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Department of Civil and Environmental Engineering, University of North Carolina at Charlotte, Charlotte, NC, U.S.A.

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1 Papers

Real-Time Bus Arrival Prediction: A Deep Learning Approach for Enhanced Urban Mobility

Topics: OR in Transportation; Predictive Analytics

Authors: Narges Rashvand ¹ , Sanaz Hosseini ² , Mona Azarbayjani ³ and Hamed Tabkhi ¹

Affiliations: ¹ Department of Electrical and Computer Engineering, University of North Carolina at Charlotte, Charlotte, NC, U.S.A. , ² Department of Civil and Environmental Engineering, University of North Carolina at Charlotte, Charlotte, NC, U.S.A. , ³ Department of Architecture, University of North Carolina at Charlotte, Charlotte, NC, U.S.A.

Digital Object Identifier: 10.5220/0012365500003639

Proceedings of ICORES 2024, ISBN: 978-989-758-681-1

Quick Abstract

In urban settings, bus transit stands as a significant mode of public transportation, yet faces hurdles in delivering accurate and reliable arrival times. This discrepancy often culminates in delays and a decline in ridership, particularly in areas with a heavy reliance on bus transit. A prevalent challenge is the mismatch between actual bus arrival times and their scheduled counterparts, leading to disruptions in fixed schedules. Our study, utilizing New York City bus data, reveals an average delay of approximately eight minutes between scheduled and actual bus arrival times. This research introduces an innovative, AI-based, data-driven methodology for predicting bus arrival times at various transit points (stations), offering a collective prediction for all bus lines within large metropolitan areas. Through the deployment of a fully connected neural network, our method elevates the accuracy and efficiency of public bus transit systems. Our comprehensive evaluation encompasses over 200 bus lines and 2 million data points, showcasing an error margin of under 40 seconds for arrival time estimates. Additionally, the inference time for each data point in the validation set is recorded at below 0.006 ms, demonstrating the potential of our Neural-Net based approach in substantially enhancing the punctuality and reliability of bus transit systems.