Prediction of Road Surface Quality during Cycling Using Smartphone Accelerometer Data

Litzenberger, Stefan; Christensen, Trine; Hofstätter, Otto; Sabo, Anton

doi:10.3390/proceedings2060217

Cited by 9 publications

(6 citation statements)

References 7 publications

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“…Ride vibration and factors such as roadway and traffic settings, including frequent vehicle engagement, stops, steep slopes, and impediments such as humps and curbs, dramatically reduce bicycle riding [44,45]. Comfort decreases as vibration intensity rises [46][47][48]. Vibrations are caused mainly by an uneven road surface [25,48].…”

Section: Literature Reviewmentioning

confidence: 99%

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Utilizing Intelligent Portable Bicycle Lights to Assess Urban Bicycle Infrastructure Surfaces

et al. 2023

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Vibration from bicycle infrastructure affects the cyclists’ comfort and the choice of this transportation mode. This study uses smart portable bicycle lights to measure the vibration and quantify the level of cycling comfort on cycling infrastructure. A total of 28 bicycle streets and paths were selected in the city of Hasselt, Belgium, as the case study area. Six volunteer cyclists were recruited for the vibration sensitivity test of the device before the actual data collection. The results showed no considerable difference in the vibration recorded separately on each tested bicycle surface. The average vibration values vary from 1 to 17.78, indicating that riding comfort varies significantly across different surfaces. Asphalt and concrete roads had the lowest vibration and were the most comfortable in the study area. In contrast, cobblestone-paved bike paths were the least comfortable because of higher vibration. A comfort level map was developed based on the relationship between cycle vibration and subjective perception of comfort level. Twenty cyclists participated in the perception of vibration test. The comfort level is inversely correlated with the vibration. This methodology is adaptable to any other setting. Additionally, practitioners can use it to check and track the quality of the surface of the bicycle infrastructure over time.

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Section: Literature Reviewmentioning

confidence: 99%

“…In contrast, vertical acceleration or vibration is essential in sensor and detecting technology. Many researchers have studied its effect on bicycle riders' comfort over the years [19,25,28,47,48]. Cyclists perceive surface quality through bicycle vibrations, one of the most common sources of discomfort [36].…”

Section: Literature Reviewmentioning

confidence: 99%

Utilizing Intelligent Portable Bicycle Lights to Assess Urban Bicycle Infrastructure Surfaces

et al. 2023

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“…The XGBoost comes in two versions: the Linear Booster and Tree Booster (Chen and Guestrin, 2016). It is noteworthy that these machine learning algorithms have been used in many application fields in the literature including cycling and transportation (Dadashova et al, 2020; Khasawneh et al, 2020; Litzenberger et al, 2018; Mahmoud et al, 2021; Munira and Sener, 2020; Sun and Mobasheri, 2017). To compare their performance, we tuned each model parameter separately to minimize the mean absolute error (MAE) and to maximize R 2 .…”

Section: Machine Learning Analysismentioning

confidence: 99%

Quantifying changes in bicycle volumes using crowdsourced data

Al-Ramini

Takallou

Piatkowski

et al. 2022

Environment and Planning B: Urban Analytics and City Science

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Most cities in the United States lack comprehensive or connected bicycle infrastructure; therefore, inexpensive and easy-to-implement solutions for connecting existing bicycle infrastructure are increasingly being employed. Signage is one of the promising solutions. However, the necessary data for evaluating its effect on cycling ridership is lacking. To overcome this challenge, this study tests the potential of using readily-available crowdsourced data in concert with machine-learning methods to provide insight into signage intervention effectiveness. We do this by assessing a natural experiment to identify the potential effects of adding or replacing signage within existing bicycle infrastructure in 2019 in the city of Omaha, Nebraska. Specifically, we first visually compare cycling traffic changes in 2019 to those from the previous two years (2017–2018) using data extracted from the Strava fitness app. Then, we use a new three-step machine-learning approach to quantify the impact of signage while controlling for weather, demographics, and street characteristics. The steps are as follows: Step 1 (modeling and validation) build and train a model from the available 2017 crowdsourced data (i.e., Strava, Census, and weather) that accurately predicts the cycling traffic data for any street within the study area in 2018; Step 2 (prediction) use the model from Step 1 to predict bicycle traffic in 2019 while assuming new signage was not added; Step 3 (impact evaluation) use the difference in prediction from actual traffic in 2019 as evidence of the likely impact of signage. While our work does not demonstrate causality, it does demonstrate an inexpensive method, using readily-available data, to identify changing trends in bicycling over the same time that new infrastructure investments are being added.

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“…(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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Prediction of Road Surface Quality during Cycling Using Smartphone Accelerometer Data

Cited by 9 publications

References 7 publications

Utilizing Intelligent Portable Bicycle Lights to Assess Urban Bicycle Infrastructure Surfaces

Utilizing Intelligent Portable Bicycle Lights to Assess Urban Bicycle Infrastructure Surfaces

Quantifying changes in bicycle volumes using crowdsourced data

On metrics to assess road bicycle dynamic comfort during impacts

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