Research Article
E-Go Bicycle Intelligent Speed Adaptation System for Catching the Green Light
@INPROCEEDINGS{10.1007/978-3-031-63989-0_10, author={Khalil Ben Fredj and Akhil Reddy Pallamreddy and Geert Heijenk and Paul Havinga and Yanqiu Huang}, title={E-Go Bicycle Intelligent Speed Adaptation System for Catching the Green Light}, proceedings={Mobile and Ubiquitous Systems: Computing, Networking and Services. 20th EAI International Conference, MobiQuitous 2023, Melbourne, VIC, Australia, November 14--17, 2023, Proceedings, Part I}, proceedings_a={MOBIQUITOUS}, year={2024}, month={7}, keywords={Smart e-bikes Intelligent Speed Adaptation Privacy Preserving Fuzzy Logic Control Bluetooth Low Energy RSSI distance estimation SPaT}, doi={10.1007/978-3-031-63989-0_10} }- Khalil Ben Fredj
Akhil Reddy Pallamreddy
Geert Heijenk
Paul Havinga
Yanqiu Huang
Year: 2024
E-Go Bicycle Intelligent Speed Adaptation System for Catching the Green Light
MOBIQUITOUS
Springer
DOI: 10.1007/978-3-031-63989-0_10
Abstract
The expanding growth of electric bikes in recent years underscores their increasing importance as a sustainable and eco-friendly mode of transportation. With zero emissions and the ability to ease urban congestion, e-bikes are becoming a pivotal solution in promoting greener and more efficient commuting habits. However, signalized intersections and frequent stops at traffic lights (TL) are considered uncomfortable for cyclists. This article introduces a personalized and privacy-preserving Intelligent Speed Adaption (ISA) system that helps cyclists adapt to the required speed to catch the green light. In our system design, traffic lights are augmented with Bluetooth Low Energy (BLE) beaconing devices which allow connected e-bikes to get the remaining green light phase duration, estimate the distance to the intersection, and assist the cyclist to catch the green light when necessary. We address the speed adaption problem as a convex optimization problem to ensure smooth and safe acceleration. In addition, a fuzzy logic controller is used to control motor power to reach the recommended speed while considering the human pedal power. We generate different scenarios with various initial velocities, time to red (TTR), slope of the road, and human pedal power to evaluate the system’s performance. The results demonstrate that ISA improves the probability of crossing the traffic light by about 77% compared to the absence of speed adaptation.
