GIFs to Augment Content Vocabulary for Hotel Management Learners

Gifs (Graphic Interchange Format) are looping videos with no sound and captures our attention. In this experimental study we decided to incorporate Gifs to develop learners’ content vocabulary. The study has chosen 60 students from two hotel management colleges. Research findings reveal that the Gifs did contribute to students’ improvement in vocabulary. Since they had active attention and grasped the vocabulary used though Gifs. Keywords: Graphic interchange format, Content and language integrated learning, content vocabulary, Vocabulary instruction

Active Transportation Counts From Existing On-Street Signal And Detection Infrastructure

The goal of agencies is to provide a safe and reliable multimodal transportation system that connects people and helps communities and economy thrive. In order to fulfill this mission, it is important to have a robust understanding of the usage of each mode within the system as well as the linkages between modes. Currently there is no systemic accounting of pedestrian traffic across the transportation network, making it difficult to understand systemic crash safety and prioritize projects. Analysts must rely on imperfect proxies such as land use density, race, income, and transit usage in the absence of measured pedestrian traffic data. A noteworthy possibility is to utilize existing traffic signal infrastructure to count pedestrians by using push-button actuation systems as permanent traffic counters. This presentation reports on the findings of a research study that sought to examine the feasibility of estimating pedestrian volumes from existing on-street infrastructure, such as pedestrian push-buttons, at a large scale.https://pdxscholar.library.pdx.edu/trec_seminar/1251/thumbnail.jp

Bayesian updating of simulated household travel survey data for small/medium metropolitan areas

This thesis tests an approach for generating simulated travel survey data that has local characteristics incorporated in it. Travel survey data are generally required for estimating and calibrating travel demand models for a region. The high cost associated with travel surveys puts them beyond the budget of most small/medium MPOs. Therefore simulation of travel survey data provides a viable alternative for these data starved regions to generate data. The simulated data is produced by combining socio-demographic data along with a national survey data set. Updating the simulated data distributions with the distributions obtained by surveying a small sample of local households, adds a local element to the simulated data set. The updating procedure using a small local sample of households is tested for two regions, which had previously conducted household travel surveys. The local sample was drawn from the travel survey and results obtained after updating were compared to those from the travel surveys in order to assess the performance of updating. Comparisons of trip attributes (trip rates, mode shares, departure times and trip lengths) in the two study areas show the updating has succeeded in bringing the updated values closer to the survey values in the majority of cases. The anomalies, which were seen in a few cases, were attributed to the lack of representativeness of the local sample, the inability of the simulation to capture all variations and the contextual differences between the regions. The concept of updating a simulated travel data set using local sample distributions in order to generate an updated simulated travel data set is explained here. While updating in general was found to move the updated trip attributes in the correct direction and towards the survey values, further testing such as comparing the population values estimated from the survey data and the updated simulated data need to be carried out in order to generate conclusive evidence on the benefit of updating. The main beneficiaries of this method are small/medium metropolitan areas who can use this method to produce synthetic travel data for running their travel demand models at a much lower expense

Webinar: Collecting and Analyzing Pedestrian Behaviors at Intersections Using LiDAR Tracking Technologies

The aim of this research is to investigate pedestrian behavior at signalized intersections using state-of-the-art LIDAR sensing technologies and to use this data, along with vehicular data, to develop a more effective multimodal signal control system. In the presentation, the presenters will describe a novel method of collecting pedestrian crossing behaviors at signalized intersection, discuss the findings and implications of data analytics, and introduce a novel approach to dynamic flashing yellow arrow mechanisms to reduce the conflict between left turn vehicles and crossing pedestrians.https://pdxscholar.library.pdx.edu/trec_webinar/1077/thumbnail.jp

Emission model sensitivity analysis: The value of smart phone weight-mile tax truck data

This research serves to evaluate the potential use of a system developed by the Oregon Department of Transportation (ODOT) for emission estimates. The data collection system developed by ODOT – Truck Road Use Electronics (TRUE) – includes a smart phone application with a Global Positioning System (GPS) device and microprocessor. Previous research with the TRUE data served to demonstrate its use for important ancillary applications such as highly accurate trip generation rates and m obility performance measures. In addition, it was shown that the TRUE data has strong potential use for safety, accessibility and connectivity, system condition and environmental stewardship performance measures. This new research builds on that past work and evaluates the potential use of the TRUE data for emissions estimates that take into account truck type details, truck weight and detailed speed profiles. A sensitivity analysis using the U.S. Environmental Protection Agency's (EPA) Motor Vehicle Emissi on Simulator 2010b (MOVES2010b) is performed in order to understand the level of error that might be encountered when such detailed data are not available. The impact of grade on emissions estimates is also considered. Results indicate that TRUE data in in tegration with Oregon Department of Transportation (ODOT) weight - mile tax (WMT) data will greatly improve the accuracy of emissions estimations at the project and regional level

Online Survey of Driver Comprehension of the Flashing Yellow Arrow for Right-Turn Signal Indications

This paper presents the results of an online survey of licensed driver comprehension of the right-turn signal displays with a focus on the flashing yellow arrow (FYA) and also including the circular green and red and red arrow. Recruitment postcards were mailed to a random sample of 9,872 residents in Oregon. The online survey yielded 399 responses. The open-ended responses were coded for comprehension and analyzed. The results suggest that FYA for right turns is well understood by Oregon drivers despite its current novelty (only two locations at the time of the research). Importantly, survey respondents were more likely to recognize the yielding requirement of the permissive movement and associate the yielding with pedestrians with the FYA over the circular green (CG) display. The research also confirmed that the expected driver response to the red arrow display for right turns is not well understood (only 52% of the respondents correctly stated the expected driver response). Binary logistic regression modeling revealed that the driver’s age and their educational level were significant factors in comprehension

New LiDAR System Pinpoints Pedestrian Behavior to Improve Eficiency and Safety at Intersections

Pedestrian safety is critical to improving walkability in cities. To that end, NITC researchers have developed a system for collecting pedestrian behavior data using LiDAR sensors. Tested at two intersections in Texas and soon to be tested at another in Salt Lake City, Utah, the new software created by a multi-university research team is able to reliably observe pedestrian behavior and can help reduce conflicts between pedestrians and vehicles at signalized intersections. The Utah Department of Transportation (UDOT) is already working on implementing this new LiDAR system to improve data collection at intersections

Understanding Factors Affecting Arterial Reliability Performance Metrics

In recent years, the importance of travel time reliability has become equally important as average travel time. However, the majority focus of travel time research is average travel time or travel time reliability on freeways. In addition, the identification of specific factors (i.e., peak hours, nighttime hours, etc.) and their effects on average travel time and travel time variability are often unknown. The current study addresses these two issues through a travel time-based study on urban arterials. Using travel times collected via Bluetooth data, a series of analyses are conducted to understand factors affecting reliability metrics on urban arterials. Analyses include outlier detection, a detailed descriptive analysis of select corridors, median travel time analysis, assessment of travel time reliability metrics recommended by the Federal Highway Administration (FHWA), and a bivariate Tobit model. Results show that day of the week, time of day, and holidays have varying effects on average travel time, travel time reliability, and travel time variability. Results also show that evening peak hours have the greatest effects in regards to increasing travel time, nighttime hours have the greatest effects in regards to decreasing travel time, and directionality plays a vital role in all travel time-related metrics