This session includes papers that describe innovations in collecting and analyzing pedestrian and bicyclist data, including crowdsource data, GPS data from bicyclists, using LiDAR for counting pedestrians, and an innovative method to expand pedestrian counts.
Bicycle Safety Analysis at Intersections Using Crowdsourced Data
Moatz Saad, University of Central FloridaShow Abstract
Mohamed Abdel-Aty, University of Central Florida
Jaeyoung Lee, University of Central Florida
Qing Cai, University of Central Florida
Cycling is encouraged in countries around the world as an economic, energy efficient, and sustainable mode of transportation. Although there are many studies that focused on analyzing bicycle safety, they have limitations because of the shortage of bicycle exposure data. This study represents a major step forward in estimating safety performance functions for bicycle crashes at intersections by using crowdsourced data from STRAVA. Several adjustments considering the population distribution and field observations were made to overcome the disproportionate representation of the STRAVA data. The adjusted STRAVA data which entails bicycle exposure information was used as input to develop safety performance functions. The functions are negative binomial models aimed at predicting frequencies of bicycle crashes at intersections. The developed model was compared with three counterparts: the model using the un-adjusted STRAVA data, the model using the STRAVA data with field observation data adjustments only, and the model using the STRAVA data with adjusted population. The results revealed that the case of STRAVA data with both population and field observation data adjustments had the best performance in bicycle crash modeling. The results also addressed several key factors (e.g., signal control system, intersection size, bike lanes), which are associated with bicycle safety at intersections. Additionally, the safety-in-numbers effect was acknowledged when bicycle crash rates decreased as bicycle activities increased. The study concluded that crowdsourced data is a reliable source for exploring bicycle safety after the appropriate adjustments.
Extraction and Investigation of Bicycle Cruising Speeds from GPS Data Using Time-Series Clustering
Elmira Berjisian, University of British ColumbiaShow Abstract
Alexander Bigazzi, University of British Columbia
Cycling speed is an important variable for infrastructure design, mode and route choice models, traffic simulation, safety and health impact assessments, and more. Most of these applications currently assume a constant average or desired cycling speed; a better understanding of how and why cycling speeds vary is needed. Several studies have reported and modeled observed cycling speeds. This study is unique in specifically investigating cruising speed, defined as a steady-state speed that a cyclist maintains given a fixed set of environmental factors. Cruising or desired speed is needed for microsimulation and speed choice modeling, and can provide insight into microscopic cycling behavior. Three time-series clustering methods are applied to identify cruising, idling, acceleration, and deceleration events in 230 hours of naturalistic bicycle travel data collected by GPS in Vancouver, Canada. Stability and consistency of the clustering and extracted cruising speeds are examined, and relationships with personal, environmental, and trip variables are investigated using mixed-effect models. Average cruising speed on level ground is 18 km/hr (4 km/hr standard deviation among cyclists), falling by 0.3 km/hr per 1% grade. Inter-personal variation in cruising speed is high, including individual sensitivity to road grade. The cruising speed extraction method can be used to investigate cruising speeds in other contexts (different cities, terrain, seasons, road surfaces, bicycle types, populations, etc.), and the speed results can be used in cycling facility design, traffic microsimulation models, and other applications. In future work, a bicycle speed choice model will be calibrated to the measured cruising speeds.
Development and Evaluation of a 2D LiDAR Real-Time Pedestrian Counting System for High-Volume Conditions
Asad Lesani, McGill UniversityShow Abstract
Ehsan Nateghinia, McGill University
Luis Fernando Miranda-Moreno, McGill University
Counting high pedestrian volumes in non-motorized facilities has been a challenge. There are several methods and technologies that may address this demand, including manual counting in the field or from video data, and automated counting using sensors such as video, laser, etc. This paper introduces a real-time system for counting high pedestrian volumes in non-motorized facilities employing Laser Technology Light Detection and Ranging (LiDAR). The proposed system processes distance measurements from a two-dimensional LiDAR sensor with 16 distinct laser channels and an angular resolution of 3 degrees between each channel. The counting system processes the distance measurements using a proposed clustering algorithm to detect, count, and identify the directions of pedestrians. The system’s performance has been evaluated by comparing its directional counting results with manually counted ground truth data at the disaggregate and aggregate (15-minutes i¬ntervals) levels at two different sidewalks. The results showed that the proposed system accurately counted more than 97% of the pedestrians at the disaggregate level, with less than 1.1% of them experiencing false direction detection. The results also revealed that the over-count error is less than 0.65% and that the under-count errors are around 1.3% and 2.7% for the two selected sites. At the aggregate level (15-minutes intervals), the average absolute percentage deviations (AAPDs) were around 1.6% and 4.3% while the weighted AAPDs (the weights are the ratio of pedestrian volumes at each time interval to the total volume) were less than 1.5% and 3.5% for the first and second sites, respectively.
Pedestrian Count Expansion Methods: Bridging the Gap Between Land Use Groups and Empirical Clusters
Aditya Medury, Safe Transportation Research and Education CenterShow Abstract
Julia Griswold, University of California, Berkeley
Louis Huang, University of California, Berkeley
Offer Grembek, University of California, Berkeley
Count expansion methods are a useful tool for creating long-term pedestrian or bicyclist volume estimates from short-term counts for safety analysis or planning purposes. Expansion factors can be developed based on the trends from automated counters set up for long periods of time. Evidence has shown that the activity patterns can vary between sites so that there is potential to create more accurate estimates by grouping similar long-term count trends into factor groups. There are two common approaches to developing factor groups in pedestrian and bicycle count expansion studies. The land use classification approach has the advantage of being simple to apply to short-term count locations based on attributes of the surrounding area, but it requires assumptions by the researchers about which characteristics correlate with different activity patterns. Empirical clustering approaches can potentially create more distinct clusters by effectively matching locations with similar patterns, but they do not present an easy way to apply the resulting factor groups to appropriate short-term count sites. This study connects the two approaches and takes advantage of the benefits of both by using objective measures of the surrounding land use to model to model membership in the empirical cluster groups.