People | Locations | Statistics |
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Ziakopoulos, Apostolos | Athens |
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Vigliani, Alessandro | Turin |
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Catani, Jacopo | Rome |
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Statheros, Thomas | Stevenage |
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Utriainen, Roni | Tampere |
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Guglieri, Giorgio | Turin |
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Martínez Sánchez, Joaquín |
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Tobolar, Jakub |
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Volodarets, M. |
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Piwowar, Piotr |
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Tennoy, Aud | Oslo |
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Matos, Ana Rita |
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Cicevic, Svetlana |
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Sommer, Carsten | Kassel |
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Liu, Meiqi |
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Pirdavani, Ali | Hasselt |
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Niklaß, Malte |
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Lima, Pedro | Braga |
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Turunen, Anu W. |
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Antunes, Carlos Henggeler |
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Krasnov, Oleg A. |
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Lopes, Joao P. |
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Turan, Osman |
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Lučanin, Vojkan | Belgrade |
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Tanaskovic, Jovan |
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Oort, Neils Van
Delft University of Technology
in Cooperation with on an Cooperation-Score of 37%
Topics
- data
- driver
- modeling
- railway train
- weight
- estimating
- chemical element
- trajectory
- timetable scheduling
- geometry
- railway traffic
- wheel
- law
- resistance
- brake
- vehicle dynamic
- recommendation
- calibration
- state of the art
- ecodriving
- automatic train operation
- train operation
- urban travel
- public transport
- automobile
- road
- survey
- bicycle
- behavior
- urban area
- modal split
- cyclist
- stated preference
- multinomial logit
- mobility service
- case study
- simulation
- passenger
- optimisation
- costs
- vehicle occupant
- implementation
- logistics
- bus
- tramway
- timetable
- printed publicity
- electric vehicle
- traffic mode
- age
- traveller
- taxicab
- density
- bicycling
- experiment
- travel
- travel time
- last mile
- sensitivity
- taxi service
- e-bike
- mode choice
- land use
- shared mobility
- weekday
- e-scooter
- mobility concept
- weekend
- local public transport
- E-moped
- assessment
- industry
- cladding
- climate change
- propulsion
- contaminant
- carbon
- power train
- employed
- accumulator
- life cycle analysis
- gas
- production
- environmental impact
- energy storage system
- warehousing
- fuel
- fuel cell
- diesel engine
- traction
- wind
- hydrogen
- passenger service
- railway network
- greenhouse gas
- regional railway
- electrolysis
- algorithm
- attention
- electromagnetic spectrum
- investment
- electrification
- constraint
- bottleneck
- quality of service
- hydrogen fuel
- perception
- planning
- urban transit
- shopping
- recreation
- work trip
- automatic vehicle location
- braking
- estimate
- Statistic
- filter
- traffic simulation
- equation
- calculator
- speed measurement
- ponding
- vehicle
- ridesharing
- design
- determinant
- market
- fare
- discount
- ridesourcing
- demand responsive transportation
- ridehailing
- infrastructure
- stakeholder
- supervision
- automation
- vehicle characteristic
- control device
- noise
- ion
- submarine
- fuel consumption
- dynamic programming
- coordination
- railway station
- vehicle performance
- electric train
- railway undertaking
- comfort
- decision making
- parking duration
- flexibility
- supporting
- bicycle parking
- driving
- hub
- urbanisation
- built environment
- driver licence
- on-demand ride service
- evening
- machinery
- learning
- machine learning
- fare collection
- abstract
- operations research
- computer science
- city
- agent-based modeling
- automotive engineering
- architecture
- trip length
- traffic assignment
- route choice
- technological innovation
- waiting time
- door to door service
- autonomous vehicle
- forecasting
- traffic behavior
- meta-analysis
- autonomous automobile
- operating speed
- crash
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- traffic safety
- positioning
- face
- crosswalk
- alignment
- mobility pattern
- customer
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- urban mobility
- mobility-as-a-service
- cluster analysis
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- urban sprawl
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- private enterprise
- mechanical engineering
- mining
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- information system
- surveillance
- smart card
- choice model
- reliability
- headway
- crossheading
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- exhaust gas
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- structural engineering
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- departure time
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- transport hub
- data file
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- theory
- graph
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- planning method
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- decomposition
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- performance evaluation
- itinerary
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- big data
- level of service
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- expected value
- AIDS
- arrival and departure
- on time performance
- scheduling
- crowd
- running time
- library
- random variable
- engineering service
- layover time
- crossover
- impact study
- lowering
- philosophy
- intersection
- seat
- traffic control
- traffic control center
- show 275 more
Publications (85/85 displayed)
- 2023A Literature Review on Train Motion Model Calibrationcitations
- 2022Potential of on-demand services for urban travelcitations
- 2022Quantification and control of disruption propagation in multi-level public transport networkscitations
- 2022Preferences for first and last mile shared mobility between stops and activity locations
- 2022Life Cycle Assessment of Alternative Traction Options for Non-Electrified Regional Railway Lines
- 2022Train motion model calibration
- 2022Analysis of hydrogen-powered propulsion system alternatives for diesel-electric regional trainscitations
- 2022Optimal network electrification plan for operation of battery-electric multiple unit regional trains
- 2022Perceived and actual travel times in a multi-modal urban public transport network: comparing survey and AVL datacitations
- 2022Real-time train motion parameter estimation using an Unscented Kalman Filtercitations
- 2021Quantification and control of disruption propagation in multi-level public transport networkscitations
- 2021What are the determinants of the willingness to share rides in pooled on-demand services?citations
- 2021Deployment Scenarios for First/Last-Mile Operations With Driverless Shuttles Based on Literature Review and Stakeholder Surveycitations
- 2021Reducing fuel consumption and related emissions through optimal sizing of energy storage systems for diesel-electric trainscitations
- 2021Insights into factors affecting the combined bicycle-transit modecitations
- 2021Travellers’ preferences towards existing and emerging means of first/last mile transport: a case study for the Almere centrum railway station in the Netherlandscitations
- 2021Unsupervised approach towards analysing the public transport bunching swings formation phenomenoncitations
- 2020Integrated route choice and assignment model for fixed and flexible public transport systemscitations
- 2020Adoption of Shared Automated Vehicles as Access and Egress Mode of Public Transportcitations
- 2020Tram drivers' perceived safety and driving stress evaluationcitations
- 2020Drivers and barriers in adopting Mobility as a Service (MaaS) – A latent class cluster analysis of attitudescitations
- 2020Impacts of replacing a fixed transit line by a Demand Responsive Transit system
- 2020Unsupervised approach towards analysing the public transport bunching swings formation phenomenoncitations
- 2019Calibrating Route Choice Sets for an Urban Public Transport Network using Smart Card Datacitations
- 2019Supporting the Implementation of Headway-Based Holding Strategies
- 2019Sustainability of Railway Passenger Services
- 2019Robust Control for Regulating Frequent Bus Service: Supporting the Implementation of Headway-Based Holding Strategiescitations
- 2019Passenger Travel Time Reliability for Multimodal Public Transport Journeyscitations
- 2019Where shall we sync? Clustering passenger flows to identify urban public transport hubs and their key synchronization prioritiescitations
- 2018Identification and quantification of link vulnerability in multi-level public transport networkscitations
- 2018Passenger-oriented optimization of lines in a mass transit system
- 2018Will car users change their mobility patterns with Mobility as a Service (MaaS) and microtransit? – A latent class cluster analysis
- 2018Analysing the trip and user characteristics of the combined bicycle and transit modecitations
- 2018Identification and quantification of link vulnerability in multi-level public transport networks: a passenger perspectivecitations
- 2018Improving predictions of public transport usage during disturbances based on smart card datacitations
- 2018The Potential of Demand-Responsive Transport as a Complement to Public Transportcitations
- 2018The Potential of Demand-Responsive Transport as a Complement to Public Transport: An Assessment Framework and an Empirical Evaluationcitations
- 2018Assessing and Improving Operational Strategies for the Benefit of Passengers in Rail-Bound Urban Transport Systemscitations
- 2017OV-potentie blijkt uit datafusie GSM en chipkaart
- 2017Investigating potential transit ridership by fusing smartcard and global system for mobile communications datacitations
- 2017Krijgt MaaS de auto uit de stad?
- 2017Modelling multimodal transit networks integration of bus networks with walking and cyclingcitations
- 2017Flexibility or Uncertainty? Forecasting Modal Shift Towards Demand Responsive Public Transport
- 2017Kansen voor het OV: data van gsm en OV-chipkaart combineren
- 2017Urban Demand Responsive Transport in the Mobility as a Service Ecosystem: Its Role and Potential Market Share
- 2017Ridership evaluation and prediction in public transport by processing smart card data: A Dutch approach and example
- 2017A robust transfer inference algorithm for public transport journeys during disruptionscitations
- 2017Investigating Potential Transit Ridership by Fusing Smartcard Data and GSM Datacitations
- 2017Performance Assessment of Fixed and Flexible Public Transport in a Multi Agent Simulation Frameworkcitations
- 2017Flexibel vervoer onstuitbaar
- 2016Incorporating enhanced service reliability of public transport in cost-benefit analysescitations
- 2016Measuring Passenger Travel Time Reliability using Smartcard Data
- 2016Exposing the role of exposure: Public transport network risk analysiscitations
- 2016Measuring Passenger Travel Time Reliability Using Smart Card Data
- 2016Vervoer op afroep is niet meer te stuiten
- 2016Waar liggen kansen voor OV
- 2016Betrouwbare OV netwerken
- 2016Short-Term Prediction of Ridership on Public Transport with Smart Card Datacitations
- 2015Robuustheid van multi-level openbaar vervoer netwerken
- 2015Robustness of multi-level public transport networks: A methodology to quantify robustness from a passenger perspective
- 2015Robuustheid van OV-netwerken meten. Door de redactie
- 2015Uit de wetenschap: OV meten en OV-netwerken meten. Door de redactie
- 2015Data driven improvements in public transport: the Dutch examplecitations
- 2015Short-term prediction of ridership on public transport with smart card datacitations
- 2015Improving Public Transport Decision Making, Planning and Operations by Using Big Data: Cases from Sweden and the Netherlandscitations
- 2015Multi-Modal Data Fusion for Big Events [Research News]citations
- 2014Rol van de wetenschap in de mobiliteitssector
- 2014Service reliability in a network context: Impacts of synchronizing schedules in long headway servicescitations
- 2014Incorporating service reliability in public transport design and performance requirements: International survey results and recommendationscitations
- 2014Service Reliability in a Network Contextcitations
- 2012Optimizing public transport planning and operations using automatic vehicle location data: The Dutch example
- 2012The impact of scheduling on service reliability: Trip-time determination and holding points in long-headway servicescitations
- 2010Reliability Improvement in Short Headway Transit Servicescitations
- 2010Rail transit network design supported by an open source simulation library: Towards reliability improvement
- 2010Impact of Rail Terminal Design on Transit Service Reliabilitycitations
- 2010Line Length versus Operational Reliabilitycitations
- 2010Control of Public Transportation Operations to Improve Reliabilitycitations
- 2009Reliability assessment of urban rail transit networks; methodology and case study
- 2009On-time vehicles at RandstadRail
- 2009Service reliability: A key factor
- 2009Line length versus operational reliability: Network design dilemma in Urban public transportationcitations
- 2009Regularity analysis for optimizing urban transit network designcitations
- 2009Control of public transportation operations to improve reliability: Theory and practicecitations
- 2008The role of infrastructures on public transport service reliabilitycitations
- 2008Using a rail simulation library to assess impacts of transit network planning on operational qualitycitations
Places of action
document
Flexibility or Uncertainty? Forecasting Modal Shift Towards Demand Responsive Public Transport
Abstract
The urban transportation sector is undergoing significant changes. Firstly, the shared-economy paradigm has led to both car sharing schemes and Transportation Network Companies (TNCs, such as Uber or Lyft). And secondly, on-going technology developments are underway to make autonomous driving a reality.<br/><br/>However, individual, even if not privately owned, modes of transport are unlikely to be able to cope with all the transport demand in urban areas. The public transportation sector can potentially combine collective transport with the latest societal and technological innovations leading to collective demand responsive transport (DRT). Demand responsive transport can be described as a collective on-demand mode of transport which provides flexible mobility in time and possibly also in space.<br/><br/>In the last decades, DRT was primarily conceived as a mode of transport for rural areas or to assist the elderly and the disabled. Lately, however, research studies have started considering the feasibility of this mode of transport for urban settings. Recent investigations of DRT have located their case studies in Lisbon (Portugal) (Martinez et al., 2015) and Hino (Japan) (Atasoy et al., 2015). These studies focus mainly on the algorithms behind such a system. Helsinki (Finland) even implemented a real pilot between 2012-2015. Nevertheless, there is still lack of knowledge on how the shift away from fixed schedules and routes is perceived by the users. Are those aspects perceived positively as injecting flexibility to the system (“I am not fixed to a timetable”) or are they perceived negatively as inducing uncertainty to the trip ( “Will I get a suitable match next time?”)?. Our study aims at providing a broader understanding of the fuzzy variable time inherent to DRT in the mode choice decision.<br/><br/>Different studies have addressed mode shift including DRT services and reliability aspects. Ryley et al. (2014) includes the deviation from the expected starting time of the trip as one of the attributes when comparing bus or car to DRT. Khattak et al. (2004) asks respondents to rate different reliability and flexibility statements concerning DRT. In Diana (2010), respondents rate their mode shift propensity and different cognitive and affective modal attitudes. However, none of the mentioned approaches quantifies the different flexibility-uncertainty characteristics that surround DRT and includes these perceptions in the overall SP experiment. Our approach is expected to shed light into how the time attributes that appear in DRT influence mode choice. Next to the flexible-uncertain characteristics surrounding DRT, our study aims at studying the perception of DRT versus conventional public transport as well as examining mode choice preference against the car and individual on-demand services (Taxi and Uber).<br/><br/>To deal with these questions, a stated preference (SP) experiment is performed. To avoid overloading respondents, the new attributes related to flexibility-uncertainty that appear in the DRT mode will be understood as part of a time construct in an HII (Hierarchical Information Integration) experiment. HII is a method for handling multi-attribute judgement problems with a large number of attributes; in it, a logical decomposition of the decision problem takes place (Louviere, 1984). The HII can be seen as a separated SP experiment in which different levels of the variables that define the construct are rated. This methodology makes it easier for abstract concepts (such as the flexibility-uncertainty construct in our research) to be quantified. The HII experiment will be quantified in a 9 point scale. The outcome of our HII experiment will then be incorporated in the higher-level mode choice experiment as a flexibility-uncertainty attribute of DRT. Richter et al. (2012) also included HII in their mode choice SP experiment to analyse quality of connection, comfort and information.<br/><br/>To answer our research questions, a web-based survey among the Dutch population is designed, conducted and analysed. In the Netherlands, 91% of the households have access to the internet and 81% of the Dutch population uses internet daily or almost daily (CBS, 2016). Hence, a web-based survey is considered sufficient to get a representative sample of the population. A stated preference survey, as opposed to revealed preference survey is performed since the envisioned DRT system is not yet established. Jain et al. (2017) reviews existing studies on DRT services and identifies shopping and social trips as the most recurrent trip purposes for the use of DRT systems. Based on this finding, our SP is explained in a context of a free-time trip (as opposed to a business or a commuting trip). We also fix the context to an urban/suburban context (as opposed to rural context), since we want to study the impact that DRT services can have in the city. The addressed variables are walking time, waiting time, in-vehicl...
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