吉林大学学报(工学版) ›› 2022, Vol. 52 ›› Issue (11): 2618-2625.doi: 10.13229/j.cnki.jdxbgxb20210346

• 交通运输工程·土木工程 • 上一篇    

电动自行车出行特性及骑行决策行为建模

董春娇1(),董黛悦1,2,诸葛承祥3,甄理1   

  1. 1.北京交通大学 综合交通运输大数据应用技术交通运输行业重点实验室，北京 100044
    2.浙江省交通运输科学研究院 交通发展研究中心，杭州 310023
    3.香港理工大学 土地测量及地理资讯学系，香港 999077
  • 收稿日期:2021-04-20 出版日期:2022-11-01 发布日期:2022-11-16
  • 作者简介:董春娇（1982-），女，教授，博士. 研究方向：交通规划，交通安全，交通流理论，出行行为分析. E-mail： cjdong@bjtu.edu.cn
  • 基金资助:
    国家重点研发计划项目(2019YFF0301400)

Trip characteristics and decision⁃making behaviors modeling of electric bicycles riding

Chun-jiao DONG1(),Dai-yue DONG1,2,Cheng-xiang ZHU-GE3,Li ZHEN1   

  1. 1.Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport，Ministry of Transport，Beijing Jiaotong University，Beijing 100044，China
    2.Transportation Development Research Center，Zhejiang Scientific Research Institute of Transport，Hangzhou 310023，China
    3.The Department of Land Surveying and Geo-Informatics，The Hong Kong Polytechnic University，Hongkong 999077，China
  • Received:2021-04-20 Online:2022-11-01 Published:2022-11-16

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摘要：

通过开展问卷调查和断面交通流参数调查，分析了电动自行车出行特性和交通流量、速度特征。针对电动自行车正常骑行、占用机动车道骑行、在机动车之间穿插骑行和逆向骑行4种电动自行车骑行决策行为，建立了基于Elman神经网络和多元logistic回归的电动自行车骑行决策行为模型。以深圳市福田区为例，进行了实证研究，结果表明：基于Elman神经网络的电动自行骑行决策行为模型的预测效果明显优于基于多元logistic回归的电动自行车骑行决策行为模型，平均预测精度为91.62%，相比多元logistic回归模型提高了9.93%；构建的多元logistic回归模型能揭示影响因素和骑行决策行为之间的关联关系，可为制定减少电动自行车不安全骑行决策行为的策略和政策提供理论支撑。

关键词: 交通工程, 城市交通, 电动自行车, 骑行决策行为, Elman神经网络, 多元logistic回归

Abstract:

Through questionnaire survey and cross-section traffic flow parameter survey， the characteristics of electric bicycle travel， traffic flow and speed are analyzed. Aiming at four kinds of decision-making behaviors of electric bike， such as normal riding， occupying motorway riding， interleaving between vehicles and reverse riding， a decision-making behavior model of electric bike riding based on Elman neural network and multiple logistic regression was established. Finally， taking Futian District of Shenzhen as an example， this paper makes an empirical study. The results show that： the prediction effect of electric bicycle riding decision-making behavior model based on Elman neural network is significantly better than that based on multiple logistic regression， with an average prediction accuracy of 91.62%， which is 9.93% higher than that of multiple logistic regression model； the established multiple logistic regression model can reveal the relationship between influencing factors and riding decision-making behavior. The relationship can provide theoretical support for the formulation of strategies and policies to reduce the unsafe riding behavior of electric bikes.

Key words: traffic engineering, urban traffic, electric bike, riding decision behavior, Elman neural network, multivariate logistic regression

中图分类号: 

  • U491.1

图1

电动自行车出行特性"

图2

电动自行车流流量和速度调查地点分布图"

表1

电动自行车小时流量调查结果"

道路等级平均总流量/（辆?h?1）正常骑行占用机动车道骑行在机动车之间穿插骑行逆向骑行
数量占比/%数量占比/%数量占比/%数量占比/%
主干路298.0035.0011.15166.0055.203.001.3594.0032.30
次干路464.33157.1736.28216.3348.6554.007.4324.677.60
支路554.5083.5047.95407.0045.5564.006.500.000.00
平均值449.10118.0033.59244.4049.3445.806.0333.6011.02

图3

电动自行车骑行速度调查结果"

图4

基于Elman神经网络的电动自行车骑行决策行为模型"

图5

预测结果对比分析"

表2

基于多元logistic回归的电动自行车骑行决策行为模型参数估计结果"

决策行为变量β显著性
占道骑行常量-0.5750.456
收入5万以下-1.2260.007
收入5~10万-1.1380.006
收入10~20万-1.2840.003
收入20~50万-1.3080.005
回家时间18∶00~19∶000.7300.030
快递/外卖用途1.8760.000
车道过窄0.7840.000
机动车较少1.0370.000
公交车占道-0.5780.046
穿插骑行常量-1.8480.001
回家时间17∶00之前-1.0490.012
回家时间19∶00~22∶00-0.7390.045
出行时长0.4060.001
快递/外卖用途1.6820.001
路边停车1.0870.001
公交车停靠-0.5660.009
伺机转弯0.8270.002
逆向骑行常量-2.3470.001
回家时间17∶00~18∶000.9040.017
出行时长0.2570.005
快递/外卖用途1.1870.001
机动车流量大1.3210.001
左侧流量少0.5790.006
目的地在左侧0.5950.001
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