Katrín Halldórsdóttir

Improved methods to deduct trip legs and mode from travel surveys using wearable GPS devices: A case study from the Greater Copenhagen area

Abstract GPS data collection has become an important means of investigating travel behaviour. This is because such data ideally provide far more detailed information on route choice and travel patterns over a longer time period than possible from traditional travel survey methods. Wearing a GPS unit is furthermore less requiring for the respondents than filling out (large) questionnaires. It places however high requirements to the post-processing of the data. This study developed and tested a combined fuzzy logic and GIS-based algorithm to process raw GPS data. The algorithm is applied to GPS data collected in the highly complex large-scale multi-modal transport network of the Greater Copenhagen area. It detects trips, trip legs and distinguishes between five modes of transport. The algorithm was validated by comparing with a control questionnaire collected among the same persons and a sensitivity analysis was performed. This showed that the algorithm (i) identified corresponding trip legs for 82% of the reported trip legs, (ii) avoided classifying non-trips such as scatter around activities as trip legs, (iii) identified the correct mode of transport for more than 90% of trip legs, and (iv) were robust towards the specification of the model parameters and thresholds. The method thus makes it possible to use GPS for travel surveys in large-scale multi-modal networks.

Home-end and activity-end preferences for access to and egress from train stations in the Copenhagen region

ABSTRACT Increasing public transport use with the aim of improving the sustainability of cities should focus not only on enhancing level and quality of the service offered, but also on understanding determinants of the choice of access and egress modes to and from the railway network. This study analyzes the difference in preferences at the home-end and activity-end for travelers who have chosen train as their main travel mode while investigating the effect of policy variables such as car parking availability, bicycle parking availability and type, and bicycle on train possibility. Specifically, this study analyzes the choices between five transport modes (i.e., “walk,” “bicycle,” “car driver,” “car passenger,” “bus”) for 2921 home-end and 3658 activity-end trips. Joint mixed logit models are specified and estimated to account for heteroscedasticity and correlation across alternative modes as well as taste heterogeneity across travelers. Model estimates and pseudo-elasticities uncover the importance of travel time and underline how the improvement of walkability, bikeability, and bus service would contribute significantly to the increase in the probability of choosing sustainable modes to and from train stations. Moreover, model results emphasize the role of bicycle parking in terms of the sheer number of spaces to be increased as well as covered places to be offered at the activity end, de facto giving the possibility to leave a bicycle at that end during the night. Lastly, model results show that it is a matter of not only time and trip characteristics, but also traveler characteristics, occupation, and purpose.