Currently, the electricity grid in the Netherlands is reaching its capacity, resulting in congestion issues. One of the factors that causes the electricity grid to become overloaded is the increasing use of EVs (electric vehicles). A situation in which a large number of EV users in a certain area charge their EV at the same time can significantly increase the risk of congestion in the electricity network. To avoid such situations it is necessary to change the charging behaviour of the EV users.

The literature shows that smart charging systems, such as charging schemes, are having a high potential to solve these grid capacity problems. In the context of this study, a charging scheme is defined as a contract between an EV user and the charge card provider/electricity supplier stating, among others, at what times an EV user could charge his/her EV. However, to ensure that charging schemes can effectively contribute to solving the grid capacity problems, it is important that many EV users are willing to participate in such schemes.

Therefore, the research objective is to study how EV users will react to various charging schemes. In particular, the aim is to assess the effectiveness of appealing to someone's intrinsic motivation versus providing extrinsic incentives. First of all, with regard to the extrinsic incentives, this study specifically concerns monetary incentives. By presenting different discounts, it is possible to test whether a monetary compensation influences the willingness to participate in a charging scheme. Secondly, with regard to someone’s intrinsic motivation, this study focuses on appealing to someone's solidarity as a form of an intrinsic motivation. EV users with a certain degree of solidarity can be motivated to participate in a charging scheme when solidarity incentives are obtained (social recognition, togetherness and the development of friendships). By emphasizing the social benefits of a charging scheme, solidarity incentives could possibly increase the willingness to participate in a charging scheme.

Since the objective of the study is to research how EV users will react to various charging schemes, a survey containing a discrete choice experiment is used. To be able to examine the effectiveness of providing an extrinsic incentive (monetary compensation) versus appealing to someone's solidarity by emphasizing the social benefits of a charging scheme, the survey contains two charging scheme versions, an incentive-based version and an intrinsic-based version. By using two survey versions, it is possible to examine separately whether it is more effective to appeal to someone's intrinsic motivation versus providing a financial compensation.

The results show that the willingness to participate in a charging scheme was generally higher in the intrinsic-based version than in the incentive-based version. Based on this, it can be concluded, within the boundaries of the selected attribute ranges, that appealing to someone's solidarity by emphasizing the social benefits is more effective than offering a financial compensation. This is also substantiated by the fact that the financial compensation attribute included in the incentive-based charging schemes does not influence the willingness to participate.

If people repeat choices in daily travel behaviour under stable conditions, over time this can form habitual travel behaviour and lead to inertia. If travellers face a new metro line, to what extent does their habitual travel behaviour affect their willingness to use this new transport alternative. This study investigate the inertia effects in a multimodal public transport network using a fully revealed preference data set. Habitual route choice behaviour is quantified prior to the opening of the new metro line. We then use the quantitative metric for habitual behaviour as an indicator in the situation after the metro line opens to reflect the inertia effects of individuals. The outcome of the route discrete choice model reveal that the inertia effects for the new metro line are significant. The interaction effect of habitual travellers with the new transport alternative show that there is an additional effect for the new alternative if travellers are classified as habitual. Furthermore, not accounting for inertia effects leads to bias in travel demand estimations of the new transport alternative. The model with the inertia effect improves model fit and is able to more accurately.

The Dutch Ministry of Infrastructure and Water Management is committed to enhancing the accessibility and sustainability of the Dutch mobility system. Working from home (WFH) has been discussed as a policy lever to decrease commute travel and, thereby, congestion for decades. During the COVID-19 pandemic, this discussion regained momentum. WFH was a key factor for the decrease in commute travel by public transport and car in the Netherlands, which decreased congestion. Against this background, the Ministry wants to stimulate WFH post-pandemic to retain some of the sustainability and accessibility benefits identified during the pandemic. WFH can enhance the accessibility of the mobility system thanks to reduced commute trips during peak hours.
Although the stimulation of WFH may be a policy lever to increase the accessibility of the mobility system, the full effect of WFH on overall personal mobility is unknown. Studying pre-, during and post-COVID-19 measures data, this study assesses the impact of a change in WFH on activity-travel patterns. Commute travel, non-work travel, and mode use together define activity-travel patterns in this research. The main research question is as follows: How did changes in WFH influence activity-travel patterns during the pandemic in the Netherlands? To answer this question, data from the Netherlands Mobility Panel (MPN), collected between 2019-2022 is used. Latent class analysis is used to identify seven distinct activity-travel patterns. Next, two latent transition models are estimated. The first model, for 2019-2021, studies the effect of an increase in WFH on transitions in activity-travel patterns. The second model, for 2021-2022, studies the effect of a decrease in WFH on transitions in activity-travel patterns.

The application of LTA, a longitudinal clustering method, sheds light on complex transition behaviour in the population during- and post-COVID-19 measures. The results show that activity-travel patterns before a change in WFH and the level of an increase in WFH matter for the impact of WFH on transitions. Depending on the initial class membership, differences in transitions emerged. To a certain extent, all effects of WFH on travel, substitution, complementarity, modification, and neutrality exist in the population, but these depend on initial class membership. The results for an increase in WFH are significant, while results for a decrease are not. For the time being, these findings do not exclude the existence of structural changes in working and travel behaviour. Significant effects on transitions appear for a small and a large increase in WFH. After an increase in WFH, most classes are more prone to take up another activity-travel pattern. In both cases, most classes have an increased probability of transitioning towards the low mobility class, which underpins more sustainable patterns. Nonetheless, more diverse results appear for a small increase in WFH, and significant transitions towards other car profiles appear. Analysing the impact of a small increase on class sizes confirms this. Overall, the car class sizes remain relatively high after a small increase in WFH. Thus, an increase by 1-3 days working from home may not lead to more sustainable activity-travel patterns in terms of trip rates per purpose and mode. Nevertheless, it needs to be kept in mind that this study only analysed trip rates per purpose and mode, a small increase in WFH may still lead to a peak-travel reduction, which benefits accessibility. Further analyses should investigate peak-travel to conclude on the accessibility benefits of small increases in WFH. At the same time, governments and employers should take measures to counter-act low mobility associated with WFH which is detrimental for physical health.

Autonomous vehicles are expected to become an inevitable reality in the coming years, as their uncovered benefits are increasingly proven to be positive, such as increased safety and traffic efficiency. However, their impacts on travel choices have often been represented with an assumed reduction in the travel time penalty, which oversimplified AVs' subsequent effects activity-travel scheduling choices. As such, models and assessment methods do not accurately represent travel behavior implications of autonomous vehicles. Thus, using survey data, this thesis identifies possible rearrangements in activity-travel schedules, classifies the respondents into classes with similar profiles of expected rearrangements, and identifies further classification on the basis of socio-economic, personal, and travel characteristics. The survey to be used is one in which respondents were asked to report a full, regular working day activity schedule using their currently preferred mode of transport, then report the schedule as they expect it to be if they could use an autonomous vehicle. The initial exploration of the data identified the occurrence of activities on-board (work, spare-time, meals, and getting ready), changes to the duration of activities outside travel, as well as travel (delay of work-bound trips, and advancement of home-bond trips). Next, we used latent class models to cluster the responses with respect to on-board activity duration changes, stationary activity duration changes, and travel departure time changes. The clustering uncovered types of classes: no change, single activity on-board (work and spare-time), multiple activities on-board. Interactions between stationary and on-board activities were identified, with some direct activity transfer to travel episodes being common (with work, meals, and getting ready), while other activities were generally not transferred (spare-time). Finally, the addition of personal characteristics and demographics highlighted the limited influence of socio-economic factors, with the exception of education, on activity changes. In contrast, the most significant factors were mostly associated with work (daily time pressure and the ability to do work in the car) and travel time duration. An important insight uncovered was that the travel changes were limited and not as dramatic as expected, highlighting that the value of time impacts alone are not as representative as expected.

Automated vehicles (AV) are expected to change our future way of transportation and bring many positive effects, such as better road/vehicle safety, reduced environmental costs but also increased on-board productivity. The commonly adopted approach to capture the effect of increased on-board productivity is to reduce the ‘penalty’ associated with travel time. This implies that people will show less aversion to longer travel times (i.e. congestion), prioritise arriving close to the preferred arrival time and thus, increase the peak congestion. However, a distinction should be made between the type of performed activity, since this could likely affect the departure time preferences and thus can have an impact on the congestion pattern. Previous work has shown that travellers with fully automated vehicles in which home activities could better be performed, preferred to depart earlier, and conversely travellers with automated vehicles in which work activities could better be performed, preferred depart later. In addition, the results suggested that automated vehicles could likely increase severe congestion in the future. However, these insights have been obtained with the use of a theoretical single link setting. Up to this point differentiation between activities has not yet been investigated for real-life road networks in which route choice can be incorporated. Therefore this research aims to investigate network effects of activity based departure time choice with fully automated vehicles. It uses the extended α-β-γ scheduling preferences by B. Pudāne and implemented these within a traffic simulation model. This model was assigned to a case study network of the ‘Haaglanden’ region. Results showed that travellers which use AVs to engage in home activities, increase congestion more to the beginning of the morning peak. Vice versa, travellers which use AVs to engage in work activities move congestion more to the end of the morning peak. Travellers which use AVs for both home and work activities would increase congestion in the middle. Considering route choice, a shift in vehicle-kilomtres (VKM) could be observed from the main road network to the underlying road network with the introduction of AVs. This has a negative impact regarding traffic safety, noise and air pollution. Although one might argue that future AVs will have increased safety levels and a more eco-friendly way of driving. Lastly, it was observed that with mixed traffic situations, non-AV users were ‘forced’ to shift their departure times out of the peak moments, move to less congested periods. Conversely, AV users were less affected by these longer travel times and prioritised their preferred arrival times. It is recommended that further research is conducted to include the dynamic modelling element of blocking back. In addition, the extended α-β-γ model parameter values should be assessed to arrive at more substantiated values. Moreover, it suggested to further investigate the impact of differentiation towards road capacities for AVs. Furthermore, this study did not investigate heterogeneity among travellers’ preferences, nor did it take into account endogeneity through which people may assign themselves to a certain type of activity. It is suggest to take these aspects into account in further research. This study assumed full automation, whereas it would be interesting to investigate the effects with lower automation as well. Lastly, an important extension of this research would be to also capture the effect of value of travel time differentiation with respect to route choice, as this study did not differentiate in probabilities to choose a specific route per vehicle type.

Although fully-automated vehicles (FAVs) are not yet allowed on public roads and are barely present in people’s daily lives, according to experts their wide implementation will happen within the next few decades. The technology is expected to bring major benefits in terms of safety, traffic flow efficiency and environmental impact. Furthermore, by eliminating the necessity of actively steering the vehicle, automated vehicles (AVs) are expected to bring extra free time during the day to perform activities, as well as increase mobility of some excluded societal groups (e.g. elderly). However, taking away the burden of driving might also bring negative results. The convenience of the technology might encourage people to travel further and more often. Moreover, AVs users might transfer some of the activities from the traditional environments into the vehicle (e.g. work). The associated rearrangement of daily activity plans might have an impact on travel and residential location choices, possibly leading to further increase in overall travel distance. In such way, the expected positive impact of the technology could be compromised.
For that reason, it is necessary to examine behavioural response to AVs, namely the way travel time will be used in the vehicle and its consequences for daily activity plans, travel patterns and residential location choices. In order to fill in this scientific gap five focus groups were conducted. The participants were confronted with a simplified scenario of the future after broad AVs implementation and were asked a set of questions regarding the aforementioned topics. It was found that, in contrary to the current modes, time inside AVs will be perceived not in terms of burden or opportunity, but rather in terms of pure opportunity. The participants perceive AV time as an extension of either work or private time. In context of daily activity plans it brings about a number of possible behavioral adaptations. In case of regular commuters, a willingness to use the in-vehicle time to release pressure from daily life was often identified, whereas people with less stationary jobs claim that the AV would allow them to bring more flexibility into their daily plans, or move their actual office or even the entire business into the vehicle.
However, the findings regarding daily activity plans do not provide a clear answer on their impact on travel and location decisions. At the same time, a number of advantages of AVs for irregular, long travels was identified, and many participants explicitly expressed their willingness to increase the amount of such travels. The impact of AVs on residential location choices appears to be limited: although people are not willing to move because of the AV, they claim it would still play a significant role in their relocation decisions if it was necessary for other reasons. Moreover, some AV-specific phenomena were discovered, which might have an important significance on the actual AV use: namely increased pressure to perform onboard activities and feeling of “being trapped” in the vehicle. Furthermore, the participants pointed at the striking easiness of making travel decisions in case of AVs, compared to other modes. Although the results bring a valuable indication of possible behavioral adaptation to the technology, one must also take into account limitations of the study. The most important of them is the speculative, imaginary character of the focus groups discussions, simplified future scenario and subjective character of qualitative analysis. Therefore, in the future studies on behavioural adaptation to AVs it is suggested to take a more experimental path, aiming at emulating the actual AV experience as far as possible. In that way the credibility of the findings from the present study could be further validated and extended.