Exploring the drive-by sensing power of bus fleet through active scheduling

TL;DR

This paper introduces an active scheduling approach for bus fleets to enhance urban sensing capabilities, optimizing sensor deployment and bus routes to improve data collection efficiency and reduce costs.

Contribution

It develops a nonlinear integer programming model and a batch scheduling algorithm to actively coordinate sensor-equipped buses across multiple lines, which is a novel approach compared to existing static assignment methods.

Findings

Active scheduling improves sensing quality by up to 32.1%.

Operational costs are reduced by 1.0%.

Sensor investment can be cut by over 33% with active scheduling.

Abstract

Vehicle-based mobile sensing (a.k.a drive-by sensing) is an important means of surveying urban environment by leveraging the mobility of public or private transport vehicles. Buses, for their extensive spatial coverage and reliable operations, have received much attention in drive-by sensing. Existing studies have focused on the assignment of sensors to a set of lines or buses with no operational intervention, which is typically formulated as set covering or subset selection problems. This paper aims to boost the sensing power of bus fleets through active scheduling, by allowing instrumented buses to circulate across multiple lines to deliver optimal sensing outcome. We consider a fleet consisting of instrumented and normal buses, and jointly optimize sensor assignment, bus dispatch, and intra- or inter-line relocations, with the objectives of maximizing sensing quality and minimizing operational costs, while serving all timetabled trips. By making general assumptions on the sensing utility function, we formulate the problem as a nonlinear integer program based on a time-expanded network. A batch scheduling algorithm is developed following linearization techniques to solve the problem efficiently, which is tested in a real-world case study in Chengdu, China. The results show that the proposed scheme can improve the sensing objective by 12.0%-20.5% (single-line scheduling) and 16.3%-32.1% (multi-line scheduling), respectively, while managing to save operational costs by 1.0%. Importantly, to achieve the same level of sensing quality, we found that the sensor investment can be reduced by over 33% when considering active bus scheduling. Comprehensive comparative and sensitivity analyses are presented to generate managerial insights and recommendations for practice.