A State-Space Model for Assimilating Passenger and Vehicle Flow Data with User Feedback in a Transit Network

TL;DR

This paper proposes a state-space model using Kalman filter principles to estimate transit network crowdedness and incorporate passenger feedback via a participatory smartphone system for real-time data assimilation.

Contribution

It introduces a novel framework combining state-space modeling with user feedback to improve crowdedness estimation in transit networks.

Findings

Effective integration of passenger feedback improves crowdedness estimates.

The model can assimilate various data sources like smartcard and wireless monitoring.

Real-time feedback enhances system responsiveness and accuracy.

Abstract

This note explores the idea of utilising a state-space model, congruent with the underlying equations of the Kalman filter with control input, for reconstructing the state of crowdedness in a transit network. The envisaged role of the proposed scheme is twofold: first, to provide an estimate of the state of crowdedness given input data on vehicle movement, on passenger inflow/outflow at stations and on measured crowdedness; second, to trigger localised requests for feedback based on the estimated system state as well as on the data assimilation performance indices. The latter is applicable to a scenario where the crowdedness is measured through passenger feedback. The feedback loop is conceptualised to be realised with a participatory crowd-sensing smartphone-based system in which reported perceived levels of crowdedness are assimilated in near-real-time with the aim of improving the estimation of the model state. Presented model is also applicable for assimilating other relevant measurements, for instance, vehicle weighing, automatic passenger counting, aggregated smartcard data or passive wireless device monitoring data.