大数据
计算机科学
公共交通
贝叶斯网络
预测分析
打开数据
步伐
服务(商务)
分析
贝叶斯概率
数据科学
数据挖掘
运输工程
万维网
工程类
机器学习
人工智能
业务
大地测量学
营销
地理
作者
Alfredo Cuzzocrea,Carson K. Leung,Mojtaba Hajian,Marshall D. Jackson
标识
DOI:10.1109/dasc/picom/cbdcom/cy55231.2022.9927788
摘要
Today, various types of valuable data can be collected with ease and at a rapid pace. In recent years, many governments, researchers, and organizations have been driven by open data pioneers, to make their data available for public. Transportation data, such as public bus performance data, is an example of open big data. The analyzing of these open big data can be used in social services. For example, bus service operators might get a vision into time delays in bus services by processing and mining public bus performance data. Then, making ameliorative steps (e.g., adding more buses, rerouting some bus routes, etc.) results in improving the feeling of the passenger. We provide a Bayesian framework, which is applied on big data obtained from transportation system. Specifically, a number of Bayesian networks have been used in our framework to predict whether a bus will arrive late or early at a specific bus stop. We investigate and establish the optimum network settings and/or parameter permutations for each (bus stop, bus route, arrival time)-triplet. The results demonstrate that the proposed Bayesian framework effectively supports predictive analytics on big transportation data collected from the City of Winnipeg, Manitoba, Canada.
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