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Articles

Adaptability analysis methods of demand responsive transit: a review and future directions

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Pages 676-697 | Received 15 Aug 2021, Accepted 15 Dec 2022, Published online: 08 Jan 2023

References

  • Adnan, M., Pereira, F. C., Lima Azevedo, C. M., Basak, K., Lovric, M., Raveau Feliu, S., Zhu, Y., Ferreira, J., Zegras, C., & Ben-Akiva, M. E. (2016). SimMobility: A multi-scale integrated agent-based simulation platform. In 95th Annual Meeting of the Transportation Research Board Forthcoming in Transportation Research Record. The National Academies of Sciences, Engineering, and Medicine, Washington, DC.
  • Azevedo, C. L., Marczuk, K., Raveau, S., Soh, H., Adnan, M., Basak, K., Loganathan, H., Deshmunkh, N., Lee, D.-H., Frazzoli, E., & Ben-Akiva, M. (2016). Microsimulation of demand and supply of autonomous mobility on demand. Transportation Research Record, 2564(1), 21–30. https://doi.org/10.3141/2564-03
  • Babalik-Sutcliffe, E. (2002). Urban rail systems: Analysis of the factors behind success. Transport Reviews, 22(4), 415–447. https://doi.org/10.1080/01441640210124875
  • Balog, J. N. (1997). Guidebook for attracting paratransit patrons to fixed-route services. Transit cooperative research program (Report 24). Transportation Research Board.
  • Barfod, M. B., Salling, K. B., & Leleur, S. (2011). Composite decision support by combining cost-benefit and multi-criteria decision analysis. Decision Support Systems, 51(1), 167–175. https://doi.org/10.1016/j.dss.2010.12.005
  • Basu, R., Araldo, A., Akkinepally, A. P., Biran, B. N., Basak, K., Seshadri, R., Deshmukh, N., Kumar, N., Azevedo, C. L., & Ben-Akiva, M. (2018). Automated mobility-on-demand vs. mass transit: A multimodal activity-driven agent-based simulation approach. Transportation Research Record, 2672(8), 608–618. https://doi.org/10.1177/0361198118758630
  • Beria, P., Maltese, I., & Mariotti, I. (2012). Multicriteria versus cost benefit analysis: A comparative perspective in the assessment of sustainable mobility. European Transport Research Review, 4(3), 137–152. https://doi.org/10.1007/s12544-012-0074-9
  • Bischoff, J., Führer, K., & Maciejewski, M. (2019). Impact assessment of autonomous DRT systems. Transportation Research Procedia, 41, 440–446. https://doi.org/10.1016/j.trpro.2019.09.074
  • Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99(Suppl. 3), 7280–7287. https://doi.org/10.1073/pnas.082080899
  • Bösch, P. M., Becker, F., Becker, H., & Axhausen, K. W. (2018). Cost-based analysis of autonomous mobility services. Transport Policy, 64, 76–91. https://doi.org/10.1016/j.tranpol.2017.09.005
  • Brake, J., Mulley, C., Nelson, J. D., & Wright, S. (2007). Key lessons learned from recent experience with flexible transport services. Transport Policy, 14(6), 458–466. https://doi.org/10.1016/j.tranpol.2007.09.001
  • Brake, J., Nelson, J. D., & Wright, S. (2004). Demand responsive transport: Towards the emergence of a new market segment. Journal of Transport Geography, 12(4), 323–337. https://doi.org/10.1016/j.jtrangeo.2004.08.011
  • Bruzzone, F., Scorrano, M., & Nocera, S. (2021). The combination of e-bike-sharing and demand-responsive transport systems in rural areas: A case study of Velenje. Research in Transportation Business & Management, 40, 100570. https://doi.org/10.1016/j.rtbm.2020.100570
  • Chen, S., Akkinepally, A. P., De Azevedo, C. L., & Ben-Akiva, M. (2020). Formulation and solution approach for calibrating activity-based travel demand model-system via microsimulation. Transportation Research Part C: Emerging Technologies, 119, 102650. https://doi.org/10.1016/j.trc.2020.102650
  • Cich, G., Knapen, L., Maciejewski, M., Yasar, A., Bellemans, T., & Janssens, D. (2017). Modeling demand responsive transport using SARL and MATSim. Procedia Computer Science, 109, 1074–1079. https://doi.org/10.1016/j.procs.2017.05.387
  • Ciotlaus, M., Moldovan, D., Clitan, A., & Muntean, L. (2017). Choosing an environmentally-friendly public transportation system using multiple-criteria analysis. Case study: Bistrita Nasaud, Romania. Procedia Engineering, 181, 396–403. https://doi.org/10.1016/j.proeng.2017.02.407
  • Coogan, M., Spitz, G., Adler, T., McGuckin, N., Kuzmyak, R., & Karash, K. (2018). Understanding changes in demographics, preferences, and markets for public transportation. Transit cooperative research program (Report 201). Transportation Research Board. https://doi.org/10.17226/25160
  • Currie, G., & Fournier, N. (2020). Why most DRT/micro-transits fail - What the survivors tell us about progress. Research in Transportation Economics, 83, 100895. https://doi.org/10.1016/j.retrec.2020.100895
  • Currie, G., & Wong, T. (2020). Workshop 4 report: Realising the potential benefits of demand-responsive travel. Research in Transportation Economics, 83, 100912. https://doi.org/10.1016/j.retrec.2020.100912
  • Daganzo, C. F. (1978). An approximate analytic model of many-to-many demand responsive transportation systems. Transportation Research, 12(5), 325–333. https://doi.org/10.1016/0041-1647(78)90007-2
  • Daganzo, C. F. (2010). Structure of competitive transit networks. Transportation Research Part B: Methodological, 44(4), 434–446. https://doi.org/10.1016/j.trb.2009.11.001
  • Daganzo, C. F., & Ouyang, Y. (2019). A general model of demand-responsive transportation services: From taxi to ridesharing to dial-a-ride. Transportation Research Part B: Methodological, 126, 213–224. https://doi.org/10.1016/j.trb.2019.06.001
  • Daniels, R., & Mulley, C. (2012). Flexible transport services: Overcoming barriers to implementation in low-density urban areas. Urban Policy and Research, 30(1), 59–76. https://doi.org/10.1080/08111146.2012.660872
  • Demenezes, J. T., & Falcocchio, J. C. (1983). Factors influencing transit use in European and U.S. cities. Transportation Research Record, 936, 44–47. https://trid.trb.org/view/204648
  • Devika, R., & Harikrishna, M. (2020). Analysis of factors influencing mode shift to public transit in a developing country. IOP Conference Series: Earth and Environmental Science, 491(1), 012054 (7pp). https://doi.org/10.1088/1755-1315/491/1/012054
  • Diana, M., Quadrifoglio, L., & Pronello, C. (2009). A methodology for comparing distances traveled by performance-equivalent fixed-route and demand responsive transit services. Transportation Planning and Technology, 32(4), 377–399. https://doi.org/10.1080/03081060903119618
  • Djavadian, S., & Chow, J. (2017). An agent-based day-to-day adjustment process for modeling ‘mobility as a service’ with a two-sided flexible transport market. Transportation Research Part B: Methodological, 104, 36–57. https://doi.org/10.1016/j.trb.2017.06.015
  • Edwards, D., & Watkins, K. (2013). Comparing fixed-route and demand-responsive feeder transit systems in real-world settings. Transportation Research Record, 2352(1), 128–135. https://doi.org/10.3141/2352-15
  • Eliasson, J., & Lundberg, M. (2012). Do cost–benefit analyses influence transport investment decisions? Experiences from the Swedish Transport Investment Plan 2010–21. Transport Reviews, 32(1), 29–48. https://doi.org/10.1080/01441647.2011.582541
  • Ellis, E. H., & McCollom, B. E. (2009). Guidebook for rural demand-response transportation: Measuring, assessing, and improving performance (Vol. 136). Transportation Research Board.
  • Errico, F., Crainic, T. G., Malucelli, F., & Nonato, M. (2013). A survey on planning semi-flexible transit systems: Methodological issues and a unifying framework. Transportation Research Part C: Emerging Technologies, 36, 324–338. https://doi.org/10.1016/j.trc.2013.08.010
  • Fielbaum, A., Jara-Diaz, S., & Gschwender, A. (2017). A parametric description of cities for the normative analysis of transport systems. Networks and Spatial Economics, 17(2), 343–365. https://doi.org/10.1007/s11067-016-9329-7
  • Franco, P., Johnston, R., & Mccormick, E. (2020). Demand responsive transport: Generation of activity patterns from mobile phone network data to support the operation of new mobility services. Transportation Research Part A: Policy and Practice, 131, 244–266. https://doi.org/10.1016/j.tra.2019.09.038
  • Frasher, M. (2020). Modelling and control of the collaborative behavior of adaptive autonomous agents [Doctoral dissertation]. Mälardalen University.
  • Hägerstraand, T. (1970). What about people in regional science? Papers in Regional Science, 24(1), 7–24. https://doi.org/10.1111/j.1435-5597.1970.tb01464.x
  • Horni, A., Nagel, K., & Axhausen, K. W. (2016). The multi-agent transport simulation MATSim. Ubiquity Press. https://doi.org/10.5334/baw
  • Hörl, S., Ruch, C., Becker, F., Frazzoli, E., & Axhausen, K. W. (2019). Fleet operational policies for automated mobility: A simulation assessment for Zurich. Transportation Research Part C: Emerging Technologies, 102, 20–31. https://doi.org/10.1016/j.trc.2019.02.020
  • Hunkin, S., & Krell, K. (2018). Demand responsive transport, A Policy Brief from the Policy Learning Platform on Low-carbon economy. Interreg Europe. https://interregeurope.eu
  • Kaddoura, I., Leich, G., & Nagel, K. (2020). The impact of pricing and service area design on the modal shift towards demand responsive transit. Procedia Computer Science, 170, 807–812. https://doi.org/10.1016/j.procs.2020.03.152
  • Kagho, G. O., & Axhausen, K. W. (2019). An agent-based MATSim scenario for Lagos, Nigeria. In 19th Swiss Transport Research Conference (STRC 2019). STRC. https://doi.org/10.3929/ethz-b-000342817
  • Kamargianni, M., Matyas, M., Li, W., Muscat, J., & Yfantis, L. (2018). The MaaS dictionary. MaaSLab, Energy Institute, University College London.
  • KFH Group. (2008). Guidebook for measuring, assessing, and improving performance of demand-response transportation. Transit cooperative research program (Report 124). Transportation Research Board. https://doi.org/10.17226/23112
  • Koffman, D. (2004). Operational experiences with flexible transit services. Transit cooperative research program (Synthesis 53). Transportation Research Board. https://doi.org/10.17226/23364
  • Li, X., & Quadrifoglio, L. (2010). Feeder transit services: Choosing between fixed and demand responsive policy. Transportation Research Part C: Emerging Technologies, 18(5), 770–780. https://doi.org/10.1016/j.trc.2009.05.015
  • Lu, X. L., Zhang, X., Jie, Y. U., & Zou, N. (2015). Research of a comprehensive evaluation method for customised flexible transit system. Journal of Highway and Transportation Research and Development, 32(05), 135–140. https://doi.org/10.3969/j.issn.1002-0268.2015.05.022
  • Macharis, C., & Bernardini, A. (2015). Reviewing the use of multi-criteria decision analysis for the evaluation of transport projects: Time for a multi-actor approach. Transport Policy, 37, 177–186. https://doi.org/10.1016/j.tranpol.2014.11.002
  • Mageean, J., & Nelson, J. D. (2003). The evaluation of demand responsive transport services in Europe. Journal of Transport Geography, 11(4), 255–270. https://doi.org/10.1016/S0966-6923(03)00026-7
  • McIntosh, J., Trubka, R., Kenworthy, J., & Newman, P. (2014). The role of urban form and transit in city car dependence: Analysis of 26 global cities from 1960 to 2000. Transportation Research Part D: Transport and Environment, 33, 95–110. https://doi.org/10.1016/j.trd.2014.08.013
  • Mishra, S., Mehran, B., & Sahu, P. K. (2020). Assessment of delivery models for semi-flexible transit operation in low-demand conditions. Transport Policy, 99, 275–287. https://doi.org/10.1016/j.tranpol.2020.09.004
  • Mulley, C., Nelson, J., Teal, R., Wright, S., & Daniels, R. (2012). Barriers to implementing flexible transport services: An international comparison of the experiences in Australia, Europe and USA. Research in Transportation Business & Management, 3, 3–11. https://doi.org/10.1016/j.rtbm.2012.04.001
  • Narayan, S. (2020). Design and analysis of on-demand mobility systems [Doctoral dissertation]. Delft University of Technology.
  • Nguyen-Hoang, P., & Yeung, R. (2010). What is paratransit worth? Transportation Research Part A: Policy and Practice, 44(10), 841–853. https://doi.org/10.1016/j.tra.2010.08.006
  • Nourbakhsh, S. M., & Ouyang, Y. (2012). A structured flexible transit system for low demand areas. Transportation Research Part B: Methodological, 46(1), 204–216. https://doi.org/10.1016/j.trb.2011.07.014
  • Oh, S., Lentzakis, A. F., Seshadri, R., & Ben-Akiva, M. (2021). Impacts of automated mobility-on-demand on traffic dynamics, energy and emissions: A case study of Singapore. Simulation Modelling Practice and Theory, 110(1-3), 102327. https://doi.org/10.1016/j.simpat.2021.102327
  • Oh, S., Seshadri, R., Azevedo, C. L., Kumar, N., Basak, K., & Ben-Akiva, M. (2020). Assessing the impacts of automated mobility-on-demand through agent-based simulation: A study of Singapore. Transportation Research Part A: Policy and Practice, 138, 367–388. https://doi.org/10.1016/j.tra.2020.06.004
  • Oke, J. B., Akkinepally, A. P., Chen, S., Xie, Y., & Ben-Akiva, M. (2020). Evaluating the systemic effects of automated mobility-on-demand services via large-scale agent-based simulation of auto-dependent prototype cities. Transportation Research Part A: Policy and Practice, 140, 98–126. https://doi.org/10.1016/j.tra.2020.06.013
  • Outwater, M., Sana, B., Ferdous, N., Woodford, B., & Lobb, J. (2014). Characteristics of premium transit services that affect choice of mode. Transit cooperative research program (Report 166). Transportation Research Board.
  • Papanikolaou, A., & Basbas, S. (2021). Analytical models for comparing demand responsive transport with bus services in low demand interurban areas. Transportation Letters, 13(4), 255–262. https://doi.org/10.1080/19427867.2020.1716474
  • Perera, S., Ho, C., & Hensher, D. (2020). Resurgence of demand responsive transit services – Insights from BRIDJ trials in inner west of Sydney, Australia. Research in Transportation Economics, 83, 100904. https://doi.org/10.1016/j.retrec.2020.100904
  • Potts, J. T., Marshall, M. A., Crockett, E. C., & Washington, J. (2010). Guide for planning and operating flexible public transportation services. Transit cooperative research program (Report 140). Transportation Research Board.
  • Qian, X., & Ukkusuri, S. V. (2017). Taxi market equilibrium with third-party hailing service. Transportation Research Part B: Methodological, 100, 43–63. https://doi.org/10.1016/j.trb.2017.01.012
  • Qiu, F., Li, W. Q., & Haghani, A. (2015). An exploration of the demand limit for flex-route as feeder transit services: A case study in Salt Lake city. Public Transport, 7(2), 259–276. https://doi.org/10.1007/s12469-014-0097-9
  • Qiu, F., Shen, J., Zhang, X., & An, C. (2015). Demi-flexible operating policies to promote the performance of public transit in low-demand areas. Transportation Research Part A: Policy and Practice, 80, 215–230. https://doi.org/10.1016/j.tra.2015.08.003
  • Quadrifoglio, L., & Li, X. (2009). A methodology to derive the critical demand density for designing and operating feeder transit services. Transportation Research Part B: Methodological, 43(10), 922–935. https://doi.org/10.1016/j.trb.2009.04.003
  • Rasouli, S., & Timmermans, H. (2014). Activity-based models of travel demand: Promises, progress and prospects. International Journal of Urban Sciences, 18(1), 31–60. https://doi.org/10.1080/12265934.2013.835118
  • Scheltes, A., & de Almeida Correia, G. H. (2017). Exploring the use of automated vehicles as last mile connection of train trips through an agent-based simulation model: An application to Delft, Netherlands. International Journal of Transportation Science and Technology, 6(1), 28–41. https://doi.org/10.1016/j.ijtst.2017.05.004
  • Schlüter, J., Bossert, A., Roessy, P., & Kersting, M. (2021). Impact assessment of autonomous demand responsive transport as a link between urban and rural areas. Research in Transportation Business & Management, 39, 100613. https://doi.org/10.1016/j.rtbm.2020.100613
  • Shen, Y., Zhang, H., & Zhao, J. (2018). Integrating shared autonomous vehicle in public transportation system: A supply-side simulation of the first-mile service in Singapore. Transportation Research Part A: Policy and Practice, 113, 125–136. https://doi.org/10.1016/j.tra.2018.04.004
  • Tan, K. (2016). The research on the critical demand density of mobility allowance shuttle transit (MAST) [M.A. thesis]. Changsha University of Science & Technology.
  • Taylor, B. D., & Fink, C. (2003). The factors influencing transit ridership: A review and analysis of the ridership literature. University of California Transportation Center Working Papers. https://escholarship.org/uc/item/3xk9j8m2
  • Taylor, B. D., Miller, D., Iseki, H., & Fink, C. (2009). Nature and/or nurture? Analyzing the determinants of transit ridership across US urbanized areas. Transportation Research Part A: Policy and Practice, 43(1), 60–77. https://doi.org/10.1016/j.tra.2008.06.007
  • Thomopoulos, N., Grant-Muller, S., & Tight, M. R. (2009). Incorporating equity considerations in transport infrastructure evaluation: Current practice and a proposed methodology. Evaluation and Program Planning, 32(4), 351–359. https://doi.org/10.1016/j.evalprogplan.2009.06.013
  • Tirachini, A. (2014). The economics and engineering of bus stops: Spacing, design and congestion. Transportation Research Part A: Policy and Practice, 59, 37–57. https://doi.org/10.1016/j.tra.2013.10.010
  • Viergutz, K., & Schmidt, C. (2019). Demand responsive - vs. conventional public transportation: A MATSim study about the rural town of Colditz, Germany. Procedia Computer Science, 151, 69–76. https://doi.org/10.1016/j.procs.2019.04.013
  • Volinski, J. (2019). Microtransit or general public demand-response transit services: State of the practice. transit cooperative research program (Report 166). Transportation Research Board. https://doi.org/10.17226/25414
  • Vuchic, V. R. (2017). Urban transit: Operations, planning, and economics. John Wiley & Sons. (Original work published 2005).
  • Wang, Y. H. (1993). On the number of successes in independent trials. Statistica Sinica, 3(2), 295–312. https://www.jstor.org/stable/24304959
  • Wee, B. V. (2012). How suitable is CBA for the ex-ante evaluation of transport projects and policies? A discussion from the perspective of ethics. Transport Policy, 19(1), 1–7. https://doi.org/10.1016/j.tranpol.2011.07.001
  • Wee, B. V., & Borjesson, M. (2015). How to make CBA more suitable for evaluating cycling policies. Transport Policy, 44, 117–124. https://doi.org/10.1016/j.tranpol.2015.07.005
  • Wee, B. V., & Molin, E. (2012). Transport and ethics: Dilemmas for CBA researchers. An interview-based study from the Netherlands. Transport Policy, 24, 30–36. https://doi.org/10.1016/j.tranpol.2012.06.021
  • Weisbrod, G., & Duncan, C. (2016). Integrating multiple economic analysis methods for more effective decision making: Three-dimensional framework. Transportation Research Record: Journal of the Transportation Research Board, 2597(1), 99–107. https://doi.org/10.3141/2597-13
  • Wen, J., Chen, Y., Nassir, N., & Zhao, J. (2018). Transit-oriented autonomous vehicle operation with integrated demand-supply interaction. Transportation Research Part C: Emerging Technologies, 97, 216–234. https://doi.org/10.1016/j.trc.2018.10.018
  • Zhai, Z., Yang, Y., Shen, Y., Ji, Y., & Du, Y. (2020). Assessing the impacts of autonomous bus-on-demand based on agent-based simulation: A case study of Fuyang, Zhejiang, China. Journal of Advanced Transportation, 2020(135), 1–15. https://doi.org/10.1155/2020/7981791
  • Zhang, X. (2014). Study on comprehensive evaluation system of flexible transit system [M.A. thesis] Shandong University.
  • Zheng, Y., Li, W., & Qiu, F. (2018). A methodology for choosing between route deviation and point deviation policies for flexible transit services. Journal of Advanced Transportation, 2018, 1–12. https://doi.org/10.1155/2018/6292410
  • Zhuge, C. X. (2014). Dynamic evolution mechanism of urban transport-land use based on self-oranganizing theory [Doctoral dissertation]. Beijing Jiaotong University.
  • Zwick, F., Kuehnel, N., Moeckel, R., & Axhausen, K. W. (2021). Agent-based simulation of city-wide autonomous ride-pooling and the impact on traffic noise. Transportation Research Part D: Transport and Environment, 90, 102673. https://doi.org/10.1016/j.trd.2020.102673

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