Mobility Panel Research Articles

The influence of panel effects and inertia on travel cost elasticities for car use and public transport

Studies on the impact of changes in travel costs on car and public transport use are typically based on cross-sectional travel survey data or time series analysis and do not capture intrapersonal variation in travel patterns, which can result in biased cost elasticities. This paper examines the influence of panel effects and inertia in travel behaviour on travel cost sensitiveness, based on four waves of the Mobility Panel for the Netherlands (comprising around 90,000 trips). This paper analyses the monetary costs of travel. Panel effects reflect (within wave) intrapersonal variations in mode choice, based on three-day trip diary data available for each wave. The impact of intrapersonal variation on cost sensitiveness is shown by comparing mode choice models with panel effects (mixed logit mode choice models with error components) and without panel effects (multinomial logit models). Inertia represents variability in mode choice between waves, measured as the effect of mode choice decisions made in a previous wave on the decisions made in the current wave. Additionally, all mode choice models include socio-economic and spatial variables but also mode preferences and life events. The effect of inertia on travel cost elasticities is measured by estimating mixed logit mode choice models with and without inertia effects. The main conclusion is that the inclusion of intrapersonal effects tends to increase cost sensitiveness whereas the inclusion of inertia effects decreases travel cost sensitiveness for car and public transport modes. Car users are identified as inert travellers, whereas public transport users show a lower tendency to maintain their usual mode choice. This paper reveals the inertia effects over four waves of repeated respondent’s data repeated yearly.

What is the role of carsharing toward a more sustainable transport behavior? Analysis of data from 80 major German cities

The supply of as well as the demand for carsharing has been increasing in recent years. Yet, these dynamics and carsharing's function as catalyzer toward a more sustainable transport behavior in German cities are still not fully understood. For this reason, we analyze supply data for 80 large cities in Germany regarding fleet numbers and supply densities. We combine this with analyses about carsharing (non-) members by drawing on the datasets Mobility in Germany and the German Mobility Panel. We find that carsharing members use walking, cycling, or public transport 1.4–1.5 times more often and motorized individual transport, respectively, less often than nonmembers. Based on this, we build user groups regarding the people's usage of the environmental alliance (i.e., walk, bicycle, public transport). In the subsequent multinomial logistic regression analysis for the years 2013, 2015, and 2017, we find that both increasing carsharing supply and being carsharing member are not effective measures to increase the share of walking, cycling, and public transport. The availability of mobility tools, especially the number of cars in households, are more important predictors.

Causal relations between body-mass index, self-rated health and active travel: An empirical study based on longitudinal data

IntroductionIt has been estimated that physical inactivity accounts for roughly 10% of premature mortality globally in any given year. Active travel (walking and cycling) has been promoted as an effective means to stimulate physical activity. However, many of the available studies on the relation between active travel and health are based on cross-sectional data and are therefore unable to determine the direction of causation. This study aims to unravel the bidirectional relationships between active travel measured by the active modes bicycle, e-bike and walking, on the one hand, and two health outcomes, namely body-mass index (BMI) and self-rated health (SRH), on the other. MethodsTo provide an initial assessment of the relationship between active travel and the two health outcomes, multivariate regression models are estimated. To study the direction of causation, Random-Intercept Cross-Lagged Panel Models (RI-CLPM) are estimated using three waves of the Netherlands Mobility Panel (MPN). Active travel is measured as travelled distances and trips with the bicycle, e-bike and walking. BMI is calculated based on weight and height, SRH is measured with a single question. ResultsThe regression models show that a higher BMI and lower SRH are associated with less walking and cycling, while being obese is associated with more e-bike use. The results of the RI-CLPM indicate that cycling distance has a positive effect on SRH. Furthermore, walking distance has a negative effect on BMI and BMI has a negative effect on bicycle use among people without obesity. No relationships between BMI and active travel are found for people with obesity. ConclusionThe results highlight the importance of longitudinal analyses when estimating the relationship between active travel and health. In addition, the results suggest that, relatively speaking, the increasing overweight and obesity rates may result in a decrease of bicycle use.

E-bike user groups and substitution effects: evidence from longitudinal travel data in the Netherlands

In recent years, the e-bike has become increasingly popular in many European countries. With higher speeds and less effort needed, the e-bike is a promising mode of transport to many, and it is considered a good alternative for certain car trips by policy-makers and planners. A major limitation of many studies that investigate such substitution effects of the e-bike, is their reliance on cross-sectional data which do not allow an assessment of within-person travel mode changes. As a consequence, there is currently no consensus about the e-bike’s potential to replace car trips. Furthermore, there has been little research focusing on heterogeneity among e-bike users. In this respect, it is likely that different groups exist that use the e-bike for different reasons (e.g. leisure vs commute travel), something which will also influence possible substitution patterns. This paper contributes to the literature in two ways: (1) it presents a statistical analysis to assess the extent to which e-bike trips are substituting trips by other travel modes based on longitudinal data; (2) it reveals different user groups among the e-bike population. A Random Intercept Cross-Lagged Panel Model is estimated using five waves of data from the Netherlands Mobility Panel. Furthermore, a Latent Class Analysis is performed using data from the Dutch national travel survey. Results show that, when using longitudinal data, the substitution effects between e-bike and the competing travel modes of car and public transport are not as significant as reported in earlier research. In general, e-bike trips only significantly reduce conventional bicycle trips in the Netherlands, which can be regarded an unwanted effect from a policy-viewpoint. For commuting, the e-bike also substitutes car trips. Furthermore, results show that there are five different user groups with their own distinct behaviour patterns and socio-demographic characteristics. They also show that groups that use the e-bike primarily for commuting or education are growing at a much higher rate than groups that mainly use the e-bike for leisure and shopping purposes.

Exploring the relationship between life events, mode preferences and mode use of young adults: A 3-year cross-lagged panel analysis in the Netherlands

This paper examines the impact of life events on transport mode preferences and the frequency of mode use of young adults in the Netherlands, using data from three waves (2014, 2015 and 2016) of the Netherlands Mobility Panel. The database used for this paper contains 1,180 young adults (18–39 years) who participated in all three waves. Cross-lagged structural equation panel models were estimated to examine the longitudinal relationship between life events (childbirth, moving home or a new job) and travel behaviour. We investigated the relationship between frequency of mode use and mode preference over time, and the impact of life events on mode preference and frequency of mode use. Young adults showed very stable behaviour over time: frequency of mode use and mode preference are good predictors of frequency of mode use and mode preference in the future. The results show that changes in the frequency of mode use have a stronger effect on changes in mode preferences than vice versa. In addition, young adults subjected to life events are more likely to change travel behaviour. Car use and car preference are found to increase significantly after childbirth. Bicycle use and preference for the bicycle were more likely to increase for young adults who moved home. Changing jobs showed a negative association with bicycle use. These life-changing moments could offer a window of opportunity for policymakers and other parties to create more awareness of alternative, more sustainable, modes of transport.

Changing mobility patterns in the Netherlands during COVID-19 outbreak

ABSTRACT The COVID-19 outbreak and associated measures taken had an enormous impact on society as well as a disruptive, but not necessarily negative, impact on mobility. The Ministry of Infrastructure and Water Management received the most recent insights from the Dutch Mobility Panel (DMP) on a weekly basis. These insights were used to monitor the travel behaviour and to analyse changes in the behaviour of different groups and usage of modes of transport during COVID-19. The analysis shows an enormous decrease in travel at the beginning of the implementation of the so-called ‘intelligent’ lockdown and gradual increase again towards comparable levels as before this ‘intelligent lockdown, although the distribution over time, motives and used modes has changed. It becomes clear that not everyone needs to travel during peak hours and commuter travel is also not the main reason for the increase in car usage. Furthermore, cycling has shown to be an alternative option for travellers and public transport is hardly used anymore. If it is possible to sustain the lower level of car usage and integrate public transport as an important alternative for travel again, the COVID-19 impact on mobility could have a substantial remaining positive impact on mobility.

Reducing vehicle cold start emissions through carbon pricing: evidence from Germany

A large proportion of local pollutants originating from the road transport sector is generated during the so-called cold-start phase of driving, that is, the first few minutes of driving after a car has stood inactive for several hours. Drawing on data from the German Mobility Panel (MOP), this paper analyzes the factors that affect the frequency of cold starts, approximated here by the number of car tours that a household takes over the course of a week. Based on fixed-effects panel estimations, we find a negative and statistically significant effect of fuel prices on the number of tours and, hence, cold starts. Using our estimates to explore the spatial implications arising from fuel price increases stipulated under Germany’s Climate Programme 2030, we find substantial impacts on the number of avoided tours even for modest fuel price increases of 20 cents per liter, particularly in urban areas. This outcome lends support to using carbon pricing as a means to improve both global climate and local air quality, pointing to a co-benefit of climate policy.

Drive Less, Drive Better, or Both? Behavioral Adjustments to Fuel Price Changes in Germany

The demand for motor fuel should decline when its price rises, but how exactly does that happen? Do people drive less, do they drive more carefully to conserve fuel, or do they do both? To answer these questions, we use data from the German Mobility Panel from 2004 to 2019, taking advantage of the fluctuations in motor fuel prices over time and across locales to see how they affect Vehicle Kilometers Traveled (VKT) and on-road fuel economy (expressed in kilometers per liter). Our reduced-form regressions show that while the VKTs driven by gasoline cars decrease when the price of gasoline rises, their fuel economy tends to get worse. It is unclear why this happens. Perhaps attempts to save on gasoline—cutting on solo driving, forgoing long trips on the highway, driving more in the city—end up compromising the fuel economy. By contrast, both the VKTs and the fuel economy of diesel cars appear to be insensitive to changes in the price of diesel. Latent class models confirm our main findings, including the fact that while fuel prices, car attributes, and household and location characteristics explain much of the variation in the VKTs, it remains difficult to capture the determinants of on-road fuel economy. Since the price elasticity of fuel consumption is the difference between the price elasticity of VKT and the price elasticity of the fuel economy, our results suggest that the fuel economy might be the “weakest link” of price-based policies that seek to address environmental externalities, such as a carbon tax.

Reducing Vehicle Cold Start Emissions through Carbon Pricing: Evidence from Germany

A large proportion of local pollutants originating from the road transport sector is generated during the so-called cold-start phase of driving, that is, the first few minutes of driving after a car has stood inactive for several hours. Drawing on data from the German Mobility Panel (MOP), this paper analyzes the factors that affect the frequency of cold starts, approximated here by the number of car tours that a household takes over the course of a week. Based on fixed-effects panel estimations, we find a negative and statistically significant effect of fuel prices on the number of tours and, hence, cold starts. Using our estimates to explore the spatial implications arising from fuel price increases stipulated under Germany’s Climate Programme 2030, we find substantial impacts on the number of avoided tours even for modest fuel price increases of 20 cents per liter, particularly in urban areas. This outcome lends support to using carbon pricing as a means to improve both global climate and local air quality, pointing to a co-benefit of climate policy.

Tracing the Effects of the Covid-19 Pandemic on Car Usage in Germany - an Analysis of the German Mobility Panel

Being a vast external influence, the COVID-19 pandemic causes major changes in travel behavior on the individual level. This exceptional situation and the political measures such as the lockdown lead to decreased overall travel demand and shifts in mode choice. To understand these effects, the analysis of car usage in private households offers explanatory insights. To this aim, this study provides a differentiation of car usage before and during the COVID -19 pandemic in spring 2020. Furthermore, insights into structural changes of car usage in private households based on German Mobility Panel data are presented. The results show that during the COVID -19 pandemic car usage was affected, and overall mileage decreased. Especially newer cars with large cubic capacity show a large decrease in mileage by 39 % between 2019 and 2020. In addition, we see that the type of car usage (business/private) and the household characteristics are related to different magnitudes of change in car usage. Overall, it becomes evident that examining identical households and cars before and during the pandemic allows for a deeper understanding of the impacts of the COVID -19 pandemic.

Image-based activity pattern segmentation using longitudinal data of the German Mobility Panel

In this paper, we present an approach to segment people based on a visualization of the longitudinal week activity data from the German Mobility Panel. In order to perform segmentations, different clustering methods are commonly used. Most of the approaches require comprehensive prior knowledge about the input data, e.g., condensing information to cluster-forming variables. As this may influence the method itself, we used images with a high degree of freedom. These images show week activity schedules of people, including all trips and activities with their purposes, modes as well as their duration or their temporal position within the week. Thus, we answer the question whether using only this type of image data as input will produce reasonable clustering results as well. For the clustering, we extracted the images from an existing tool, processed them for the method and finally used them again to select the final cluster solution based on the visual impression of cluster assignments. Our results are meaningful as we identified seven activity patterns (clusters) using this visual validation. The approach is confirmed by the data-based analysis of the cluster solution showing also interpretable key figures for all patterns. Thus, we show an approach taking into account many aspects of travel behavior as an input to clustering, while ensuring the interpretability of solutions. Usually, key figures from the data are used for validation, but this practice may obscure some aspects of the longitudinal data, which are visible when looking on the images as validation.

Early adopters of Mobility-as-a-Service in the Netherlands

The concept of Mobility-as-a-Service (MaaS) is rapidly gaining momentum. Parties involved are eager to learn more about its potential uptake, effects on travel behaviour, and users. We focus on the latter, as we attempt to reveal the profile of groups within the Dutch population that have a relatively high likelihood of adopting MaaS in the near future, apart from the actual supply side.MaaS is a transport concept integrating existing and new mobility services on a digital platform, providing customised door-to-door transportation options. Based on common denominators of MaaS as found in the literature, we have established five indicators to identify early adopters: innovativeness, being tech-savvy, needing travel information, having a multimodal mindset, and wanting freedom of choice. These five indicators are the building blocks of our Latent Demand for MaaS Index (LDMI), and were constructed using 26 statements and questions from a special survey conducted in 2018 among participants of the Netherlands Mobility Panel (MPN). The features derived from the MPN serve as independent variables in a regression analysis of the indicators used to ascertain the profile of early adopters.The results of our model indicate that early adopters are likely to be highly mobile, have a high socio-economic status, high levels of education and high personal incomes. Young people are more eager to adopt MaaS than older adults. Early adopters are healthy, active and frequent users of trains and planes. The characteristics of MaaS's early adopters overlap in numerous ways with those of innovative mobility services users and with the general characteristics of early adopters as found in innovation studies.

How COVID-19 and the Dutch ‘intelligent lockdown’ change activities, work and travel behaviour: Evidence from longitudinal data in the Netherlands

COVID-19 has massively affected the lives of people all over the world. This paper presents first insights in current and potential future effects of the virus and the Dutch government's ‘intelligent lockdown’ on people's activities and travel behaviour. Findings are based on a representative sample of about 2500 respondents from the Netherlands Mobility Panel (MPN). We show that approximately 80% of people reduced their activities outdoors, with a stronger decrease for older people. 44% of workers started or increased the amount of hours working from home and 30% have more remote meetings. Most of these workers report positive experiences. Students and school pupils, however, are mostly not happy with following education from home. Furthermore, the amount of trips and distance travelled dropped by 55% and 68% respectively when compared to the fall of 2019. So-called ‘roundtrips’ (e.g. a walking or cycling tour) gained in popularity. People are currently more positive towards the car and far more negative towards public transport. Changes in outdoor activities seem to be temporal, with over 90% of people who currently reduced their outdoor activities not expecting to continue this behaviour in the future after corona. However, 27% of home-workers expect to work from home more often in the future. In addition, 20% of people expect to cycle and walk more and 20% expect to fly less in the future. These findings show that the coronavirus crisis might result in structural behavioural changes, although future longitudinal analyses are needed to observe these possible structural effects.

Trip chain complexity: a comparison among latent classes of daily mobility patterns

This paper studies the relationship between trip chain complexity and daily travel behaviour of travellers. While trip chain complexity is conventionally investigated between travel modes, our scope is the more aggregated level of a person’s activity-travel pattern. Using data from the Netherlands Mobility Panel, a latent class cluster analysis was performed to group people with similar mode choice behaviour in distinct mobility pattern classes. All trip chains were assigned to both a travel mode and the mobility pattern class of the traveller. Subsequently, differences in trip chain complexity distributions were analysed between travel modes and between mobility pattern classes. Results indicate considerable differences between travel modes, particularly between multimodal and unimodal trip chains, but also between the unimodal travel modes car, bicycle, walking and public transport trip chains. No substantial differences in trip chain complexity were found between mobility pattern classes. Independently of the included travel modes, the distributions of trip chain complexity degrees were similar across mobility pattern classes. This means that personal circumstances such as the number of working hours or household members are not systematically translated into specific mobility patterns.

The experienced mode choice set and its determinants: Commuting trips in the Netherlands

Abstract Active modes take up an increasingly important place on the global policy-making agenda. In the Netherlands, a country that is well-known for its high shares of walking and cycling, the government aims at achieving a modal shift among 200,000 commuting car drivers towards using the bicycle. To this end, policy measures need to be introduced. When the aim is to achieve a modal switch over an enduring period of time, it is more relevant to know the likelihood of including or excluding a mode in the mode choice set, compared to choosing a mode for a single trip. Therefore, we investigate the formation of the experienced choice set (set of modes used over a long period of time), where the aim is to identify determinants that influence the inclusion or exclusion of a mode in this set. We estimate discrete choice models, based on survey data from the Netherlands Mobility Panel (MPN) and a complementary survey, where individuals were asked to report the frequency of using certain modes of transport for commuting trips over the course of half a year. This study shows that the experienced choice set for commuting is unimodal for the majority of the individuals, and remains constant over time for most individuals. Reimbursement by the employer for using a certain mode is the most important determinant influencing the experienced choice set, followed by ownership characteristics and urban density. We show that the mode choice set formation depends on more determinants than previously assumed.

Inter- and Intrapersonal variation in destination choice

This paper examines spatial-temporal inter- and intrapersonal variation in destination choice, based on longitudinal smartphone data for the Netherlands. Mixed logit destination choice models were estimated using two years of data (2014 and 2015) from the Dutch Mobile Mobility Panel, in which over 68,000 trips for 442 respondents were recorded with a smartphone app during an annual four-week measurement period. A distinction was made between trips to compulsory activities (such as work) and trips for discretionary purposes (such as recreation) as they are associated with different trip characteristics. Discrete destination alternatives were defined based on individuals’ behaviour in terms of repeatedly visited destinations and the statistical distribution of a spatial repetition index. The model results show that intrapersonal variation in destination choice, departure time and mode choice was relatively high for less frequently visited locations, which indicates novelty-seeking behaviour in destination choice. Furthermore, we found a strong connection between activity, departure time, and destination choice. And, mode choice and departure time choice were highly repetitive for destinations visited repetitively (e.g. work), but not for discretionary activities.

Modal shift implications of e-bike use in the Netherlands: Moving towards sustainability?

This paper investigates the modal shift patterns of e-bike users in the Dutch context. We focus on the change in e-bikers’ travel behavior to assess whether this change benefits sustainability. Our study provides direct ecologically valid evidence on modal shift by using a longitudinal dataset from the Netherlands Mobility Panel survey. We examine e-bikers’ modal shift patterns before and after acquiring an e-bike. The findings indicate that after e-bike adoptions, conventional bike use reduces significantly, while car use reduces less strongly. Nonetheless, the share of car kilometers is much larger than that of conventional bikes at the baseline. Besides, the emission rate per passenger kilometer of an e-bike is several times lower than that of a car. These imply a net environmental gain after e-bike adoptions. The present study also sheds light on modal shifts at a disaggregated level by investigating those e-bikers who are more likely to drive less after e-bike adoption. The findings suggest that e-bikers younger than 50 and those around retirement age (60–69) seem more likely to step out of their cars. Additionally, people living in rural areas tend to be more likely to reduce their car use than their counterparts in highly urbanized areas. Based on our findings, we present policy recommendations for achieving a greener shift in mobility systems.

Linear, non-linear, bi-directional? Testing the nature of the relationship between mobility and satisfaction with life

Travel is one of the most important facilitators of life and has been widely acknowledged as a prerequisite for economic and social activity. Research developing in recent decades has found that limitations to mobility and accessibility can reduce satisfaction with life. To date, research in this field assumes a linear and one-way relationship between the two—i.e. that ‘more mobility’ results in ‘more life satisfaction’. Yet diminishing marginal returns on happiness are found in many related fields such as economics, and there is always the possibility that happier people travel more than unhappy people. To the authors’ knowledge, this paper presents the first attempt to look for evidence of a non-linear relationship between mobility (measured as trip-making) and life satisfaction, and the first to test the direction of causality between the two factors. It uses a sample of some 1500 adults in the Netherlands Mobility Panel. Linear and segmented regression models were used to associate trip-making with satisfaction with life, when controlling for income, age, self-rated health and other demographics. Counter to expectations, five different model specifications suggest that the relationship between trip-making and satisfaction with life is linear. Furthermore, a structural equation model found that the relationship between mobility and satisfaction could run in either direction. This study questions many of the assumptions made about the relationship between transport and subjective well-being. Given the increasing prominence of this topic, much research is needed to further explore these complex relationships.

Longitudinal changes in transport-related and recreational walking: The role of life events

Walking is a common form of physical activity and has a considerable impact on public health. Walking behavior may change over time due to life events, including residential relocation. Only a few studies based on longitudinal data have examined the impacts of life events on walking behavior. The present study investigated the extent to which life events lead to changes in transport-related and recreational walking duration, by analyzing longitudinal panel data from the Netherlands Mobility Panel (MPN) for the years 2013 and 2015. In total, 1185 respondents aged 18 or older who completed both survey waves were included in our sample. Multilevel mixed-effects Tobit regression models were fitted and showed that both childbirth and relocation to less urbanized areas were related to an increase in transport-related walking over time. No significant associations of life events with recreational walking were observed. Findings suggest that transport-related walking is more likely to be influenced by changes in the household composition and residential relocation than recreational walking. Further longitudinal research is needed to verify our findings and gain insight into the mechanisms underlying these relationships.

Do changes in travellers’ attitudes towards car use and ownership over time affect travel mode choice? A latent transition approach in the Netherlands

Abstract This paper examines how changes in travellers’ attitudes towards car use and ownership change over time and how these changes influence car use, based on the 2014 and 2016 waves of the Netherlands Mobility Panel (N = 1640). A latent transition model was estimated to investigate transitions between different segments based on travellers’ attitudes towards car use and ownership, and the latent transition probabilities were used to predict changes in mode use. Four latent classes were found to reflect the participants’ attitudes: cost-sensitive, car-minded, environmentally aware and social-conscious travellers. Most of the participants remained in the same class between 2014 and 2016, which suggests that attitudes towards car use and ownership are stable over time. Also, the results indicate that car use and car ownership may be less widespread among younger adults. Only when younger adults face life events, such as moving, starting a job or become parents, transitioning to more car-oriented profiles appears more likely. Changes in attitudes towards car use and car ownership do not significantly affect car use (number of trips per day), except for the social-conscious travellers who switched to the car-minded class. This suggests that, in most cases, a more positive or negative attitude towards car use and ownership does not directly affect the frequency of car use.