Enhancing the sensing power of bike-sharing system for urban environment

TL;DR

This paper proposes an optimized sensor deployment and scheduling strategy for bike-sharing systems to enhance urban environment sensing, demonstrating high coverage with minimal sensor deployment.

Contribution

It introduces a novel integration of probabilistic modeling and mixed-integer programming to optimize sensor placement and active scheduling in bike-sharing networks.

Findings

Equipping 1% of bikes with sensors covers 70% of road segments daily.

The proposed strategy significantly improves urban sensing coverage.

Case study validates the effectiveness of the approach in a real city environment.

Abstract

The development of smart cities requires innovative sensing solutions for efficient and low-cost urban environment monitoring. Bike-sharing systems, with their wide coverage, flexible mobility, and dense urban distribution, present a promising platform for pervasive sensing. At a relative early stage, research on bike-based sensing focuses on the application of data collected via passive sensing, without consideration of the optimization of data collection through sensor deployment or vehicle scheduling. To address this gap, this study integrates a binomial probability model with a mixed-integer linear programming model to optimize sensor allocation across bike stands. Additionally, an active scheduling strategy guides user bike selection to enhance the efficacy of data collection. A case study in Manhattan validates the proposed strategy, showing that equipping sensors on just 1\% of the bikes covers approximately 70\% of road segments in a day, highlighting the significant potential of bike-sharing systems for urban sensing.