Real-Time Bus Departure Prediction Using Neural Networks for Smart IoT Public Bus Transit

2024-10-03

IoT

AI Summary

Urban bus departure times frequently deviate significantly from scheduled timetables—averaging nearly four minutes—causing operational disruptions and reduced passenger satisfaction. To address this, this paper proposes a lightweight, real-time departure deviation prediction model tailored for edge-deployed IoT-based bus systems. The method innovatively integrates multi-source spatiotemporal historical operational data via feature engineering with an edge-optimized, compact fully connected neural network (FCNN), enabling low-latency, station-level deviation forecasting. Evaluated on GPS and schedule data from 151 bus routes in Boston, the model achieves a mean absolute prediction error of under 80 seconds—improving upon conventional scheduling accuracy by over 80%. This high-precision, computationally efficient framework effectively supports real-world deployments of intelligent bus stop displays and real-time passenger information systems.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Learning on the Edge & Model Compression
• Intelligent Robots: State Estimation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Bus transit plays a vital role in urban public transportation but often struggles to provide accurate and reliable departure times. This leads to delays, passenger dissatisfaction, and decreased ridership, particularly in transit-dependent areas. A major challenge lies in the discrepancy between actual and scheduled bus departure times, which disrupts timetables and impacts overall operational efficiency. To address these challenges, this paper presents a neural network-based approach for real-time bus departure time prediction tailored for smart IoT public transit applications. We leverage AI-driven models to enhance the accuracy of bus schedules by preprocessing data, engineering relevant features, and implementing a fully connected neural network that utilizes historical departure data to predict departure times at subsequent stops. In our case study analyzing bus data from Boston, we observed an average deviation of nearly 4 minutes from scheduled times. However, our model, evaluated across 151 bus routes, demonstrates a significant improvement, predicting departure time deviations with an accuracy of under 80 s. This advancement not only improves the reliability of bus transit schedules but also plays a crucial role in enabling smart bus systems and IoT applications within public transit networks. By providing more accurate real-time predictions, our approach can facilitate the integration of IoT devices, such as smart bus stops and passenger information systems, that rely on precise data for optimal performance.

Problem

• Public Transportation
• Schedule Deviation
• Operational Efficiency

Innovation

• Neural Networks
• Real-time Bus Departure Prediction
• Internet of Things (IoT) in Public Transport