(walk\), \(bike\), \(taxi\), Figure 6 illustrates the ML-TZSA methodology for optimising the transfer points and path of a journey profile. Figure 6 depicts the  
Figure 5: Neural network structure.  
steps performed in one journey leg-iteration of Figure 3 (main loop) for the case where the current transport mode is _bike_. Firstly, the _origins_, which are the inputs of the many-to-many [PERSON]'s algorithm, are the nodes in cluster 1 that can be reached along the walking path tree (black lines) from the origin. The many-to-many [PERSON]'s algorithm generates cycling shortest-path trees (green lines) from cluster 1 to cluster 2. The next step is to commit to one of the cycling sub-trees, and thereby fix the transfer point of cluster 1 and also a single walking path within the walking path tree. For this, machine-learning predictions (depicted as curved dashed lines) are used to estimate arrival times at cluster 3's sampled transfer point (centroid), via taxi, starting from each possible cluster 2 transfer point. In the example, a transfer from bike to taxi at node 9 is found to minimise the expected arrival time at cluster 3's sampled transfer point, which corresponds to the cycling path sub-tree starting at node 7 (thick green lines), which is then fixed as the transfer point in cluster 1. As a result, nodes 9, 10, 13 and 15 become the origins for the next iteration of Figure 3, as well as being the candidate locations for the next transfer point in cluster 2. In summary, machine-learning predictions for the next journey leg are used to decide on a path through the previous leg's many-to-many shortest-path tree and commit to a single path sub-tree of the current leg's many-to-many shortest-path tree. The issue that machine-learning predictions can exhibit inaccuracy is not too critical given that the machine-learning predictions are only used to choose a sub-tree from a many-to-many shortest-path tree.  
To conclude the presentation of the proposed methodology, we note that the aim of this approach is to generate a large number of alternative journeys with attractive features regarding cost, time, CO\({}_{2}\), convenience and calorie expenditure. So, while our journey profile transfer point optimisation procedure is driven by time minimisation, constraining the transfer points to limited size transfer zones provides the mechanism by which the objective criteria characteristics of the original journey profiles are preserved. On a case-by-case basis, it is also possible to further limit the candidate transfer points to those that do not impact the objective values too much. In any case, the proposed approach is intended to augment the offerings of MaaS apps rather than replace their existing ones, be they optimal or not. Regarding the issue of presenting too many journey options to commuters, the alternatives can be ordered according to different preference criteria (possibly listed in a drop-down menu). Another option is to present those optimising each individual objective  
Figure 6: Diagram illustrating how machine-learning predictions of travel times of the subsequent leg of a multi-modal journey, starting from different possible transfer zones, are used to select the transfer point of the previous leg.  
in addition to a selection of those balancing conflicting criteria, perhaps identified using a clustering algorithm.