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Bicycling

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CNN-GRU-AM for Shared Bicycles Demand Forecasting.

Computational intelligence and neuroscience
The demand forecast of shared bicycles directly determines the utilization rate of vehicles and projects operation benefits. Accurate prediction based on the existing operating data can reduce unnecessary delivery. Since the use of shared bicycles is...

Crash severity analysis of vulnerable road users using machine learning.

PloS one
Road crash fatality is a universal problem of the transportation system. A massive death toll caused annually due to road crash incidents, and among them, vulnerable road users (VRU) are endangered with high crash severity. This paper focuses on empl...

Bike-Sharing Demand Prediction at Community Level under COVID-19 Using Deep Learning.

Sensors (Basel, Switzerland)
An important question in planning and designing bike-sharing services is to support the user's travel demand by allocating bikes at the stations in an efficient and reliable manner which may require accurate short-time demand prediction. This study f...

Smart Electrically Assisted Bicycles as Health Monitoring Systems: A Review.

Sensors (Basel, Switzerland)
This paper aims to provide a review of the electrically assisted bicycles (also known as e-bikes) used for recovery of the rider's physical and physiological information, monitoring of their health state, and adjusting the "medical" assistance accord...

Green Recycling Supplier Selection of Shared Bicycles: Interval-Valued Pythagorean Fuzzy Hybrid Weighted Methods Based on Self-Confidence Level.

International journal of environmental research and public health
In the face of practical problems such as the increasing demand for shared bicycles and the number of faulty vehicles which are hard to handle and repair in time, shared bicycles operators tend to outsource recycling services to suppliers. To solve t...

Cadence Detection in Road Cycling Using Saddle Tube Motion and Machine Learning.

Sensors (Basel, Switzerland)
Most commercial cadence-measurement systems in road cycling are strictly limited in their function to the measurement of cadence. Other relevant signals, such as roll angle, inclination or a round kick evaluation, cannot be measured with them. This w...

Automated classification of time-activity-location patterns for improved estimation of personal exposure to air pollution.

Environmental health : a global access science source
BACKGROUND: Air pollution epidemiology has primarily relied on measurements from fixed outdoor air quality monitoring stations to derive population-scale exposure. Characterisation of individual time-activity-location patterns is critical for accurat...

The Future of Road Safety: Challenges and Opportunities.

The Milbank quarterly
Policy Points Traditional approaches to addressing motor vehicle crashes are yielding diminishing returns. A comprehensive strategy known as the Safe Systems approach shows promise in both advancing safety and equity and reducing motor vehicle crashe...

Forward dynamics computational modelling of a cyclist fall with the inclusion of protective response using deep learning-based human pose estimation.

Journal of biomechanics
Single bicycle crashes, i.e., falls and impacts not involving a collision with another road user, are a significantly underestimated road safety problem. The motions and behaviours of falling people, or fall kinematics, are often investigated in the ...

Comparing the advantages and disadvantages of physics-based and neural network-based modelling for predicting cycling power.

Journal of biomechanics
Models of physical phenomena can be developed using two distinct approaches: using expert knowledge of the underlying physical principles or using experimental data to train a neural network. Here, our aim was to better understand the advantages and ...