## 1 Introduction
[10] proposed an A\({}^{\star}\)[12] style shortest-path journey-planning algorithm (which is a goal-directed version of [PERSON]'s shortest-path algorithm [13]) that only allows car usage in the first legs of journeys, as in park-and-ride journeys. Ref. [11] required journey-mode sequences as a commuter input constraint in their multi-modal travel-time minimisation approach. The problem considered here considers a unique objective function, that of the simultaneous minimisation of cost, travel time, inconvenience (measured as the sum of walking and waiting time), CO\({}_{2}\) emissions and calories expended. Prior to this, the most general objective function considered was that of [8], who considered cost in addition to travel time and number of transfers. Even for this objective function, acceptable query response times could not be attained without solution-space-reduction-based heuristics, by imposing structural constraints on journeys including mode-sequences. This work considers mode choice and their sequence a commuter choice issue, and the challenge addressed in this work is to rapidly generate all of the worthy alternatives. Regarding how this work extends that of [7], the focus of the previous work was an investigation of exact and heuristic solution methodologies set within small to moderate sized problem instances such that optimal solutions could still be computed within a reasonable amount of time. Time complexity analyses and computational results led to the conclusion that, for this new problem, the number of transport network nodes that are considered as potential multi-modal transfer points is a huge and computationally prohibitive factor influencing query response times. It was recommended that future work should investigate algorithms based on a transfer-point-sample-approach, a strategy that should most effectively reduce the size of the solution space. This work builds on the insights of [7] and proposes a methodology based on such a strategy. The Pareto set generation process of the multi-modal multi-objective journey-planning problem is decomposed into two steps. Firstly a Pareto set of journey profiles is generated, where a journey profile is defined as the combination of a _mode sequence_ and corresponding _transfer-zone sequence_. That is, solution space is reduced via a transfer-zone sampling approach. Secondly, the exact transfer locations within the transfer zones of the journey profiles are then optimised by a procedure guided by machine-learning travel-time predictions. The proposed approach is referred to as ML-TZSA, reflecting its use of machine learning and transfer-zone sampling. Figure 1 depicts the four main steps of ML-TZSA: (i) _Transfer-zone sampling_, in which the transport networks of interest are divided into a set of transfer zones (Section 4.1); (ii) _Inter-transfer-zone objective criteria contributions_, in which objective value contributions corresponding to single transport mode trips between each pair of transfer zones are calculated (Section 4.2); (iii) _Journey profile Pareto set generation_, in which a Pareto set of mutually non-dominated journey profiles, for a real-time query, is generated based on the inter-transfer-zone objective criteria contributions (Section 4.3); (iv) _Transfer-zone constrained transfer point optimisation_, in which the transfer points and paths of the journey profiles are optimised in a procedure guided by machine-learning travel-time predictions (Section 5). Phases (i) and (ii) are offline pre-processing tasks that prepare the algorithm to be able to handle any query between any pair of locations within the input transport network, while phases (ii) and (iv) are online query response tasks. On the whole, this approach side steps the issue of the very large number of possible sequences of transfer locations without ruling any out.