While social segregation is often assessed using static data concerning residential areas, the extent to which people with diverse background travel to the same destinations may offer an additional perspective on the extent of urban segregation. This study further contributes to the measurement of activity-based social segregation between multiple groups using public transport smart card data. In particular, social segregation is quantified using the ordinal information theory index to measure the income group mix at public transport journey destination zones. The method is applied to the public transport smart card data of Stockholm County, Sweden. Applying the index on 2017–2020 data sets for a selected week, shows significant differences between income groups’ segregation along the radial public transport corridors following the opening of a major rail project in the summer of 2017. The overall slight decrease in segregation over the years can be linked to declining segregation in the city center as a travel destination and its public transport hubs. Increasing zonal segregation is observed in suburban and rural zones with commuter train stations. This method helps to quantify social segregation, enriching the analysis of urban segregation and can aid in evaluating policies based on the dynamics of social life.

Validation of travel demand models, although recognised as important, is seldom undertaken. This study adds to the scarce literature in this field by undertaking an external validation of a multi-modal transit route choice model. The model was estimated using smart card data for the urban transit network of Amsterdam before the introduction of a new metro line and is used to predict changes in travel behaviour after the network change. To validate, the model was checked for changes in estimated parameters between the two time periods, and predictive ability was evaluated at different aggregation levels. Although most model parameters were found to be unstable between the two contexts, the predictive performance at all levels was similar to the locally estimated model. Moreover, individual choices and transit mode-share predictions were found to be close to the observed ones. The errors were relatively larger for the link and route-level predictions, some of which could be attributed to the assumptions made regarding consideration choice set given as input to the model. On comparing alternative model specifications, using generic instead of mode-specific travel attributes lead to a strong degradation in predictive performance. Conversely, a model incorporating overlap between routes, with a better model fit in the base period, did not offer a clear improvement in prediction performance. The study highlights the need to validate transit route choice models before using them for deriving policy recommendations, especially in this data-rich age in which it can often be undertaken at a relatively low additional cost.

Perceived travel times of travelers are usually longer than actually realized travel times, implying that passengers’ experience of travel time savings is different from objectively calculated savings. This study provides additional empirical evidence on this topic, by comparing the passengers’ perceived travel times reported in an (online) survey with their corresponding actual in-vehicle travel times from Automatic Vehicle Location (AVL) data. The case study involves the metro, tram and bus network of Amsterdam, the Netherlands. On average, travelers perceive their travel time to be 1.9 min (11%) longer than their actual realized travel time. The perceived values match the scheduled values slightly better than the actually realized values. Furthermore, we found a larger travel time over-perception for metro compared to tram and bus. This is a counter-intuitive result, since the metro has been found to have a less negative travel time perception than busses in the public transport choice modelling literature. When the travel purpose is considered, the leisure time purposes recreation and shopping have a significantly smaller travel time over-perception than work-related journeys. Opening a new metro line did not have a significant influence on the travel time perception of travelers in Amsterdam.

Circuity of transit networks, defined as the ratio of network to Euclidean distance traveled from origin to destination stop, has been known to influence travel behavior. In addition to the longer time spent in travel, for networks where fare is based on distance traveled, higher circuity also means higher fare for the same Euclidean distance. This makes circuity relevant from an equity perspective. Using a case study of the urban transit network of Amsterdam in the Netherlands, this study explores the role of transit circuity on the disparity in distance traveled by travelers' income profile and its implications on travel times and costs for networks with distance-based fares. The analysis is based on travel patterns from smart card data for bus, tram, and metro modes, combined with neighborhood level income data. Results reveal that in Amsterdam, the higher the share of high income people living in proximity to a transit stop, the lower the circuity of journeys from the stop, when controlled for the Euclidean distance covered and spatial auto-correlation. The uneven distribution of circuity exacerbates the disparity in distance traveled, and hence fare paid between the income groups. However, the travel time per Euclidean distance favors the low income group, possibly due to the circuitous routes serving these areas being compensated by higher travel speeds. This study highlights the role of transit network design in determining its equity outcomes and emphasizes the importance of considering equity during route and fare planning. The process followed can be adapted to examine equity for other urban networks.

Capturing unobserved correlation between overlapping routes is a non-trivial problem in route choice modelling. For urban transit networks, research so far has been inconclusive on how this overlap is perceived by travellers. We estimate a series of path size correction logit (PSCL) models to account for alternative specifications of route overlap, including a new definition of overlap in terms of transfer nodes is proposed for multi-leg journeys. Our estimation is performed on smart card data from Amsterdam. The results indicate that the overlap between transit routes is valued positively when incorporated using either link-based, leg-based or transfer node-based PSC individually, with the transfer node-based PSC resulting in the best model fit. When considered simultaneously, the overlap of transfer nodes is valued positively by the travellers, but the subsequent overlap of journey legs is valued negatively, implying that travellers prefer having multiple (distinct) travel options at common transfer locations.

The north-south metro line in Amsterdam became operational in summer 2018, accompanied by changes to the existing bus, metro, and tram network in the city. In this paper we undertake an ex-post analysis of the transportation impacts of the network change. Using two sets of smart card transactions, of 5-6 weeks each, and corresponding Automatic Vehicle Location (AVL) data, a before-after comparison is made, concerning ridership, travel times, number of transfers, and travel time reliability. The results show a 4% increase in network wide working day ridership and a strong shift from tram and bus to metro. On an average working day, more than 6,000 hours of travel time is saved. 21% of travellers have more than 1 minute shorter travel time and 13% of travellers have more than 1 minute travel time increase. Furthermore, slightly fewer transfers are made, and the aggregated effect on travel time reliability is marginally positive. For an average working day (7am to 7pm), the resulting daily societal benefits of the new public transport network are approximately €54,200. On a yearly basis the transport related societal benefits are approximately 22 million euros. Doing an ex-post analysis is not common in the literature and in practice, and therefore in a lot of cases the realized benefits of large infrastructural investments remain unknown. This study provides an example of scientific methodology development using multiple data sources, that enables such ex-post evaluations, leading to improvements in public transport assessment and planning.

Transport accessibility experienced by an individual depends on their needs and abilities, as represented by their individual characteristics, such as age, income and gender. Although important from an equity perspective, the individual component of accessibility is currently ignored in most transport equity studies. This paper evaluates the impact of including individual characteristics into logsum-based accessibility measures for transport equity analysis. Using data from the London Travel Demand Survey (LTDS) 2011–13, two alternate logsum measures of accessibility are specified – with and without individual characteristics. An empirical analysis of spatial, social and economic equity is conducted using both the measures, and the outcomes are compared. The results clearly demonstrate that ignoring individual characteristics in logsum measures of accessibility can lead to unreliable outcomes for social and economic equity analysis, but do not add significant value when aggregated across large geographical zones for spatial equity analysis. Overall, ignoring individual characteristics masks the disparity in distribution of accessibility, as measured by the Gini index. Although not straightforward, the difference between accessibility patterns using the two logsum measures also yields insights into the possible causes of inequity, which can provide actionable inputs to policy makers. The study highlights that personal needs and abilities are often responsible for accessibility variations among individuals and ignoring them can result in a misleading picture of equity, as demonstrated quantitatively in this paper.