Real time bus arrival time prediction system under Indian traffic condition

B. Dhivyabharathi, B. A. Kumar, L. Vanajakshi
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引用次数: 16

Abstract

The estimation of bus travel time and providing accurate information about bus arrival time to passengers are important to make public transport system more user-friendly and thus enhance its competitiveness among various transportation modes. However, for the system to be effective, the information provided to passengers should be highly reliable. The model and technique used for prediction plays a major role in enhancing the accuracy and reliability of the system. The present study proposes a model based approach for accurate prediction of bus travel times for the development of a real time passenger information system under heterogeneous traffic conditions that exist in India. The proposed model considers the predicted bus travel time as the sum of the median of historical bus travel times, random variations in travel time over time, and a model evolution error. In order to capture the random variations in travel time, a model based approach with Particle filtering technique is used, wherein inputs are obtained using k-NN algorithm. The results obtained from the implementation of the above method are compared with the measured travel time data and the prediction accuracy is quantified using the Mean Absolute Percentage Error (MAPE). The Performance of the proposed method showed a clear improvement in prediction accuracy when compared with an existing model based approach using Kalman filter that was reported to be work well under similar traffic conditions.
印度交通状况下公交到达时间实时预测系统
公交出行时间的估算,为乘客提供准确的公交到达时间信息,对于提高公共交通系统的用户友好性,从而提高公共交通系统在各种交通方式中的竞争力具有重要意义。然而,为了使系统有效,提供给乘客的信息应该是高度可靠的。用于预测的模型和技术在提高系统的准确性和可靠性方面起着重要作用。本研究提出了一种基于模型的方法,用于在印度存在的异构交通条件下开发实时乘客信息系统,以准确预测公交行驶时间。该模型将预测的公交出行时间考虑为历史公交出行时间的中位数、出行时间随时间的随机变化和模型演化误差的总和。为了捕获旅行时间的随机变化,采用了基于模型的方法和粒子滤波技术,其中输入使用k-NN算法获得。将上述方法的实现结果与实测旅行时间数据进行了比较,并利用平均绝对百分比误差(MAPE)对预测精度进行了量化。与现有的基于模型的卡尔曼滤波方法相比,该方法的预测精度有明显提高,卡尔曼滤波方法在类似的交通条件下也能很好地工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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