There is growing interest in formalized programs to collect data and understand bicycle activity. Bicycle transportation has become a central priority for transportation agencies invested in improving sustainability, livability, and public health outcomes. Constrained infrastructure spending has motivated research into understanding where bicycle improvements can yield the maximum net benefit in terms of increased ridership, comfort, and safety. The goal of encouraging new bicycle trips has also motivated research to understand what inadequacies may exist in current bicycle networks that could hinder the participation of less competent or confident cyclists. Crowdsourced data can be potentially used to gather data with finer granularity and to support the development of a new generation of bicycle related models. The first objective of this research project is to develop a smartphone application to fill some ODOT’s data gaps regarding cyclists’ routes, users, and comfort levels. While ORcycle is not the first smartphone application to collect bicycle travel data, it is the first statewide deployment of a smartphone application collecting bicycle specific safety data in addition to travel data. ORcycle has also added many user friendly features and increased the depth to which transportation planners and researchers can understand users’ characteristics and their cycling preferences. The second objective of this research project is to collect data to estimate cyclists Level of Traffic Stress (LTS) and data to prioritize infrastructure and safety improvements. The ultimate goal of this effort is to provide data and models that will support ODOT’s planning efforts to improve bicycle facilities and policies. The remainder of this report is organized as follows: Chapter 2: A review of existing LTS and Level of Service (LOS) literature and estimation methods. Chapter 3: A summary description of existing bicycle and infrastructure smartphone applications. Chapter 4: A review of ongoing efforts to collect cyclists and GIS data at the state, metropolitan, and local level in the state of Oregon. Chapter 5: An introduction to the ORcycle smartphone application developed in this project and a review of its basic parts: trips, safety reports, crash reports, cyclists’ socio-demographic data, and cyclists’ attitude. Chapter 6: Descriptive statistics for the ORcycle data collected during the pilot study between November 2014 and March 2015. This section also analyzes sample bias and includes an exploratory study of LTS utilizing a subset of ORcycle data. Chapter 7: Final chapter that describes potential applications of the ORcycle data such as LTS modeling, prioritization of network improvements, crash and injury risk models, determination of Oregon’s cyclists types, and improved route choice models. This chapter ends with lessons learned and final thoughts. (Author/publisher)
Samenvatting