Accurate estimates of bicycle and pedestrian volume inform safety studies, trend monitoring, and infrastructure improvements. The Federal Highway Administration’s Traffic Monitoring Guide advises current practice for estimation of nonmotorized traffic. While methodologies have been developed to minimize error in estimation of annual average daily nonmotorized traffic (AADNT), challenges persist. This study provides new guidance for monitoring and volume estimation of nonmotorized traffic. Using continuous count data from 102 sites across six cities, the findings confirm that mean absolute percent error (MAPE) in estimated AADNT is minimized when seven-day short duration counts are collected in June through September and for 24-h counts, when data are collected Tuesdays through Thursdays (except for pedestrian-only counts). MAPE across all days (except holidays) and seasons was 34% for 24-h and 20–22% for seven-day short duration counts. The magnitude of bicycle and pedestrian volumes did not significantly affect estimation errors. For factor groups larger than one counter, the length of short duration samples may influence accuracy of AADNT estimates more than the number of counters per group, all else equal. To maximize precision of estimates of AADNT, four or more counters per factor group for bicycle and five or more for pedestrian travel monitoring are recommended. These findings provide guidance for practitioners seeking to establish or improve nonmotorized traffic monitoring programs.

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Modeling procedures in transportation planning depend on the quality of data collected from personal travel surveys, which in turn depend on the data-collection technique. All conventional data-collection techniques rely on respondents to report the time, distance, and location attributes of a trip, among other things. Respondents rarely know addresses that they visit with sufficient detail to permit accurate geocoding. Also, it has been observed that short trips are underreported. Earlier studies proved the feasibility of using the Global Positioning System (GPS) as an alternative to acquire error-free, high-quality information on trip-making behavior. However, all GPS survey methodologies tested relied on the respondent to enter information into a personal data assistant (PDA) as the trip is being made and to intervene in other ways to record all data for each trip. This adds the expense of a PDA and its power supply and puts a burden on the respondent. A method that uses GPS technology with less complexity, involving less cost and minimal user intervention while making the trip, is tested and explained. Additional trip attributes that cannot be recorded by the GPS receiver were obtained after the survey period by prompted recall, in which the respondents were aided with maps displaying their travel paths. Analysis of the data showed that this method performed very well. However, a still-larger survey is needed to estimate the benefits.

This research explored driver comprehension and behaviors in Oregon with respect to right-turn signal displays focusing on the Flashing Yellow Arrow (FYA) in a driving simulator. A counterbalanced, factorial design was chosen to explore three independent variables: signal indication type and active display, length of the right-turn bay, and presence of pedestrians. Driver decision-making and visual attention were considered. Data were obtained from 46 participants (21 women, 25 men) turning right 736 times in 16 experimental scenarios. A Mixed-effects Ordered Probit Model and a Linear mixed model were used to examine the influence of driver demographics on observed performance. Results suggest that the FYA indication improves driver comprehension and behavioral responses to the permissive right-turn condition. When presented with the FYA indication in the presence of pedestrians, nearly all drivers exhibited caution while turning and yielding to pedestrians and stopping when necessary. For the same turning maneuver, drivers presented with a circular green (CG) indication were less likely to exhibit correct behavior. At least for Oregon drivers, another clear finding was a general lack of understanding of the steady red arrow (SRA) display for right turns. Most drivers assume the SRA indication requires a different response than the circular red (CR) and remain stopped during the entire red interval, thus resulting in efficiency losses. These findings suggest that transportation agencies could potentially improve driver yielding behavior and pedestrian safety at signalized intersections with high volumes of permissive right turns from exclusive right-turn lanes by using the FYA display in lieu of a steady CG display.

Over the last decade, the Oregon DOT and other agencies have systematically implemented many pedestrian crossing enhancements (PCEs) across the state. This study explored the safety performance of these enhanced crossing in Oregon. Detailed data were collected on 191 crossings. Supplemental data items included crossing location information, route characteristics, surrounding land use and crossing enhancement descriptions. Pedestrian volume at the crossing locations was a highly desirable but unavailable data element. To characterize pedestrian activity, a method was developed to estimate ranges for pedestrian crosswalk activity levels based on the land use classification at the census block level and the presence of pedestrian traffic generators such as bus stops, schools, shopping centers and hospitals within a 0.25-mile radius. Each crosswalk was categorized into one of six levels of activityvery low, low, medium-low, medium, mediumhigh and high. Crash data for the 2007-2014 period were assembled for the safety analysis. After filtering, 62 pedestrian crashes and 746 rear-end crashes were retained for further analysis. The crash data were merged and analyzed. Crash patterns and risk ratios were explored. The most important trend observed was a shift (reduction) in the pedestrian crash severity after the installation of the crosswalk treatments. This shift was from fatal and injury A crash type to lower severity crashes of injury B and injury C. For pedestrian crashes, increases in the risk ratio were observed for increases in the number of lanes, the posted speed, and estimated pedestrian activity level. Similar trends were observed for rear-end crashes. Due to data limitations, subsequent safety analysis focused on installations of RRFB crossing enhancements. A CMF for RRFB installations was estimated. The CMFS for pedestrian crashes are 0.64 +/-0.26 using a simple before-after analysis; for rear-end crashes: 0.93 +/-0.22 using an empirical Bayes analysis approach.

Bicycling and walking have gained increased attention recently; however, systematic bicycle and pedestrian counts are still scarce. At intersections, transportation agencies are interested in counting bicycles and pedestrians and leveraging for counting purposes, if possible, existing signal detection equipment. This study evaluated four counting technologies: inductive loops and a thermal camera to count bicycles and passive infrared counters and pedestrian signal actuation data to count pedestrians. The four technologies were tested in a parking lot (controlled environment) and in an intersection (real-world environment). The findings revealed that while the inductive loops and thermal camera counted bicycles accurately in a controlled environment, the loops and cameras failed to do so at an intersection. Passive infrared counters were found to count pedestrians accurately at the intersection sidewalk, and pedestrian signal actuation data could be a cost-effective surrogate for pedestrian demand at signalized intersections.

Accurate travel time prediction–estimation is important for advanced traveler information systems and advanced traffic management systems. Traffic managers and operators are interested in estimating optimal sensor density for new construction and retrofits. In addition, with the development of vehicle-tracking technologies, they may be interested in estimating optimal probe vehicle percentage. Unlike most studies focusing on data-driven models, this paper extends some limited previous work and describes a concept developed from first principles of traffic flow. The goal is to establish analytical relationships between travel time prediction–estimation accuracy and sensor spacing by means of two basic travel time prediction–estimation algorithms, as well as to probe vehicle penetration rate. The methods are based on computing the magnitude of under- and overprediction–estimation of total travel time (TTT) during shock passages in a time–space plane by using the midpoint method for online travel time prediction and the Coifman method for offline travel time estimation. Three shock wave configurations are assessed with each method so as to consider representative traffic dynamics situations. TTT prediction–estimation errors are calculated and expressed as a function of sensor spacing and probe vehicle percentage. Optimal sensor spacing is calculated with consideration of the tradeoff between TTT estimation error and sensor deployment cost. The results from this study can provide simple and effective support for detector placement and probe vehicle deployment, especially along a freeway corridor with existing detectors. Optimal sensor spacing results are analyzed and compared for various methods of travel time estimation during different types of shock waves.