Abstract / Executive Summary

Urban bicycle usage has gained in importance across many cities with progressive transportation policies. According to \cite{hu_more_2017}, as of this paper's publication,  "there are more than 450,000 daily bike trips in New York City, up from 170,000 in 2005, an increase that has outpaced population and employment growth".  About one in five bike trips is by a commuter. Biking serves as an important transportation option to many around the world and we argue that the need for effective bicycle lane maintenance should be a top concern for municipalities.
Conventionally, street maintenance is an expensive, often inaccurate, and time intensive process that either uses subjective data prone to error or uses very precise data that is very expensive to collect citywide. There exists a clear need for cities to adopt a cost-effective and data intensive maintenance practice that can scale to the entire city and be performed frequently. These needs have been explored through the Street Quality Identification also known as the SQUID project \cite{adibhatla_digitizing_2016} to develop standardized methods for digital street inspection. This work extends the SQUID project and repurposes it for citywide bike lane measurements.
In this paper, we describe the development of a data and analytics framework to measure bicycle lane quality using street imagery and accelerometer data obtained from an open source smartphone application, OpenStreetCam (OSC)  \cite{telenav_openstreetmap_nodate} . This framework can be used in crowdsourced or situated settings with the overall purpose being the standardized measurement of citywide bike lane quality.

Introduction

In recent years, bike based mobility in cities around the world has been growing rapidly. Compared to cars, bikes are cheaper, safer, more sustainable, allow for higher traffic flows, and require far less space for parking. Many city governments have invested in a permanent bike-sharing infrastructure and programs in an effort to improve transit conditions within the urban core.