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    Posts tagged smartcard

    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

    Insights into door-to-door travel patterns of public transport passengers

    Public transport enables fast and reliable station to station journeys. To assess passenger travel patterns and to infer actual quality of service, smartcard and AVL data offer great opportunities. There is, however, an increasing interest in insights into access and egress dynamics of public transport riders as well. What is the size of a stop’s catchment area, which modes are used, and how long and reliable are access and egress times? The answers to these and other questions enable optimization of the total mobility system, thereby also increasing public transport ridership and efficiency. Sufficient biking access of public transport stops (routes and parking), for instance, offer opportunities to increase public transport stopping distances, thereby increasing operational speed and reliability, without compromising accessibility of service areas. We developed a methodology to calculate and demonstrate these dynamics by using new and existing data technologies, namely AVL, survey and new promising app.

    Find the Transit Data Conference abstract HERE and our presentation HERE

    Improving predictions of the impact of disturbances on public transport usage based on smart card data

    The availability of smart card data from public transport travelling the last decades allows analyzing current and predicting future public transport usage. Public transport models are commonly applied to predict ridership due to structural network changes, using a calibrated parameter set. Predicting the impact of planned disturbances, like temporary track closures, on public transport ridership is however an unexplored area. In the Netherlands, this area becomes increasingly important, given the many track closures operators are confronted with the last and upcoming years. We investigated the passenger impact of four planned disturbances on the public transport network of Den Haag, the Netherlands, by comparing predicted and realized public transport ridership using smart card data. A two-step search procedure is applied to find a parameter set resulting in higher prediction accuracy. We found that in-vehicle time in rail-replacing bus services is perceived ≈1.1 times more negatively compared to in-vehicle time perception in the initial tram line. Besides, passengers do not seem to perceive the theoretical benefit of the usually higher frequency of rail-replacement bus services compared to the frequency of the replaced tram line. At last, no higher waiting time perception for temporary rail-replacement services could be found, compared to regular tram and bus services. The new parameter set leads to substantially higher prediction accuracy compared to the default parameter set. It supports public transport operators to better predict the required supply of rail-replacement services and to predict the impact on their revenues.

    Read our TRB paper HERE

    Find the poster HERE

    Investigating potential transit ridership by fusing smartcard and GSM data

    The public transport industry faces challenges to cater for the variety of mobility patterns and corresponding needs and preferences of passengers. Travel habit surveys provide information on the overall travel demand as well as its spatial variation. However, it often does not include information on temporal variations. By means of data fusion of smartcard and Global System for Mobile Communications (GSM) data, spatial and temporal patterns of public transport usage versus the overall travel demand are examined. The analysis is performed by contrasting different spatial and temporal levels of smartcard and GSM data. The methodology is applied to a case study in Rotterdam, the Netherlands, to analyze whether the current service span is adequate. The results suggest that there is potential demand for 10 extending public transport service span on both ends. In the early mornings, right before transit operations are resumed, an hour-on-hour increase in visitor occupancy of 33-88% is observed in several zones, thereby showing potential demand for additional public transport services. The proposed data fusion method showed to be valuable in supporting tactical transit planning and decision making and can easily be applied to other origin-destination transport data.

    Read our TRB paper HERE

    Find our presentation HERE

    Waar liggen kansen voor OV: datafusie GSM en chipkaart

    De grootste uitdaging van de openbaar vervoer sector is om tegemoet te komen aan de verscheidenheid aan reispatronen, en de bijbehorende behoeften en preferenties, van reizigers. Het beter matchen van vraag en aanbod levert zowel een kwaliteitssprong als kostenreductie op en heeft dus alle aandacht. Bestaande databronnen helpen, maar zijn nog niet afdoende. De combinatie van nieuwe bronnen biedt echter hoopgevende resultaten. Door een innovatieve methodiek kunnen GSM- en anonieme chipkaartdata gecombineerd worden om de OV potentie in kaart te brengen.

    Bestaande onderzoeken (zoals OViN) geven informatie over de totale reisbehoefte en de ruimtelijke spreiding hiervan. Deze huishoudsurveys bieden veelal echter geen inzicht in de spreiding van deze reisbehoefte over de tijd. Een nieuwe methodiek om GSM- met anonieme OV chipkaartdata te koppelen, geeft die inzichten wel. Door middel van deze datafusie kunnen zowel de ruimtelijke als temporele patronen van OV gebruik vergeleken worden met de totale ruimtelijke en temporele reispatronen. Dit geeft inzicht in de (mis)match van vraag en aanbod in ruimte én tijd. Ideaal dus als eerste stap voor het verbeteren van deze match: OV potentie kan zo worden opgespoord.
    Deze methode is toegepast in een case study in Rotterdam om te onderzoeken of het huidige OV bedieningsinterval voldoende aansluit bij de latente vraag. De resultaten demonstreren dat er potentie is om het OV bedieningsinterval zowel in de vroege ochtend als in de late avond uit te breiden. In de vroege ochtend, juist voordat het OV wordt opgestart, kan een uur-tot-uur toename in bezoekersaantallen van 33% tot zelfs 88% worden waargenomen in diverse delen van de Rotterdamse regio. Dit illustreert de potentiële vraag voor extra openbaar vervoer aanbod in de vroege ochtend. Op vergelijkbare wijze is deze analyse uitgevoerd voor het OV aanbod in de late avond.
    Deze innovatieve methode van datafusie is van grote toegevoegde waarde te zijn gebleken ter ondersteuning van OV planning. Deze datafusie methode kan ook eenvoudig worden toegepast op andere herkomst-bestemmingsdata.

    Lees het CVS paper HIER

    Betrouwbare OV netwerken: Reizigersperspectief centraal dankzij anonieme chipkaartdata

    Voor het openbaar vervoer is betrouwbaarheid een kwaliteitsfactor van belang.
    Terwijl we een beetje vertraging met de auto wel oké vinden, is elk minuutje
    dat een bus, trein of tram te laat arriveert, er echt één te veel. Vervoerders en
    openbaarvervoerautoriteiten zijn dan ook continu op zoek naar mogelijkheden
    om de betrouwbaarheid te verbeteren. Maar hoe bepaal je eigenlijk of
    een maatregel werkt? Wat is een goede maat voor betrouwbaarheid? In
    deze bijdrage maken we een boeiend uitstapje naar de wereld van haltes,
    overstappen en OV-chipkaarten.

    Lees het artikel uit NM magazine HIER
    Lees het uitgebreide wetenschappelijke artikel HIER

    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

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