Statistical and machine learning approach for planning dial-a-ride systems

Door-to-door transportation service for elderly and persons with disabilities is often called dial-a-ride (DAR), and is usually provided by transit agencies through private contractors. Growth in DAR ridership is reported across the United States and this tendency will likely continue due to aging population. Such trends encourage development of models that can provide decision support in planning new DAR systems or expanding existing ones. Several statistical models were previously developed to predict the required DAR system capacity, given various characteristics of the service region, level-of-service requirements and operator constraints. The authors' work contributes to this line of research by proposing statistical and machine learning approaches that provide more accurate predictions over a wider range of scenarios. This is accomplished through transformation of variables and application of generalized linear model and support vector regression. Proposed models are built into an online tool that can help transit planners and policy makers: (a) estimate the capacity and operating cost of a DAR system needed to provide the desired level of service, (b) explore tradeoffs between system costs and levels of service, and (c) compare the cost of providing DAR service with other transportation alternatives (e.g., taxi, conventional transit).

• Record URL:
  • https://doi.org/10.1016/j.tra.2016.05.006
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0965856416303883
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09658564
• Supplemental Notes:
  • Abstract reprinted with permission of Elsevier.
• Authors:
  • Markovic, Nikola
  • Kim, Myungseob (Edward)
  • Schonfeld, Paul
• Publication Date: 2016-7

Language

• English

Media Info

• Media Type: Web
• Features: Figures; Maps; References; Tables;
• Pagination: pp 41-55
• Serial:
  • Transportation Research Part A: Policy and Practice
  • Volume: 89
  • Publisher: Elsevier
  • ISSN: 0965-8564
  • Serial URL: http://www.sciencedirect.com/science/journal/09658564

Subject/Index Terms

• TRT Terms: Decision support systems; Linear regression analysis; Machine learning; Mathematical prediction; Paratransit services; Planning; Statistics
• Subject Areas: Planning and Forecasting; Public Transportation;

Filing Info

• Accession Number: 01603853
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 30 2016 9:17AM