Effect of Routing Constraints on Learning Efficiency of Destination Recommender Systems in Mobility-on-Demand Services

Gyugeun Yoon, Joseph Y.J. Chow, Assel Dmitriyeva, Daniel Fay

Research output: Contribution to journalArticlepeer-review


With Mobility-as-a-Service platforms moving toward vertical service expansion, we propose a destination recommender system for Mobility-on-Demand (MOD) services that explicitly considers dynamic vehicle routing constraints as a form of a 'physical internet search engine'. It incorporates a routing algorithm to build vehicle routes and an upper confidence bound based algorithm for a generalized linear contextual bandit algorithm to identify alternatives which are acceptable to passengers. As a contextual bandit algorithm, the added context from the routing subproblem makes it unclear how effective learning is under such circumstances. We propose a new simulation experimental framework to evaluate the impact of adding the routing constraints to the destination recommender algorithm. The proposed algorithm is first tested on a 7 by 7 grid network and performs better than benchmarks that include random alternatives, selecting the highest rating, or selecting the destination with the smallest vehicle routing cost increase. The RecoMOD algorithm also reduces average increases in vehicle travel costs compared to using random or highest rating recommendation. Its application to Manhattan dataset with ratings for 1,012 destinations reveals that a higher customer arrival rate and faster vehicle speeds lead to better acceptance rates. While these two results sound contradictory, they provide important managerial insights for MOD operators.

Original languageEnglish (US)
Pages (from-to)4021-4036
Number of pages16
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number5
StatePublished - May 1 2022


  • Mobility-on-Demand
  • contextual bandit algorithm
  • destination recommendation
  • insertion heuristics
  • physical Internet

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications


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