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Personalized Demand-Responsive Transit Service

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  • Cayford, Randall
  • Yim, Y. B. Youngbin

Abstract

Providing easy access to the public transit service is the goal of the California transit agencies. Many travelers cannot take an express transit because they often cannot park and ride. Smart DRT (demand responsive transit) Feeder is a system that collects transit riders from neighborhoods and takes them to transit stations. This system will use APTS (Advanced Public Transit System) technologies to make the feeder service convenient and reliable. The concept is very simple. When demand is high, Smart Feeder will use the fixed-route fixedschedule service. When demand is low, it will use the on-demand service. For the on-demand service, customers will use an automated dial-a-ride system or the Internet for making a reservation or receiving a confirmation. Feeder vehicles will be equipped with Automated Vehicle Location (AVL) and on-board wireless computer devices. The first part of this paper describes the results of a user survey on the demand for and on the desired characteristics of a Smart Feeder system for the city of Millbrae. The second part of the paper describes the resulting design and implementation of the automated dial-a-ride system that will be used for the demand responsive transit service. The system uses an automated phone-in system for reservations, computerized dispatching over a wireless communication channel to the bus driver, and an automated callback system for customer notifications. User requests for pickup are collected and a computerized scheduling system acts as a broker between the multiple user requests and the transit agency to determine the optimal departure time and route that minimizes customer wait time and maximizes the number of passengers per trip. The system requires no dispatchers and operates in real time without requiring advance reservations.

Suggested Citation

  • Cayford, Randall & Yim, Y. B. Youngbin, 2004. "Personalized Demand-Responsive Transit Service," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt29j111ts, Institute of Transportation Studies, UC Berkeley.
    • Handle: RePEc:cdl:itsrrp:qt29j111ts
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    Citations

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    Cited by:

    1. Rahimi, Mahour & Amirgholy, Mahyar & Gonzales, Eric J., 2018. "System modeling of demand responsive transportation services: Evaluating cost efficiency of service and coordinated taxi usage," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 112(C), pages 66-83.
    2. Alan Lee & Martin Savelsbergh, 2017. "An extended demand responsive connector," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 6(1), pages 25-50, March.
    3. Kumar, Pramesh & Khani, Alireza, 2022. "Planning of integrated mobility-on-demand and urban transit networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 166(C), pages 499-521.
    4. Babak Mehran & Yongzhe Yang & Sushreeta Mishra, 2020. "Analytical models for comparing operational costs of regular bus and semi-flexible transit services," Public Transport, Springer, vol. 12(1), pages 147-169, March.
    5. Yu Hao & Yunxia Guo & Haitao Wu, 2022. "The role of information and communication technology on green total factor energy efficiency: Does environmental regulation work?," Business Strategy and the Environment, Wiley Blackwell, vol. 31(1), pages 403-424, January.
    6. Chandra, Shailesh & Quadrifoglio, Luca, 2013. "A model for estimating the optimal cycle length of demand responsive feeder transit services," Transportation Research Part B: Methodological, Elsevier, vol. 51(C), pages 1-16.
    7. Stiglic, M. & Agatz, N.A.H. & Savelsbergh, M.W.P. & Gradisar, M., 2016. "Enhancing Urban Mobility: Integrating Ride-sharing and Public Transit," ERIM Report Series Research in Management ERS-2016-006-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    8. Quadrifoglio, Luca & Li, Xiugang, 2009. "A methodology to derive the critical demand density for designing and operating feeder transit services," Transportation Research Part B: Methodological, Elsevier, vol. 43(10), pages 922-935, December.
    9. Md Hishamur Rahman & Ye Chen & Shijie Chen & Yanshuo Sun & Muhammad Imran Younus Siddiqui & Matthew Mohebbi & Nikola Marković, 2025. "Enhancing Underutilized Bus Routes with Advance Reservations and Semiflexible Routing," Transportation Science, INFORMS, vol. 59(5), pages 909-931, September.
    10. Bürstlein, Johanna & López, David & Farooq, Bilal, 2021. "Exploring first-mile on-demand transit solutions for North American suburbia: A case study of Markham, Canada," Transportation Research Part A: Policy and Practice, Elsevier, vol. 153(C), pages 261-283.
    11. Sergei Dytckov & Jan A. Persson & Fabian Lorig & Paul Davidsson, 2022. "Potential Benefits of Demand Responsive Transport in Rural Areas: A Simulation Study in Lolland, Denmark," Sustainability, MDPI, vol. 14(6), pages 1-21, March.

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