Traffic behavior spectrum analysis method based on regional road live data

doi:10.11772/j.issn.1001-9081.2018081699

Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (3): 907-912.DOI: 10.11772/j.issn.1001-9081.2018081699

Previous Articles Next Articles

Traffic behavior spectrum analysis method based on regional road live data

HUANG Fengyu^1,2, WU Yefu^1,2, CHEN Jingren^1,2, WU Bing^1,2

1. College of Computer Science and Technology, Wuhan University of Technology, Wuhan Hubei 430063, China;
2. Hubei Key Laboratory of Transportation Internet of Things(Wuhan University of Technology), Wuhan Hubei 430063, China

Received:2018-08-17 Revised:2018-10-21 Online:2019-03-11 Published:2019-03-10

基于区域道路实况数据的交通行为谱分析方法

黄丰雨^1,2, 吴业福^1,2, 陈镜任^1,2, 吴冰^1,2

1. 武汉理工大学计算机科学与技术学院, 武汉 430063;
2. 交通物联网技术湖北省重点实验室(武汉理工大学), 武汉 430063

通讯作者: 吴业福
作者简介:黄丰雨(1994-),男,江西抚州人,硕士研究生,主要研究方向:计算机网络、智能交通;吴业福(1964-),男,湖北红安人,副教授,博士,主要研究方向:交通信息与安全、人工智能;陈镜任(1996-),女,江西南昌人,硕士研究生,主要研究方向:机器学习、大数据分析、交通安全;吴冰(1992-),男,湖北武汉人,硕士,主要研究方向:大数据分析、交通安全。

Abstract

Abstract:

Aiming at the problem that the characteristic and evaluation indexes for reasearch of traffic behavior spectrum are incompleted both at home and abroad and quantitative analysis cannot be performed in the research, the corresponding characteristic and evaluation indexes were defined to establish a complete traffic behavior spectrum system with quantitative analysis of regional traffic behavior data. Firstly, based on the characteristics of traffic behavior, an improved Analytic Hierarchy Process (AHP) was used to classify the traffic order types. Secondly, Real-Time System Integration (RTSI) algorithm with multi-data fusion was used to comprehensively evaluate the traffic safety of a certain road. Finally, a traffic behavior spectrum analysis tool was developed, calculating traffic safety index of a road section according to the traffic live data, and analyzing traffic behavior in the section more completely.

Key words: traffic behavior spectrum, analysis tool of traffic behavior spectrum, regional road live data, traffic behavior characteristic, multi-data fusion

摘要：

针对国内外有关交通行为谱研究的特征指标和评价指标不完善，且不能定量分析等问题，设计并定义了相应的特征指标和评价指标以建立完整的、能够定量化的分析区域交通行为数据的交通行为谱体系。首先基于交通行为特征，采用改进的层次分析法（AHP）对交通秩序类型进行了分类；其次采用多数据融合的实时系统集成（RTSI）算法对某路段交通安全性进行综合评判。最后开发了交通行为谱分析工具，该工具能根据交通实况数据计算区域路段的交通安全指数，较为完备地分析该路段内的交通行为。

关键词: 交通行为谱, 交通行为谱分析工具, 区域道路实况数据, 交通行为特征, 多数据融合

CLC Number:

TP391.4

HUANG Fengyu, WU Yefu, CHEN Jingren, WU Bing. Traffic behavior spectrum analysis method based on regional road live data[J]. Journal of Computer Applications, 2019, 39(3): 907-912.

黄丰雨, 吴业福, 陈镜任, 吴冰. 基于区域道路实况数据的交通行为谱分析方法[J]. 计算机应用, 2019, 39(3): 907-912.

References

[1] 陈旭.交通秩序数字化管理平台[D].长春:吉林大学,2016:28-31.(CHEN X. Digital management platform of traffic order[D]. Changchun:Jilin University, 2016:28-31.)
[2] 汪益敏,郭继幸,李林生,等.基于层次分析法的公路运营期路基状况评价[J].华南理工大学学报(自然科学版),2017,45(6):37-43.(WANG Y M, GUO J X, LI L S, et al. Condition evaluation of subgrade in highway operation period based on analytical hierarchy process[J]. Journal of South China University of Technology (Natural Science Edition), 2017, 45(6):37-43.)
[3] 刘家玮.道路交通环境对交通安全的影响及解决对策分析[J].中国管理信息化,2017,20(18):207-208.(LIU J W. Influence of road traffic environment on traffic safety and analysis of countermeasures[J]. China Management Informationization, 2017, 20(18):207-208.)
[4] 王坤峰,李镇江,汤淑明.基于多特征融合的视频交通数据采集方法[J].自动化学报,2011,37(3):322-330.(WANG K F, LI Z J, TANG S M. Visual traffic data collection approach based on multi-features fusion[J]. Acta Automatica Sinica, 2011, 37(3):322-330.)
[5] YU H, GUO M. A self-adapting data collection approach in wireless sensor networks for urban traffic information monitoring[C]//Proceedings of the 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems. Piscataway, NJ:IEEE, 2010:196-200.
[6] LUO C, WU F, SUN J, et al. Efficient measurement generation and pervasive sparsity for compressive data gathering[J]. IEEE Transactions on Wireless Communications, 2010, 9(12):3728-3738.
[7] 郭礼照,杨三强,周良川,等.基于模糊层次分析法的高速公路交通安全综合评价[J].交通科技与经济,2017,19(2):8-12.(GUO L Z, YANG S Q, ZHOU L C, et al. Traffic safety comprehensive assessment of expressway based on fuzzy analytic hierarchy process[J]. Technology and Economy in Areas of Communications, 2017, 19(2):8-12.)
[8] WEN H, CHEN X. Traffic safety evaluation model for mountain road under bad weather based fuzzy comprehensive evaluation[C]//Proceedings of the 2011 International Conference on Multimedia Technology. Piscataway, NJ:IEEE, 2011:1865-1868.
[9] 李金花,刘莉,张宁,等.基于仿真的城市道路交叉口交通安全评价[J]. 中国安全科学学报,2016,26(12):41-46.(LI J H, LIU L, ZHANG N, et al. Simulation-based evaluation of urban road intersection traffic safety[J]. China Safety Science Journal, 2016, 26(12):41-46.)
[10] 罗强,胡三根,龚华炜,等.城市道路交通安全评价体系研究与模型构建[J].广西大学学报(自然科学版),2017,42(2):587-592.(LUO Q, HU S G, GONG H W, et al. Model building and research of urban road traffic safety evaluation system[J]. Journal of Guangxi University (Natural Science Edition), 2017, 42(2):587-592.)
[11] 李文勇,余子威,王涛,等.基于交叉口复杂度的交叉口秩序模型[J]. 广西大学学报(自然科学版),2016,41(5):1538-1544.(LI W Y, YU Z W, WANG T, et al. An intersection order model based on intersection complexity[J]. Journal of Guangxi University (Natural Science Edition),2016, 41(5):1538-1544.)
[12] TANG T-Q, WANG Y-P, YANG X-B, et al. A multilane traffic flow model accounting for lane width, lane-changing and the number of lanes[J]. Networks and Spatial Economics, 2014, 14(3/4):465-483.
[13] LI S B, LIN Y, REN H L, et al. Research on control strategies and optimal signal timing for traffic congestion based on prediction[J]. Applied Mechanics and Materials, 2011,40/41:858-865.
[14] 张惠玲,尹宝计.限速值对信号交叉口交通安全的影响分析[J].中国安全科学学报,2015,25(2):112-116.(ZHANG H L, YIN B J. Analyzing effects of speed limit on traffic safety at signalized intersection[J]. China Safety Science Journal, 2015, 25(2):112-116.)
[15] 张南,秦兵杰.基于交通冲突技术的高速公路合流区段安全性研究[J].综合运输,2016,38(10):55-59.(ZHANG N, QIN B J. Safety evaluation of freeway merge segment based on traffic conflict technique[J]. China Transportation Review, 2016, 38(10):55-59.)

Traffic behavior spectrum analysis method based on regional road live data

基于区域道路实况数据的交通行为谱分析方法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

[1]	. Privacy preserving localization of surveillance images based on large vision models [J]. Journal of Computer Applications, 0, (): 0-0.
[2]	. Data augmentation method for abnormal passenger behavior in elevators based on dynamic graph convolutional network [J]. Journal of Computer Applications, 0, (): 0-0.
[3]	. Text-based person retrieval method based on multi-granularity shared semantic center association [J]. Journal of Computer Applications, 0, (): 0-0.
[4]	. Distribution adaptation and dynamic curriculum pseudo-labeling framework for semi-supervised fire detection [J]. Journal of Computer Applications, 0, (): 0-0.
[5]	. Image Caption Method Based on Swin-Transformer and Multi-Scale Feature Fusion [J]. Journal of Computer Applications, 0, (): 0-0.
[6]	Wei XIONG, Yibo CHEN, Lizhen ZHANG, Qian YANG, Qin ZOU. Self-supervised monocular depth estimation using multi-frame sequence images [J]. Journal of Computer Applications, 2024, 44(12): 3907-3914.
[7]	Keyi FU, Gaocai WANG, Man WU. Few-shot object detection method based on improved region proposal network and feature aggregation [J]. Journal of Computer Applications, 2024, 44(12): 3790-3797.
[8]	Chenghao YANG, Jie HU, Hongjun WANG, Bo PENG. Incomplete multi-view clustering algorithm based on attention mechanism [J]. Journal of Computer Applications, 2024, 44(12): 3784-3789.
[9]	Peng FANG, Fan ZHAO, Baoquan WANG, Yi WANG, Tonghai JIANG. Development， technologies and applications of blockchain 3.0 [J]. Journal of Computer Applications, 2024, 44(12): 3647-3657.
[10]	Yudong PANG, Zhixing LI, Weijie LIU, Tianhao LI, Ningning WANG. Small target detection model in overlooking scenes on tower cranes based on improved real-time detection Transformer [J]. Journal of Computer Applications, 2024, 44(12): 3922-3929.
[11]	Xin ZHAO, Xinjie LI, Jian XU, Buyun LIU, Xiang BI. Parallel medical image registration model based on convolutional neural network and Transformer [J]. Journal of Computer Applications, 2024, 44(12): 3915-3921.
[12]	. Multi-stage point cloud completion network based on adaptive neighborhood feature fusion [J]. Journal of Computer Applications, 0, (): 0-0.
[13]	. Neural architecture search for multi-tissue segmentation using convolutional and transformer-based networks in glioma segmentation [J]. Journal of Computer Applications, 0, (): 0-0.
[14]	CHEN Wei, SHI Changyong, MA Chuanxiang. Crop disease recognition method based on multimodal data fusion#br# #br# [J]. Journal of Computer Applications, 0, (): 0-0.
[15]	. State-of-The-Art Survey on Hardware Acceleration for Ray Tracing [J]. Journal of Computer Applications, 0, (): 0-0.