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

• URL: http://arxiv.org/abs/2303.15495v3
• Date: Mon, 4 Mar 2024 15:42:31 GMT
• Title: Real-Time Bus Arrival Prediction: A Deep Learning Approach for Enhanced Urban Mobility
• Authors: Narges Rashvand, Sanaz Sadat Hosseini, Mona Azarbayjani, Hamed Tabkhi
• Abstract summary: A prevalent challenge is the mismatch between actual bus arrival times and their scheduled counterparts, leading to disruptions in fixed schedules. This research introduces an innovative, AI-based, data-driven methodology for predicting bus arrival times at various transit points (stations) Through the deployment of a fully connected neural network, our method elevates the accuracy and efficiency of public bus transit systems.
• Score: 2.1374208474242815
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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