Combining environmental quality assessment of bicycle infrastructures with vertical acceleration measurements

Nuñez, Javier Yesid Mahecha; Bisconsini, Danilo Rinaldi; Silva, Antônio Nélson Rodrigues da

doi:10.1016/j.tra.2018.10.032

Cited by 13 publications

(11 citation statements)

References 29 publications

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“…In order to register this variable, Bil et al [ 11 ] used a system with a GPS and a data logger with accelerometers (MSR145s); Chou et al [ 12 ] equipped a bicycle with an accelerometer, a distance measurement instrument, a speed meter, and a data acquisition system; and Ambroz et al [ 13 ] used a system based on Raspberry Pi with two tri-axial accelerometers, a wire potentiometer, and a magnetic reed-switch based speed sensor. In addition to these sensors, vertical acceleration measurements for assessing the quality of bicycle infrastructures have also been collected using a smartphone [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

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

Pérez-Zuriaga

Llopis-Castelló

Just-Martínez

et al. 2022

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

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

Pérez-Zuriaga

Llopis-Castelló

Just-Martínez

et al. 2022

Sensors

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show abstract

“…(3) vibration transmissibility of bicycles, bicycle components, and cycling apparel [14][15][16][17][18][19]; (4) assessments of road surfaces [20][21][22][23][24]; (5) cyclists' sensory perception [2,[25][26][27].…”

Section: Introductionmentioning

confidence: 99%

“…Acceleration is also used in standards that relate to shock and vibration transmitted to the hand-arm system (e.g., ISO/TS 15,694:2004 [28]). Hardware in the form of accelerometers (including those embedded in smartphones) are readily available, relatively low cost, and typically straightforward to use [13,21,24]. Force measurement is technically more difficult than acceleration measurement, since it generally requires the development, fabrication, and calibration of custom transducers [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

On metrics to assess road bicycle dynamic comfort during impacts

et al. 2021

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A road bicycle's dynamic comfort relates to its capacity to filter vibration generated by the road surface. Typically, four quantities have been used to assess a road bicycle's dynamic comfort, acceleration, force, power, and energy; however, little has been done to compare the effectiveness of these in distinguishing between impact events. The aim of this study was to assess the ability of these four quantities when measured at a cyclist's hands, to discriminate between small changes in the level of an impact load applied at the front wheel of a road bicycle. With a rider seated on a bicycle, acceleration and force time signals were recorded at the left and right hands using instrumented brake hoods during a series of impacts at the front wheel on a bicycle treadmill. Six derived parameters of the acceleration, force, and power time signals were considered: discrete values: maximum, peak; mean values: root-mean-square, root-mean-quad; ratio values: crest factor, shock content quotient. Integral values were used for the energy. Various criteria were developed to assess the performance of these parameters and whether they should be recommended as road bicycle dynamic comfort metrics for impact events. The criteria were related to three characteristics: the consistency of the measurements, the parameter's statistical discrimination power, and how well changes in the parameter matched corresponding changes in impact level. The energy and root-mean-square value of power were found to be the top performers and are recommended as road bicycle dynamic comfort metrics for impact events. All acceleration-based parameters are not recommended. The remaining parameters demonstrated mixed results.

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“…Smartphones provide transportation researchers with an accessible means to couple detailed location information with data from the phones’ internal sensors (accelerometer, barometer, microphone, camera, etc. ), paired instruments with additional sensors, and traveler-reported input ( 11 – 13 ). Particularly for active travel research, smartphones enable researchers to increase data resolution and accuracy while decreasing participant burden and potentially observation bias.…”

mentioning

confidence: 99%

Instrumentation to Measure On-Road Cyclist Noise Exposure: Considerations for Study Design with Smartphones and Sound Level Meters

Albitar

Bigazzi

2023

Transportation Research Record: Journal of the Transportation R

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Cyclist noise exposure has implications for health, comfort, and safety. Methods used for in situ measurement of on-road noise levels for cyclists vary, and the effects of key study design factors have not been investigated. To enable reliable research into cyclist noise exposure, this study aims to determine the accuracy of smartphone noise measurements in comparison with a sound level meter (SLM) reference instrument, and how noise levels are affected by travel speed, air speed, sensor placement, and use of a windscreen. Field data were collected with paired instruments in a typical urban cycling scenario, and comparisons made varying one design factor at a time (smartphone versus SLM, with versus without windscreen, handlebar versus shoulder placement, etc.). Results show that smartphones can generate reliable measurements (compared with SLM) of high-resolution (1-s) cyclist exposure for C-weighted noise, but not A-weighted noise. Sensor placement and windscreen have small effects on noise readings, but air speed and travel speed greatly affect measured noise levels. Future studies measuring on-road noise must consider the effects of wind- and bicycle-generated noise to ensure internal validity. Studies should also consider both study objectives and instrumentation when selecting a noise measure (frequency weighting). Research is needed into bicycle noise generation and perception of traffic noise by cyclists to enhance the reliability of future studies.

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Combining environmental quality assessment of bicycle infrastructures with vertical acceleration measurements

Cited by 13 publications

References 29 publications

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

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

On metrics to assess road bicycle dynamic comfort during impacts

Instrumentation to Measure On-Road Cyclist Noise Exposure: Considerations for Study Design with Smartphones and Sound Level Meters

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