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

Figures (9)

• Figure 1: Methodological framework.
• Figure 2: Left: Road network of Manhattan, New York City, which consists of 3,330 nodes and 6,055 road segments. Right: Locations of 646 bike-sharing stands in Manhattan.
• Figure 3: Empirical relationship between $n^s$ and average coverage $\bar{N}_{s,e}$.
• Figure 4: The visit probability $p_{s,e}$ of each bike from a fixed bike stand to different road sections. The blue five-pointed star represents the location of the bike stand, and the heatmap shows the values of the parameter $p_{s,e}$.
• Figure 5: Sensing reward $\Phi$ for three methods across various total number of sensors $N_s$.