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Research on Short-Term Prediction Method of Demand Numberin Urban Public Bicycle Based on the ARIMA Model(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2016年03期
Page:
36-
Research Field:
计算机工程
Publishing date:

Info

Title:
Research on Short-Term Prediction Method of Demand Numberin Urban Public Bicycle Based on the ARIMA Model
Author(s):
Lin Yanping; Dou Wanfeng
School of Computer Science and Technology，Nanjing Normal University，Nanjing 210023，China
Keywords:
public bicycle; ARIMA model; demand number; short-term prediction
PACS:
U491.1
DOI:
10.3969/j.issn.1672-1292.2016.03.006
Abstract:
Prediction occupies an important position in study of urban public bicycle. Analyzing and predicting the demand numbers at every station in future can provide a basis，which managers allocate bicycles and the users make travel plan in advance. It is necessary to use the Autoregressive Integrated Moving Average（ARIMA）model，which models the demand number time series of public bicycle during peak hours of the week. Comparing with prediction error of the Baseline method，the results show that the average relative error of the value of the prediction and the actual are both lower than the Baseline prediction method for different stations. The prediction precision of the ARIMA model is relatively high，and the prediction result is credible. It provides theory and method of the prediction for management and use of the urban public bicycle.

References:

［1］ 姚遥，周扬军. 杭州市公共自行车系统规划［J］. 城市交通，2009，7（4）：30 -38.YAO Y，ZHOU Y J. Bike-sharing planning system in Hangzhou［J］. Urban transport of China，2009，7（４）：30-38. （in Chinese）
［2］ 曹静，宫建，杨孝宽. 解决北京市潮汐性交通拥堵的措施研究［J］. 武汉理工大学学报（交通科学与工程版），2009，33（6）：1 116-1 119.
CAO J，GONG J，YANG X K. Analysis of resolving reversible traffic congestion in Beijing［J］. Journal of Wuhan university of technology（transportation science & engineering edition），2009，33（６）：1 116-1 119. （in Chinese）
［3］ 徐叶冉子，沈瑾. 基于圆分布法和时间序列模型的公共自行车需求量分析［J］. 工业工程，2014，17（2）：54 -63.
XU Y R Z，SHEN J. Demand analysis of public bicycle system based on circular distribution method and time series model［J］. Industrial engineering journal，2014，17（2）：54-63. （in Chinese）
［4］ OLIVER O B，JAMES C，MICHAEL B. Mining bicycle sharing data for generating insights into sustainable transport systems［J］. Journal of transport geography，2014，34：262-273.
［5］ C ME E，OUKHELLOU L. Model-based count series clustering for bike sharing system usage mining，a case study with the V’lib’s system of Paris［J］. ACM transactions on intelligent systems and technology，2014，5（3）：1-21.
［6］ ADVAIT S，NEAL L，CECILIA M. Comparing cities’ cycling patterns using online shared bicycle maps［J］. Transportation，2015，42：541-559.
［7］ BOX G E P，JENKINS G M. Time series analysis：forecasting and control［M］. San Francisco：Holden Day，1976.
［8］ JONATHAN C，TIEBEI L，DAVID R，et al. Spatio-temporal patterns of a public bicycle sharing program：the effect of weather and calendar events［J］. Journal of transport geography，2014，41：292-305.

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Last Update: 2016-09-30