An Intelligent Predictive Analytics System for Transportation Analytics on Open Data Towards the Development of a Smart City

被引:38
|
作者
Audu, Abdul-Rasheed A. [1 ]
Cuzzocrea, Alfredo [2 ]
Leung, Carson K. [1 ]
MacLeod, Keaton A. [1 ]
Ohin, Nibrasul, I [1 ]
Pulgar-Vidal, Nadege C. [1 ]
机构
[1] Univ Manitoba, Winnipeg, MB, Canada
[2] Univ Trieste, Trieste, Italy
基金
加拿大自然科学与工程研究理事会;
关键词
Intelligent system; Transportation analytics; Open data; Public transportation; Bus; Bus delay; Data analytics; Frequent pattern mining; Predictive analytics; Smart city; TRAVEL-TIME PREDICTION;
D O I
10.1007/978-3-030-22354-0_21
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
As time is a precious asset, bus riders would desire to get accurate information about bus arrival time. Although different research approaches have been developed to correctly predict bus arrival time, very few of them produce highly precise and accurate results based on open data. In this paper, we present an intelligent system designed for transportation analytics on open data such as bus delay data. Specifically, the system accesses open data to analyze public transport data-such as historical bus arrival time-for urban analytics; it then conducts data analytics and mining to discover frequent patterns. Based on the discovered patterns, the system makes predictions on whether the bus arrives on time or is being late. Evaluation on real-life open data provided by a Canadian city show the effectiveness and prediction accuracy of our intelligent system in transportation analytics on open data. The results are encouraging towards the goal of developing smart cities.
引用
收藏
页码:224 / 236
页数:13
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