Recommendation of feeder bus routes using neural network embedding-based optimization

Despite the existence of vast bus and subway networks, the demand for taxis in the morning commute hours is substantial in metropolitan areas. This kind of morning traffic can be resolved by means of feeder buses connecting residential areas with popular transit points. To assist the design of feeder bus routes, this study proposes an optimization approach based on road2vec, which is applied to real-time taxi global positioning system (GPS) data. Road2vec is a neural network-based embedding methodology that extracts road name vectors considering the movement patterns of vehicles. Subsequently, the k-means clustering analysis is applied to those vectors to identify the major taxi transit clusters during the commute hours. For each cluster, the authors suggest a feeder bus route that can best reflect the taxi trajectory patterns. To find intermediate stops between the departure and origin of a feeder bus route, the authors solve an integer programming to maximize the cosine similarity between the origin road vector and the departure road vector subtracted by the road vectors of intermediate stops. The suggested routes based on the authors' method differ from the existing routes in that they have a tendency to pass through residential areas, transit stations, and schools. In addition, the result suggests that the model developed in this study finds bus routes that could be suitable for feeder buses by accommodating areas where the demand for taxis is high in the morning. The authors road2vec approach is expected to contribute to a reduction in traffic during rush hours.

• Record URL:
  • https://doi.org/10.1016/j.tra.2019.05.005
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0965856418305731
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09658564
• Supplemental Notes:
  • © 2019 Published by Elsevier Ltd. Abstract reprinted with permission of Elsevier.
• Authors:
  • Park, Chung
  • Lee, Jungpyo
  • Sohn, So Young
• Publication Date: 2019-8

Language

• English

Media Info

• Media Type: Web
• Features: Figures; Maps; References; Tables;
• Pagination: pp 329-341
• Serial:
  • Transportation Research Part A: Policy and Practice
  • Volume: 126
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0965-8564
  • Serial URL: http://www.sciencedirect.com/science/journal/09658564

Subject/Index Terms

• TRT Terms: Bus routes; Embedded systems; Feeder buses; Integer programming; Neural networks; Optimization; Peak hour traffic; Residential areas; Traffic congestion
• Subject Areas: Operations and Traffic Management; Planning and Forecasting; Public Transportation;

Filing Info

• Accession Number: 01711740
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 22 2019 8:00AM