Advancements in Intelligent Transportation Systems and Micromobility

The field of transportation research is moving towards increased safety, efficiency, and sustainability. Recent studies have focused on leveraging crowd-sourced data, advanced machine learning models, and high-resolution satellite imagery to improve our understanding of transportation phenomena. Notable advancements include the development of novel intention-sharing mechanisms for cooperative electrically power-assisted cycles, scalable dynamic origin-destination demand estimation frameworks, and comprehensive machine learning frameworks for micromobility demand prediction. These innovations have the potential to significantly enhance the safety and efficiency of transportation systems, particularly for vulnerable road users. Noteworthy papers in this area include:

  • Evaluating Redundancy Mitigation in Vulnerable Road User Awareness Messages for Bicycles, which proposes an adapted redundancy mitigation mechanism to balance channel load reduction and VRU awareness.
  • V2X Intention Sharing for Cooperative Electrically Power-Assisted Cycles, which introduces a novel intention-sharing mechanism for EPACs, enhancing the ETSI VRU Awareness Message protocol.
  • A Comprehensive Machine Learning Framework for Micromobility Demand Prediction, which integrates spatial, temporal, and network dependencies for improved micromobility demand forecasting.

Sources

Hitchhiking Rides Dataset: Two decades of crowd-sourced records on stochastic traveling

Evaluating Redundancy Mitigation in Vulnerable Road User Awareness Messages for Bicycles

V2X Intention Sharing for Cooperative Electrically Power-Assisted Cycles

Scalable Dynamic Origin-Destination Demand Estimation Enhanced by High-Resolution Satellite Imagery Data

Do Electric Vehicles Induce More Motion Sickness Than Fuel Vehicles? A Survey Study in China

Reducing Motion Sickness in Passengers of Autonomous Personal Mobility Vehicles by Presenting a Driving Path

Origin-Destination Travel Demand Estimation: An Approach That Scales Worldwide, and Its Application to Five Metropolitan Highway Networks

Time Series Foundation Models are Flow Predictors

A Comprehensive Machine Learning Framework for Micromobility Demand Prediction

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