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For the third year, we're hosting our two-part data science course in Portland, OR. You can register for one part or the other– or attend both at a discount: Data Science Course - Part 2: Intermediate Scientific Computing for Planners, Engineers, and Scientists
Did you ever feel you are “drinking from a hose” with the amount of data you are attempting to analyze? Have you been frustrated with the tedious steps in your data processing and analysis process and thinking, “There’s gotta be a better way to do things”? Are you curious what the buzz of data science is about? If any of your answers are yes, then this course is for you.
Classes will all be hands-on sessions with lecture, discussions and labs. Participants can choose to sign up for one or both courses. For more information, download the syllabus (PDF). This course was developed as part of a NITC education project: Introduction to Scientific Computing for Planners, Engineers, and Scientists.
Agenda: Part One -...
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Summary: Where and when does overcrowding happen on TriMet's bus network? Which routes have the best on-time performance? Portland State University and TriMet have collaborated to make this kind of data available to anybody through Portal, PSU's transportation data archive for the Portland/Vancouver region. This presentation will cover the use of General Transit Feed Specification (GTFS) data for mapping TriMet’s performance data and the development of Portal’s innovative transit application. In the MAP-21 era of performance management, see how tools like Portal can support enhanced agency decision-making as well as community engagement.
Bio: Jon Makler researches and teaches about transportation planning and engineering at Portland State University. His research portfolio centers on intelligent transportation systems, including how they can be harnessed to benefit the environment and how the data they generate can support operational strategies and planning decisions. Since moving to Oregon 9 years ago, he has worked at Metro, the City of Portland and OTREC, the federally-funded research center housed at PSU. His previous employers were the North Jersey Transportation Planning Authority, the Harvard Kennedy School, IBI Group and Sarah...
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Abstract: The concept of accessibility has long been theorized as a principal determinant of household residential choice behavior. Research on this influence is extensive but the empirical results have been mixed, with some research suggesting that accessibility is becoming a relatively insignificant influence on housing choices. Further, the measurement of accessibility must contend with complications arising from the increasing prevalence of trip-chains, non-work activities, and multi-worker households, as well as reconcile person-specific travel needs with household residential decisions. This paper contributes to the literature by addressing the gap framed by these issues and presents a novel residential choice model with three main elements of innovation. First, it operationalized a time-space prism (TSP) accessibility measure, which the authors believe to be the first application of its kind in a residential choice model. Second, it represented the choice sets in a building-level framework, the lowest level of spatial disaggregation available for modeling residential choices. Third, it explicitly examined the influence of non-work accessibility at both the local- and person-level. This residential choice model was applied in the...
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The City of Portland is exploring how distributed “Internet of Things” (IoT) sensor systems can be used to improve the available data that is usable by city engineers, planners, and the public to help inform transportation operations, enable assessments of public health and equity, advance Portland’s Climate Action Plan goals, and...
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For the third year, we're hosting our two-part data science course in Portland, OR. You can register for one part or the other– or attend both at a discount: Data Science Course, Part 1: Introduction to Scientific Computing for Planners, Engineers, and Scientists
Did you ever feel you are “drinking from a hose” with the amount of data you are attempting to analyze? Have you been frustrated with the tedious steps in your data processing and analysis process and thinking, “There’s gotta be a better way to do things”? Are you curious what the buzz of data science is about? If any of your answers are yes, then this course is for you.
Classes will all be hands-on sessions with lecture, discussions and labs. Participants can choose to sign up for one or both courses. For more information, download the syllabus (PDF). This course was developed as part of a NITC education project: Introduction to Scientific Computing for Planners, Engineers, and Scientists.
Agenda: Part Two - Intermediate Course
- Transforming, visualizing, and modeling data
- ...
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Summary: Real-world traffic trends observed in PORTAL and INRIX traffic data are used to expand the performance measures that can be obtained from Portland Metro's travel demand model to include the number of hours of congestion that can be expected during a typical weekday and travel time reliability measures for congested freeway corridors.
Bio: Michael Mauch, a senior data analyst and project manager with DKS Associates, has over 20 years of experience in transportation data analysis, applications programming, mathematical model building and transportation demand forecasting. Over the years, Mike has been project manager and has led the technical analyses for numerous large transportation data collection and data analysis projects including BRT and rail transit studies, CIP updates, transportation corridor studies, trip and parking generation studies, corridor capacity analysis, General and Master Plan Updates, incident management cost effectiveness analysis and numerous EIRs. In addition to working with DKS, Mike currently holds a variable-time position as a Research Engineer with UC Berkeley’s Institute of Transportation Studies. He has taught “Traffic Flow Theory”, “Transit Operations”, and “Computer Programming & Numerical...
Read moreThe proliferation of information technology in the transportation field has opened up opportunities for communication and analysis of the performance of transportation facilities. The Highway Capacity Manual relies on rules of thumb and small data samples to generate levels of service to assess performance, but modern detection technology gives us the opportunity to better capture the dynamism of these systems and examine their performance from many perspectives. Travelers, operations staff, and researchers can benefit from measurements that provide information such as travel time, effectiveness of signal coordination, and traffic density. In particular, inductive loop detectors show promise as a tool to collect the data necessary to generate such information. But while their use for this purpose on restricted‐access facilities is well understood, a great many challenges remain in using loop detectors to measure the performance of surface streets.
This thesis proposes 6 methods for estimating arterial travel time. Estimates are compared to simulated data visually, with input/output diagrams; and statistically, with travel times. Methods for estimating travel time are applied to aggregated data and to varying detector densities and evaluated as above. Conclusions are drawn about which method provides the best estimates, what levels of data aggregation can still provide useful information, and what the effects of detector density are on the quality of estimates....
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