data science | National Institute for Transportation and Communities

Development of the Idaho Statewide Travel Demand Model Trip Matrices Using Cell Phone OD Data and Origin Destination Matrix Estimation

Topic: Airsage cell phone data and its application in travel modeling
Summary: As part of the initial phase of development for the Idaho Statewide Travel Demand Model, Parsons Brinckerhoff developed a base year auto and truck trip matrix using AirSage cell phone OD data, a statewide network in Cube, traffic counts, and origin-destination matrix estimation (ODME) procedures. To begin, the 4000+ statewide zone system was aggregated into a 700 super zone system for collecting the cell phone OD data. Next, the cell phone data was collected for the month of September 2013 for the following market segments: Average weekday resident HBW, HBO, NHB, and visitor NHB trips. The cell phone trips were then disaggregated to zones using each zone’s share of super zone population and employment. These initial trip matrices were assigned to the daily statewide network using free flow travel time for route impedance and iteratively adjusted to minimize the difference between the estimated link volumes and traffic counts by user class.

This iterative trip matrix balancing procedure, also known as ODME, converged nicely by user class and facility type and produced reasonable flows. The resulting trip matrix trip length frequencies matched fairly well with the...

Transit Scheduling/Routing Data Applications

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Abstract: TriMet collects detailed ridership data from automatic passenger counters on buses and trains. In addition, an automatic vehicle location system provides specific information on how well buses and trains adhere to preset schedules. This presentation is an overview of how TriMet uses these data in designing and managing the transit network, ranging from developing regional service policies to making minor schedule adjustments on a bus line.

Speaker Bio: Ken Zatarain is TriMet Director of Service Planning and Scheduling. He has had several other positions at TriMet. Prior to joining TriMet, he worked at the federal and local government levels. Ken has a degree in Regional and City Planning from the University of North Carolina.

PORTAL: Lessons from Developing an Archived Data User Service in Portland, Oregon

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Transportation and Road Weather

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Transportation and Road Weather

Where: Room 204 of the Distance Learning Center Wing of the Urban Center at PSU
Speaker: Rhonda Young, University of Wyoming
Topic: Transportation and Road Weather

Summary: Weather has a tremendous impact on the transportation system and is one of the largest contributors to non-recurring delay and increased crashes. Road weather is the a multidisciplinary area involving transportation engineers and meteorologists who are working to mitigate the negative effects of weather on the operation and maintenance practices of transportation systems. The talk covers the broad types of issues being studied in this field and looks at ways in which technology is playing a large part in the proposed solutions. The issue of weather as a complicating factor in the use of connected and autonomous vehicle technologies is also discussed.

Bio: Rhonda Young is an associate professor in the Department of Civil and Architectural Engineering at the University of Wyoming since 2002 and teaches graduate and undergraduate classes in Traffic Operations, Transportation Planning, Transportation Design and Traffic Safety. She completed her master and PhD degrees in Civil Engineering at the University of Washington and...

TriMet CAD / AVL System Data

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Abstract: TriMet has used a computer aided dispatch (CAD)/automatic vehicle location (AVL) system to manage bus and rail operations since the late 1990s. TriMet is currently in the process of updating the CAD/AVL system, and anticipates improvements in bus tracking and performance monitoring. This presentation will show how TriMet uses data from the system to support intelligent transportation systems (ITS) such as TransitTracker and automatic stop announcements in buses and trains, as well as to analyze transit operations such as on time performance and passenger loads.

Speaker Bios: Steve Callas is the Manager of Service and Performance Analysis at TriMet in Portland Oregon, where he is responsible for operations performance monitoring and analysis. This includes analyzing TriMet’s comprehensive automatic vehicle location and automatic passenger counter data archive. Additionally, Steve is involved in various transit operations research projection in conjunction with Portland State University and OTREC. Steve has been with TriMet for over 15 years.

David is an operations analyst with TriMet. He is involved in AVL data mining and analysis, safety analysis, automatic stop announcements, transit signal priority, and real-time...

The End of Commuting

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Data Science Course - Part 1: Introduction to Scientific Computing for Planners, Engineers, and Scientists

This summer we're hosting a two-part data science course. You can register for one or the other– or attend both parts at a discount: Data Science Course 2018, Part 2: Intermediate/Advanced Scientific Computing for Planners, Engineers, and Scientists.

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CLICK HERE TO REGISTER

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, ...

Assessing Impacts of Time Use on Children's Physical Fitness in Relation to Risk for Obesity and Diabetes

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Summary: Researchers from the transportation, planning and health fields share the common goal of promoting physically active lifestyle. One challenge that researchers often face is the measurement of physical activity, particularly among children. This is because the sporadic nature of children’s physical activity patterns makes it difficult to recall and quantify such activities. Additionally, children’s lower cognitive functioning compared to adults prevents them from accurately recalling their activities. This presentation will describe the design and application of a novel self-report instrument - the Graphs for Recalling Activity Time (GReAT) - for measuring children’s activity time use patterns. The instrument was applied in a study of children’s risk for obesity and diabetes in a predominately Hispanic community in Milwaukee, WI. Time-use data for two weekdays and one weekend day were collected for various physical and sedentary activities. The data was then assessed against measurements of the children’s cardiovascular fitness, weight status and insulin resistance through exploratory analysis and structured equation modeling. Findings on GReAT’s reliability and new evidence on the impacts of time-use in different activities on children’s risk for...

Adjustment Factors for Estimating Miles Traveled by Non-Motorized Traffic

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Abstract: Traffic counts are an important piece of information used by transportation planners; however, while count programs are common for motor vehicles most efforts at counting non-motorized traffic – cyclists and pedestrians – are minimal. Long-term, continuous counts of non-motorized traffic can be used to estimate month of year and day of week adjustment factors that can be used to scale short-duration counts to estimates of annual average daily traffic. Here we present results from continuous counts of non-motorized traffic at 6 locations on off-street trails in Minneapolis, MN using two types of automated counters (active infrared and inductive loop detectors). We found that traffic volumes varied significantly by location, but the month of year and day of week patterns were mostly consistent across locations and mode (i.e., cycling, walking, or mixed mode). We give examples of how this information could be used to extrapolate short-duration counts to estimates of annual average daily traffic as well as Bicycle Miles Traveled (BMT) and Pedestrian Miles Traveled (PMT) for defined lengths of off-street trails. More research is needed to determine if non-motorized traffic patterns (and subsequently our adjustment factors) for off-street...

The Future of Advanced Travel Demand Modeling: Positive Theory, Behavioral Model, and Process Data

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