Browsing by Subject "travel time"

Accessibility – what can be reached from a given point in space and how – is an essential field of study to measure the physical structure of cities, travel mode choices of residents, and the competitiveness of areas. Researchers increasingly acknowledge that accessibility is a fundamental concept on understanding how urban regions work and its position in future development of cities is paramount. Travel time is considered an intuitive measure to indicate accessibility and a strong predictor of mode choice, and usually, private car is the fastest mode of transport in urban environments. A central issue which stems from private cars and accessibility is the process of searching for parking. An understudied issue, the rather stressful activity is engaged in when arriving by car at the general area of desired parking, but no space is available. Motorists are then forced to continue search for parking, significantly contributing to urban congestion. In catering to mobility rather than accessibility, the modern urban planning has made it challenging to move away from private cars toward alternative, often more sustainable, modes of transport. Travel time studies, and more specifically, parking studies, can produce accurate data to aid in this transformation. In this thesis, a parking related research survey was developed and conducted in the Helsinki Capital Region, Finland. Adhering to the door-to-door approach, the survey respondents were enquired how long it took for them to find a parking place and park their car, and walk from the car to the destination in different postal code areas of Helsinki Capital Region. To explain a hypothetical variation in parking process durations (searching for parking, and walking to one's destination) in different areas, additional questions, such as the time of the day of parking, were presented. The invitation to respond to the survey was mostly spread on the social media platform Facebook. The survey, filled out with a web application specifically programmed for this thesis, received 5200 data rows from over 1000 unique visitors. The survey results indicate that there are spatial differences in parking process durations in different postal code areas of the Helsinki Capital Region. The inner city of Helsinki was experienced as the most difficult location to park in with regional subcenters such as Matinkylä, Espoo and Tikkurila, Vantaa, receiving relatively long parking process durations. Short parking process durations were reported from scarcely built areas but more often than not these areas had extreme values reported. Interestingly, area familiarity did not necessarily translate to faster parking process, while the type of the usual parking place was a better indicator. Out of the spatial explanatory variables added in the survey data processing, zones of urban structure (yhdyskuntarakenteen vyöhykkeet) could be used to find statistically significant differences in the parking process between variable groups and study area municipalities. Making use of the Helsinki Region Travel Time Matrix, a dataset developed by the research group Digital Geography Lab of the University of Helsinki, the thesis survey data was compared to total travel chain durations. The thesis survey data indicates that the proportion of time it takes to park one's car and walk to one's destination is a much larger part of the entire travel chain than previously estimated in the dataset. The parking process times are proportionally largest in the inner city of Helsinki, where the reported parking process duration exceeds that of the actual driving segment. This thesis, its entire version history, and all of the scripts developed for it have been made available at GitHub: https://github.com/sampoves/thesis-data-analysis.

Transportation in cities is facing the challenges of congestion and environmental impact caused by the increase in traffic flows. These issues can be reduced by promoting more sustainable transport modes, such as cycling. To increase its modal share, cycling has to be an attractive and competitive choice compared to other travel modes. Digital Geography Lab in University of Helsinki has developed comparable measures for modelling accessibility with different travel modes in Helsinki region. However, cycling is missing from the data because it has been previously modelled with simplistic assumptions of constant travel speed. Little research has been carried out to assess the applicability of this assumption. The main objective of this thesis is to develop a more realistic GIS model for calculating optimal routes and travel times of cycling in Helsinki region taking into account the feasibility of the model. Other objectives are to find out what factors affect cyclists' travel speed and can the environmental factors be used as impedances in the travel time model, what kind of spatial differences the cycling speeds have, and how realistic it is to model cyclists' travel times with constant speed on a regional scale. According to previous research, among the various things affecting cycling some of the main environmental factors are slope, junctions and traffic lights. The effects of these factors to cycling speeds in Helsinki region were analysed based on individual cycling routes and on a route and segment level from the whole data with linear regression models. GPS data of cycling was collected from volunteers who had been tracking their cycling in Helsinki region with mobile sports applications. Basic background information of the cyclists was also collected to analyse the variations in speed between different background variables. Road network for cycling and walking by Helsinki Region Transport was used as the modelling network. A GIS-based map-matching method for the cycling GPS data was developed by applying a method developed for map-matching GPS data of cars. Slope was calculated for route segments using NLS 2 meter digital elevation model and the traffic light information was derived from Digiroad. Python scripts used in modelling are available on GitHub. The cycling speeds vary by cycling frequency: cyclists who stated to cycle almost every day of the week, 3-5 times a week, or a few times a week have median speeds of 24 km/h, 22 km/h and 18 km/h, respectively. Uphill slope and signalized junctions decelerate and downhill slopes accelerate cycling speeds on individual routes. Looking at the whole data, speed has a weak negative correlation between slope and different junction types. On a regional scale the effect of signalized junctions is the greatest, whereas uphill slope has the greatest effect on route-based mean speeds. The regression models do not explain the variation in cycling speeds very well (R2 ≈ 0.1) so a travel time model based on constant speeds corresponding to the different median speeds of frequent and less frequent cyclists was implemented on the network. Spatial examination shows that mean cycling speeds in parts of central Helsinki are 0.8 times slower than in rest of the area, so the cycling speeds of the model were slowed down on those segments. Slope, traffic lights and other junctions affect cycling speeds on an individual level but not on the regional scale. Based on model validation the travel times of the constant speed model correlate strongly with the real travel times of the GPS data. The model taking into account the slower parts of central Helsinki is marginally better but the difference is only slight and affecting only the routes going via the city centre. The difference in travel times caused by different constant speeds is much greater. Constant speed can hence be seen as an adequate assumption to model cyclists' travel times in Helsinki region but the personal and spatial differences in cycling speeds should be taken into account.