The management of shared services such as parking and ride sharing can be challenging in urban areas. However, the efficient management of shared services, particularly parking, has been shown to dramatically reduce congestion and pollution. In this proposal, the team proposes exploring the commercial viability of a methodology to predict the demand for shared services found in cities. The product being proposed is a predictive parking application with both long and short term components. Not only will the product assist individuals in finding a parking space but also it will influence their travel demand patterns. Drivers will be able to proactively plan their trips and explore other options available to them based on the information obtained from the predictive model.
The benefits of the predictive model go beyond the demand side. On the supply side, garage operators could use the predictive information to better manage their facilities. For example, a predicted higher than normal demand for parking spots could allow a garage operator to artificially increase the facility?s capacity by making provisions for valet parking. Further downstream, the information provided by the predictive module will be used to dynamically price parking spaces, a functionality that will help address some of the rigidities in the automobile parking market. The proposed innovation, if successful, will improve the management of shared services in urban areas. Additionally, the proposed innovation has the potential increases in small business activity and entrepreneurship in the areas of shared services such as carsharing, homesharing, and ridesharing
