Brian Ferris

Gaussian Processes for Signal Strength-Based Location Estimation

Estimating the location of a mobile device or a robot from wireless signal strength has become an area of highly active research. The key problem in this context stems from the complexity of how signals propagate through space, especially in the presence of obstacles such as buildings, walls or people. In this paper we show how Gaussian processes can be used to generate a likelihood model for signal strength measurements. We also show how parameters of the model, such as signal noise and spatial correlation between measurements, can be learned from data via hyperparameter estimation. Experiments using WiFi indoor data and GSM cellphone connectivity demonstrate the superior performance of our approach.

OneBusAway: results from providing real-time arrival information for public transit

Public transit systems play an important role in combating traffic congestion, reducing carbon emissions, and promoting compact, sustainable urban communities. The usability of public transit can be significantly enhanced by providing good traveler information systems. We describe OneBusAway, a set of transit tools focused on providing real-time arrival information for Seattle-area bus riders. We then present results from a survey of OneBusAway users that show a set of important positive outcomes: strongly increased overall satisfaction with public transit, decreased waiting time, increased transit trips per week, increased feelings of safety, and even a health benefit in terms of increased distance walked when using transit. Finally, we discuss the design and policy implications of these results and plans for future research in this area.

Learning to navigate through crowded environments

The goal of this research is to enable mobile robots to navigate through crowded environments such as indoor shopping malls, airports, or downtown side walks. The key research question addressed in this paper is how to learn planners that generate human-like motion behavior. Our approach uses inverse reinforcement learning (IRL) to learn human-like navigation behavior based on example paths. Since robots have only limited sensing, we extend existing IRL methods to the case of partially observable environments. We demonstrate the capabilities of our approach using a realistic crowd flow simulator in which we modeled multiple scenarios in crowded environments. We show that our planner learned to guide the robot along the flow of people when the environment is crowded, and along the shortest path if no people are around.

OneBusAway: A Transit Traveler Information System

Public transit is an important tool for those looking to ease their commutes, reduce their car dependence, or perhaps minimize their environmental impact. Unfortunately, the usability of transit systems often leaves much to be desired, to the point of deterring new riders. Tools on the web and mobile devices are increasingly being used to help tame confusing transit systems. OneBusAway is one such set of tools, providing access to real-time transit information for Seattle bus riders through a variety of interfaces, including web (http://onebusaway.org), phone, SMS, and mobile devices. We describe the current system, and then discuss current and planned research that builds on it to use increasingly-powerful smart mobile devices to provide location and context-aware tools for navigating transit systems.

Implications for Location Systems in Indoor Wayfinding for Individuals with Cognitive Impairments

We studied an indoor wayfinding application for individuals with cognitive impairments using Wizard-of-Oz techniques. This gave us insights into the technological considerations for the location system underlying such an application. We have abstracted these into generally desirable properties for location-aware systems for wayfinding. In addition to general requirements for accuracy and robustness, we discuss what other information about the environment should be provided that supports wayfinding. Our work showed that combining rich environment information with user location is key to making indoor wayfinding applications feasible

Beyond Context-Sensitive Solutions: Using Value-Sensitive Design to Identify Needed Transit Information Tools

Although the practice of Context-Sensitive Solutions instructs transportation designers to respect community values, the focus is typically on the surroundings of the project rather than the direct and indirect stakeholders who will be affected by a solution. Therefore, the team designing the OneBusAway transit traveler information system turned to the Value-Sensitive Design (VSD) process from information and computer science to help determine what transit rider information tools to build next. Through conceptual, empirical, and technical investigations, the OneBusAway team has developed a list of potential transit information tools and begun to prioritize projects based on the needs of riders of all types and effects on indirect stakeholders. The use of VSD has helped guide the use of limited resources, so that they are spent meeting the actual needs of the larger public-transit-using community. The principles of VSD can be applied throughout the transportation industry, especially when considering broader transportation planning goals.

Pervasive Computing for Transit and Transport

As computing technologies become more pervasive, many new forms of mobility will emerge, with corresponding changes in peoples transport behavior. The last few years have revealed indicators of these changes. For example, checking road conditions or train timings while commuting between work and home has become common practice. The growing availability of computing, sensing, and communication technologies, coupled with the opportunities that lie ahead, prompted this special issue on pervasive computing in transit and transport. The three articles in this issue cover just a small part of the entire research space but should inspire you to imagine new ways in which computing can affect transportation.