**4.1 Survey study**

A pilot survey has been undertaken to explore the validity of the underpinning idea of the proposed hypermode concept. 50 respondents have been interviewed at Imperial College London. The sample includes academic, technicians and administrative staff as well as students, to ensure that behaviour in different user categories is captured. The respondents have been interviewed face-to-face to ensure an in-depth and comprehensive grasp of their decision-making processes.

#### **4.2 Survey design**

The respondents were presented with two different scenarios:

SCENARIO 1. The regular commuting trip home from the College at the end of the day, which is a Revealed Preference scenario. The origin is the same for all respondents but the destinations vary, with some at walking distance and others outside of London.

SCENARIO 2. A hypothetical trip from the College to Sloane Square (a shopping destination 2.1 km away from the origin) at the end of the working day. This is a Stated Preference scenario.

In the first scenario the respondent is asked what modes are available for his/her trip. An open question is then asked to describe the decision making process that shortlists the possible mode options or leads to a specific mode choice. Afterwards they are asked if any of the following real-time events may affect their final mode choice:

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**Table 2.**

What do you do? Wait for the bus even if you may arrive later than

Use the 12 minutes for other errands and then go back to

Consider alternative buses **Consider alternative modes**

*Survey scenarios for the bus.*

expected

the bus stop

*Adaptive Travel Mode Choice in the Era of Mobility as a Service (MaaS): Literature Review…*

If the respondent's explanation of the decision making process at the open question is in line with the adaptive behaviour, as confirmed by answering "yes" to any of the above real-time events (1 to 4), then this user behaviour is related to

In the Stated Preference scenario (Scenario 2) the modes available to the user are the same as those shown in **Figure 2** (with possibly different routes and access points), and are associated with given average costs and travel times. The user is asked what would his preferred mode option be in the described scenario. Depending on the preferred mode, a range of real-time events are presented to the respondent, which may lead him/her to reassess the original choice. For example, **Table 2** shows the situation presented to the respondent who selects bus as the

*DOI: http://dx.doi.org/10.5772/intechopen.98432*

Two different types of trips are considered:

• Important appointment, (on-time arrival is needed)

Since trip purpose is likely to be an influencing factor of mode choice. Real-time events relevant to other preferred modes are also included in the

• Disruptions on the tube once the user reaches the tube station.

• Availability of bike at the docking station if the user chooses bike sharing;

The key point for Scenario 2 is to understand if the user would either consider

alternative transport modes in a specific situation or stick to the initial mode preference regardless of any real-time events. In the first case the user is associated with the hypermode behaviour. In Scenario 2 both the origin and destination are located in central London, which is not necessary true in Scenario 1. This could have

**Preferred mode Bus**

will arrive in 12 minutes.

Real-time event You arrive at the bus stop and the information system says that your bus

Purpose of trip Leisure (e.g. shopping) Appointment

(on-time arrival is crucial)

• Leisure (e.g. shopping, visiting friends)

survey; a few examples are provided below.

• Wet weather if the user chooses walking;

hypermode.

preferred mode.


*Adaptive Travel Mode Choice in the Era of Mobility as a Service (MaaS): Literature Review… DOI: http://dx.doi.org/10.5772/intechopen.98432*

If the respondent's explanation of the decision making process at the open question is in line with the adaptive behaviour, as confirmed by answering "yes" to any of the above real-time events (1 to 4), then this user behaviour is related to hypermode.

In the Stated Preference scenario (Scenario 2) the modes available to the user are the same as those shown in **Figure 2** (with possibly different routes and access points), and are associated with given average costs and travel times. The user is asked what would his preferred mode option be in the described scenario. Depending on the preferred mode, a range of real-time events are presented to the respondent, which may lead him/her to reassess the original choice. For example, **Table 2** shows the situation presented to the respondent who selects bus as the preferred mode.

Two different types of trips are considered:


Since trip purpose is likely to be an influencing factor of mode choice. Real-time events relevant to other preferred modes are also included in the survey; a few examples are provided below.


The key point for Scenario 2 is to understand if the user would either consider alternative transport modes in a specific situation or stick to the initial mode preference regardless of any real-time events. In the first case the user is associated with the hypermode behaviour. In Scenario 2 both the origin and destination are located in central London, which is not necessary true in Scenario 1. This could have


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

that we try to demonstrate.

real-time events.

**4.1 Survey study**

**4.2 Survey design**

outside of London.

mode choice:

4.Weather

5.Other, specify.

Stated Preference scenario.

1.Real-time bus arrival time

3.Disruptions on the tube

described in Section 4.1.

All of these illustrative examples have one thing in common: The pre-defined feasible modes are re-interpreted and re-ranked with the influence of real-time information, which is dynamic and stochastic in nature. This highlights the key difference between the traditional mode choice model and the adaptive behaviour

Note that it is possible that the repetitive occurring of a negative real-time event

To further support the relevance and likelihood of such adaptive behaviours, we conduct a qualitative survey to validate the behavioural soundness of this subject, as

A pilot survey has been undertaken to explore the validity of the underpinning idea of the proposed hypermode concept. 50 respondents have been interviewed at Imperial College London. The sample includes academic, technicians and administrative staff as well as students, to ensure that behaviour in different user categories is captured. The respondents have been interviewed face-to-face to ensure an in-depth and comprehensive grasp of their decision-making processes.

SCENARIO 1. The regular commuting trip home from the College at the end of the day, which is a Revealed Preference scenario. The origin is the same for all respondents but the destinations vary, with some at walking distance and others

SCENARIO 2. A hypothetical trip from the College to Sloane Square (a shopping destination 2.1 km away from the origin) at the end of the working day. This is a

In the first scenario the respondent is asked what modes are available for his/her trip. An open question is then asked to describe the decision making process that shortlists the possible mode options or leads to a specific mode choice. Afterwards they are asked if any of the following real-time events may affect their final

The respondents were presented with two different scenarios:

2.Bike availability at docking stations for bike-sharing service

on a day-to-day basis may lead to the exclusion of a mode from the feasible set. For example, if a user constantly finds the bike-sharing station empty, he/she may exclude bike-sharing as one of the feasible modes in his/her planned behaviour. This, however, does not contradict the mode choice behaviour that we propose here. In fact, it still falls within the scope of the proposed two-stage decision-making process, i.e. in the forming of feasible mode choice set (see Level 1 of **Figure 1**). In most cases, the feasible mode set contains more than one element, and the realization of a particular mode choice (or sequence of mode choices) must thus rely on

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potentially influenced the results as more mode options are available to reach the destinations in Scenario 2, while in the first scenario the users with destination far away may have quite limited mode choices. To avoid potential bias, in Scenario 1 the respondents with destination outside of London are asked to consider the trip from the College to the station in central London from which they take a train; this allows plenty of mode options to be available to all users.
