New approaches to managing parking--including urban parking lots, curbside, and residential parking--are presented in this session.
A Framework for Optimal Allocation of Curbside Space
You Kong, Southwest Jiaotong UniversityShow Abstract View Presentation
Scott Le Vine, Transpo Group/SUNY
Alejandro Henao, National Renewable Energy Laboratory (NREL)
Stanley Young, National Renewable Energy Laboratory (NREL)
The emergence of various new forms of urban mobility services in recent years is leading to new pressures on curbside space. Municipalities, the entities typically responsible for managing the curbside, are in many instances handling these growing pressures by reallocating portions of the curbside away from traditional uses (such as metered and residential parking) in favor of uses such as ridehailing, scooter and bike-share corrals. As yet, however, such actions are being undertaken on an ad-hoc basis, due to the rapidly growing complexity of the curbside and the lack of standard analytical approaches. This lack of analytical capability is due to the traditional focus of transportation network modeling being focused predominantly on the interaction of supply and demand on links and nodes, with limited focus on link edges (the curbside). In this paper we address this research need by proposing a framework for modeling inter-modal competition for curbside space, inspired by the classical Bid-Rent Model of urban land use, intended to support curb managers to move towards maximizing the aspects of economic welfare that relate to curb access. We then present a simple numerical case study to demonstrate the properties of the proposed model, showing its tractability, flexibility, and intuitive sensitivity to systematic variation in inputs. The framework demonstrates the type of adaptive and evolving approach needed to maximize benefits from increasingly dynamic curb management strategies. The paper concludes with a brief discussion of future research needs to advance this line of inquiry.
Testing Curbside Management Strategies to Mitigate the Impacts of Ridehailing Services on Traffic
Andisheh Ranjbari, University of WashingtonShow Abstract View Presentation
Jose Machado, University of Washington
Giacomo Dalla Chiara, University of Washington
Don MacKenzie, University of Washington
Anne Goodchild, University of Washington Seattle Campus: University of Washington
Increased ride-hailing use leads to increased pick-up and drop-off activity. This may slow traffic or cause delays as vehicles increase curb use, conduct pick-up and drop-off activity directly in the travel lane, or slow to find and connect with passengers. How should cities respond to this change in an effort to keep travel lanes operating smoothly and efficiently? This research evaluates two strategies in Seattle, WA, in an area where large numbers of workers commute using ride-hailing services: 1) a curb allocation change from paid parking to passenger load zone (PLZ), and 2) a geofencing approach by transportation network companies (TNCs) which directs their drivers and passengers to designated pick-up and drop-off locations on a block. An array of data on street and curb activity along three study block-faces was collected, using video and sensor technology as well as in-person observations. Data was collected in three phases: 1) the baseline, 2) after the new PLZs were added, expanding total PLZ curb length from 20 feet to 274 feet, and 3) after geofencing was added to the expanded PLZs. The added PLZ spaces were open to any passenger vehicle (not just TNC vehicles) weekdays 7-10am and 2-7pm. The results found that the increased PLZ allocation and geofencing strategy reduced the number of pick-ups/drop-offs in the travel lane, reduced dwell times, increased curb use compliance, and increased TNC user satisfaction. The two-pronged strategy however, had no observable effect on travel speeds or traffic safety.
Linking Residential Parking to Automobile Transportation Impact Outcomes at a Development Level
Kristina Currans, University of ArizonaShow Abstract View Presentation
Gabriella Abou-Zeid, ICF
Nicole Iroz-Elardo, University of Arizona
Although there exists a well-studied relationship between parking policies and automobile demand, conventional practices evaluating transportation impacts of new land development tends to ignore this. In this paper, we (a) explore literature linking parking policies and vehicle use (including vehicle trip generation, vehicle miles traveled (VMT), and trip length) through the lens development-level evaluations (e.g., TIAs); (b) develop a conceptual map linking development-level parking characteristics and vehicle use outcomes based on previously supported theory and frameworks; and (c) evaluate and discuss the conventional approach to identify steps forward to operationalize this link, specifically for residential development. Our findings indicate a significant and noteworthy dearth of studies incorporating parking constraints into travel behavior studies—including, but not limited to: parking supply, costs or pricing, and travel demand management strategies, such as the impacts of (un)bundled parking in housing costs. Disregarding parking in TIAs ignores a significant indicator in automobile use. Further, unconstrained parking may encourage increases car ownership, trips, and VMT in areas with robust alternative-mode networks and accessibility, thus creating greater demand for vehicle travel than would otherwise occur. The conceptual map offers a means for operationalizing the links between: the built environment; socioeconomic and demographic characteristics; fixed and variable travel costs; and vehicle use. Implications for practice and future research are explored.
Parking Resource Allocation Optimization Based on Travel Destination Data
Haonan Guo, Beijing Jiaotong UniversityShow Abstract
Xuedong Yan, Beijing Jiaotong University
Yun Wang, Beijing Jiaotong University
Yu Zhou, National University of Singapore
Yunlin Guan, Beijing Jiaotong University
Yan Huang, Beijing Jiaotong University
Parking presents a big challenge in metropolitan areas, where the spatial distribution of parking resources supply and demand is unbalanced. This study aims to balance the demand and supply for parking slots in city and provide operational suggestions for urban parking managers. To achieve this goal, this paper presents a destination-around parking resource allocation optimization method, and an integer linear model is formulated for simultaneous optimization of parking lot location and parking resource allocation problem. We mine the true and accurate private travel destination information from traffic big data and arrange parking slots based on the distribution of travel destinations to provide more convenient services for users. A simple real-life case is used to test the performance of our method. Numerical experiments show that more users can easily find parking slots under the optimization results.