Yue Bao

Crowd-sourced, randomly located vehicles in large-scale on-demand mobility systems result in uncertainty in customer waiting times, posing challenges for service reliability and system efficiency. We theoretically establish that, under nearest-eligible-vehicle assignment and the stated independence condition, any nondegenerate limit of suitably normalized customer waiting-time minima must belong to the minimum-GEV family. Under a lower-bounded spatial Poisson benchmark, we derive how the cumulative spatial intensity of eligible idle vehicles determines the minimum-GEV location, scale, and shape parameters. Numerical simulations show close finite-sample agreement between fitted minimum-GEV distributions and simulated waiting-time distributions under the specified designs, including expected eligible-set sizes as low as ten and random selection among the first three order statistics. Observations from e-hailing trajectory data provide descriptive distributional evidence that GEV distributions approximate customer waiting times. We also formulate a generalized-cost comparison across providers. A lower-bounded observed-quote benchmark links provider quotes to physical pickup-time distributions, while a behavioral benchmark uses independent minimum-Gumbel generalized-cost disturbances with a common scale and yields the Multinomial Logit (MNL) form. Moreover, we analytically derive a unique stationary throughput turning point associated with the Wild Goose Chase (WGC) phenomenon under an uncontrolled, lower-bounded benchmark. The resulting turning point provides a stress-test benchmark for examining how assignment, admission, dispatch limits, and repositioning alter stationary throughput.

This paper examines how employer-provided housing allowances and overtime subsidies reshape household behavior, commuting, and urban spatial structure. We develop a monocentric spatial general equilibrium model in which households choose residential location, housing consumption, composite-good consumption, overtime, and leisure, with commuting time endogenously determined by residential location and congestion. A CBD firm provides a proximity housing allowance and a threshold-based overtime subsidy. The model captures how workplace benefits affect not only household income and time allocation, but also rents, residential density, firm expenditure, and the distribution of gains across households, firms, and absentee landlords. In the benchmark calibration, the housing allowance reduces average commuting time by 5.2% but raises firm expenditure by 1.5%. The overtime subsidy reduces commuting time by 10.4%, increases average overtime by 11.2%, and raises firm expenditure by 6.1%. A higher eligibility threshold narrows program coverage and can lower firm expenditure, but may create sharper behavioral responses and less smooth residential density profiles. Combining the two benefits smooths some density irregularities, but remains subject to rent capitalization. These model-based results show that employer-provided benefits should be evaluated jointly in terms of commuting, labor supply, leisure, housing-market incidence, and firm expenditure, rather than by commuting-time reductions alone.

1. Due to the rapid expansion of online and mobile technologies, on-demand ride services have experienced significant growth and widespread consumer acceptance, becoming an indispensable component ...