Using network segments in the spatial representation of travel time isochronesJeff Allen, University of Toronto
Steven Farber, University of Toronto Scarborough
Isochrones are often used for visual analysis of mobility and accessibility in urban areas. We will discuss an alternative method to conventional isochrones; using computed travel times to classify network segments rather than generating isolines or polygons. We will outline the data, tools, and geoprocessing steps required to make these kind of maps as well as discuss visualization considerations for different scales, subject matter, and for static and interactive maps. Further discussion will include their advantages and disadvantages when compared to conventional isochrones, particularly in terms of classification options and mapping in conjunction with other data. Finally, we will comment on how this method results in potential benefits for subsequent spatial analysis and how it can be scaled for multiple origin points, travel modes, departure times, and transit scenarios.
View Slides »
Husky Lines Mobile App: Adapting transportation studies to our changing technologies Elisabeth Leaf, University of Washington, Urban Studies
Britta Ricker, Ph.D. University of Washington
Alexa Brockamp, University of Washington
The Husky Lines research project takes a mixed methods approach to identifying barriers to public transit usage for the student population of the Tacoma campus of the University of Washington. The first step was to illuminate existing public transit deserts and simultaneously implement a student survey to measure student perceptions of transit use. Based on these findings, the team is recommending new bus stops and bus lines to better serve the student population in an effort to increase usage of public transportation by the students. Taking this approach a step further, this specific study aims to collect perceptions of daily commute and actual daily commute patterns. A mobile application, tapping into built-in sensors, measures actual commute patterns and is augmented with a traditional travel diary to measure perception of commutes. Finally, this study provides an example of how mobile technology can be used to support transportation surveys.
View Slides »Mapping Air PopulationMichael Peterson, University of Nebraska at Omaha
Paul Hunt, University of Nebraska at Omaha
Air population refers to the total number of people flying above the earth at any point in time. These people form a distinct and separate population from those still physically connected to earth. Real-time air population can be estimated by using an extensive network of ground aircraft sensors based on ADS-B (Automated Dependent Surveillance-Broadcast). An aircraft determines its position via GPS and broadcasts its position along with its identification, aircraft type, altitude and speed. Most commercial passenger aircraft are equipped with ADS-B transponders. The total number of passengers is calculated by multiplying the number of seats for each aircraft by the current seat occupancy rate. Using this method, the estimated air population is determined for the contiguous airspace over the United States. The air population is further divided by each state. In the interactive, real-time mapping system, maps are provided to show total state air population and the density of air population.
View Slides »
Mapping Real-Time Flight Data -
10 Minute TalkPaul Hunt, University of Nebraska at OmahaMichael Peterson, University of Nebraska at Omaha
The Federal Aviation Administration (FAA) tracks flights through a combination of flight plans and radar. This data is publicly available through subscription to the FAA's National Aerospace System (NAS). Live flight data can be acquired by requesting access and connecting through a Virtual Private Network (VPN) into the FAA System Wide Information Management (SWIM). This daunting process requires security and hardware infrastructure and heavy coordination with FAA liaisons. Alternatively, many private companies, such as FlightAware, are already connected to SWIM and augment this information with a network of ground stations that acquire ADS-B signals from airplanes. A cloud-based system is demonstrated for mapping this data in real-time using a series of JavaScript AJAX requests. The requests return geographically referenced JavaScript Object Notation (GeoJSON) data that is mapped on-the-fly using web-based mapping APIs. The data is further analyzed to determine the number and type of planes flying above each US state.
