Road grade estimates for bicycle travel analysis on a street network

Abstract

Road grade is a major factor influencing walking and cycling speed, energy expenditure, and travel behaviour. Bicycling research and commercial travel applications typically use digital elevation models to estimate road grades. The goal of this research is to determine the best methods of obtaining road grade data for bicycle travel analysis on an urban street network. Multiple elevation data sources are collected for eight sample locations in the city of Vancouver, Canada. Road grade extraction algorithms are applied and compared to precise ground surveying data. Estimates of cycling power and energy are used to assess the road grade accuracy in a specific analytical context. Road grades on all 35 elevated roadways of at least 30 m in the city are also characterized and classified into 3 modalities of road grade distribution. Results show that road elevations and grades extracted from raw Light Detection and Ranging (LIDAR) data are the most accurate where directly measured grades are unavailable. Interpolation of digital elevation models (DEM) and digital surface models (DSM) can provide adequate grade estimates on non-elevated roads, but are highly inaccurate on elevated structures, leading to substantial errors in cycling power and energy estimates. Short elevated network links (under 100 m) often have unimodal grade distributions which can be approximated with straight-line elevation profiles, but longer elevated structures (such as arched spans) have more grade variability. Recommendations are made for estimating grades on a network with and without LIDAR data, with the caveat that propagation of errors in grade data should be considered in walking and cycling analysis.