Cities worldwide, including Stockholm, are attracting more residents, making providing public transport services challenging and passenger overcrowding a new norm. Crowding negatively affects passengers' travel experience and the operations of the public transport system. So far, little attention has been given to how the new urban developments contribute to public transport crowding. Empirical knowledge of how new residents impact on the crowding conditions in the system can guide tailored policy initiatives such as infrastructure investments and calibration of the ex-ante public transport models (macroscopic network assignment models).

The CAPA-CITY project utilizes the large-scale Access-kort data available for most of the trips in Region Stockholm. The project's primary goal is to identify the network-wide public transport crowding implications and capacity needs of new urban development areas. The research team focused on capturing the non-local effects of urban developments that have consequences for the crowding experienced, not limited to those experienced by travelers originating from or destined to these areas. To this end, the team proposes a workflow for supporting planners and policy-makers in assessing the crowding implications and capacity requirements induced by urban developments.

We demonstrate the proposed workflow for various newly developed areas in Region Stockholm, accounting for diverse characteristics in terms of size, type (e.g., residential/business/mixed), location (e.g., central/peripheral), proximity to a high-capacity public transportation connection and in terms of socioeconomic characteristics. To this end, we perform a before-after analysis utilizing Access-kort data accounting for a sufficient time period before the urban development and during/after the completion of the construction phase.

Last, it is important to mention that the development and monitoring of the public transport system of Region Stockholm should rely on the best empirical evidence available to support evidence-based decision-making and set the right priorities. The proposed workflow in the CAPA-CITY project can assist in more efficient public transport planning in relation to new urban developments, supporting the initial planning from planners in Region Stockholm.

This work aims to explore the use of machine learning techniques, particularly clustering and cluster evolution tracking, to analyze travel patterns in public transportation in a city and provide valuable insights for urban transit planning and optimization. Clustering involves identifying and grouping similar objects, such as passengers with different ticket types, and distinguishing them from dissimilar objects in other groups. Over time, groups can change, so tracking this change can provide more detailed and valuable insights than analyzing data in aggregates. Clustering and cluster evolution tracking can reveal groups of passengers that are more or less affected by changes such as seasonality or fare increases. We propose a framework called DUGET (Dynamic User Grouping and Evolution Tracking), which clusters anonymized users based on their ticket choices and temporal travel patterns using a multi-step approach. The clusters are then tracked over time using Jaccard similarity based on memberships, allowing for the analysis and visualization of changes. Our experiments using a real-world public transportation dataset collected in Stockholm, Sweden, show the feasibility of tracking change over time in public transportation by examining passenger behavior as a temporal aggregate. The framework we propose is generalizable and can be used for future projects to understand trends in groups of objects.

Policymakers must find efficient public transport solutions to promote sustainability and provide efficient urban mobility in the course of urban growth. A growing number of research papers are applying Geographically weighted regression (GWR) to model the relationship between public transport demand and its influential factors. However, few studies have considered the rapid development of journey inference from ticket transaction data. Similarly, the potential of GWR to analyze spatio-temporal changes that reflect changes in transportation supply and thus provide a measure for evaluating the local success of transport supply changes has yet to be exploited. In this paper, we use inferred journeys from smart card inferences as the dependent variable and analyze how public transport demand responds to a set of explanatory variables, emphasizing transport supply. Consequently, GWR and its successor Multiscale Geographically Weighted Regression (MGWR) are applied to analyze the spatially varying impact of transport supply changes for seven consecutive time frames between autumn 2017 and spring 2020, allowing conclusions about local changes in transport demand, as well as the benchmarking of transport supply changes. The (M)GWR framework's predictive power is evaluated by training the model with past transport supply data and testing the model with data from the following consecutive years. The conducted analyses reveal that the (M)GWR model, using inferred journeys and transport supply data, can retrospectively predict the impact of transport supply changes on travel behavior and thus provides conclusions about the success of transport policies.

Public transport crowding exposure is known to cause discomfort, stress and dissatisfaction. However, the distribution and equity of crowding exposure across socioeconomic groups has been largely unexplored. This paper opens a new research topic connecting crowding exposure in public transport to travelers’ socioeconomic characteristics. We present a framework for assessing the equity of in-vehicle crowding exposure based on automatic data sources. Two metrics are considered for quantifying the travelers’ in-vehicle crowding exposure: (1) the excess perceived travel time and (2) the relative excess perceived travel time. The proposed methodology computes the two metrics based on travel diaries and in-vehicle loads inferred from automated fare collection data. We implement Lorenz curves, Gini and Suits coefficients to evaluate horizontal (across the population) and vertical equity (considering income as well as mobility ability and need). The vertical equity is further discussed using clusters of socioeconomic groups and results from spatial lag regression models to assess the distribution of crowding exposure across socioeconomic characteristics. The results for the Stockholm Region case study indicate that crowding exposure varies substantially across the service area, with the highest values found in the denser urban areas close to Stockholm City. We find that the distribution across socioeconomic groups is relatively even, but travelers from areas that are wealthier, higher educated, have higher share of rental housing or lower vehicle ownership areas tend to be exposed to more crowding. The paper provides tools to support public transport planners in decision-making, showing where to intervene to reduce crowding exposure efficiently to achieve urban equity and sustainability.

Background

This project followed-up on a project called FairAccess which was granted in Trafik och Region 2018.In FairAccess, we processed Access card data and performed a sequence of inferences to derive timedependent origin-destination matrices for the entire Region Stockholm system. Tap-in records werematched with corresponding inferred tap-out locations and time stamps for about 80% of all records.Moreover, we implemented an algorithm to generate a journey database based on our transferinference method. We used the outputs of this process to evaluate the impacts of the fare schemechange (i.e. from zone-based to flat fare) on different user profiles. Access card products and zonalattributes were used for analysing policy impacts on different market segments.The “Unravelling Mobility Patterns using Longitudinal Smart Card Data” project was granted on May27, 2020 and the contract was signed on July 17, 2020. In this project, we capitalise on the capabilitiesof the inferences performed in previous work to conduct a series of market segmentation andadvanced data analytics to empirically analysis demand patterns for public transport in the StockholmCounty. The growing travel demand in Stockholm County is accompanied by an increased diversity ofsub-centres within the region as well as in individual travel patterns. It is thus increasingly importantto understand how demand patterns evolve over time, what the key market segments are and howdifferent users are affected by changes in service provision. The latter is studied in the contact of theopening of the Citybanan project.As stated in the SLL Research and Innovation Plan, the development of transport solutions for theStockholm region requires new knowledge regarding travellers’ needs and preferences, and theimpacts for different types of travellers. 

The COVID-19 pandemic has changed travel behaviour and reduced the use of public transport throughout the world, but the reduction has not been uniform. In this study we analyse the propensity to stop travelling by public transport during COVID-19 for the holders of 1.8 million smart cards in Stockholm, Sweden. We suggest two models for explaining the change in travel pattern, linking socioeconomic data with the probability to stop travelling. We find that education level, income and age are strong predictors, but that workplace type also substantially affect the propensity of public transport travel. Furthermore, we use clustering to divide the population into five separate social groups, serving as a more intuitive understanding of how the pandemic has affected different citizens’ propensity to use public transport. The results can guide policy makers on how to better tail e.g. bus supply to local demand, either through an increased understanding of differences based on the results or by further incorporating the results into a transport simulation models.

We analyze the distribution of transit subsidies across population groups in Stockholm. We develop a novel methodology that takes into account that the subsidy per passenger varies across transit links, since production costs and load factors vary. With this, we calculate the subsidy per trip in the transit network and analyze the distribution of subsidies across population groups. The average subsidy rate in Stockholm is 44%, but the variation across trips turns out to be large: while 34% of the trips are not subsidized at all but generates a profit, 16% of the trips have a subsidy rate higher than 2/3. We calculate the concentration index to explore the distribution of subsidies across income groups. The average subsidy per person is similar for all income groups, except for the top income quintile. This holds not only for the current flat-fare system, but also for distance-based fares and fares with a constant subsidy rate. Transit subsidies is hence not effective as a redistribution policy in Stockholm. The largest systematic variation we find is across residential areas: the average subsidy per person is five times higher in the peripheral areas of the region compared to the regional core, and the subsidy per trip is ten times higher.

A fair distribution of public transport benefits is a commonly stated goal of agencies and operators of public transport. However, it is less complicated and costly to provide accessibility in some parts of cities and their surroundings than in other parts. Densely populated areas, and areas situated closer to the city center therefore often have higher public transport accessibility than remote or sparsely populated areas. Neglecting these realities results with an unrealistic assessment of equity in service provision and hampers their consideration when setting policy goals. In this study, we propose a framework for investigating equity in the distribution of accessibility, where the suggested goal is to provide residents with equal accessibility for equally dense and central areas. For the Stockholm County, we show that accessibility may seem to be distributed horizontally inequitable and vertically regressive. However, once controlling for how dense and close to the city center residents live, while still being horizontally inequitable the distribution of accessibility in Stockholm County is found progressive, i.e., benefiting those with lower incomes. We demonstrate the proposed method for the case of skip-stop train operations and find that it shifts our constructed accessibility measure toward a more horizontally inequitable and vertically progressive state. We conclude that our proposed method can be a potent way for public transport agencies to measure and concretize equity goals and evaluate policy changes.