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Browsing by Subject "työmatkat"

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  • Jalkanen, Pinja-Liina Jannika (2020)
    Large-scale transport infrastructure projects change our daily mobility patterns, as they change the geographical accessibility of the places where we spend most of our time, such as our homes and workplaces. Thus, there is a clear need for advance evaluation of the effects of those projects. Traditionally, however, the available methods have imposed severe limitations for both measuring accessibility and surveying mobility, and despite modern data collection methods enabled by the ever-present mobile phones, surveying mobility remains challenging due to data accessibility restrictions. Furthermore it would not enable any advance evaluation of mobility changes. However, using a modern accessibility dataset instead of a mobility one does offer a possible answer. In my study, I set out to investigate this possibility. I combined a modern, multimodal and longitudinal accessibility dataset, the Helsinki Region Travel Time Matrix (TTM), with a spatially compatible, census-based longitudinal commuting dataset to evaluate the aggregated journey times in the Helsinki Capital Region (HCR), the area covered by the TTM, and estimated the shares of different transport modes based on a previously published travel survey. Armed with this combined dataset, I assessed the changes in aggregated journey times between the three years that were included in the TTM dataset – 2013, 2015 and 2018 – by statistical district to estimate its usability for these kind of advance mobility evaluations. As a small subset of the commuting dataset was classified by industry, I also assessed regional differences between industries. My results demonstrate that for travel by public transport, the effects of new transport projects are plausibly identifiable in these aggregated patterns, with a number of areas served by several new, large-scale public transport infrastructure projects – the Ring Rail, the trunk bus lane 560 and the Western extension of the metro line – being outliers in the results. For travel by private car and for the industry-level changes, the results are more inconclusive, possibly due to absence of massive projects affecting the road network throughout the dataset timeframe, potential inaccuracies in the source data and limitations of the industry-classified part of the dataset. In conclusion, a modern accessibility dataset such as the TTM can be plausibly used to estimate the mobility effects of large-scale public transport infrastructure projects, although the final accuracy of the results is likely to be heavily dependent of the precision of the original datasets, which should be taken into account when such assessments are made. Further research is clearly needed to assess the effects of diurnal variations in travel times and the effects of more precise transport mode preference data.