Document Type


Date of Award


Degree Name

Doctor of Philosophy in Computing Sciences - (Ph.D.)


Computer Science

First Advisor

Cristian Borcea

Second Advisor

Reza Curtmola

Third Advisor

Xiaoning Ding

Fourth Advisor

Narain Gehani

Fifth Advisor

Tamer Nadeem


Finding a free, curbside parking spaces in metropolitan areas, especially during rush hours, is difficult for drivers. The difficulty arises from not knowing where the available spaces may be at that time; and, even if the spaces are known, many vehicles may pursue the same spaces, causing serious parking contention and traffic congestion. This dissertation presents three cost-effective and easily deployable free parking assignment systems that optimize the travel time of the drivers.

The first contribution is the Free Parking System (FPS), a centralized solution that solves the curbside parking problem. Unlike existing solutions, FPS is cost-effective, as it does not need any sensing infrastructure. It relies on drivers' cooperation to maintain the parking availability information. FPS reduces parking space contention because it provides individual space assignments to drivers. The system consists of two components: a mobile app running on the drivers' smart phones that submits parking requests and guides drivers to their parking spaces, and a central server that manages the parking assignment process. The main novelty of FPS consists of its parking assignment algorithm, FPA, which combines a system-wide objective ("social welfare") with a modified compound laxity algorithm to minimize the total travel time for all drivers. The simulation results demonstrate that compared to a baseline solution, which mimics the way people search for parking today, and a greedy parking assignment algorithm, FPA reduces the total travel time for all drivers. Furthermore, FPA provides substantial improvements even when many parking spaces are occupied by drivers who do not use FPS.

The second contribution is the Distributed Free Parking System (DFPS), which solves the two intrinsic problems of the centralized FPS: scalability, as the server has to perform intensive computation and communication with the drivers; and privacy, as the drivers have to disclose their destinations to the server. DFPS solves the scalability problem by using the smart phones of the drivers to cooperatively compute and forward to drivers the parking assignments, and a centralized dispatcher to receive and distribute parking requests. The parked drivers in DFPS are structured in a K-D tree, which is used to serve new parking requests in a distributed fashion. DFPS removes the computation and substantially reduces the communication handled by the dispatcher. DFPS solves the privacy problem through an entropy-based cloaking technique that runs on drivers' smart phones and conceals drivers' destinations from the dispatcher. DFPS provides a distributed version of FPA, which optimizes the total travel time for all drivers, while preserving driver's destination privacy. The evaluation demonstrates that DFPS obtains better travel time performance than a centralized system, while protecting the privacy of drivers' destinations and removing the computation and communication bottleneck from the server.

The third contribution is the Multi-Destination Vehicular Route Planning (MDVRP) system, which applies FPS to the multi-destination route planning problem. Specifically, MDVRP proposes an efficient solution for people in a city who drive their cars to visit several destinations, where they need to park for a while, but do not care about the visiting order. This instance of the multi-destination route planning problem is novel in terms of its constraints: the real-time traffic conditions and the real-time free parking conditions in the city. MDVRP uses TDTSP-FPA, a novel algorithm that finds the most efficient order to visit the destinations and also assigns free curbside parking spaces that minimize the total travel time for drivers. To evaluate MDVRP, a novel experimental platform that simulates real, multi-destination driver trips of over two million drivers, is built. Experimental results from a prototype implementation show that TDTSP-FPA delivers the best performance when compared to three baseline algorithms.