PDPTW formulation for real-time public transit dispatch, feedback on approach?
I've been working on a conceptual framework for an autonomous on-demand public transit system. The core dispatch problem is formulated as a variant of the PDPTW with the following objective:
min F(π) = α·W(π) + β·D(π) + γ·(1−OCC(π))
where W is average passenger waiting time, D is deadhead km ratio, and OCC is average fleet occupancy. The weights α, β, γ sum to 1 and are configurable by the operator.
For the solver I've proposed an LNS approach (Ropke & Pisinger 2006) with worst removal + regret-based insertion, running in 30-second dispatch cycles.
A few questions for people with more OR experience:
Is LNS the right choice here, or would a rolling horizon approach with column generation be worth the added complexity for a real-time system?
For the demand prediction module, I've proposed LSTM-based spatiotemporal forecasting. Are there better architectures for this specific problem (short-horizon, high spatial granularity)?
The conceptual simulation suggests ~20-24% deadhead ratio. Does this seem reasonable for a system operating in low-density suburban areas?
Full write-up (preprint link)
https://papers.ssrn.com/sol3/papers.cfm?abstract\_id=6513843