Privacy-preserving vehicle assignment for mobility-on-demand systems

Abstract

Abstract-Urban transportation is being transformed by mobility-on-demand (MoD) systems. One of the goals of MoD systems is to provide personalized transportation services to passengers. This process is facilitated by a centralized operator that coordinates the assignment of vehicles to individual passengers, based on location data. However, current approaches assume that accurate positioning information for passengers and vehicles is readily available. This assumption raises privacy concerns. In this work, we address this issue by proposing a method that protects passengers' drop-off locations (i.e., their travel destinations). Formally, we solve a batch assignment problem that routes vehicles at obfuscated origin locations to passenger locations (since origin locations correspond to previous drop-off locations), such that the mean waiting time is minimized. Our main contributions are two-fold. First, we formalize the notion of privacy for continuous vehicleto-passenger assignment in MoD systems, and integrate a privacy mechanism that provides formal guarantees. Second, we present a scalable algorithm that takes advantage of superfluous (idle) vehicles in the system, combining multiple iterations of the Hungarian algorithm to allocate a redundant number of vehicles to a single passenger. As a result, we are able to reduce the performance deterioration induced by the privacy mechanism. We evaluate our methods on a real, large-scale data set consisting of over 11 million taxi rides (specifying vehicle availability and passenger requests), recorded over a month's duration, in the area of Manhattan, New York. Our work demonstrates that privacy can be integrated into MoD systems without incurring a significant loss of performance, and moreover, that this loss can be further minimized at the cost of deploying additional (redundant) vehicles into the fleet.

Robust Assignment Using Redundant Robots on Transport Networks With Uncertain Travel Time

“…For task assignment problems with a continuous influx of tasks, our framework would need to be extended. In previous work, we implemented such a mechanism through a sliding window approach that keeps a reserve of robots to accommodate future (unknown) task demands [31]. This idea should be further extended to optimize the re-balancing of the robot distribution, as a function of predictions of spatiotemporal demand distributions.…”

“…Travel time uncertainty also arises due to the deterioration of positioning accuracy of GNSSbased solutions in urban canyons [2]. Furthermore, recent methods consider it desirable to actively obfuscate true robot state information (e.g., robot positioning), to ensure privacy across a variety of applications [6,31]. Yet irrespective of the source of uncertainty, it follows that any discrepancies around true robot states cause a degradation in the system's overall performance, and can lead to cascading effects.…”

Multi-Objective Analysis of Ridesharing in Automated Mobility-on-Demand

“…The early works in AMoD focused on the development of models and algorithmic tools for on-demand fleets with single-occupancy vehicles [6,14,17,20,15]. However, one of the main promises of on-demand systems is the ability to implement massive ridesharing.…”

Efficient Mobility-on-Demand System with Ride-Sharing

“…Other aspects of MOD systems were also explored. A privacypreserving algorithm was developed [50] to protect the location information of passengers without a significant performance hit. Continuous approximation [51] is used to study the dynamics of the fleet and the influence of large fleet to congestion as well as the fleet routing problem in a congested network [29,52].…”