Posts tagged smartcard

Impact assessment of new North/South metro line in Amsterdam

Large infrastructural projects are usually evaluated ex-ante before the decision to build the project is taken. However, after construction and opening of such project a thorough ex-post analysis is rare. In this paper we present an overview of such an evaluation study conducted in Amsterdam, capital of The Netherlands, including some first results. Research themes in the study are public transport, mobility and accessibility, public space and liveability and spatial economics. In this paper we focus on effects on public transport.

The new north-south metro line in Amsterdam became operational in summer 2018. This was accompanied by changes to the existing bus and tram network to provide feeder services to the new line, as well as to remove duplicate routes. Apart from adding significant capacity to the public transport network, the new line and the accompanying changes to the network are expected to improve travel times, reliability, accessibility and comfort levels (at least on average; not for all individual travellers).
The changes in such service quality attributes is expected to lead to a change in travel behaviour in terms of public transport route choice, mode choice (between public transport and private modes or within public transport), destination choice, departure time choice or addition of new trips (induced demand).
The objective of this study is to identify the main effects of the new metro line on existing and new passengers. We pay attention to the following aspects:
– Passenger volumes.
– Travel times, where the following distinction can be made:
o in-vehicle time;
o waiting time at the first stop;
o transfer walking time;
o transfer waiting time.
– Number of transfers.
– Network flows / crowding in vehicles.
– Reliability: travel time variance on the journey level.
– Accessibility: number of inhabitants and jobs reachable within a travel time budget.

Data sources for the study are GTFS timetable data (open source), Smart card data (both from within the city of Amsterdam as for the regional feeder bus services) and Automated Vehicle Location data. To measure perceived quality of the PT network, a survey is conducted among inhabitants of Amsterdam. In this survey approximately 3.800 respondents were asked about the travel time perceptions of their last PT trip, both before and after opening of the metro line. Finally, for a sample of travellers the entire trip is followed by a GPS tracking app.

Impacts of replacing a fixed transit line by a Demand Responsive Transit system

The diffusion of the smartphone and the urban sprawl is pushing both private and public actors to revisit the concept of the demand-responsive transit (DRT). Mokumflex is a DRT pilot program that replaced the regular bus service in low-density areas of Amsterdam, the Netherlands, for 12 months. The close collaboration with the private enterprise that conducted the system but also with the local bus operator allowed the authors to have access to precise databases, giving this article empirical information for both the situation before and after the implementation. These insights help to understand DRT systems and support (future) design of DRT and transit systems. A few indicators were chosen for the comparison: distances, ridership, costs, Greenhouse Gases (GHG), emissions and population’s perception. The ridership dropped, however, for being “demand-tailored”, the mileage per passenger reduced, improving the costs and GHG emissions. In regards to population’s perception, the system enjoyed a good evaluation.

Find the Thredbo presentation of Felipe Coutinho HERE and the paper HERE

The advantages of multi-modal concessions, two analyses in the Netherlands

Public transport authorities are aiming for more integrated concessions, including bus, train services, to provide a better experience for travellers. This paper describes the analysis of the effect of multimodal concessions.First, the Dutch Province of Limburg moved from uni-modal to a multimodal concession. The paper analyses effects of that choice had for network design, travel times (using weighted generalized travel time), travel costs, patronage (using smart card data analysis), and coordinative interactions between operator and authority (based on interviews). Second, the paper analyses three different forms of coordination between bus and train services, using the STO model. It compares three regional concession in the Netherlands in Limburg, Fryslân, and Groningen. They represent one region with a multi-modal concession under net-cost, one region with multiple unimodal concessions under net-cost and one region with multiple unimodal concessions under mixed forms of contract. The paper concludes that multi-modal concessions provide some real-world advantages to travellers and authorities. However, to what extent these advantages materialize is dependent on a number of key factors, including the type of contracts used, the number of transport authorities active in the concession area and the role that the transport authority wants to take up.

Find the Thredbo presentation of Gerald Hoekstra HERE and the paper HERE

Impact analysis of a new metro line in Amsterdam using automated data sources

A new metro line (the north-south line) was opened in Amsterdam in July 2018, adding significant capacity to the existing urban public transport network consisting of bus, tram and metro modes. The opening of the metro line was accompanied by changes to the existing bus and tram network, such as removal of duplicate routes and addition of feeder routes.

Traditionally, the impact of such a network change was measured either ex-ante or post-op based on surveys or model forecasts (Vuk 2005; Knowles 1996; Engebretsen, Christiansen, and Strand 2017). However, with the availability of automated data sources such as the smart card data, the exact impact on transit demand and service quality can now be measured. However, so far this has been limited to analysing the changes in travel times and reliability at a trip level (Fu and Gu 2018), excluding transfers.
This research utilises smart card and AVL data to study the impact of the new line on travel patterns (passenger flows), travel times and reliability from a passenger perspective by considering journeys including transfers. The metrics are calculated at a stop-cluster level, enabling also a distributional analysis of the impacts. Such a post-op analysis of any policy intervention or network change could be used to refine the demand predictive ex-ante tools.

Check the Transit Data workshop contributions of Malvika Dixit: Presentation and Extended abstract

Forecasting bus ridership with trip planner usage data: a machine learning application

Currently, public transport gives much attention to environmental impact, costs and traveler satisfaction. Good short-term demand forecasting models can help improve these performance indicators. It can help prevent denied boarding and overcrowding in busses by detecting insufficient capacity beforehand. It could be used to operate more economically by decreasing the frequency or the size of the bus if there is overcapacity. Moreover, it could help operators plan their busses during incidental occasions like big public events where little information is known. Finally, it could be used to reliably inform the travelers on the current crowdedness.
This study investigates the usefulness of a new data source; the usage data of a trip planner. In the Netherlands there are multiple trip planners available for users to help find the most optimal (multimodal) journeys. These trip planners require a date, a time and an origin and destination, which they use to construct multiple alternative journeys from which the user can choose. For this study the data of 9292 was used, being the major trip planner in the Netherlands including all public transport modes.
We developed a model for forecasting the number of people boarding and a model for forecasting the number of people alighting at a certain stop. These forecasts are defined at the vehicle-stop level. By summing the number of people boarding and subtracting the number of people alighting along the trip the forecasted number of passengers after a stop is calculated.

We compare five different machine learning models: multiple linear regression, decision tree, random forests, neural networks and support vector regression with a radial basis kernel. We compare these models with two simple rules: 1 predict the same number as last week, and 2 predict the historic average as number. The models are implemented in the Scikit-Learn library of Python. The data is stored in a PostgresSQL database.
The trip planner datasets and smart card dataset are merged and preprocessed. The resulted dataset is rather sparse; a lot of stops have zero passengers boarding or alighting or requests suggesting to do so. Therefore we investigated if subsampling is needed. From the datasets useful data is selected and features are constructed. The features are standardized. Different number of features are tested, these features are selected based on recursive elimination using a simple random forests model. Finally, the hyperparameters of the models are tuned and the optimal configurations are stored. The scores are validated by using cross validation.

Find more details in the following contributions by Jop van Roosmalen: Transit Data workshop presentation and MSc thesis

Combining Speed Adjustment and Holding Control for Regularity based Transit Operations

Vehicle bunching often occurs in high-frequency transit systems leading to deterioration of service reliability. It is thus necessary to control vehicles during operations. Holding control is a common solution for this situation, but it may result in longer vehicle running times. Speed adjustments can contribute to more regular operations while preventing prolonged trip times. This paper proposes a control strategy, which combines these two strategies to maintain the regularity of transit operations. The findings based on simulation study for trunk bus services in the Netherlands suggest that combining the two strategies implies both the positive and negative attributes of each control.

Find the MT-ITS presentation and paper of Aishah Imram HERE and HERE

Calibrating Route Choice Sets for an Urban Public Transport Network using Smart Card Data

Identifying the set of alternatives from which travellers choose their routes is a crucial step in estimation and application of route choice models. These models are necessary for the prediction of network flows that are vital for the planning of public transport networks. However, choice set identification is typically difficult because while selected routes are observed, those considered are not. Approaches proposed in literature are not completely satisfactory, either lacking transferability across networks (observation-driven methods) or requiring strong assumptions regarding traveller behaviour (uncalibrated choice set generation methodologies (CSGM)). Therefore, this study proposes a constrained enumeration CSGM that applies the non-compensatory decision model, elimination-by-aspects, for choice set formation. Subjective assumptions of traveller preferences are avoided by calibrating the decision model using observed route choice behaviour from smart card data, which is becoming increasingly available in public transport systems around the world. The calibration procedure also returns two key insights regarding choice set formation behaviour: (i) the ranking of different attributes by their importance, and (ii) the acceptable detours for each attribute. To demonstrate the methodology and investigate choice set formation behaviour, the tram and bus networks of The Hague, Netherlands are used as a case study.

Find the MT-ITS paper and presentation of Sanmay Shelat HERE and HERE

Passenger Travel Time Reliability for Multi-Modal Public Transport Journeys

Urban transit networks typically consist of multiple modes and the journeys may involve a transfer within or across modes. Hence, 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 uni-modal transfers only or fail to include all components of travel time in reliability measurement. This study extends the existing ‘Reliability Buffer Time’ metric to journeys with multi-modal 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 services. By using a consistent method for all journeys in the network, reliability can be compared between different 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 modes. It can also be used as an input to behavioral models such as mode, route or departure time choice models.

Find the TRB paper and presentation of Malvika Dixit HERE and HERE

Robust Control for Regulating Frequent Bus Services: Supporting the Implementation of Headway-based Holding Strategies

Reliability is a key determinant of the quality of a transit service. Control is needed in order to deal with the stochastic nature of high-frequency bus services and to improve service reliability. In this study, we focus on holding control, both schedule- and headway-based strategies. An assessment framework is developed to systematically assess the effect of different strategies on passengers, the operator and transport authority. This framework can be applied by operators and authorities in order to determine what holding strategy is most beneficial to regulate headways, and thus solve related problems. In this research knowledge is gained about what service characteristics affect the performance of holding strategies and the robustness of these strategies in disrupted situations, by using scenarios. The framework is applied to a case study of a high-frequency regional bus line in the Netherlands. Based on the simulation results, we identified the line characteristics that are important for the performance of schedule- and headway-based strategies and determined how robust different strategies are in case of disruptions. Headway-based control strategies better mitigate irregularity along the line, especially when there are disruptions. However, schedule-based control strategies are currently easier to implement, because it does not require large changes in practice, and the performance of both strategies is generally equal in regular, undisrupted situations. In this paper, insights into what the concerns are for operators with respect to technical adaptations, logistical changes and behavioral aspects when using a headway-based strategy are given.

Find the TRB paper and presentation of Ellen van der Werff HERE and HERE

A data-driven approach to infer spatial characteristics and service reliability of public transport hubs

Public transport hubs play an important and a central role in public transport networks by connecting several public transport lines from one or multiple network levels. Hubs can be characterized by a large relative and absolute number of transferring passengers between public transport services within the same network level and/or between different network levels. Hubs are especially important with respect to service reliability of passenger journeys, since missing connections at hubs can substantially increase the nominal and perceived passenger journey travel time. The availability of AFC and AVL data allows an in-depth analysis of hub definition, identification, characterization and reliability performance evaluation. Such analysis enables optimisation of synchronisation of schedules, thereby increase the level of service reliability.

Find our TransitData2017 presentation HERE

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