**4.3 Survey results**

Some characteristics of the sample respondents are reported in **Table 3**.

**Table 4** shows the percentages of respondents associated with the hypermode behaviour. We also use the sample size (50) to calculate the 95% binomial proportion confidence interval. Here, the category "Either scenario" accounts for those who show the hypermode behaviour in either Scenarios 1 or 2.

The results of this exploratory survey show that the vast majority of the respondents follow an adaptive behaviour, which is in line with the hypermode concept. In the first scenario, 34% of the respondents consider initially a set of feasible modes for their trips, and their final mode choices are determined/affected by real-time events.

In the second scenario, 86% of the respondents indicate that they would consider alternative modes once adverse real-time events occur. This result refers to the overall responses for the two travel purposes (leisure/appointment) (i.e. respondents who consider alternative modes for at least one of the two travel purposes are associated with hypermode). The analysis of the responses for each travel purpose indicates that:


This difference is easily understandable as the urge to reach the destination on time offers another motivation to reconsider other modes and justifies the associated effort.

A more detailed analysis of Scenario 2 identifies the pattern of mode switches under the two different trip purposes. The results are reported in **Table 5**.


#### **Table 3.**

*Age and gender of respondents.*


**17**

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

Walking 50% 41% Tube 37% 39% Bus 13% 17% Bike-sharing 0% 2% Cycling 0% 0 Taxi 0% 0

**Mode initially considered % of switches (Leisure) % of switches (Appointment)**

The adaptive behaviour is more evident in Scenario 2. This may be explained by the fact that the respondents were referring to their regular commuting trip in the first scenario, and were less likely to abandon their preferred mode due to extensive learning of the preferred and alternative modes based on past experience. On the other hand, in the more hypothetical scenario (Scenario 2), the travel environment is new to the commuters, who might be more inclined to consider different modes

The inertia in decision-making may also play a role in the sense that users may be inclined to stick to one specific mode of transport even though it may not appear to be the most rational choice at the moment. This choice behaviour is known as bounded rationality. In the second scenario, despite their familiarity with the area, the users were more prone to consider different modes, as their experience on specific trips is relatively limited. The adaptive behaviour is more evident when

**Table 5** partially illustrates the relevance of users' adaptive behaviour to planning. In particular, it shows the percentage of travellers who abandon their initial (static) mode choice in reaction to real-time events. For example, when there is a interruption/delay of tube service, 39% of travellers will switch to other modes, possibly at nearby access points. Such information is crucial for planning service interruption at tube stations (such as scheduled maintenance or train operation): the planner need to take into account the increase in demand for other modes in the vicinity of the tube station to avoid heavy congestion and/or shortage of supplies.

Many researchers and stakeholders of the transport sector see Mobility as a Service (MaaS) as the mobility of the future. However, a lot of uncertainty lye under this travel solution. The same first statement is actually uncertain, considering that it depends on MaaS diffusion, which in turn depends on the adopted business model, on its financial convenience and on the membership rate, which in turn depend on what kind of services are offered, their level of service and their price. On the other hand, first MaaS applications have not helped to clarify the financial

Furthermore, MaaS is also generally associated with many virtuos impacts which can be sinthesized by saying that it goes in the direction of a sustainable mobility, by aiding and supporting intermodality. However, also this statement is not clearly confirmed by the literature. In fact, put different services in the

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

due to the lack of experience.

**Table 5.**

*Mode switches in Scenario 2.*

**5. Conclusions**

**5.1 Remarks on MaaS**

convenience of a MaaS.

on-time arrival at the destination is important.

#### **Table 4.**

*Survey results with 95% confidence levels for the hypermode behaviour. Sample size: 50.*


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

#### **Table 5.**

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

plenty of mode options to be available to all users.

**4.3 Survey results**

indicates that:

ated effort.

of adverse real-time events.

presence of adverse real-time events.

N respondent 45–64 28%

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

Some characteristics of the sample respondents are reported in **Table 3**. **Table 4** shows the percentages of respondents associated with the hypermode behaviour. We also use the sample size (50) to calculate the 95% binomial proportion confidence interval. Here, the category "Either scenario" accounts for those

The results of this exploratory survey show that the vast majority of the respondents follow an adaptive behaviour, which is in line with the hypermode concept. In the first scenario, 34% of the respondents consider initially a set of feasible modes for their trips, and their final mode choices are determined/affected by real-time events. In the second scenario, 86% of the respondents indicate that they would consider alternative modes once adverse real-time events occur. This result refers to the overall responses for the two travel purposes (leisure/appointment) (i.e. respondents who consider alternative modes for at least one of the two travel purposes are associated with hypermode). The analysis of the responses for each travel purpose

• [Leisure] 60% of the respondents re-assess the available modes in the presence

• [Appointment] 82% of the respondents re-assess the available modes in the

This difference is easily understandable as the urge to reach the destination on time offers another motivation to reconsider other modes and justifies the associ-

A more detailed analysis of Scenario 2 identifies the pattern of mode switches

N respondent 18–25 24% N female respondent 38% N respondent 26–44 48% N male respondent 62%

Average percentage 34% 86% 92% **Confidence interval [21%, 49%] [73%, 94%] [81%, 98%]**

*Survey results with 95% confidence levels for the hypermode behaviour. Sample size: 50.*

**Scenario 1 Scenario 2 Either scenario**

under the two different trip purposes. The results are reported in **Table 5**.

**Age of respondents Gender of respondents**

who show the hypermode behaviour in either Scenarios 1 or 2.

**16**

**Table 4.**

**Table 3.**

*Age and gender of respondents.*

*Mode switches in Scenario 2.*

The adaptive behaviour is more evident in Scenario 2. This may be explained by the fact that the respondents were referring to their regular commuting trip in the first scenario, and were less likely to abandon their preferred mode due to extensive learning of the preferred and alternative modes based on past experience. On the other hand, in the more hypothetical scenario (Scenario 2), the travel environment is new to the commuters, who might be more inclined to consider different modes due to the lack of experience.

The inertia in decision-making may also play a role in the sense that users may be inclined to stick to one specific mode of transport even though it may not appear to be the most rational choice at the moment. This choice behaviour is known as bounded rationality. In the second scenario, despite their familiarity with the area, the users were more prone to consider different modes, as their experience on specific trips is relatively limited. The adaptive behaviour is more evident when on-time arrival at the destination is important.

**Table 5** partially illustrates the relevance of users' adaptive behaviour to planning. In particular, it shows the percentage of travellers who abandon their initial (static) mode choice in reaction to real-time events. For example, when there is a interruption/delay of tube service, 39% of travellers will switch to other modes, possibly at nearby access points. Such information is crucial for planning service interruption at tube stations (such as scheduled maintenance or train operation): the planner need to take into account the increase in demand for other modes in the vicinity of the tube station to avoid heavy congestion and/or shortage of supplies.
