Examining impacts of time-based pricing strategies in public transportation: A study of Singapore
Tài liệu tham khảo
Adnan, 2016, SimMobility: A multi-scale integrated agent-based simulation platform
Anas, 2011, Reducing urban road transportation externalities: Road pricing in theory and in practice, Rev. Environ. Econ. Policy, 5, 66, 10.1093/reep/req019
Andrea, B., Todd, L., & Gopinath, M. (2009). Transport Demand Management, training document. Division 44, Water, Energy and Transport,GTZ Germany. https://www.sutp.org/files/contents/documents/resources/H_Training-Material/GIZ_SUTP_TM_Transportation-Demand-Management_EN.pdf (Accessed August, 2018).
Antoniou, 2015, W-spsa in practice: Approximation of weight matrices and calibration of traffic simulation models, Transport. Res. Part C: Emerg. Technol., 59, 129, 10.1016/j.trc.2015.04.030
Azevedo, 2017, SimMobility Short-term: An integrated microscopic mobility simulator, Transp. Res. Rec., 2622, 13, 10.3141/2622-02
Batarce, 2016, Valuing crowding in public transport: implications for cost-benefit analysis, Transport. Res. Part A: Policy Practice, 91, 358
Ben-Akiva, 2013, Methodological issues in modelling time-of-travel preferences, Transportmetrica A: Transport Sci., 9, 846, 10.1080/18128602.2012.686532
Ben-Akiva, M., Bowman, J., Ramming, S., & Walker, J., 1998. Behavioral realism in urban transportation planning models. Transportation Models in the Policy-Making Process: Uses, Misuses and Lessons for the Future, 46.
Ben-Akiva, 2010, Traffic simulation with dynamit, 363
Bianchi, 1998, Modelling new pricing strategies for the Santiago Metro, Transp. Policy, 5, 223, 10.1016/S0967-070X(98)00025-0
Bowman, 2001, Activity-based disaggregate travel demand model system with activity schedules, Transport. Res. Part A: Policy Practice, 35, 1
Burguillo, 2017, The new public transport pricing in Madrid Metropolitan Area: A welfare analysis, Res. Transport. Econ., 62, 25, 10.1016/j.retrec.2017.02.005
Castiglione, J., Bradley, M., & Gliebe, J., 2015. Activity-based travel demand models: A primer(No. SHRP 2 Report S2-C46-RR-1). Transportation Research Board Publication.
Cats, 2014, Public transport pricing policy: empirical evidence from a fare-free scheme in Tallinn, Estonia, Transport. Res. Record, 2415, 89, 10.3141/2415-10
Cats, 2017, The prospects of fare-free public transport: evidence from Tallinn, Transportation, 44, 1083, 10.1007/s11116-016-9695-5
Chakirov, A., & Erath, A., 2011. Use of public transport smart card fare payment data for travel behaviour analysis in Singapore. Arbeitsberichte Verkehrs-und Raumplanung, 729.
Chen, 2018, Impact of congestion pricing schemes on emissions and temporal shift of freight transport, Transport. Res. Part E: Logist. Transport. Rev., 118, 77, 10.1016/j.tre.2018.07.006
Cheong, 2013, Transport policies and patterns: A comparison of five Asian cities, Journeys, 69
Choo C., 2017. No more free travel, but more commuters to benefit from discounted fares for pre-peak travel from Dec 29, 2017, News article Today Online, https://www.todayonline.com/singapore/morning-pre-peak-travel-fares-be-reduced-ptc (Accessed, April, 2018).
Christina M., 2015. Time to spread out the peak! - The Transport Planning Society, Bursary Award Paper for The Transport Planning Society, Accessed online on 24th April, 2018, https://tps.org.uk/public/downloads/N8P8I/Melina%20Christina.pdf, (Accessed, April, 2018).
Cipriani, E., Mannini, L., Montemarani, B., Nigro, M., Petrelli, M., 2019. Congestion pricing policies: Design and assessment for the city of Rome, Italy. Transport Policy, 80, 127–135. -New.
de Palma, 2011, Traffic congestion pricing methodologies and technologies, Transport. Res. Part C: Emerg. Technol., 19, 1377, 10.1016/j.trc.2011.02.010
Ge, 2015, Solving traffic congestion from the demand side, Promet-Traffic Transport., 27, 529, 10.7307/ptt.v27i6.1734
Gwee, 2013, Review of Time-Based Public Transport Fare Pricing, Journeys
Halvorsen, 2016, Reducing subway crowding: analysis of an off-peak discount experiment in Hong Kong, Transp. Res. Rec., 2544, 38, 10.3141/2544-05
Halvorsen, 2019, Demand management of congested public transport systems: a conceptual framework and application using smart card data, Transportation, 1
Jacob, M.S., 2018. An estimation of short-and long-term price elasticity of bus demand in São Paulo and a study of its implications on fare subsidies policy (Doctoral dissertation).
Kaddoura, 2015, Optimal public transport pricing: Towards an agent-based marginal social cost approach, J. Transport Econ. Policy (JTEP), 49, 200
Kębłowski, 2019, Why (not) abolish fares? Exploring the global geography of fare-free public transport, Transportation, 1
Lan, 2010, Effects of temporally differential fares on taipei metro riders' mode and time-of-day choices, Int. J. Transport Econ., 37, 97
Land Transport Authority, Singapore (2018), Free Pre-Peak Travel Extended Until 30 June 2016, https://www.lta.gov.sg/apps/news/page.aspx?c=2&id=02518312-ad79-43d6-948d-05729743a222 (Accessed, April 2018).
Lindsey, C.R., & Verhoef, E.T., 2000. Traffic congestion and congestion pricing (No. 00-101/3). Tinbergen Institute Discussion Paper.
Lipscombe, P., 2016. Transit Fare Policy An International Best Practices Review for Metro Vancouver, Prepared for City of Vancuover. https://sustain.ubc.ca/sites/sustain.ubc.ca/files/GCS/2016%20Project%20Reports/Transit%20Fare%20Policy%20Best%20Practices%20Review_Lipscombe%20_2016.pdf (Accessed September, 2019).
Litman, 2017, Understanding transport demands and elasticities, Victoria Transport Policy Institute.
Lovrić, 2016, Evaluating off-peak pricing strategies in public transportation with an activity-based approach, Transp. Res. Rec., 2544, 10, 10.3141/2544-02
Lu, Y., Adnan, M., Basak, K., Pereira, F.C., Carrion, C., Saber, V.H., ... & Ben-Akiva, M.E., 2015. Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In 94th Annual Meeting of the Transportation Research Board, Washington, DC.
Moyo, O.M., 2014. Calibration of public transit routing for multi-agent simulation (MATSIM). PhD Thesis, Technical University, Berlin, Germany. https://pdfs.semanticscholar.org/fa5a/8ed651f7c7dcf85599b0266edb2bc10b8a51.pdf (Accessed, September, 2019).
Paulley, 2006, The demand for public transport: The effects of fares, quality of service, income and car ownership, Transp. Policy, 13, 295, 10.1016/j.tranpol.2005.12.004
Peer, 2016, Train commuters’ scheduling preferences: Evidence from a large-scale peak avoidance experiment, Transport. Res. Part B: Methodol., 83, 314, 10.1016/j.trb.2015.11.017
Perone, J.S., 2002. Advantages and disadvantages of fare-free transit policy(No. NCTR-473-133). National Center for Transit Research, Center for Urban Transportation Research, University of South Florida.
Sarkar, 2016, Elasticity Model for easing peak hour demand for Metrorail transport System, World Acad. Sci., Eng. Technol., Int. J. Soc., Behav., Educ., Econ., Busin. Indus. Eng., 10, 2466
Siyu, 2015
Smith, 2009, 476
Štraub, 2019, Free fare policy as a tool for sustainable development of public transport services, Human Geographies, 13, 45
Tan, 2015, New path size formulation in path size logit for route choice modeling in public transport networks, Transp. Res. Rec., 2538, 11, 10.3141/2538-02
Tavassoli, 2019, Calibrating a transit assignment model using smart card data in a large-scale multi-modal transit network, Transportation
Tawfik, 2014
Tirachini, 2016, Valuation of sitting and standing in metro trains using revealed preferences, Transp. Policy, 47, 94, 10.1016/j.tranpol.2015.12.004
Theseira, W.E., Qiyan, O., 2018. The effect of free travel on commuter trip timings: Evidence from transit card data, in: Presented at LTA-UITP Singapore International Transport Congress and Exhibition (SITCE).
Volinski, J., 2012. Implementation and outcomes of fare-free transit systems, TCRP Synthesis 101, Transportation Research Board Publication.
Yang, 2018, Managing rail transit peak-hour congestion with a fare-reward scheme, Transport. Res. Part B: Methodol., 110, 122, 10.1016/j.trb.2018.02.005
Zhang, 2017, Improved Calibration Method for Dynamic Traffic Assignment Models: Constrained Extended Kalman Filter, Transp. Res. Rec., 2667, 142, 10.3141/2667-14
Zhu, 2014, Synthetic population generation at disaggregated spatial scales for land use and transportation microsimulation, Transp. Res. Rec., 2429, 168, 10.3141/2429-18