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Robert C. Hampshire
Researcher at University of Michigan
Publications - 75
Citations - 2372
Robert C. Hampshire is an academic researcher from University of Michigan. The author has contributed to research in topics: Parking guidance and information & Population. The author has an hindex of 20, co-authored 74 publications receiving 1965 citations. Previous affiliations of Robert C. Hampshire include Princeton University & IBM.
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Inventory rebalancing and vehicle routing in bike sharing systems
TL;DR: A new cluster-first route-second heuristic is proposed, in which a polynomial-size Clustering Problem simultaneously considers the service level feasibility and approximate routing costs and shows that it outperforms a pure mixed-integer programming formulation and a constraint programming approach.
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Large-Scale Vehicle Sharing Systems: Analysis of Vélib'
TL;DR: A quantitative analysis of the pioneering large-scale bicycle sharing system, Vélib' in Paris, France, provides key insights on the functioning of such systems and serves to inform policy makers in other urban communities wanting to explore bicycle-sharing systems.
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An Empirical Analysis of Bike Sharing Usage and Rebalancing: Evidence from Barcelona and Seville
TL;DR: A mixed linear model is developed to estimate the influence of bicycle infrastructure, socio-demographic characteristics and land-use characteristics on customer arrivals and departures and provides a template for examining bicycle rebalancing in different contexts, and a tool to improve system management of bicycle sharing systems.
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An empirical analysis of bike sharing usage and rebalancing: Evidence from Barcelona and Seville
TL;DR: In this paper, a mixed linear model was developed to estimate the influence of bicycle infrastructure, socio-demographic characteristics and land-use characteristics on customer arrivals and departures in bicycle sharing systems.
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Is the Curb 80% Full or 20% Empty? Assessing the Impacts of San Francisco's Parking Pricing Experiment
TL;DR: In this paper, the authors evaluate the relationship between occupancy rules and metrics of direct policy interest, such as the probability of finding a parking space, the amount of cruising, and show how cruising and arrival rates can be simulated or estimated from hourly occupancy data.