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.

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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.

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We evaluate a large scale network change in Amsterdam, the Netherlands, where a regional network of many direct bus lines is replaced by a new metro line as a trunk line and buses serving as feeder lines. For the analysis we use realized trip times from automated vehicle location data and ridership details from smart card data. In the new network, we observe a strong shift to the new transfer stations. On the other hand, stops and lines keeping their direct connection in the new feeder network are used more than other stops and lines. From northern direction, the leg on the faster trunk line is too short to compensate for the additional transfer, leading to a slight increase in travel time. From the south, travel time has become shorter: a longer leg is travelled on the new metro line. Finally, all new feeder lines operate more reliably than the former direct lines. These findings can help guide ex-ante evaluations for other networks considering transition to a trunk and feeder system.@en

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.

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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.

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The built environment is an important determinant of travel demand and mode choice. Establishing the relationship between the built environment and transit use using direct models can help planners predict the impact of neighborhood-level changes, that are otherwise overlooked. However, limited research has compared the impacts of the built environment for different networks and for individual transit modes.
This paper addresses this gap by developing built environment and transit use models for three multimodal networks, Amsterdam, Boston and Melbourne, using a consistent methodology. A sample of train, tram and bus sites with similar station-area built environments are selected and tested to establish if impacts differ by mode. It is the first study that develops neighborhood-level indicators for multiple locations using a consistent approach.
This study compares results for ordinary least squares regression and two-stage least squares (2SLS) regression to examine the impact of transit supply endogeneity on results. Instrumented values are derived for bus and tram frequency in Melbourne and bus frequency in Boston. For other mode and city combinations, the 2SLS approach is less effective at removing endogeneity.
Results confirm that different associations exist between the built environment and transit modes, after accounting for mode location bias, and that this is true in multiple networks. Local access and pedestrian connectivity are more important for bus use than other modes. Tram is related to commercial density. This finding is consistent for all samples. Land use mix and bicycle connectivity also tend to be associated with higher tram use. Train use is highest where opportunities exist to transfer with bus. Population density is commonly linked to ridership, but its significance varies by mode and network.
More research is needed to understand the behavioral factors driving modal differences to help planners target interventions that result in optimal integration of land use with transit modes.
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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.

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De Noord/Zuidlijn (NZL) is een nieuwe metroverbinding die sinds juli 2018 de noord- en zuidzijde van Amsterdam met elkaar verbindt. In een omvangrijke, vier jaar durende, studie worden de vervoerkundige, ruimtelijke en economische effecten onderzocht. Deze paper beschrijft de effecten op reizigers, reistijden, aantal overstappen en betrouwbaarheid, waarbij de situatie voor en na de ingebruikname van de Noord/Zuidlijn met elkaar worden vergeleken. Voor de analyse is gebruik gemaakt van twee databronnen: automatische voertuiglocatiedata en geanonimiseerde OV-chipkaartdata. Het gaat om grote datasets: de OV-chipkaartdata bevat ongeveer 56 miljoen rittransacties (van 11 weken). Die ritten zijn vervolgens samengevoegd tot reizen, indien door reizigers wordt overgestapt. Om de overstap te identificeren is een nieuwe methode ontwikkeld met aanvullende criteria op het standaard 35 minutencriterium. De aanvullende criteria leiden ertoe dat het aantal overstappen grofweg 25% lager uitkomt dan gebruikelijk in de Nederlandse praktijk. Voor de analyse van de reistijden zijn de wachttijden bij tram en bus geschat op basis van gerealiseerde tussenaankomsttijden. Bij metro wordt ingecheckt op het station (bij de poortjes) en zit de wachttijd dus al in het de gemeten reistijd. Uit de analyses blijkt dat als gevolg van de ingebruikname van de Noord/Zuidlijn een flinke verschuiving heeft plaatsgevonden van bus en tram naar metro. Per saldo is er een toename van het OV-gebruik van 4% ten opzichte van een jaar eerder. Gemiddeld over alle reizen is het effect van het nieuwe netwerk inclusief Noord/Zuidlijn dat reizen 43 seconden per reis korter duren, ofwel in totaal ongeveer 7.500 uur per gemiddelde werkdag. Er zijn echter grote verschillen tussen reisrelaties. 21% van de reizigers heeft een reistijdwinst van meer dan 1 minuut, 13% van de reizigers heeft een reistijdverlies van meer dan 1 minuut. De grotere reistijdwinsten van meer dan 10 minuten komen terecht bij een relatief klein aandeel van 3% van de reizigers. Ook zien we een kleine afname van het gemiddeld aantal overstappen, die geheel wordt veroorzaakt door nieuwe reizigers met de Noord/Zuidlijn. Wanneer we alleen kijken naar de bestaande reizigers, dan zien we wel een lichte toename (5%) van het aantal overstappen. Ten slotte is ook de betrouwbaarheid van het OV-netwerk verbeterd. De buffertijd is afgenomen met gemiddeld 17 seconden per reiziger.@en

Urban transit networks typically consist of multiple modes and the journeys may involve a transfer within or across modes. Therefore, the passenger experience of travel time reliability is based on the whole journey experience including the transfers. Although the impact of transfers on reliability has been highlighted in the literature, the existing indicators either focus on unimodal transfers only or fail to include all components of travel time in reliability measurement. This study extends the existing “reliability buffer time” metric to transit journeys with multimodal transfers and develops a methodology to calculate it using a combination of smartcard and automatic vehicle location data. The developed methodology is applied to a real-life case study for the Amsterdam transit network consisting of bus, metro, and tram lines. By using a consistent method for all journeys in the network, reliability can be compared between different transit modes or between multiple routes for the same origin–destination pair. The developed metric can be used to study the reliability impacts of policies affecting multiple transit modes. It can also be used as an input to behavioral models such as mode, route, or departure time choice models.@en

In daily practice, public transport authorities and operators are constantly searching for improvements in public transport operations. To this end, it is necessary to identify inefficiencies and bottlenecks in the current public transport services. In this paper, we propose a method to automatically detect bottlenecks in the public transport network, using Automatic Vehicle Location data. A tool is developed to automatically process AVL data to identify bottlenecks for the current situation. This tool is applied to Amsterdam, capital of the Netherlands, where a new metro line will come into operation in the summer of 2018. The results show that bottlenecks are mainly found on radial lines and in the inner city. Therefore we expect that the operations of the tram network will improve in terms of operating speed and reliability due to the opening of the metro line, since the tram lines are expected to become less crowded and fewer lines will traverse the inner city.@en