Exploiting Multi-modal Contextual Sensing for City-bus's Stay Location Characterization: Towards Sub-60 Seconds Accurate Arrival Time Prediction

TL;DR

This paper introduces BuStop, a multi-modal sensing system using smartphones to accurately characterize bus stop types and predict arrival times within 60 seconds, enhancing real-time city transportation information.

Contribution

The paper presents BuStop, a novel multi-modal sensing framework that accurately characterizes bus stop types and improves arrival time predictions in urban transit systems.

Findings

High accuracy in identifying different stay location types

Stay location characterization reduces arrival time prediction deviation to under 60 seconds

Effective use of multi-modal data from smartphones enhances transit information systems

Abstract

Intelligent city transportation systems are one of the core infrastructures of a smart city. The true ingenuity of such an infrastructure lies in providing the commuters with real-time information about citywide transports like public buses, allowing her to pre-plan the travel. However, providing prior information for transportation systems like public buses in real-time is inherently challenging because of the diverse nature of different stay-locations that a public bus stops. Although straightforward factors stay duration, extracted from unimodal sources like GPS, at these locations look erratic, a thorough analysis of public bus GPS trails for 720km of bus travels at the city of Durgapur, a semi-urban city in India, reveals that several other fine-grained contextual features can characterize these locations accurately. Accordingly, we develop BuStop, a system for extracting and characterizing the stay locations from multi-modal sensing using commuters' smartphones. Using this multi-modal information BuStop extracts a set of granular contextual features that allow the system to differentiate among the different stay-location types. A thorough analysis of BuStop using the collected dataset indicates that the system works with high accuracy in identifying different stay locations like regular bus stops, random ad-hoc stops, stops due to traffic congestion stops at traffic signals, and stops at sharp turns. Additionally, we also develop a proof-of-concept setup on top of BuStop to analyze the potential of the framework in predicting expected arrival time, a critical piece of information required to pre-plan travel, at any given bus stop. Subsequent analysis of the PoC framework, through simulation over the test dataset, shows that characterizing the stay-locations indeed helps make more accurate arrival time predictions with deviations less than 60s from the ground-truth arrival time.