In an online GPS tracking system, a fundamental trade-off exists between timeliness, the average time interval between a recorded change in location and a change in the reported location; accuracy, the average error between the actual location and the reported location; and uplink usage, the average amount of data used per second of tracking. While tracking efficiency has been addressed in the literature, our thrifty tracking system presents the first unified view of timeliness, accuracy, and uplink usage, allowing the user to specify desired targets for any two of these objectives, while optimizing the third. We also provide a closed-form characterization of this three-way trade-off, and demonstrate that our system converges to the predicted performance.

A typical online GPS tracking system uses a cellular uplink to report the location of a device to a central server, and in a study based on 1.6 billion location updates we find at least 90% are sent with a fixed 1--300 second period. Through experiments with the cost of cellular data transmission we also find that every packet sent incurs significant overhead.

This paper describes a process for automatically inferring maps from large collections of opportunistically collected GPS traces. In this type of dataset, there is often a great disparity in terms of coverage. For example, a freeway may be represented by thousands of trips, whereas a residential road may only have a handful of observations. Additionally, while modern GPS receivers typically produce high-quality location estimates, errors over 100 meters are not uncommon, especially near tall buildings or under dense tree coverage. Combined, GPS trace disparity and error present a formidable challenge for the current state of the art in map inference. By tuning the parameters of existing algorithms, a user may choose to remove spurious roads created by GPS noise, or admit less-frequently traveled roads, but not both.

We address the problem of inferring road maps from large-scale GPS traces that have relatively low resolution and sampling frequency. Unlike past published work that requires high-resolution traces with dense sampling, we focus on situations with coarse granularity data, such as that obtained from thousands of taxis in Shanghai, which transmit their location as seldom as once per minute. Such data sources can be made available inexpensively as byproducts of existing processes, rather than having to drive every road with high-quality GPS instrumentation just for map building - and having to re-drive roads for periodic updates. Although the challenges in using opportunistic probe data are significant, successful mining algorithms could potentially enable the creation of continuously updated maps at very low cost.

In order to facilitate the introduction of transit tracking and arrival time prediction in smaller transit agencies, we investigate an automatic, smartphone-based system which we call EasyTracker. To use EasyTracker, a transit agency must obtain smartphones, install an app, and place a phone in each transit vehicle. Our goal is to require no other input.

EasyTracker is an automated system that assists small public or private transit agencies in deploying bus tracking and arrival time prediction. This demo will showcase how data from GPS sensors embedded in smartphones can be automatically processed in order to accurately estimate routes, bus stop locations, schedules, and make annotated maps with real-time bus tracking and arrival time predictions. We will also demonstrate a website portal which transit agencies can use to further interact with their bus transit systems.

Real-time transit tracking is gaining popularity as a means for transit agencies to improve the rider experience. However, many transit agencies lack either the funding or initiative to provide such tracking services. In this paper, we describe a crowd-sourced alternative to official transit tracking, which we call cooperative transit tracking.

A transit navigation system is described that integrates real-time transit and user tracking with existing transit schedules to improve the transit riding experience.