Issue No. 02 - June (2017 vol. 3)
Preeti Goel , Department of Computing and Information System, University of Melbourne, Melbourne, Vic., Australia
Lars Kulik , Department of Computing and Information System, University of Melbourne, Melbourne, Vic., Australia
Kotagiri Ramamohanarao , Department of Computing and Information System, University of Melbourne, Melbourne, Vic., Australia
Car occupancy rates (travelers per vehicle) are currently very low in most developed countries, for example, on average between 1.15 and 1.25 in Australia. Enabling shared rides on short notice can be an effective solution to counter the problem of increasing traffic through the use of the untapped transportation capacity. Common inhibitors for the uptake of ride sharing services are privacy and safety concerns. We present an approach to ride sharing where the pick up/drop off locations for passengers are selected from a fixed set, which has the advantage of increased safety through video surveillance. We present a scheme that chooses optimally fixed locations of Pick up Points (PuPs) and aims to maximize the car occupancy rates while preserving user privacy and safety. Our method enhances privacy as the users do not need to provide their precise home/work locations. We have extended the well studied 1-coverage problem, i.e., to cover an area with the minimum number of circles of a given radius
 , to road networks. The challenges for road networks are the varying population densities of suburbs which requires circles of different radii. The aim is to ensure that every point of a city's area is covered by at least one PuP while minimizing the total number of PuPs. By ensuring that we have different circle radii for PuPs the anonymity of individuals is the same throughout. Using Voronoi diagrams we present a k-anonymity model that guarantees a minimum number of individuals covered by every PuP. Our problem is a multi objective problem where we aim to maximize coverage, k-anonymity and privacy provided by the system to its users while facilitating ride sharing. Through greedy randomized adaptive search procedure (GRASP) we find out the Pareto front of solutions and evaluate their impact on ride sharing.
Privacy, Roads, Sociology, Statistics, Automobiles, Safety
P. Goel, L. Kulik and K. Ramamohanarao, "Optimal Pick up Point Selection for Effective Ride Sharing," in IEEE Transactions on Big Data, vol. 3, no. 2, pp. 154-168, 2017.