Real-Time and Predictive Analytics for Smart Public Transportation Decision Support System
Real-Time and Predictive Analytics for Smart Public Transportation Decision Support System
复制标题
智能公共交通决策支持系统的实时和预测分析
DOI：
10.1109/smartcomp.2016.7501714
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
A. Dubey
中科院分区：
文献类型：
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作者：
Fangzhou Sun;Yao Pan;Jules White;A. Dubey
公交是城市交通基础设施中的重要组成部分。然而，由于缺乏关于公交车位置和延误时间的实时信息，公共交通往往很难使用，在存在运营延误和服务警报的情况下，乘客很难预测公交车何时到达并计划出行。由于许多因素，如交通拥堵、运营延误、在每个站点装载乘客所需的时间不同，准确跟踪车辆并通知乘客估计到达时间是具有挑战性的。在本文中，我们介绍了一个公交决策支持系统，既可以用来预测公交到站时间，也可以用来预测公交到达时间。该系统使用流式实时公交车位置数据(每分钟更新一次)和历史到达和发车数据(可用于选定站点)来预测公交车到达时间。我们的方法结合了聚类分析和卡尔曼滤波与共享路径段模型，以产生更准确的到达时间预测。实验表明，与城市目前使用的基本到达时间预测模型相比，我们的系统在预测到达延迟一个小时时，平均减少了25%的到达时间预测误差，在15分钟内预测未来时间窗口时，平均减少了47%的到达时间预测误差。
Public bus transit plays an important role in city transportation infrastructure. However, public bus transit is often difficult to use because of lack of real- time information about bus locations and delay time, which in the presence of operational delays and service alerts makes it difficult for riders to predict when buses will arrive and plan trips. Precisely tracking vehicle and informing riders of estimated times of arrival is challenging due to a number of factors, such as traffic congestion, operational delays, varying times taken to load passengers at each stop. In this paper, we introduce a public transportation decision support system for both short-term as well as long-term prediction of arrival bus times. The system uses streaming real-time bus position data, which is updated once every minute, and historical arrival and departure data - available for select stops to predict bus arrival times. Our approach combines clustering analysis and Kalman filters with a shared route segment model in order to produce more accurate arrival time predictions. Experiments show that compared to the basic arrival time prediction model that is currently being used by the city, our system reduces arrival time prediction errors by 25% on average when predicting the arrival delay an hour ahead and 47% when predicting within a 15 minute future time window.