**3.2 Factors affecting adaptive mode choices**

**Table 1** illustrates the proposed approach and a non-exhaustive list of factors affecting choice probabilities on the two choice levels.

The realization of a specific mode choice is therefore the consequence of the mode first belonging to the feasible set (choice level 1), and then actually chosen within such set with given real-time events (choice level 2).

**Figure 1** illustrates, in further detail, individual components of the decision making processes with inputs and outputs of the two levels of choices.

Any of the traditional mode choice models can be applied to calculate the probability at the first level. Once the probabilities of all possible modes are calculated, the set of feasible modes can be formed, which is a quite standard procedure and thus omitted here. In the second level of the decision making process, the feasible modes are subject to re-interpretation and their probabilities are reassessed based on real-time events. For example, if walking is the preferred mode with the highest probability at the first level, and the weather is rainy in real time, the probability associated with walking decreases.

The whole procedure may be easily formalized in a compact formulation coherent with existing assignment models, thus may be implemented for simulation any transportation system (see technical report [88]).

*Models and Technologies for Smart, Sustainable and Safe Transportation Systems*

effects of real-time events on both pre-trip and en-route mode choices.

investigates an adaptive mode choice behaviour and presents the results of an empirical study undertaken to validate the approach. It focuses on the potential

For reason that will become clear below, this adaptive mode choice will be hereafter called "*hypermode"*, in analogy to the hyperpath concept proposed for the route choice in transit assignment [84]. The hyperpath approach suggests that travellers first identify a set of attractive lines that connect their origin–destination (O-D) pair; then they choose a specific service according to certain strategies. Such strategies can be based on the minimization of travel time, waiting time, walking distance, or the number of transfers A more complex strategy can also consider the influence of real-time information on path choices [85, 86]. In an analogous way, the hypermode concept stipulates that travellers identify a set of feasible modes for their target trip and may make their final decisions later based on real-time events. These adaptive mode choices have been recently facilitated by the development of Information and Communication Technologies (ICT) such as smartphones, as well as Intelligent Transport Systems (ITS) such as vehicle tracking and prediction. For example, travellers can now make informed mode choices based on estimated time of arrival of buses/trains/trams, or the availability of shared bikes at any given docking station. Such adaptive travel behaviour is suitable for dense urban areas, where plenty of mode options and access points are available to travellers, and walking is always an option especially for short trips. Given that 50% of the trips in urban areas in Europe are shorter than 5 km [87], the hypermode concept enjoys wide empirical support. This extra modelling dimension could lead to a significant yet challenging advancement in the modelling of multimodal transport networks. This section illustrates this notion by proposing a conceptual analytical frame-

In this section, we formally introduce the *hypermode* concept, which is analogous

to the hyperpath concept proposed for the route choice in public transit assignment [84]. The hyperpath approach suggests that a traveller first identifies a set of attractive lines that connect the origin–destination (O-D) pairs. Then, he/she chooses a specific service according to a certain strategy, which can be based on the minimization of travel/waiting time, amount of walking, or number of transfers. A more sophisticated strategy can also take into account the influence of real-time information on path choices [86]. In an analogous way, the hypermode concept stipulates that travellers identify a set of feasible modes for their target trip and may later make their final decisions based on real-time events. These adaptive mode choices have been recently facilitated by the development of Intelligent Transport

Systems (ITS), and Information and Communication Technologies (ICT).

times, and financial costs of using different modes.

The underpinning decision making process involved in the hypermode concept

1.The user identifies a set of feasible travel modes for the trip, which are accessible at the same physical location or nearby. On this level, the decision making is strategic (i.e. not real-time), and is affected by static characteristics such as user preferences, socio-economic characteristics, average/historical travel

2.Right before a trip is made, the user evaluates real-time events in order to select a specific mode of transport from the aforementioned feasible set. The realtime event includes but is not limited to: availability of vehicles (relevant to

**10**

work along with a few examples.

is articulated in two levels.

**3.1 Decision-making architecture**


#### **Table 1.**

*The two choice levels and influencing factors in the hypermode approach.*

**Figure 1.**

*Flow chart representation of the hypermode concept.*
