Posts tagged smartcard

Data driven enhancement of public transport planning and operations: service reliability improvements and ridership predictions

Automatic Vehicle Location (AVL) and smartcard data are of great value in planning, design and operations of public transport. We developed a transport demand model, which utilizes smartcard data for overall and what-if analyses, by converting these data into passengers per line and OD-matrixes and allowing network changes on top of a base scenario. This new generation model serves in addition to the existing range of transport demand models and approaches. It proved itself in practice during a case study in The Hague, where it helped the operator gain valuable insights into the effect of small network changes, such as a higher frequency.
Data also supports measures to improve service reliability. We introduced a new network design dilemma, namely the length of a transit line vs. its reliability. Long lines offer many direct connections, thereby saving transfers. However, the variability in operation is often negatively related to the length of a line, leading to poorer schedule adherence and additional waiting time for passengers. A data driven case study shows that in the case of long lines with large variability, enhanced reliability resulting from splitting the line could result in less additional travel time. This advantage compensates for the additional time of transferring if the transfer point is well chosen.

Read the full paper here: TRA Conference 2016 Van Oort Data driven enhancement of PT

or check the poster: TRA2016 Conference Poster

New generation of public transport models: predicting ridership by smartcard data

In the public transport industry we observe the rise of a new generation of transport demand models. We applied Dutch smart card data for analysis of passenger volumes and routing and performed what-if analyses by using existing transport planning software. We focused specifically on public transport operators by providing them relative simple (easy to build, low calculation time) models to perform these what-if analyses. The data, including transfer information, is converted to passengers per line and an OD-matrix between stops. This matrix is assigned to the network to reproduce the measured passenger flows. After this step, what-if analysis becomes possible. The effects of line changes on route choice can already be investigated when fixed demand is assumed. However, by introducing an elastic demand model the realism of the modeled effects is improved, because network changes induce changes in level of service, which affects the demand for public transportation. This elastic demand model was applied on a case study in The Hague. We imported the smart card data into a transport model and connected the data with the network. The tool turned out to be very valuable for the operator to gain insights into the effects of small network changes.
In addition to this basic model, we also applied a capacity constrained assignment method. The most important aspects on which passengers base their choice for public transport travelling are the perceived travel time, costs, reliability and comfort. Despite this importance, comfort is often not explicitly considered when predicting demand. The case study results indicate that not considering capacity and comfort effects can lead to a substantial underestimation of effects of certain measures aiming to improve public transport. This means that benefits of measures that reduce crowding for both passengers and operators can now be quantified and incorporated in the decision-making process. We also illustrate that this extended modelling framework can be applied in practice, requiring short calculation times and leading to better predictions of public transport demand.

Find our ETC 2015 presentation HERE

Improving public transport decision making, planning and operations by using Big Data: Cases from Sweden and the Netherlands

New big data (sources) in the public transport industry enable to deal with major challenges such as elevating efficiency, increasing passenger ridership and satisfaction and facilitate the information flow between service providers and service users. This paper presents two actual cases from the Netherlands and Sweden in which automated data sources were utilized to support the planning and operational processes. The cases illustrate the benefits of using smartcard and vehicle positioning data. Due to the data (processing), valuable insights were gained helping to make the right choices and improve the public transport system.

Read our paper: Workshop paper IEEE ITSC 2015 and check our presentation: Presentation IEEE ITSC15

Urban Mobility Lab: benut databerg

CROW-KpVV hield op 28 mei in Utrecht de eerste landelijke kennisdag over het benutten van data in het openbaar vervoer. Het delen van data levert veel op, maar is nog geen gemeengoed. Tijdens de bijeenkomst stond onder andere het Urban Mobility Lab in de schijnwerpers: een proeftuin vol data over vervoerpatronen in Amsterdam.

Lees het hele artikel: Urban Mobility Lab in OV Magazine

Data-driven public transport ridership prediction approach including comfort aspects

The most important aspects on which passengers base their choice whether to travel by public transport are the perceived travel time, costs, reliability and comfort. Despite its importance, comfort is often not explicitly considered when predicting demand for public transport. In this paper, we include comfort level in a modelling framework by incorporating capacity in the public transport assignment. This modelling framework is applied in the public transport model of HTM, the urban public transport operator of The Hague. The current transportation demand is directly derived from smart card data and future demand is estimated using an elasticity based approach. The case study results indicate that not considering capacity and comfort effects can lead to a substantial underestimation of effects of certain measures aiming to improve public transport (up to 30%). We also illustrate that this extended modelling framework can be applied in practice: it has a short computation time and leads to better predictions of public transport demand.

 

Check our presentation: Presentation CASPT2015
Read our full paper: Van Oort et al: Datadriven PT modelling CASPT2015

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