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.

翻译：在城市环境中，公交运输作为公共交通的重要模式，在提供准确可靠的到达时间方面面临挑战。这种偏差通常导致延误和乘客量下降，尤其在高度依赖公交运输的区域。普遍存在的问题是实际公交到达时间与计划时间不匹配，导致固定时刻表紊乱。本研究利用纽约市公交数据，揭示了计划与实际公交到达时间之间存在约八分钟的平均延误。本项研究提出了一种创新的、基于人工智能的数据驱动方法，用于预测各交通站点（车站）的公交到达时间，并为大都市区域内所有公交线路提供集体预测。通过部署全连接神经网络，我们的方法提升了公共公交系统的准确性和效率。我们的综合评估涵盖了超过200条公交线路和200万个数据点，显示到达时间估计的误差范围低于40秒。此外，验证集中每个数据点的推理时间记录在0.006毫秒以下，这证明了基于神经网络的方法在显著提升公交系统准点性和可靠性方面的潜力。