Posts tagged AVL
Operations of zero-emission buses: impacts of charging methods and mechanisms on costs and the level of service
To limit global warming and strive for more liveable and sustainable cities, innovative zero-emission buses are on the rise all around the world. For now, only trolley, battery and fuel-cell electric vehicles can be classified as (on the pipe) zero-emission vehicles. Different charging methods, including different charging systems and power, are available to charge battery electric vehicles. However, scientific literature focused on the operation and charging scheduling of electric vehicles is scarce.
In this study, a comparison of different applied charging methods for electric buses is obtained. A new ZE-bus station simulation method is developed to assess charging methods and charging regulations with regard to their impacts on costs and level of service.
The shift to zero emission bus transport is meant for achieving more sustainable and liveable cities. However, this research concludes that this is involved with higher costs and passenger disturbances. The investment costs increase substantially. Benefits of electric operations, including vehicle propulsion cost savings up to 70 percent, are not able to compensate these high investments. (Slow) depot charging offers opportunities for operations on short distance lines. The depot location should be close to a bus station and additional fleet is required. In order to prevent fleet overcapacity, vehicles should be recharged with high charging power along the line, preferably at combined bus stations and terminals in order to prevent charging related delays. Dynamic/In-motion charging – still in its infancy stage yet – offers opportunities to prevent these delays due to combined charging and operation time.
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
Driver schedule efficiency vs. public transport robustness: A framework to quantify this trade-off based on passive data
More complex, efficient driver schedules reduce operator costs during undisrupted operations, but increase the disruption impact for passengers and operator once a disruption occurs. We develop an integrated framework to quantify the passenger and operator costs of disruptions explicitly as function of different driver schedule schemes. Since the trade-off between driver schedule efficiency and robustness can be quantified, this supports operators in their decision-making.
Read the CASPT paper by Menno Yap HERE and find the presentation HERE
Assessing disruption management strategies in rail-bound urban public transport from a passenger perspective
This paper provides a framework for generating and assessing alternatives
in case of disruptions in rail-bound urban public transport systems,. The proposed
framework considers the passenger perspective as well as the operator perspective,
for the often-used measures of detouring and short-turning. An application of the
framework demonstrates that currently used disruption management protocols often
do not lead to the optimal solution from the passenger perspective. Furthermore, the
optimal choice between alternatives from passenger perspective shows to be
dependent on the passenger flows.
Data-driven transfer inference for public transport journeys during disruptions
Disruptions in public transport have major impact on passengers and disproportional effects on passenger satisfaction. The availability of smart card data gives opportunities to better quantify disruption impacts on passengers’ experienced journey travel time and comfort. For this, accurate journey inference from raw transaction data is required. Several rule-based algorithms exist to infer whether a passenger alighting and subsequent boarding is categorized as transfer or final destination where an activity is performed. Although this logic can infer transfers during undisrupted public transport operations, these algorithms have limitations during disruptions: disruptions and subsequent operational rescheduling measures can force passengers to travel via routes which would be non-optimal or illogical during undisrupted operations. Therefore, applying existing algorithms can lead to biased journey inference and biased disruption impact quantification. We develop and apply a new transfer inference algorithm which infers journeys from raw smart card transactions in an accurate way during both disrupted and undisrupted operations. In this algorithm we incorporate the effects of denied boarding, transferring to a vehicle of the same line (due to operator rescheduling measures as short-turning), and the use of public transport services of another operator on another network level as intermediate journey stage during disruptions. This results in an algorithm with an improved transfer inference performance compared to existing algorithms.
Find the paper 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
Monitoren van kwaliteit en beleving van multimodale OV ketens voor betere prognoses
De bereikbaarheid van steden staat onder druk. Door de toename van bewoners, bedrijven en bezoekers is de verwachting dat de stedelijke bereikbaarheid verder onder druk komt te staan. Tot voorkort was het niet goed mogelijk om de kwaliteit (reistijd, betrouwbaarheid en beleving) van de gehele OV deur-tot-deur reis en de first en last mile te meten. Deze inzichten zijn essentieel om het effect van ontwikkelingen en maatregelen in te schatten.
Samen met het ministerie van I en M en de Metropoolregio Amsterdam hebben we een werkmethode ontwikkeld en toegepast om de kwaliteit van de gehele deur-tot-deur reis te beoordelen. In de eerste maanden van 2016 is een pilot voor de werkmethode uitgevoerd tussen Amsterdam en Haarlem. In deze pilot is de kwaliteit (reistijd, betrouwbaarheid en beleving) van de deur-tot-deur reis onderzocht met bestaande data (OV-chipkaart en NDOV) en direct vanuit de reiziger (enquêtes en apps). Met een nieuw ontwikkelde tool is met behulp van open data van zowel het stedelijke als landelijke OV (bijv. GVB en NS) inzicht gekregen in de geleverde kwaliteit. Met behulp van een nieuwe app zijn inzichten verkregen in ketenverplaatsingen, zoals fiets-OV.
De methodiek en nieuwe tooling heeft bewezen de benodigde inzichten op te leveren. Daarnaast blijkt uit de pilot onder meer dat:
– de combinatie van gegevens een goede werkmethode oplevert voor auto, OV, fiets en combinaties daartussen en voor de gehele deur-tot-deur reis (inclusief first en last mile).
– de objectieve en subjectieve waarde van reistijd, betrouwbaarheid en beleving per stukje van de reis regelmatig van elkaar verschillen. Zo wordt een betrouwbare en gemiddeld snelle OV-reis toch beleefd als lage kwaliteit.
De resultaten van de pilot zijn veelbelovend voor verdere ontwikkeling en toepassingen.
Bekijk de Platos presentatie 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