Implementation of a Low-Cost Data Acquisition System on an E-Scooter for Micromobility Research

Abstract

In recent years, cities are experiencing changes in the ways of moving around, increasing the use of micromobility vehicles. Bicycles are the most widespread transport mode and, therefore, cyclists’ behaviour, safety, and comfort have been widely studied. However, the use of other personal mobility vehicles is increasing, especially e-scooters, and related studies are scarce. This paper proposes a low-cost open-source data acquisition system to be installed on an e-scooter. This system is based on Raspberry Pi and allows collecting speed, acceleration, and position of the e-scooter, the lateral clearance during meeting and overtaking manoeuvres, and the vibrations experienced by the micromobility users when riding on a bike lane. The system has been evaluated and tested on a bike lane segment to ensure the accuracy and reliability of the collected data. As a result, the use of the proposed system allows highway engineers and urban mobility planners to analyse the behaviour, safety, and comfort of the users of e-scooters. Additionally, the system can be easily adapted to another micromobility vehicle and used to assess pavement condition and micromobility users’ riding comfort on a cycling network when the budget is limited.

BSafe-360: An All-in-One Naturalistic Cycling Data Collection Tool

“…Thus, combining several sensors to assemble one IB for real-world or virtual reality data collection can become extremely expensive. Even for low-cost DASs that do not use commercial sensors but require an IB-like assembly, such as the one proposed by Pérez-Zuriaga et al (2022) [ 28 ] (∼€395.00 or ∼$425.00), cost more than BSafe-360 (∼$300.00) without accounting for the bicycle/e-scooter purchase. These costs also hinder the production of multiple units.…”

Torque Measurement and Control for Electric-Assisted Bike Considering Different External Load Conditions

“…References Torque control for assisted E-bike [1,2,24,38,39] Instantaneous pedaling torque waveform [4][5][6][7] Pedaling torque component analysis [8][9][10][11][12][13][14] Factors affecting riding [15][16][17][18][19][20][21][22][23][25][26][27][28][29][30][31][32][33][34][35]] Recharge control for assisted E-bike [36,37] To overcome these limitations on existing E-bike assisted torque control, this paper's motivation is to find the best-suited assisted torque considering external loads. It is shown that the overall cycling torque is affected by external loads, including the cyclist's weight, wind resistance, rolling resistance, and the road slope.…”

“…In (19), ωk−1 is calculated based on the two prior time steps, t k−1 and t k−2 . It is noted that the position interpolation is under the average speed assumption in (19) without instantaneous motor acceleration and deceleration. For E-bike applications, this assumption is still valid during normal cycling conditions.…”

“…It has been noted that cycling quality is different with respect to the different physiological factors of cyclists. The sources in [ 15 , 16 , 17 , 18 , 19 , 20 , 21 ] detail that cycling quality can be affected by various factors. These include the cyclist’s sex, purpose, cadence, speed, acceleration, vibration, experience, as well as the weather.…”

“…By contrast, [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ] investigate the cyclist’s pedaling dynamic with no motor-assisted torque assumed. Moreover, [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ] focus on E-bike cycling performance with respect to human behaviors, including heartbeat, gender, and weight. The authors of [ 40 , 41 ] further evaluate recharge control for assisted motor output.…”