Global positioning system (GPS)

CURA hosting public lectures on data-driven urban science and planning, October 20 and 22

The Center for Urban and Regional Analysis (CURA) at The Ohio State University is pleased to announce two special lectures on data-driven urban science and planning :

Tuesday, October 20th, 3:30pm – 5:00pm, “Big steps” in Knowlton Hall.  Robert Goodspeed, University of Michigan.  Tools of Collaborative Inquiry: A discussion of the use of geographic information systems (GIS) and planning support systems for more intelligent urban planning.

Dr. Goodspeed is an Assistant Professor in the Taubman College of Architecture & Urban Planning, University of Michigan.  He has been named as “Leading Thinker in Urban Planning and Technology” by Planetizen.

Thursday, October 22nd,  12:00 – 1:30pm, Derby Hall 1080. Martin Raubal, Swiss Federal Institute of Technology (ETH).  Investigating human behavior in urban environments: How novel data sources, methods, and technologies provide opportunities for scientists to investigate human mobility and behavior in urban environments.

Dr. Raubal is a Professor in the Department of  Civil, Environmental and Geomatic Engineering at ETH in Zurich, Switzerland.  He is one of the world’s leading experts on mobile GIS, location based services and cognitive engineering for geospatial services.  Pizza and beverages will be provided!

Please join us for this opportunity to hear about the data-driven revolution in urban science and planning!

More information here:  https://cura.osu.edu/events

Estimating the most likely space–time paths, dwell times and path uncertainties from vehicle trajectory data: A time geographic method

Tang J, Song Y, Miller HJ, Zhou X (2015) “Estimating the most likely space–time paths, dwell times and path uncertainties from vehicle trajectory data: A time geographic method,” Transportation Research Part C, http://dx.doi.org/10.1016/j.trc.2015.08.014

Highlights
• Develop a time-dependent graph model to estimate their likely space–time paths.
• Find network-time paths, link travel times and dwell times at possible intermediate stops.
• Develop a dynamic programming algorithm for both offline and real-time applications.
• Use the potential path area for all feasible network–time paths to estimate path uncertainty.

Abstract.  Global Positioning System and other location-based services record vehicles’ spatial locations at discrete time stamps. Considering these recorded locations in space with given specific time stamps, this paper proposes a novel time-dependent graph model to estimate their likely space–time paths and their uncertainties within a transportation network. The proposed model adopts theories in time geography and produces the feasible network–time paths, the expected link travel times and dwell times at possible intermediate stops. A dynamic programming algorithm implements the model for both offline and real-time applications. To estimate the uncertainty, this paper also develops a method based on the potential path area for all feasible network–time paths. This paper uses a set of real-world trajectory data to illustrate the proposed model, prove the accuracy of estimated results and demonstrate the computational efficiency of the estimation algorithm.