Residential On-Site Carsharing and Off-Street Parking Policy in the San Francisco Bay Area, Research Report 11-28

In light of rising motorization, transportation planners have increasingly supported alternatives to the indiscriminate use of the car. Off-street parking policy and carsharing have emerged as credible alternatives for discouraging car ownership. This report explores an initiative that could connect these policy fields and build on their synergy: the provision of on-site carsharing service in residential developments. It evaluates the performance of on-site carsharing programs in the San Francisco Bay Area by interviewing developers, planners, and carsharing service providers. Interviews were conducted in four Bay Area cities that support the provision of carsharing as an alternative to the private automobile. Based on these interviews, this report identifies the principal factors contributing to the success or failure of on-site carsharing: the unbundling status of off-street parking in residential developments; ties to off-street parking standards; financial constraints; and the level of coordination among stakeholders. The interviews revealed that on-site carsharing has been accepted by developers, planners, and service providers, particularly in densely-populated, transit-rich communities. Nevertheless, there appears to be a gap between on-site carsharing programs and off-street parking standards, and between carsharing programs and carsharing business operations. The authors recommend that a few models for establishing carsharing policy be tested: a model designed to serve high-density cities with traditional carsharing; and another designed to serve moderately-dense communities, with new carsharing options (e.g., peer-to-peer). In the case of the latter, trip reduction can be achieved through the promotion of alternative modes along major corridors

Subtracting Foregrounds from Interferometric Measurements of the Redshifted 21 cm Emission

The ability to subtract foreground contamination from low-frequency observations is crucial to reveal the underlying 21 cm signal. The traditional line-of-sight methods can deal with the removal of diffuse emission and unresolved point sources, but not bright point sources. In this paper, we introduce a foreground cleaning technique in Fourier space, which allows us to handle all such foregrounds simultaneously and thus sidestep any special treatments to bright point sources. Its feasibility is tested with a simulated data cube for the 21 CentiMeter Array experiment. This data cube includes more realistic models for the 21 cm signal, continuum foregrounds, detector noise and frequency-dependent instrumental response. We find that a combination of two weighting schemes can be used to protect the frequency coherence of foregrounds: the uniform weighting in the uv plane and the inverse-variance weighting in the spectral fitting. The visibility spectrum is therefore well approximated by a quartic polynomial along the line of sight. With this method, we demonstrate that the huge foreground contamination can be cleaned out effectively with residuals on the order of \sim10 mK, while the spectrally smooth component of the cosmological signal is also removed, bringing about systematic underestimate in the extracted power spectrum primarily on large scales.Comment: 9 pages, 9 figures. Final published versio

Continuous Monitoring of Distributed Data Streams over a Time-based Sliding Window

The past decade has witnessed many interesting algorithms for maintaining statistics over a data stream. This paper initiates a theoretical study of algorithms for monitoring distributed data streams over a time-based sliding window (which contains a variable number of items and possibly out-of-order items). The concern is how to minimize the communication between individual streams and the root, while allowing the root, at any time, to be able to report the global statistics of all streams within a given error bound. This paper presents communication-efficient algorithms for three classical statistics, namely, basic counting, frequent items and quantiles. The worst-case communication cost over a window is $O(\frac{k} {\epsilon} \log \frac{\epsilon N}{k})$ bits for basic counting and $O(\frac{k}{\epsilon} \log \frac{N}{k})$ words for the remainings, where $k$ is the number of distributed data streams, $N$ is the total number of items in the streams that arrive or expire in the window, and $\epsilon < 1$ is the desired error bound. Matching and nearly matching lower bounds are also obtained.Comment: 12 pages, to appear in the 27th International Symposium on Theoretical Aspects of Computer Science (STACS), 201