| 引用本文: | 林培群,雷永巍,张孜,陈丽甜.面向手机信令数据的交通枢纽人流量短时预测算法[J].哈尔滨工业大学学报,2018,50(9):89.DOI:10.11918/j.issn.0367-6234.201704082 |
| LIN Peiqun,LEI Yongwei,ZHANG Zi,CHEN Litian.Short-term forecasting of urban transport hubs based on mobile phone signaling data[J].Journal of Harbin Institute of Technology,2018,50(9):89.DOI:10.11918/j.issn.0367-6234.201704082 |
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| 摘要: |
| 为实现对重点区域人群聚集动态的有效掌握，保障区域人群的及时疏运，预防群体性安全事故的发生，以广州市火车站枢纽区域为例，通过对海量手机信令数据进行信息处理，结合地理信息系统将手机信令数据映射至研究区域，实现区域人流量的实时统计，同时分析了大都市火车站枢纽区域春运人流量变化情况，得出春运期间区域人流量存在周期性变化的规律，以此为基础，构建了以平均绝对百分比误差最小的k值自适应计算模型，设计了基于手机信令数据的城市交通枢纽人流量k近邻预测算法，并以节假日与非节假日两种不同交通模式环境进行算法测试.结果表明：所建立的预测算法在两种模式下其平均绝对百分比误差PMAPE分别在6%与5%以内，均能够较为准确地对区域人流量进行预测.
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| 关键词: 城市交通 交通枢纽 手机信令数据 k近邻算法 人流量短时预测 |
| DOI：10.11918/j.issn.0367-6234.201704082 |
| 分类号:U491 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(61572233)； 广东省科技计划(2016A6,6A030313786)； 广州市科技计划(201604016091) |
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| Short-term forecasting of urban transport hubs based on mobile phone signaling data |
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LIN Peiqun1,LEI Yongwei1,2,ZHANG Zi3,CHEN Litian1
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(1. School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510640, China; 2. Guangdong Province Communications Planning and Design Institute Co., Ltd., Guangzhou 510507, China; 3. Communications Commission of Guangzhou Municipality, Guangzhou 510620, China)
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| Abstract: |
| To effectively grasp the dynamic information of key regional population aggregation, guarantee timely dispatching of regional population, and prevent group security accident, this paper took Guangzhou Railway Station hub area as an example, and obtained real-time statistics of its regional population by carrying out information processing based on massive mobile phone signaling data and mapping the data to the study area using geographic information system. Meanwhile, it analyzed the change of the population in this area during Spring Festival, and summarized the periodic variation characteristics of the regional population. Based on this, this paper constructed the k value adaptive calculation model with minimum absolute percentage error, and built the k-Nearest Neighborhood algorithm based on the mobile communication data to predict urban hub traffic. The algorithm was tested under the traffic conditions on weekends and weekdays near Guangzhou Railway Station. The results showed that the average absolute percentage error (MAPE) of the prediction algorithm was around 5%. It is more accurate to effectively predict regional population.
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| Key words: urban transport transport hub mobile phone signaling data k-Nearest Neighborhood algorithm short-term forecasting |
