A Stationless Bikeshare Proof of Concept for College Campuses

Bikeshares promote healthy lifestyles and sustainability among commuters, casual riders, and tourists. However, the central pillar of modern systems, the bike station, cannot be easily integrated into a compact college campus. Fixed stations lack the flexibility to meet the needs of college students who make quick, short-distance trips. Additionally, the necessary cost of implementing and maintaining each station prohibits increasing the number of stations for user convenience. Therefore, the team developed a stationless bikeshare based on a smartlock permanently attached to bicycles in the system. The smartlock system design incorporates several innovative approaches to provide usability, security, and reliability that overcome the limitations of a station centered design. A focus group discussion allowed the team to receive feedback on the early lock, system, and website designs, identify improvements and craft a pleasant user experience. The team designed a unique, two-step lock system that is intuitive to operate while mitigating user error. To ensure security, user access is limited through near field ii communications (NFC) technology connected to a mechatronic release system. The said system relied on a NFC module and a servo working through an Arduino microcontroller coded in the Arduino IDE. To track rentals and maintain the system, each bike is fitted with an XBee module to communicate with a scalable ZigBee mesh network. The network allows for bidirectional, real-time communication with a Meteor.js web application, which enables user and administrator functions through an intuitive user interface available on mobile and desktop. The development of an independent smartlock to replace bike stations is essential to meet the needs of the modern college student. With the goal of creating a bikeshare that better serves college students, Team BIKES has laid the framework for a system that is affordable, easily adaptable, and implementable on any university expressing an interest in bringing a bikeshare to its campus.

Agriculture, Environmental Incentive Payments, and Water Quality in the Chesapeake Bay

Nonpoint sources (NPS) pollution from agriculture is the leading source of water quality impairment in U.S. rivers and streams, and a major contributor to lakes, wetlands, estuaries and coastal waters (U.S. EPA 2016). Using data from a survey of farmers in Maryland, this dissertation examines the effects of a cost sharing policy designed to encourage adoption of conservation practices that reduce NPS pollution in the Chesapeake Bay watershed. This watershed is the site of the largest Total Maximum Daily Load (TMDL) implemented to date, making it an important setting in the U.S. for water quality policy. I study two main questions related to the reduction of NPS pollution from agriculture. First, I examine the issue of additionality of cost sharing payments by estimating the direct effect of cover crop cost sharing on the acres of cover crops, and the indirect effect of cover crop cost sharing on the acres of two other practices: conservation tillage and contour/strip cropping. A two-stage simultaneous equation approach is used to correct for voluntary self-selection into cost sharing programs and account for substitution effects among conservation practices. Quasi-random Halton sequences are employed to solve the system of equations for conservation practice acreage and to minimize the computational burden involved. By considering patterns of agronomic complementarity or substitution among conservation practices (Blum et al., 1997; USDA SARE, 2012), this analysis estimates water quality impacts of the crowding-in or crowding-out of private investment in conservation due to public incentive payments. Second, I connect the econometric behavioral results with model parameters from the EPA’s Chesapeake Bay Program to conduct a policy simulation on water quality effects. I expand the econometric model to also consider the potential loss of vegetative cover due to cropland incentive payments, or slippage (Lichtenberg and Smith-Ramirez, 2011). Econometric results are linked with the Chesapeake Bay Program watershed model to estimate the change in abatement levels and costs for nitrogen, phosphorus and sediment under various behavioral scenarios. Finally, I use inverse sampling weights to derive statewide abatement quantities and costs for each of these pollutants, comparing these with TMDL targets for agriculture in Maryland.