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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 1: Introduction to Scientific Computing for Planners, Engineers, and Scientists
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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...
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Summaries:
Identification and Characterization of PM2.5 and VOC Hot Spots on Arterial Corridor by Integrating Probe Vehicle, Traffic, and Land Use Data: The purpose of this study is to explore the use of integrated probe vehicle, traffic and land use data to identify and characterize fine particulate matter (PM2.5) and volatile organic compound (VOC) hot spot locations on urban arterial corridors. An emission hot spot is defined as a fixed location along a corridor in which the mean pollutant concentrations are consistently above the 85th percentile of pollutant concentrations when considering all other locations along the corridor during the same time period. In order to collect data for this study, an electric vehicle was equipped with instruments designed to measure PM2.5 and VOC concentrations. Second-by-second measurements were performed for each pollutant from both the right and left sides of the vehicle. Detailed meteorological, traffic and land use data is also...
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View Q&A: This document contains questions that were submitted during the webinar and the answers to them, which were not included in the broadcast due to time constaints.
Learn from experts and share your knowledge of how to count pedestrians. Are people with clipboards the only way? What technologies work and how can we use them? How can an agency improve an existing or start a new pedestrian count program? Join us for an information sharing webinar on this quickly evolving topic. We will learn from leaders in the field and encourage active audience involvement, so come prepared to share your experience!
This IBPI webinar is part of a project sponsored by FHWA to study best practices in pedestrian traffic monitoring.
Portland State University is working with ICF International and Sprinkle Consulting on a contract to the U.S. Department of Transportation, Federal Highway Administration to advise them on potential improvements to the Traffic Monitoring Guide specific to pedestrian travel.
Featuring:
- David Jones of the Federal Highway Administration will introduce the topic and...
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PRESENTATION SLIDES
Miss the presentation or want a look back? You can view the presentation slides here.
OVERVIEW
Every day transit riders ask the same question: when’s the next one coming? To answer this question, transit agencies are transitioning to providing real-time transit information through smartphones or displayed at transit stops.
The proliferation of transit planning and real time arrival tools that have hit the market over the past decade is staggering. Yet with transit ridership on the decline, agencies can’t afford to ignore the importance of providing accurate, real time information to their customers. Real-time transit information improves the reliability and efficiency of passenger travel, but barriers have prevented some transit agencies from adopting the GTFSrealtime v1.0 technology. A new NITC-funded study in May led by Sean Barbeau of the University of South Florida seeks to...
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PRESENTATION ARCHIVE
OVERVIEW
With so many Probe-Data Vendors in the market, and the fact that each offers their own unique solutions, it can be challenging to identify which vendor(s) would best meet the needs of an organization. Based on a study prepared for the Seattle Department of Transportation, this presentation will provide highlights around eight Probe-Data Vendors and their capabilities, limitations, and quality of data.
KEY LEARNING OUTCOMES
- An understanding of primary vendors that offer probe data and related products
- Key probe data sources used to put together data summaries
- An understanding of the types of platforms that exist and basic analytical capabilities
- Data quality considerations from each of the vendors offering probe data
SPEAKER
Scott Lee, CEO, IDAX Data Solutions
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Summary: A growing concern related to large-truck crashes has increased in the State of Texas in recent years due to the potential economic impacts and level of injury severity that can be sustained. Yet, studies on large truck involved crashes highlighting the contributing factors leading to injury severity have not been conducted in detail in the State of Texas especially for its interstate system. In this study, we analyze the contributing factors related to injury severity by utilizing Texas crash data based on a discrete outcome based model which accounts for possible unobserved heterogeneity related to human, vehicle and road-environment. We estimate a random parameter logit model (i.e., mixed logit) to predict the likelihood of five standard injury severity scales commonly used in Crash Records Information System (CRIS) in Texas – fatal, incapacitating, non-incapacitating, possible, and no injury (property damage only). Estimation findings indicate that the level of injury severity outcomes is highly influenced by a number of complex interactions between factors and the effects of the some factors can vary across observations. The contributing factors include drivers’ demographics, traffic flow condition, roadway geometrics, land use and temporal...
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Abstract: We propose to decompose residential self-selection by understanding its formation process. We take a life course perspective and postulate that locations experienced early in life have a lasting effect on our locational preferences in life. In other words, what was experienced spatially is a key factor contributing to our residential self-selection and our preferences in residential locations are formed long before our own self-selection begins. We further hypothesize that prior locational influence interacts with period effect such that the same location experienced in different periods may have distinct effects. Using an empirically collected dataset in the New York Metropolitan Region, we estimated a series of models to test these hypotheses. The results demonstrate that prior locational influence precedes residential self-selection. Furthermore, we show a variety-seeking behavioral pattern resulted from locations experienced during adolescence.