01590572

Component

https://doi.org/10.3141/2594-14

A case study of how big-data analytics can help to evaluate the effectiveness of existing policies and to formulate new policies is presented in this paper. The District Department of Transportation (DOT) in Washington, D.C., analyzed data for meter time limit adherence, identifying "overstays" at on-street metered parking spaces beyond the prescribed time limit. This analysis assessed the prevalence of meter overstays, citation patterns, and characteristics of that area. This information could help to determine the validity of existing time limits and develop a pricing structure that would shift longer-duration parkers to off-street garages. The analysis of overstays was conducted by using parking transaction data from the District DOT’s pay-by-cell program, transactions at networked single- and multispace meters, and parking citation data for overstays. Maps were created to identify areas experiencing historically, chronically, or persistently high rates of overstays. An assessment based on existing land use gauged whether overstays were attributable to policy flaws (not enough time to conduct business at adjacent land use) or to customers trying to "game the system" because of financial benefits (e.g., arbitrage opportunities). On the basis of a particular situation, the District DOT can consider adjusting time limits, reformulate enforcement protocols, or develop a graduated pricing strategy that minimizes the monetary incentive of parking on street.

01594376

16-3874

English

pp 107–117

2016

Transportation Research Record: Journal of the Transportation Research Board

9780309441339

Print

Figures (8) ; References (27) ; Tables (3)

Data analysis; On street parking; Parking demand; Parking violations; Pricing; Spatial analysis

District Department of Transportation

Big data

Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting; I72: Traffic and Transport Planning

PRP, TRIS, TRB, ATRI

Jan 12 2016 5:42PM

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