A Dashboard for Cities to Manage Fleets
summary
Proposes a Wardrop-style model where fleets set surge multiplier mu_i per origin, demand alpha_ij shifts, and empty-vehicle flows beta_ij rebalance so revenue equals operating cost; shows city fare cuts can perversely reduce overall rail usage because fleets rebalance or change surges. Recommends a verification-backed policy: cities publish virtual congestion at 95% capacity and require fleets to route and price against it. Draws on TCP lessons about signaling and reports multipath benefits (≈20% faster time-to-first, 5× fewer failures). Announces a dashboard project and hiring to turn these ideas into tools.
AI-written summary · about
chapters (11)
- 0:00Introduction
- 1:44What Drop Equity
- 2:47Rideshare Fleet Operator
- 5:11Cities and Fleets
- 6:23Fleet Operators
- 7:42Wardrobe Equilibrium
- 10:20Example
- 14:35Virtual Road Network
- 25:45What Makes Models Useful
- 27:10See How It Will Be Useful
- 31:50Postdocs
related talks
30mCreate Detailed Traffic Models and Simulations
16mTidy Transit: Real Life Data Modeling for Public Transportation
39mApidays Virtual Helsinki - Hendrik Wolff, Simon Fraser University
27mOpen-Source Congestion Mana...
58mTutorial
31mConnecting LF Energy’s Projects: A Mini-City Prototype for the Digital Grid Ecosys...
21mData Space to boost the European data economy
34mGrid Enhancement Platform: Operationalizing Open Source for Real-Time, Risk-Aware Gri...