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

Cafiso, Salvatore; Pappalardo, Giuseppina; Stamatiadis, Nikiforos

doi:10.3390/infrastructures6110154

Cited by 8 publications

(3 citation statements)

References 44 publications

(53 reference statements)

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“…Figure 9 shows cycle track termini at the highest risk rank followed by the roundabout. The normalized Risk Rate are also calculated to allow for comparisons with the observed risk rate reported in previous study [9]. It should be noted that in Fig.…”

Section: Validation Through Case Study and Risk Assessmentmentioning

confidence: 96%

“…Despite the promising results, they concluded that more granular data and validation work needs to be done to improve the reliability of the correlation. Perceived risk resulted consistently with frequency of CSEs in bicycle paths classified by a panel of experts analyzing video recording and speed and heading GPS data [9]. All these research mainly rely on the identification of safety-critical events via self-reporting, manual review of video footage preselected thresholds and statistical methods to analyze data.…”

Section: Observational Studiesmentioning

confidence: 99%

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Detection of anomalies in cycling behavior with convolutional neural network and deep learning

Yaqoob

Cafiso

Morabito

et al. 2023

Eur. Transp. Res. Rev.

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Background Cycling has always been considered a sustainable and healthy mode of transport. With the increasing concerns of greenhouse gases and pollution, policy makers are intended to support cycling as commuter mode of transport. Moreover, during Covid-19 period, cycling was further appreciated by citizens as an individual opportunity of mobility. Unfortunately, bicyclist safety has become a challenge with growing number of bicyclists in the 21st century. When compared to the traditional road safety network screening, availability of suitable data for bicycle based crashes is more difficult. In such framework, new technologies based smart cities may require new opportunities of data collection and analysis. Methods This research presents bicycle data requirements and treatment to get suitable information by using GPS device. Mainly, this paper proposed a deep learning-based approach “BeST-DAD” to detect anomalies and spot dangerous points on map for bicyclist to avoid a critical safety event (CSE). BeST-DAD follows Convolutional Neural Network and Autoencoder (AE) for anomaly detection. Proposed model optimization is carried out by testing different data features and BeST-DAD parameter settings, while another comparison performance is carried out between BeST-DAD and Principal Component Analysis (PCA). Result BeST-DAD over perform than traditional PCA statistical approaches for anomaly detection by achieving 77% of the F-score. When the trained model is tested with data from different users, 100% recall is recorded for individual user’s trained models. Conclusion The research results support the notion that proper GPS trajectory data and deep learning classification can be applied to identify anomalies in cycling behavior.

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Section: Validation Through Case Study and Risk Assessmentmentioning

confidence: 96%

Section: Observational Studiesmentioning

confidence: 99%

Detection of anomalies in cycling behavior with convolutional neural network and deep learning

Yaqoob

Cafiso

Morabito

et al. 2023

Eur. Transp. Res. Rev.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Another research focused on the turning behavior of cyclists by measuring steering torque and angle, and bicycle speed, acceleration, and angular velocity [15,16], whereas in [17] the hard breaking behavior was studied by analyzing speed and acceleration signals and detecting the abnormal signals after applying signal processing techniques. In another research, cycling safety was assessed from objective and perceived points of view, where the risk classification of the roadway components was compared [18].…”

Section: Introductionmentioning

confidence: 99%

Measuring Cyclist’s Inputs, the Kinematic and Dynamic Properties of a City Bicycle, and Estimating the Road Profile via Sensor Fusion

Shoman¹,

Imine²,

Johansson³

et al. 2023

Highlights Veh.

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In this paper, we present the instrumentation of a city bicycle with different sensors and devic-es in order to measure cyclists’ inputs (i.e., pedaling and steering) and the dynamical and kinematic properties of the bicycle. The instrumentation includes two tri-axial accelerometers, an Inertial Measurement Unit (IMU), GPS, a potentiometer, a laser scanner, a pedaling power meter, and speed and cadence sensors, in addition to a mobile eye tracker worn by the cyclists. After the instrumentation and adjustment of the sensors, a study was conducted in the city of Stockholm using the instrumented bicycle with the aim to evaluate cycling safety and comfort on snowy surface conditions. The outputs of this experiment will be employed further to study the interaction of cyclists with road infrastructure and other road users and their impact on cyclists’ behavior and cycling safety.

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Deep transfer learning-based anomaly detection for cycling safety

Yaqoob,

Cafiso,

Morabito

et al. 2023

Journal of Safety Research

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

Cited by 8 publications

References 44 publications

Detection of anomalies in cycling behavior with convolutional neural network and deep learning

Detection of anomalies in cycling behavior with convolutional neural network and deep learning

Measuring Cyclist’s Inputs, the Kinematic and Dynamic Properties of a City Bicycle, and Estimating the Road Profile via Sensor Fusion

Deep transfer learning-based anomaly detection for cycling safety

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