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

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

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

Assessing the Impact of Three Intersection Treatments in a Bicycling Simulator

Bicyclist safety at urban intersections is a critical element for encouraging an increase in bicycle commuting. With cyclist injury and fatality rates rising due to collisions with vehicles at signalized intersections, increasing the safety of riders continues to be an important consideration when promoting this mode of transportation. Previous research has addressed crash causality and helped to develop several roadway treatments to improve bicyclist safety, but little has been done to compare and contrast the benefits of the various treatment types. This bicycling simulator study examined the impacts of three different intersection treatments (i.e., bike box, mixing zone, and bicycle signals) to better understand their influence on bicyclists\u27 comfort, levels of stress, and riding behaviors. This improved understanding allowed researchers to make recommendations for which of the three designs proved to be most effective for reducing the risk of vehicle-bicycle collisions at signalized intersections. Forty participants successfully completed the study by responding to twenty-four scenarios while riding in the Oregon State University Bicycling Simulator. Time-space measurements revealed that the mixing zone treatment correlated with the most unpredictable riding behaviors. Analysis of the participants\u27 eye-movements revealed a lower rate of recognizing the conflict vehicle when traversing the bicycle signal treatments. Galvanic Skin Response measurements were used to measure participants stress levels but found no statistically significant results, although it was found that the mixing zone elicited slightly larger stress responses. Researchers found the bike box design to be the most versatile, providing a balance of increased safety while also requiring the participant to perceive potential danger and be ready to respond accordingly. The results of this research can provide a better understanding of how to best implement these intersection treatments to increase bicyclists\u27 safety at signalized intersections

Driver and Bicyclist Comprehension of Blue Light Detection Confirmation Systems

This study analyzed motorist and bicyclist understanding and preference of positive confirmation of detection of a bicycle by the traffic signal infrastructure using a blue light detection confirmation (BLDC). The research analyzed results of an online survey of 1,123 respondents and intercept survey of 337 respondents. The study initially found that participants of the survey did not understand the meaning of the blue light itself, but comprehension of the system rose from 40% to 50% when supplemental signs were used. Respondents overwhelmingly indicated that they preferred the sign option that included symbols, text, and a representation of the blue light, in comparison with the sign options that only included symbol and text, or text and blue dot. Additionally, respondents indicated that they “strongly agree” that the supplemental signage helped with understanding the purpose of the detection confirmation devices, that they would support the system at intersections, and that it made them feel better about waiting at an intersection with light. Including supplemental signage with the symbol, text, and blue dot could potentially improve the riding experience for users, as it was strongly preferred among the alternative sign options that were tested; however, further evaluation of sign configurations may be warranted

Multimodal Data at Signalized Intersections: Strategies for Archiving Existing and New Data Streams to Support Operations and Planning & Fusion and Integration of Arterial Performance Data

There is a growing interest in arterial system management due to the increasing amount of travel on arterials and a growing emphasis on multimodal transportation. The benefits of archiving arterial-related data are numerous. This research report describes our efforts to assemble and develop a multimodal archive for the Portland-Vancouver region. There is coverage of data sources from all modes in the metropolitan region; however, the preliminary nature of the archiving process means that some of the data are incomplete and samples. The arterial data sources available in the Portland-Vancouver region and that are covered in this report include data for various local agencies (City of Portland, Clark County, WA, TriMet and C-TRAN) covering vehicle, transit, pedestrian, and bicycle modes. We provide detailed descriptions of each data source and a spatial and temporal classification. The report describes the conceptual framework for an archive and the data collection and archival process, including the process for extracting the data from the agency systems and transferring these data to our multimodal database. Data can be made more useful though the use of improved visualization techniques. Thus as part of the project, a number of novel, online visualizations were created and implemented. These graphs and displays are summarized in this report and example visualizations are shown. As with any automated sensor system, data quality and completeness is an important issue and the challenge of automating data quality is large. Preliminary efforts to validate and monitor data quality and automate data quality processing are explored. Finally, the report presents efforts to combine transit and travel time data and signal timing and vehicle count data to generate some sample congestion measures

Pedestrian Behavior Study to Advance Pedestrian Safety in Smart Transportation Systems Using Innovative LiDAR Sensors

Pedestrian safety is critical to improving walkability in cities. Although walking trips have increased in the last decade, pedestrian safety remains a top concern. In 2020, 6,516 pedestrians were killed in traffic crashes, representing the most deaths since 1990 (NHTSA, 2020). Approximately 15% of these occurred at signalized intersections where a variety of modes converge, leading to the increased propensity of conflicts. Current signal timing and detection technologies are heavily biased towards vehicular traffic, often leading to higher delays and insufficient walk times for pedestrians, which could result in risky behaviors such as noncompliance. Current detection systems for pedestrians at signalized intersections consist primarily of push buttons. Limitations include the inability to provide feedback to the pedestrian that they have been detected, especially with older devices, and not being able to dynamically extend the walk times if the pedestrians fail to clear the crosswalk. Smart transportation systems play a vital role in enhancing mobility and safety and provide innovative techniques to connect pedestrians, vehicles, and infrastructure. Most research on smart and connected technologies is focused on vehicles; however, there is a critical need to harness the power of these technologies to study pedestrian behavior, as pedestrians are the most vulnerable users of the transportation system. While a few studies have used location technologies to detect pedestrians, this coverage is usually small and favors people with smartphones. However, the transportation system must consider a full spectrum of pedestrians and accommodate everyone. In this research, the investigators first review the previous studies on pedestrian behavior data and sensing technologies. Then the research team developed a pedestrian behavioral data collecting system based on the emerging LiDAR sensors. The system was deployed at two signalized intersections. Two studies were conducted: (a) pedestrian behaviors study at signalized intersections, analyzing the pedestrian waiting time before crossing, generalized perception-reaction time to WALK sign and crossing speed; and (b) a novel dynamic flashing yellow arrow (D-FYA) solution to separate permissive left-turn vehicles from concurrent crossing pedestrians. The results reveal that the pedestrian behaviors may have evolved compared with the recommended behaviors in the pedestrian facility design guideline (e.g., AASHTO’s “Green Book”). The D-FYA solution was also evaluated on the cabinet-in-theloop simulation platform and the improvements were promising. The findings in this study will advance the body of knowledge on equitable traffic safety, especially for pedestrian safety in the future