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基于深度學(xué)習(xí)的車(chē)聯(lián)邊緣網(wǎng)絡(luò)交通事故風(fēng)險(xiǎn)預(yù)測(cè)算法研究

趙海濤,  程慧玲,  丁儀,  張暉,  朱洪波

趙海濤, 程慧玲, 丁儀, 張暉, 朱洪波. 基于深度學(xué)習(xí)的車(chē)聯(lián)邊緣網(wǎng)絡(luò)交通事故風(fēng)險(xiǎn)預(yù)測(cè)算法研究[J]. 電子與信息學(xué)報(bào), 2020, 42(1): 50-57. doi: 10.11999/JEIT190595
引用本文: 趙海濤, 程慧玲, 丁儀, 張暉, 朱洪波. 基于深度學(xué)習(xí)的車(chē)聯(lián)邊緣網(wǎng)絡(luò)交通事故風(fēng)險(xiǎn)預(yù)測(cè)算法研究[J]. 電子與信息學(xué)報(bào), 2020, 42(1): 50-57. doi: 10.11999/JEIT190595
Haitao ZHAO, Huiling CHENG, Yi DING, Hui ZHANG, Hongbo ZHU. Research on Traffic Accident Risk Prediction Algorithm of Edge Internet of Vehicles Based on Deep Learning[J]. Journal of Electronics & Information Technology, 2020, 42(1): 50-57. doi: 10.11999/JEIT190595
Citation: Haitao ZHAO, Huiling CHENG, Yi DING, Hui ZHANG, Hongbo ZHU. Research on Traffic Accident Risk Prediction Algorithm of Edge Internet of Vehicles Based on Deep Learning[J]. Journal of Electronics & Information Technology, 2020, 42(1): 50-57. doi: 10.11999/JEIT190595

基于深度學(xué)習(xí)的車(chē)聯(lián)邊緣網(wǎng)絡(luò)交通事故風(fēng)險(xiǎn)預(yù)測(cè)算法研究

doi: 10.11999/JEIT190595 cstr: 32379.14.JEIT190595
  • 1.

    教育部泛在網(wǎng)絡(luò)健康服務(wù)系統(tǒng)工程研究中心 南京 210003

  • 2.

    江蘇省無(wú)線(xiàn)通信重點(diǎn)實(shí)驗(yàn)室 南京 210003

  • 3.

    南京郵電大學(xué)通信與信息工程學(xué)院 南京 210003

基金項(xiàng)目: 國(guó)家自然科學(xué)基金(61771252)，江蘇省自然科學(xué)基金(BK20171444)，江蘇省高校重點(diǎn)自然科學(xué)研究重大項(xiàng)目(18KJA510005)，江蘇省“六大人才高峰”B類(lèi)資助項(xiàng)目(DZXX-041)，江蘇省科協(xié)青年科技人才托舉工程資助培養(yǎng)項(xiàng)目，江蘇省研究生科研創(chuàng)新計(jì)劃項(xiàng)目(KYCX19_0949)
詳細(xì)信息
    作者簡(jiǎn)介:

    趙海濤：男，1983年生，博士，副教授，研究方向?yàn)槲锫?lián)網(wǎng)與移動(dòng)邊緣計(jì)算

    程慧玲：女，1995年生，碩士生，研究方向?yàn)橐苿?dòng)邊緣計(jì)算與人工智能

    丁儀：女，1995年生，碩士生，研究方向?yàn)槲锫?lián)網(wǎng)路由優(yōu)化和邊緣計(jì)算

    張暉：男，1982年生，博士，副教授，研究方向?yàn)槲磥?lái)無(wú)線(xiàn)網(wǎng)絡(luò)

    朱洪波：男，1956年生，博士，教授，研究方向?yàn)橐苿?dòng)通信與寬帶無(wú)線(xiàn)技術(shù)、無(wú)線(xiàn)通信與電磁兼容

    通訊作者:

    趙海濤　zhaoht@njupt.edu.cn

  • 中圖分類(lèi)號(hào): TP399

Research on Traffic Accident Risk Prediction Algorithm of Edge Internet of Vehicles Based on Deep Learning

  • 1.

    Ministry of Education Ubiquitous Network Health Service System Engineering Research Center, Nanjing 210003, China

  • 2.

    Jiangsu Key Wireless Communication Laboratory, Nanjing 210003, China

  • 3.

    College of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

Funds: The National Natural Science Foundation of China (61771252), The Natural Science Foundation Project of Jiangsu Province (BK20171444), The Jiangsu Province University Natural Science Research Major Project (18KJA510005), “The Six talents High Peaks” Class B Funding Project of Jiangsu Province (DZXX-041), The Jiangsu Provincial Association for Science and Technology Talents Entrustment Project, Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX19_0949)
  • 摘要: 針對(duì)傳統(tǒng)交通事故風(fēng)險(xiǎn)預(yù)測(cè)算法無(wú)法自動(dòng)判別數(shù)據(jù)特征，且模型表達(dá)能力差等問(wèn)題。該文提出一種基于深度學(xué)習(xí)的車(chē)聯(lián)邊緣網(wǎng)絡(luò)交通事故風(fēng)險(xiǎn)預(yù)測(cè)算法，該算法首先針對(duì)車(chē)載自組織網(wǎng)絡(luò)中采集的大量交通數(shù)據(jù)，采用邊緣服務(wù)器中建立的卷積神經(jīng)網(wǎng)絡(luò)自主提取多維特征，經(jīng)歸一化、去均值等預(yù)處理后，再將得到的新變量輸入卷積層、采樣層進(jìn)行訓(xùn)練，最后根據(jù)全連接層輸出的判別值，得到模擬預(yù)測(cè)交通事故發(fā)生的風(fēng)險(xiǎn)性。仿真結(jié)果表明，該算法被驗(yàn)證能夠預(yù)測(cè)交通事故發(fā)生的風(fēng)險(xiǎn)性，較傳統(tǒng)的機(jī)器學(xué)習(xí)算法BP神經(jīng)網(wǎng)絡(luò)、邏輯回歸具有更低的損失與更高的預(yù)測(cè)準(zhǔn)確度。
    Abstract: For the problem that the traditional traffic accident risk prediction algorithm can not automatically discriminate data features, and the model expression ability is poor, a traffic accident risk prediction algorithm based on deep learning is proposed. The algorithm firstly extracts multi-dimensional features by using the convolutional neural network established in the edge server for a large amount of traffic data collected in the edge network of vehicles. After normalization, de-equalization and other pre-processing, the new variables are input into the convolutional layer and the pooling layer for training. Finally, based on the output discrimination value of the fully connected layer, the risk of traffic accidents can be predicted by simulation. The simulation results show that the algorithm is validated to predict the risk of traffic accidents, and has lower loss and higher prediction accuracy than the traditional machine learning BP neural network algorithm and Logical Regression algorithm.
    • HTML全文

  • 圖  1  輸入層具有3個(gè)神經(jīng)元的感知機(jī)建模圖

    圖  2  含有多層隱含層的卷積神經(jīng)網(wǎng)絡(luò)交通事故風(fēng)險(xiǎn)預(yù)測(cè)建模圖

    圖  3  車(chē)聯(lián)邊緣網(wǎng)絡(luò)系統(tǒng)架構(gòu)圖

    圖  4  交通事故風(fēng)險(xiǎn)預(yù)測(cè)算法流程圖

    圖  5  卷積神經(jīng)網(wǎng)絡(luò)與BP神經(jīng)網(wǎng)絡(luò)、邏輯回歸預(yù)測(cè)損失對(duì)比圖

    圖  6  卷積神經(jīng)網(wǎng)絡(luò)較BP神經(jīng)網(wǎng)絡(luò)、邏輯回歸預(yù)測(cè)準(zhǔn)確度對(duì)比圖

    圖  7  不同激活函數(shù)對(duì)卷積、BP神經(jīng)網(wǎng)絡(luò)、邏輯回歸算法預(yù)測(cè)損失的影響對(duì)比圖

    圖  8  不同激活函數(shù)對(duì)卷積、BP神經(jīng)網(wǎng)絡(luò)、邏輯回歸算法預(yù)測(cè)準(zhǔn)確度的影響對(duì)比圖

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  • 收稿日期:  2019-08-06
  • 修回日期:  2019-11-05
  • 網(wǎng)絡(luò)出版日期:  2019-11-13
  • 刊出日期:  2020-01-21

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