7 Disaggregated analysis
7.4 Relocations
The relocators in the dataset are analysed to find out whether people change their car ownership when they move to train station areas. In the dataset are not many households that reported to have moved in the analysed waves. Both the change in car ownership before and after the move is analysed to make sure that both the preparation as the result of the move are included. Therefore, only the movers in the years 2014 and 2015 are point of interest. In total 103 households moved in the years 2014 and 2015.
Figure 7-5 visualises the changes in car ownership over time. Timestep t represents the household car ownership in the year of the reported move, 2014 or 2015. Timestep t-1 represents the reported number of household cars in the year before the move, t+1 represents the number of cars the year after the move. Gross of the movers (85%) in Figure 7-5 did not change their car ownership before and after the move. Only about eight per cent of the movers acquires a car, and about the same percentage of households disposes a car. Most of the exchanges in car ownership occur between household car ownership of zero and one. It is striking that more carless households acquired a car before the move than after the move and that single car-owning households disposed of their car after the move than before. The following sections will try to find explanations for the changes in car ownership.
Figure 7-5 Sankey diagram: Changes in household car ownership when households relocate at year t (N=103)
Train stations areas
There are hardly any movers to areas with larger train stations than before: only seven households moved to a larger largest train station type within 3km. Because the number of these movers is very low and the number of people with changes in train station types is even lower, the analysis won’t lead to significant results. However, it is still possible to analyse the effects of moving can qualitatively. The hypothesis was that households moving to larger train stations would have a larger probability to dispose their car. Table 7-7 does not show a confirmation of this hypotheses. Contrary to the expectations, there are hardly any changes among the movers to larger train stations.
Nonetheless, among the movers to smaller train stations are car acquirements before and after the move. It is striking that the number of households with zero car ownership households decreased and the number of households with one car has increased after the move. Although there are some changes in average household car ownership among the movers at locations
Disaggregated analysis
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54 without a change in train station, the proportions in number of cars per household remain constant.Only half of the households that reported that they have become in possession of a new mode of transport in the period around their relocation reported that the move itself was one of the influencing factors. Possibly, the relocations themselves may not have been the decisive factor in the choice for the acquirement of the car but the locations may supply the possibility for the acquiring of the car.
Table 7-7 Changes in household car ownership when households move at year t to larger train stations, smaller train stations or no change in largest train station type in the neighbourhood
Move to larger ‘largest train station’ (N = 7) Move to smaller ‘largest train station’ (N = 26)
Move without change in ‘largest train station’ (N = 70)
Conclusion
In the researched waves of the MPN-dataset is the number of relocations low. The goal was to find out whether train stations have a causal effect on household car ownership. The overview of the relocators showed more acquired cars before the move and more disposed cars after the move. Especially, car ownership has changed among the movers to the smaller train station types. While before the move the largest proportion of households had zero cars, had the largest proportion of households one car after the move. So, the changes in household car ownership seem related to relocating to areas with smaller train stations in proximity. However, due to the small sample was not possible to control for possible other influencing factors. Therefore, a repetition of this study after more waves of MPN is available would be beneficial to gain more statistical power.
7.5
Conclusion
This chapter aimed to find out more about the causality of the relation between train stations and household car ownership. The study to preferences to live in train station areas and the actual living locations showed that both built environment and the preferences had a significant effect on household car ownership. This also holds for travel behaviour. Nonetheless, further research is required to this effect while controlling for socio-demographics. The influence of preferences and
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55 built environment is an important factor for policymaking, this shows that in new development car ownership can be influenced by both the residential location and attracting target groups with a corresponding travel preference.The study to relocations showed that, although the number of relocators was low, households change their car ownership before and after the move. Especially, car ownership has changed among the movers to the smaller train station types. While before the move the largest proportion of households had zero cars, had the largest proportion of households one car after the move. The results point out that train stations may have a causal relation with household car ownership. Nonetheless more data is required to provide more foundation for these results.
Q3
How does household car ownership and travel behaviour differ between consonant and dissonant residents in areas with and without train stations in proximity?
In a cross-section study of the year 2014 of the Netherlands Mobility Panel, the analysis contains a comparison of household car ownership of dissonant and consonant residents. In line with previous research, are both the preference to live in a train station area and the presence of the train station areas of influence on the frequency of train use of the gatekeeper. Even more important, there is a significant difference in household car ownership between dissonant and consonant residents in train station rich areas. Nevertheless, there are some small differences in socio-demographics of the groups, which may have a (partial) explanation for the difference in household car ownership of the groups. Furthermore, there is a significant difference in household car ownership between the train station rich consonant and train station poor dissonant residents. So, both travel preference and built environment affect household car ownership.
Q4 How does household car ownership change when people move to locations with different train station proximity than before?
Changes in household car ownership of movers in the years 2014 and 2015 of the Netherlands Mobility Panel are analysed to find out whether train station areas have a decisive effect in acquiring or disposing of cars. The number of relocators in the sample is low, therefore is it not possible to have conclusions with a statistical significance. Nonetheless, the households have increasing household car ownership when the relocators move to areas with train stations with a lower number of passengers. These results indicate an effect of the built environment, but the characteristics and the reasons people have moved should be further analysed to conclude about the causality. Therefore, additional data should be acquired to get more insights into the choices in household car ownership.
Parking standards
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Parking standards
This chapter contains the analyses of parking standards of municipalities. The results of this analysis are used to highlight where parking standards can be approved. At first, a description follows of the brief analysis of the residential parking policies of municipalities. Next, a comparison follows of the CROW Key figures, the municipalities’ parking standards and the results of Chapter 5 with actual average household car ownership.