Techniques for improving routing by exploiting user input and behavior

University of Minnesota Ph.D. dissertation. October 2014. Major: Computer Science. Advisor: Loren Terveen. 1 computer file (PDF); xiii, 106 pages.This dissertation explores innovative techniques for improving the route finding process. Instead of focusing on improving the algorithm itself, I aim to improve the other factors that make the route finding experience better: personalization, map data, and presentation. I do so by making extensive use of user input (both explicit and implicit) and crowdsourcing strategies. This research uses Cyclopath, a geowiki for cyclists in the Twin Cities, MN, as a case study for the various techniques explored.The first challenge is the lack of personalization in route finding algorithms. Aside from start and end points, algorithms usually know very little about users. However, user preferences can greatly affect their ideal routes. I studied the use of community-shared tags that allow users to specify preferences for those tags instead of doing so for each individual road segment, allowing them to easily express preference for a large number of roads with little effort. Correlation between individual road segment ratings and ratings deduced from tag preferences was evidence of the utility of this technique for making personalization easier.The second challenge is missing data. The best routing algorithm is only as good as the map data underneath it. Unfortunately, maps are often incomplete. They might not have updates on the latest construction, might be missing roads in rural areas or might not include detailed information such as lanes, trails, and even shortcuts. I present an HMM-based map matching algorithm that uses GPS traces recorded by users to generate potential new road segments. Tests within Cyclopath confirmed the abundance of missing roads and the ability of this algorithm to detect them.Finally, I look at the issue of unnatural presentation of routes. The way computers relay route directions is very different from humans, who use landmarks most of the time. However, gathering useful landmarks can be difficult and is often limited to points of interest. In this research, I tested methods for crowdsourcing different types of landmarks. I show that POIs are not sufficient to represent landmarks and that there is no objective truth regarding which landmarks are more useful to users

'Sensor'ship and Spatial Data Quality

This article describes a Los Angeles-based website that collects volunteered geographic information (VGI) on outdoor advertising using the Google Street View interface. The Billboard Map website was designed to help the city regulate signage. The Los Angeles landscape is thick with advertising, and the city efforts to count total of signs has been stymied by litigation and political pressure. Because outdoor advertising is designed to be seen, the community collectively knows how many and where signs exist. As such, outdoor advertising is a perfect subject for VGI. This paper analyzes the Los Angeles community's entries in the Billboard Map website both quantitatively and qualitatively. I find that members of the public are well able to map outdoor advertisements, successfully employing the Google Street View interface to pinpoint sign locations. However, the community proved unaware of the regulatory distinctions between types of signs, mapping many more signs than those the city technically designates as billboards. Though these findings might suggest spatial data quality issues in the use of VGI for municipal record-keeping, I argue that the Billboard Map teaches an important lesson about how the public's conceptualization of the urban landscape differs from that envisioned by city planners. In particular, I argue that community members see the landscape of advertising holistically, while city agents treat the landscape as a collection of individual categories. This is important because, while Los Angeles recently banned new off-site signs, it continues to approve similar signs under new planning categories, with more in the works

15-07 App-based Crowd Sourcing of Bicycle and Pedestrian Conflict Data

Most agencies and decision-makers rely on crash and crash severity (property damage only, injury or fatality) data to assess transportation safety; however, in the context of public health where perceptions of safety may influence the willingness to adopt active transportation modes (e.g. bicycling and walking), pedestrian-vehicle and other similar conflicts may represent a better performance measure for safety assessment. For transportation safety, a clear conflict occurs when two parties’ paths cross and one of the parties must undertake an evasive maneuver (e.g. change direction or stop) to avoid a crash. Other less severe conflicts where paths cross but no evasive maneuver occurs may also impact public perceptions of safety. Most existing literature on conflicts focuses on vehicle conflicts and intersections. While some research has investigated bicycle and pedestrian conflicts, most of this has focused on the intersection environment. In this project, we propose field testing a crowd-sourced data app to better understand the continuum of conflicts (bicycle/pedestrian, bicycle/vehicle, and pedestrian/vehicle) experienced by pedestrians and cyclists; the study also tests the effectiveness of the app and its associated crowd-sourced data collection. This study assesses the data quality of the crowd sourced data and compares it to more traditional data sources while performing hot spot analysis. If widely adopted, the app will enable communities to create their own data collection efforts to identify dangerous sites within their neighborhoods. Agencies will have a valuable data source at low-cost to help inform their decision making related to bicycle and pedestrian education, enforcement, infrastructure, programs and policies

In Search of New Riders: Affective Exclusions and Bicycle Planning in Minneapolis/Saint Paul

University of Minnesota Ph.D. dissertation. September 2015. Major: Geography. Advisor: Arun Saldanha. 1 computer file (PDF); v, 257 pages.Riding a bike is typically viewed as something most people can simply do without thinking, an automatic response latent inside one’s body from childhood. Thus, in a useful way, the cliché “it’s just like riding a bike” refers to the connection between technology and the body, which can bypass the consciousness-based model of behavior in provocative ways. But at the same time, the phrase subsumes a complex relationship within a seemingly automatic response. The fact that riding a bicycle is often taken for granted erases subtle differences between how and why people ride. Rather than an innate human capacity, for many people riding a bike is an experience that offers a wide range of emotional dynamics. By examining how “riding a bike” differs depending on specific bodies, spaces, and technological relationships, we can learn how subjectivity forms in relation to social and material environments. The complex relationship the body, bicycle, and space challenges assumptions that govern urban systems. Current bicycling trends have shifted debates around bicycling in ways that challenge traditional approaches of bike planners and advocates, particularly in attempts to attract new riders. Yet without a careful understanding of how and why bicycling differs from dominant automobile-centered transportation, urban decision makers risk re-inscribing existing patterns of mobility at the expense of a more impactful future. In this dissertation, I examine how differences emerge around everyday bicycling as a relational capacity to act, locating my approach within the field of “mobilities studies.” I use the concept of the affective assemblage, a concept that describes the relational dynamics of the bicycle, bodies, and diverse kinds of urban space. I then describe how bicycle planning debates that emerged in the 1970s pivoted around assumptions that privileged specific age, gender, race, and class positions at the expense of others. I extend these debates into the present by looking at how contemporary approaches frame design debates in ways that simultaneously include and exclude certain ways of moving. Next, drawing on urban spatial theory and qualitative research, I examine how bicycle riders employ tactics based on social capacities for feeling “in place to negotiate pathways through changing urban terrain. These spatial practices are connected with a nonlinear urban landscape that displays spatial gaps fundamental to developing bicycling habits in different ways, and lay the foundation for affective difference. Next, drawing on crowd theory, I outline how patterns form around particular aspects of the bicycle assemblage, so that clothing or riding style signify a larger affective connections, combinations of emotional attitudes and capacities for action. Using interviews, I show how these patterns form an affective taxonomy that describes how different modes of experience and capacities sort bicyclists. Finally, I look at how affective difference relates to current planning policies that attempt to appeal to new riders. As decision makers have begun to recognize the limitations of traditional bicycle planning, they are experimenting with design and policy approaches aimed at diversifying the affective range of bicyclists, for example, bicycle boulevards, “open streets” events, and bike share systems Yet in practice, while these approaches circumvent automobility logics in specific ways, they remain limited by both political and institutional constraints, and the affective assumptions made by advocates

Advanced Location-Based Technologies and Services

Since the publication of the first edition in 2004, advances in mobile devices, positioning sensors, WiFi fingerprinting, and wireless communications, among others, have paved the way for developing new and advanced location-based services (LBSs). This second edition provides up-to-date information on LBSs, including WiFi fingerprinting, mobile computing, geospatial clouds, geospatial data mining, location privacy, and location-based social networking. It also includes new chapters on application areas such as LBSs for public health, indoor navigation, and advertising. In addition, the chapter on remote sensing has been revised to address advancements

Scalable Methods to Collect and Visualize Sidewalk Accessibility Data for People with Mobility Impairments

Poorly maintained sidewalks pose considerable accessibility challenges for people with mobility impairments. Despite comprehensive civil rights legislation of Americans with Disabilities Act, many city streets and sidewalks in the U.S. remain inaccessible. The problem is not just that sidewalk accessibility fundamentally affects where and how people travel in cities, but also that there are few, if any, mechanisms to determine accessible areas of a city a priori. To address this problem, my Ph.D. dissertation introduces and evaluates new scalable methods for collecting data about street-level accessibility using a combination of crowdsourcing, automated methods, and Google Street View (GSV). My dissertation has four research threads. First, we conduct a formative interview study to establish a better understanding of how people with mobility impairments currently assess accessibility in the built environment and the role of emerging location-based technologies therein. The study uncovers the existing methods for assessing accessibility of physical environment and identify useful features of future assistive technologies. Second, we develop and evaluate scalable crowdsourced accessibility data collection methods. We show that paid crowd workers recruited from an online labor marketplace can find and label accessibility attributes in GSV with accuracy of 81%. This accuracy improves to 93% with quality control mechanisms such as majority vote. Third, we design a system that combines crowdsourcing and automated methods to increase data collection efficiency. Our work shows that by combining crowdsourcing and automated methods, we can increase data collection efficiency by 13% without sacrificing accuracy. Fourth, we develop and deploy a web tool that lets volunteers to help us collect the street-level accessibility data from Washington, D.C. As of writing this dissertation, we have collected the accessibility data from 20% of the streets in D.C. We conduct a preliminary evaluation on how the said web tool is used. Finally, we implement proof-of-concept accessibility-aware applications with accessibility data collected with the help of volunteers. My dissertation contributes to the accessibility, computer science, and HCI communities by: (i) extending the knowledge of how people with mobility impairments interact with technology to navigate in cities; (ii) introducing the first work that demonstrates that GSV is a viable source for learning about the accessibility of the physical world; (iii) introducing the first method that combines crowdsourcing and automated methods to remotely collect accessibility information; (iv) deploying interactive web tools that allow volunteers to help populate the largest dataset about street-level accessibility of the world; and (v) demonstrating accessibility-aware applications that empower people with mobility impairments