research-article

Urban traffic congestion estimation and prediction based on floating car trajectory data

Authors:

Benshi ZhangAuthors Info & Claims

Future Generation Computer Systems, Volume 61, Issue C

Pages 97 - 107

https://doi.org/10.1016/j.future.2015.11.013

Published: 01 August 2016 Publication History

Abstract

Traffic flow prediction is an important precondition to alleviate traffic congestion in large-scale urban areas. Recently, some estimation and prediction methods have been proposed to predict the traffic congestion with respect to different metrics such as accuracy, instantaneity and stability. Nevertheless, there is a lack of unified method to address the three performance aspects systematically. In this paper, we propose a novel approach to estimate and predict the urban traffic congestion using floating car trajectory data efficiently. In this method, floating cars are regarded as mobile sensors, which can probe a large scale of urban traffic flows in real time. In order to estimate the traffic congestion, we make use of a new fuzzy comprehensive evaluation method in which the weights of multi-indexes are assigned according to the traffic flows. To predict the traffic congestion, an innovative traffic flow prediction method using particle swarm optimization algorithm is responsible for calculating the traffic flow parameters. Then, a congestion state fuzzy division module is applied to convert the predicted flow parameters to citizens' cognitive congestion state. Experimental results show that our proposed method has advantage in terms of accuracy, instantaneity and stability. Floating car trajectory data can be used to predict traffic congestion effectively.A Fuzzy Comprehensive Evaluation method with dynamic adaptive weight is introduced.A Traffic Flow Prediction method utilizing particle swarm optimization is proposed.Experiments verify methods' performances in accuracy, instantaneity and stability.

References

[1]

Y. Zheng, L. Capra, O. Wolfson, Urban computing: concepts, methodologies, and applications, ACM Trans. Intell. Syst. Technol., 5 (2014) 38.

Digital Library

[2]

M.B. Younes, A. Boukerche, A performance evaluation of an efficient traffic congestion detection protocol (ECODE) for intelligent transportation systems, Ad Hoc Netw., 24 (2015) 317-336.

Digital Library

[3]

Y. Zheng, Trajectory data mining: An overview, ACM Trans. Intell. Syst. Technol. (2015).

Digital Library

[4]

Q.Y. Yang, J.Z. Wang, X.M. Song, Urban traffic congestion prediction using floating car trajectory data, in: Algorithms and Architectures for Parallel Processing, Springer International Publishing, 2015, pp. 18-30.

[5]

W. Pattara-Atikom, P. Pongpaibool, S. Thajchayapong, Estimating road traffic congestion using vehicle velocity, in: Proceedings of 6th International Conference on ITS Telecommunications, Chengdu, China, 2006, pp. 1001-1004.

[6]

J. Yoon, B. Noble, M. Liu, Surface street traffic estimation, in: Proceedings of the 5th International ACM Conference on Mobile Systems, Applications and Services, New York, USA, 2007, pp. 220-232.

Digital Library

[7]

Y. Chen, L. Gao, Z. Li, et al., A new method for urban traffic state estimation based on vehicle tracking algorithm, in: Proceedings of Intelligent Transportation Systems Conference, Seattle, WA, 2007, pp. 1097-1101.

[8]

W. Shi, Q.J. Kong, Y. Liu, A GPS/GIS integrated system for urban traffic flow analysis, in: Proceedings of 11th International IEEE Conference on Intelligent Transportation Systems, Beijing, China, 2008, pp. 844-849.

[9]

Q.J. Kong, Q. Zhao, C. Wei, Efficient traffic state estimation for large-scale urban road networks, IEEE Trans Intell. Transp. Syst., 14 (2013) 398-407.

Digital Library

[10]

Q.J. Kong, Z. Li, Y. Chen, An approach to urban traffic state estimation by fusing multisource information, IEEE Trans. Intell. Transp. Syst., 10 (2009) 499-511.

Digital Library

[11]

J.D. Zhang, J. Xu, S.S. Liao, Aggregating and sampling methods for processing GPS data streams for traffic state estimation, IEEE Trans. Intell. Transp. Syst., 14 (2013) 1629-1641.

Digital Library

[12]

L. Li, X. Chen, L. Zhang, Multimodel ensemble for freeway traffic state estimations, IEEE Trans. Intell. Transp. Syst., 15 (2014) 1323-1336.

[13]

Y. Feng, J. Hourdos, G.A. Davis, Probe vehicle based real-time traffic monitoring on urban roadways, Transp. Res. C, 40 (2014) 160-178.

[14]

J. Lu, L. Cao, Congestion evaluation from traffic flow information based on fuzzy logic, in: Proceedings of the 6th International IEEE Conference on Intelligent Transportation Systems, vol. 1, Shanghai, China, 2003, pp. 50-53.

[15]

P. Pongpaibool, P. Tangamchit, K. Noodwong, Evaluation of road traffic congestion using fuzzy techniques, in: Proceeding of 2007 IEEE Region 10 Conference, Taipei, Taiwan, 2007, pp. 1-4.

[16]

Y. Chen, Y. Liu, A new method for GPS-based urban vehicle tracking using pareto frontier and fuzzy comprehensive judgment, in: Proceedings of 11th International IEEE Conference on Geoscience and Remote Sensing Symposium, Barcelona, Spain, 2007, pp. 683-686.

[17]

H. Shankar, P.L.N. Raju, K.R.M. Rao, Multi model criteria for the estimation of road traffic congestion from traffic flow information based on Fuzzy logic, J. Transp. Technol., 2 (2012) 50.

[18]

H. Su, S. Yu, Hybrid GA based online support vector machine model for short-term traffic flow forecasting, in: Advanced Parallel Processing Technologies, Springer, Berlin, Heidelberg, 2007, pp. 743-752.

Digital Library

[19]

C.T. Cao, J.M. Xu, Improved particle swarm optimized SVM for short-term traffic flow predication, in: Proceedings of the 26th Chinese Control Conference, Zhangjiajie, Hunan, China, 2007, pp. 6-9.

[20]

Y. Xu, B. Wang, Q. Kong, et al., Spatio-temporal variable selection based support vector regression for urban traffic flow prediction, in: Proceeding of the 93rd Annual Meeting of the Transportation Research Board, Washington, DC, 2014, pp. 14-1994.

[21]

W.C. Hong, Y. Dong, F. Zheng, Hybrid evolutionary algorithms in a SVR traffic flow forecasting model, Appl. Math. Comput., 217 (2011) 6733-6747.

[22]

M.W. Li, W.C. Hong, H.G. Kang, Urban traffic flow forecasting using Gauss-SVR with cat mapping, cloud model and PSO hybrid algorithm, Neurocomputing, 99 (2013) 230-240.

Digital Library

[23]

J. Wang, Q. Shi, Short-term traffic speed forecasting hybrid model based on Chaos-Wavelet analysis-support vector machine theory, Transp. Res. C, 27 (2013) 219-232.

[24]

F. Wang, G. Tan, C. Deng, et al., Real-time traffic flow forecasting model and parameter selection based on ε -SVR, in: Proceedings of the 7th IEEE World Congress on Intelligent Control and Automation, Chongqing, China, 2008, pp. 2870-2875.

[25]

J. Gong, L. Qi, M.Y. Liu, et al., Forecasting urban traffic flow by SVR, in: Proceedings of the 25th Chinese IEEE Control and Decision Conference, Guiyang, China, 2013, pp. 981-984.

[26]

H. Chen, H.A. Rakha, S. Sadek, Real-time freeway traffic state prediction: A particle filter approach, in: Proceedings of the 14th International IEEE Conference on Intelligent Transportation Systems, ITSC, Washington, DC, USA, 2011, pp. 626-631.

[27]

S. Dunne, B. Ghosh, Regime-based short-term multivariate traffic condition forecasting algorithm, J. Transp. Eng., 138 (2011) 455-466.

[28]

W. Min, L. Wynter, Real-time road traffic prediction with spatio-temporal correlations, Transp. Res. C, 19 (2011) 606-616.

[29]

X. Zhang, E. Onieva, A. Perallos, Hierarchical fuzzy rule-based system optimized with genetic algorithms for short term traffic congestion prediction, Transp. Res. C, 43 (2014) 127-142.

[30]

R. Herring, A. Hofleitner, S. Amin, et al., Using mobile phones to forecast arterial traffic through statistical learning, in: Proceedings of the 89th Transportation Research Board Annual Meeting, Washington DC, USA, 2010, pp. 10-2493.

[31]

P.S. Castro, D. Zhang, S. Li, Urban traffic modelling and prediction using large scale taxi GPS traces, in: Pervasive Computing, Springer, Berlin, Heidelberg, 2012, pp. 57-72.

Digital Library

[32]

X. Zhou, W. Wang, L. Yu, Traffic flow analysis and prediction based on GPS data of floating cars, in: Proceedings of the 2012 International Conference on Information Technology and Software Engineering, Springer, Berlin, Heidelberg, 2013, pp. 497-508.

[33]

C.C. Chang, C.J. Lin, LIBSVM: a library for support vector machines, ACM Trans. Intell. Syst. Technol., 2 (2011) 1:27.

Digital Library

[34]

J. Kennedy, R. Eberhart, Particle swarm optimization, in: Proceedings of the IEEE International Conference on Neural Networks, vol. 4 (2), 1995, pp. 1942-1948.

[35]

. http://www.datatang.com/data/44502

[36]

Beijing traffic development research center. The transportation development annual report at 2012 of Beijing city, 2012. http://www.bjtrc.org.cn/JGJS.aspx?id=5.2&Menu=GZCG.

Cited By

Ounoughi CBen Yahia S(2024)Sequence to sequence hybrid Bi-LSTM model for traffic speed predictionExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121325236:COnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121325
Liu CNiu XMa YShao SWang B(2024)TL-TSDEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108365133:PDOnline publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1016/j.engappai.2024.108365
Long QWang HLi THuang LWang KWu QLi GLiang YYu LLi YSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Practical Synthetic Human Trajectories Generation Based on Variational Point ProcessesProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599888(4561-4571)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599888
Show More Cited By

Recommendations

Urban Traffic Congestion Prediction Using Floating Car Trajectory Data
ICA3PP 2015: Proceedings, Part II, of the 15th International Conference on Algorithms and Architectures for Parallel Processing - Volume 9529

Traffic congestion prediction is an important precondition to promote urban sustainable development. Nevertheless, there is a lack of a unified prediction method to address the performance metrics, such as accuracy, instantaneity and stability, ...
Characteristics of Urban Road Non-recurrent Traffic Congestion Based on Floating Car Data
EITCE '20: Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering

Non-recurrent traffic congestion caused by traffic accidents is one of the most important factors that affect the operational efficiency of urban road networks. In this paper, the floating car data (FCD) and the traffic accident data in Wuhan were ...
A hybrid deep learning model for urban expressway lane-level mixed traffic flow prediction
Abstract
Precise real-time traffic flow prediction is crucial for route guidance and traffic fine control. With the development of autonomous driving, the mixed traffic flow state composed of Connected Automated Vehicles (CAVs) and Human-driven Vehicles (...

Comments

Information & Contributors

Information

Published In

cover image Future Generation Computer Systems

Future Generation Computer Systems Volume 61, Issue C

August 2016

138 pages

ISSN:0167-739X

Issue’s Table of Contents

Copyright © Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 August 2016

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

40
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 16 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Ounoughi CBen Yahia S(2024)Sequence to sequence hybrid Bi-LSTM model for traffic speed predictionExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121325236:COnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121325
Liu CNiu XMa YShao SWang B(2024)TL-TSDEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108365133:PDOnline publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1016/j.engappai.2024.108365
Long QWang HLi THuang LWang KWu QLi GLiang YYu LLi YSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Practical Synthetic Human Trajectories Generation Based on Variational Point ProcessesProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599888(4561-4571)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599888
Zhang JHuang QHuang YDing QTsai P(2023)DP-TrajGANFuture Generation Computer Systems10.1016/j.future.2022.12.027142:C(25-40)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1016/j.future.2022.12.027
Zhang ZYang Y(2023)Time Pattern Recognition of Traffic Flow in Terminal Area Based on Community DivisionMobile Networks and Applications10.1007/s11036-022-02079-227:6(2543-2552)Online publication date: 3-Jan-2023
https://dl.acm.org/doi/10.1007/s11036-022-02079-2
Wang LYu ZGuo BYang DMa LLiu ZXiong F(2022)Data-driven Targeted Advertising Recommendation System for Outdoor BillboardACM Transactions on Intelligent Systems and Technology10.1145/349515913:2(1-23)Online publication date: 5-Jan-2022
https://dl.acm.org/doi/10.1145/3495159
Martinez-Pastor BNogal MO’Connor ATeixeira R(2022)Identifying critical and vulnerable linksInternational Journal of Critical Infrastructure Protection10.1016/j.ijcip.2022.10057039:COnline publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1016/j.ijcip.2022.100570
Jiang PLiu ZZhang LWang J(2022)Advanced traffic congestion early warning system based on traffic flow forecasting and extenics evaluationApplied Soft Computing10.1016/j.asoc.2022.108544118:COnline publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1016/j.asoc.2022.108544
Halim ZKhan ASulaiman MAnwar SNawaz M(2022)On finding optimum commuting path in a road networkTransactions on Emerging Telecommunications Technologies10.1002/ett.378633:2Online publication date: 14-Feb-2022
https://dl.acm.org/doi/10.1002/ett.3786
Sankaranarayanan MMathew SMala C. (20ee293f-d4d9-47f8-8ce4-0ddfa2e6ff42 (2021)Road Traffic Congestion (TraCo) Estimation Using Multi-Layer Continuous Virtual Loop (MCVL)International Journal of Intelligent Information Technologies10.4018/IJIIT.202104010317:2(1-26)Online publication date: 1-Apr-2021
https://dl.acm.org/doi/10.4018/IJIIT.2021040103
Show More Cited By

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents