A Novel Acceleration Signal Processing Procedure for Cycling Safety Assessment

Abstract

With the growing rate of urban population and transport congestion, it is important for a city to have bike riding as an attractive travel choice but one of its biggest barriers for people is the perceived lack of safety. To improve the safety of urban cycling, identification of high-risk location and routes are major obstacles for safety countermeasures. Risk assessment is performed by crash data analysis, but the lack of data makes that approach less effective when applied to cyclist safety. Furthermore, the availability of data collected with the modern technologies opens the way to different approaches. This research aim is to analyse data needs and capability to identify critical cycling safety events for urban context where bicyclist behaviour can be recorded with different equipment and bicycle used as a probe vehicle to collect data. More specifically, three different sampling frequencies have been investigated to define the minimum one able to detect and recognize hard breaking. In details, a novel signal processing procedure has been proposed to correctly deal with speed and acceleration signals. Besides common signal filtering approaches, wavelet transformation and Dynamic Time Warping (DTW) techniques have been applied to remove more efficiently the instrument noise and align the signals with respect to the reference. The Euclidean distance of the DTW has been introduced as index to get the best filter parameters configuration. Obtained results, both during the calibration and the investigated real scenario, confirm that at least a GPS signal with a sampling frequency of 1Hz is needed to track the rider’s behaviour to detect events. In conclusion, with a very cheap hardware setup is possible to monitor riders’ speed and acceleration.

Smart Electrically Assisted Bicycles as Health Monitoring Systems: A Review

“…This information correlated that the heart rate can be used to estimate the state of fatigue of the cyclist [52,53,57,65,68] as well as the road difficulty to assist the user in the best manner and reduce strain during their travels. GPS and accelerometer data are mainly used for road classification as slope recognition (flat, uphill, and downhill) and road segmentation [72,73], however they can be used in other applications (e.g., fall detection [74][75][76]).…”

Road cycling safety scoring based on geospatial analysis, computer vision and machine learning

“…Murgano et Al. [16] introduce a novel methodology to detect safety events (e.g., abrupt braking) Fig. 1 a Incident causes from reported incidents since 2016 in the UCI incident database.…”

Observed Risk and User Perception of Road Infrastructure Safety Assessment for Cycling Mobility

“…For the collection of internal and external cycle data, an IPB (Figure 3) was equipped with a GNSS-video system (Video Vbox Lite ® , Buckinghamshire, UK) and a smartphone [36].…”

“…For the collection of internal and external cycle data, an IPB (Figure 3) was e with a GNSS-video system (Video Vbox Lite ® , Buckinghamshire, UK) and a sm [36]. The Video Vbox Lite (VVL) provided GNSS NMEA data (latitude, longitud tion, speed, heading) with a frequency of 10 Hz synchronized with a video record two cameras.…”