As data collection programs grow, cities need a way to systematically deploy counting equipment in a way that ensures robust pedestrian and bicyclist volume data are collected across a spectrum of use patterns and infrastructure contexts. This paper presents the findings from a deep dive into pedestrian and bicyclist volumes and exposure, including statistical modeling, as well as translating the outputs into an algorithm for systematically growing Seattle Department of Transportation’s nonmotorized count data collection program. The data collection location prioritization algorithm described in this paper provides a roadmap for cities and other agencies as they build their nonmotorized data collection programs.

随着数据收集计划的发展，城市需要一种方法来系统地部署计数设备，以确保在各种使用模式和基础设施环境中收集可靠的行人和骑自行车者的体积数据。本文介绍了深入研究行人和骑自行车者的数量和接触情况的发现，包括统计模型，以及将输出结果转换为系统地发展西雅图交通局非机动车计数数据收集程序的算法。本文描述的数据收集位置优先算法为城市和其他机构构建非机动车数据收集程序提供了路线图。