Vetenskapliga artiklar

Previous  research  shows  that  well-grounded  knowledge  of  the intended  travellers  is  essential  for  achieving  successful  DRT services.  However,  there  is  a  lack  of  understanding  of  the potential  travels  in  different  age  groups,  and  the  acceptance  of design  alternatives  that  may  affect  these.  Based  on  surveys conducted  in  Sweden,  this  paper  investigates  these  factors  in  a hypothetical DRT service, in the age groups 6–17, 18–69 and over 70  years.  A  total  of  1241  people  answered  the  questionnaires. The  results  show  that  the  age  groups  have  similar  acceptance regarding  delays  and  departure time  intervals.  Older adults  have lower  acceptance  of  digital  solutions,  and  children  and  older adults  have  stronger  requirements  for  value-added  services. About  85–90%  of  the  respondents  claim  they  would  use  the service,  at  least  occasionally.  The  results  also  show  how  a  DRT system should be configured to allow room for system efficiency gains, while still being accepted by most travellers.

In this paper, we estimate the vulnerability of railway infrastructure, switches, signals, tracks, and catenaries to different weather conditions, temperature, precipitation, snow depth, and wind speed across the entire Swedish railway network between 2006-2020. Using a method to quantify the fault rate we establish thresholds that can be useful for identifying areas of concern for operations. Results reveal that high or low temperatures have a noticeable impact on the fault rates for switches, tracks, and catenaries. High levels of precipitation are associated with higher fault rates across tracks and catenaries. Snow depth has an influence on fault rates for switches and tracks, and high wind speeds are associated with higher fault rates for tracks and catenaries. Finally, signals were found to be the most resilient asset. When comparing two dominant climate zones, notable differences were only found for track asset vulnerability.

Recent years have seen increased efforts to improve the punctuality of trains. One of the reasons for this is that punctuality is a key performance indicator concerning passenger satisfaction and punctuality can thus be seen as important to both retain current passengers as well as to attract new users. Punctuality is closely related to delays, indeed when a train is not delayed it will most likely be punctual. Trains can suffer from a delay for various reasons and one type of delays are the so-called dwell time delays. Dwell time delays arise when a train is stationary at a station for longer than scheduled. The thesis presented here focuses on why dwell time delays occur, the impact of passengers on dwell times, how dwell time delays can be measured, and finally how the risk of dwell time delays can be reduced.

Several years of passenger counts collected on board trains in Stockholm and the region of Skåne in Southern Sweden were used to study and analyse the impact of passengers on dwell times. The results of these analyses indicate that, although the volume of passengers is often seen as
the main cause of dwell time delays, this is not necessarily the case. Instead, the findings from the studies show that the volume of passengers acts as an accelerator for other aspects such as an uneven spread of passengers or passengers queuing up in front of doors. A high volume of passengers on its own is not sufficient to increase the risk of dwell time delays, but this will happen when there is a high volume of boarding passengers that are unevenly spread between the doors.

Highlights:

• Integration of e-scooter sharing with public transit in 124 European cities.
• Spatial and temporal patterns of integration in different cities.
• Nonlinear effects of city-level factors on the integration.

E-scooter sharing is a potential feeder to complement public transit for alleviating the first-and-last-mile problem. This study investigates the integration between shared e-scooters and public transit by conducting a comparative analysis in 124 European cities based on vehicle availability data. Results suggest that the integration ratios of e-scooter sharing in different cities show significant variations and range from 5.59% to 51.40% with a mean value of 31.58% and a standard deviation of 8.47%. The temporal patterns of integration ratio for first- and last-mile trips present an opposite trend. An increase in the integration ratio for first-mile trips is related to a decrease in the integration ratio for last mile in the time series. Additionally, these cities can be divided into four clusters according to their temporal variations of the integration ratios by a bottom-up hierarchical clustering method. Meanwhile, we explore the nonlinear effects of city-level factors on the integration ratio using explainable machine learning. Several factors are found to have noticeable and nonlinear influences. For example, the density of public transit stations and a higher ratio of the young are positively associated with the integration ratio to a certain extent. The results potentially support transport planners to collectively optimize and manage e-scooter sharing and public transport to facilitate multi-modal transport systems.