How Demand-Responsive Transit & Microtransit affects Transportation Systems Operations (and Efficiency)

Demand-responsive transit (DRT) and microtransit optimization has been studied using models and theoretical networks. From a strategic design perspective, continuous approximations of demand over time and space in highly theoretical networks were used to determine optimal flexible service types as a function of demand density [1], [2], [3], [4]. For tactical decision making, studies have used optimization methods in highly theoretical networks to optimize slack times [5], [6], longitudinal velocities [7], service cycle times [8], and compulsory stop selection and sequence [9]. Finally, from an operations standpoint, previous studies have evaluated policies such as dynamic stations [10], flag stops [4], point deviations[11], and optimal cycle lengths [12] in off-line settings. Few studies have also evaluated real-time operational strategies, such as optimal shuttle departure times [13] and routing/stopping decisions for rail connector services [14]. Generally, previous studies consider highly simplified or theoretical network conditions (e.g., grid networks, uniform travel times and uniform trip types), which can lead to suboptimal decision-making and unrealistic performance estimates. Though there are a number of DRT or microtransit pilots throughout the country, analysis and evaluation of real-world microtransit systems do not necessarily improve the overall system performance on efficiency, accessibility and financial sustainability. There is potential for DRT and microtransit service to be improved by innovative technologies, such as real-time demand prediction, real-time ride requests, coordination with both fixed-route mainline public transit and privately operated ride-hailing or mobility service. Both technologies of sensing, communication and service, and AI-powered algorithms could improve DRT and microtransit performance.

References

  1. L. Quadrifoglio and X. Li, “A methodology to derive the critical demand density for designing and operating feeder transit services,” Transp. Res. Part B Methodol., vol. 43, no. 10, pp. 922–935, Dec. 2009, doi: 10.1016/j.trb.2009.04.003.

  2. X. Li and L. Quadrifoglio, “Feeder transit services: Choosing between fixed and demand responsive policy,” Transp. Res. Part C Emerg. Technol., vol. 18, no. 5, pp. 770–780, Oct. 2010, doi: 10.1016/j.trc.2009.05.015.

  3. S. M. Nourbakhsh and Y. Ouyang, “A structured flexible transit system for low demand areas,” Transp. Res. Part B Methodol., vol. 46, no. 1, pp. 204–216, Jan. 2012, doi: 10.1016/j.trb.2011.07.014

  4. F. Qiu, W. Li, and A. Haghani, “A methodology for choosing between fixed‐route and flex‐route policies for transit services,” J. Adv. Transp., vol. 49, no. 3, pp. 496–509, Apr. 2015, doi: 10.1002/atr.1289.

  5. L. Fu, “Planning and Design of Flex-Route Transit Services,” Transp. Res. Rec. J. Transp. Res. Board, vol. 1791, no. 1, pp. 59–66, Jan. 2002, doi: 10.3141/1791-09.

  6. B. Smith, M. Demetsky, and P. Durvasula, “A Multiobjective Optimization Model for Flexroute Transit Service Design,” J. Public Transp., vol. 6, no. 1, pp. 81–100, Mar. 2003, doi: 10.5038/2375-0901.6.1.5.

  7. L. Quadrifoglio, R. W. Hall, and M. M. Dessouky, “Performance and Design of Mobility Allowance Shuttle Transit Services: Bounds on the Maximum Longitudinal Velocity,” Transp. Sci., vol. 40, no. 3, pp. 351–363, Aug. 2006, doi: 10.1287/trsc.1050.0137.

  8. J. Zhao and M. Dessouky, “Service capacity design problems for mobility allowance shuttle transit systems,” Transp. Res. Part B Methodol., vol. 42, no. 2, pp. 135–146, 2008.

  9. F. Errico, T. G. Crainic, F. Malucelli, and M. Nonato, “The single-line design problem for demand-adaptive transit systems: a modeling framework and decomposition approach for the stationary-demand case,” Jun. 2020, Accessed: Jul. 16, 2024. [Online]. Available: https://trid.trb.org/View/1749281

  10. F. Qiu, W. Li, and J. Zhang, “A dynamic station strategy to improve the performance of flex-route transit services,” Transp. Res. Part C Emerg. Technol., vol. 48, pp. 229–240, Nov. 2014, doi: 10.1016/j.trc.2014.09.003.

  11. Y. Zheng, W. Li, and F. Qiu, “A Methodology for Choosing between Route Deviation and Point Deviation Policies for Flexible Transit Services,” J. Adv. Transp., vol. 2018, pp. 1–12, Aug. 2018, doi: 10.1155/2018/6292410.

  12. S. Chandra and L. Quadrifoglio, “A model for estimating the optimal cycle length of demand responsive feeder transit services,” Transp. Res. Part B Methodol., vol. 51, pp. 1–16, May 2013, doi: 10.1016/j.trb.2013.01.008.

  13. Z. Wang et al., “Two-Step Coordinated Optimization Model of Mixed Demand Responsive Feeder Transit,” J. Transp. Eng. Part Syst., vol. 146, no. 3, p. 04019082, Mar. 2020, doi: 10.1061/JTEPBS.0000317.

  14. Y. Yu, R. B. Machemehl, and C. Xie, “Demand-responsive transit circulator service network design,” Transp. Res. Part E Logist. Transp. Rev., vol. 76, no. C, pp. 160–175, 2015.

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Note: Mobility COE research partners conducted this literature review in Spring of 2024 based on research available at the time. Unless otherwise noted, this content has not been updated to reflect newer research.