Skip to main content
SpringerLink
Log in

A comprehensive review on intelligent traffic management using machine learning algorithms

  • Review
  • Published:
Innovative Infrastructure Solutions Aims and scope Submit manuscript

Abstract

Traffic Clog is the main issue of the fast and evolving world. Due to the rise in the use of more private vehicles and low road network capacity managing traffic with the traditional approach is cumbersome. Pollution and productivity of individuals are highly affected due to traffic. The use of mundane methods may not be an efficient and significant solution for varying traffic congestion. Nowadays, artificial intelligence (AI) and machine learning (ML) are playing an important role in solving many real-world problems. So, to tackle this problem, use of artificial intelligence and machine learning can give optimal solutions. An AI-enabled traffic management system can provide greater leeway to vehicles as they can then be directed and controlled more by the external environment. The main aim of using AI is to decrease manual interfacing. Various algorithms have been designed to curb this problem. The traffic management system consists of tools and technologies to gather information from heterogeneous sources. This study will help in identifying hazards that may potentially degrade traffic efficiency and its overcome technique. This article presents the detailed methodology, review, challenges, and future scope of the use of various algorithms for optimizing different aspects of Traffic Management System, i.e., Smart Traffic Signal Management, Traffic Flow Prediction, Traffic Congestion Detection, and its Management, and Automatic Detection of Traffic Signal.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Availability of data and materials

All relevant data and material are presented in the main paper.

Abbreviations

AI:

Artificial intelligence

ML:

Machine learning

GPS:

Global positioning system

ITS:

Intelligent traffic system

KNN:

K-nearest neighbor

CNN:

Convolutional neural network

LSTM:

Long short-term memory

CO2 :

Carbon dioxide

COPD:

Chronic obstructive pulmonary disease

CCTV:

Closed-circuit television

IoV:

Internet of vehicles

V2I:

Vehicle to infrastructure

V2V:

Vehicle to vehicle

DSRC:

Dedicated short-range communication

VANET:

Vehicular ad hoc network

ReLU:

Rectified linear unit layer

FC:

Fully connected layer

FPS:

Frame per second

mAP:

Mean average precision

UDP:

User datagram protocol

AUC:

Area under the curve

ROI:

Return of investment

RCNN:

Recurrent convolutional neural network

YOLO:

You only look once—real-time object detection algorithm

IOU:

Intersection over union

GRU:

Gated recurrent units

OLS:

Ordinary least squares

SAE:

Social adaptive ensemble

RF:

Random forest

ANN:

Artificial neural network

STKNN:

Spatiotemporal K-nearest neighbor

GTSDB:

German traffic sign detection benchmark

GTSRB:

German traffic sign recognition benchmark

LTA:

Land transport authority

GRAM-RTM:

GRAM road-traffic monitoring

CI:

Class imbalance

DBN:

Deep belief network

C3D:

Convolutional 3D

V-ITS:

Vehicle-intelligent transport system

PeMS:

Performance measurement system

JPEA:

Jilin provincial expressway administration

RMSE:

Root-mean-square error

R2 :

R-Squared value

GA:

Genetic algorithm

LR:

Linear learning

IMSE:

Imbalanced mean squared error

GUI:

Graphical user interface

API:

Application programming interface

TCS:

Toll collection system

ILD:

Inductive loop detectors

DSRC:

Dedicated short-range communication

LBS:

Location-based service

MAPE:

Mean absolute percentage error

MdAPE:

Median absolute percentage error

SVM:

Support-vector machine

References

  1. Abbas Q (2019) V-ITS: video-based intelligent transportation system for monitoring vehicle illegal activities. Int J Adv Comput Sci Appl. https://doi.org/10.14569/IJACSA.2019.0100326

    Article  Google Scholar 

  2. Akhtar M, Moridpour S (2021) A review of traffic congestion prediction using artificial intelligence. J Adv Transp. https://doi.org/10.1155/2021/8878011

    Article  Google Scholar 

  3. Ali M, Lavanya Devi G, Neelapu R (2021) Intelligent traffic signal control system using machine learning techniques. Lect Notes Electr Eng. https://doi.org/10.1007/978-981-15-3828-5_63

    Article  Google Scholar 

  4. Subham A, Mangesh D, Ajay Y, Sagar B (n.d.) Traffic_signal_violation_monitoring_through_video_surveillance_using_CNN_ijariie10454. Retrieved 25 May 2021. http://ijariie.com/AdminUploadPdf/Traffic_signal_violation_monitoring_through_video_surveillance_using_CNN_ijariie10454.pdf

  5. Vivek KB, Ganashree KC (2020) Survey of traffic congestion detection and traffic management through CNN. Int Res J Eng Technol. www.irjet.net

  6. Bale DLT, Ugwu C, Nwachukwu EO (2016) Route optimization techniques: an overview. Int J Sci Eng Res 7(11):326

    Google Scholar 

  7. Benuwa BB, Zhan Y, Ghansah B, Wornyo DK, Kataka FB (2016) A review of deep machine learning. Int J Eng Res Afr. https://doi.org/10.4028/www.scientific.net/JERA.24.124

    Article  Google Scholar 

  8. Boukerche A, Wang J (2020) Machine learning-based traffic prediction models for intelligent transportation systems. Comput Netw. https://doi.org/10.1016/j.comnet.2020.107530

    Article  Google Scholar 

  9. Cai P, Wang Y, Lu G, Chen P, Ding C, Sun J (2016) A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting. Transp Res C Emerg Technol. https://doi.org/10.1016/j.trc.2015.11.002

    Article  Google Scholar 

  10. Cao J, Song C, Peng S, Xiao F, Song S (2019) Improved traffic sign detection and recognition algorithm for intelligent vehicles. Sensors (Switzerland). https://doi.org/10.3390/s19184021

    Article  Google Scholar 

  11. Chen K, Zhao S, Zhang D (2019) Short-term traffic flow prediction based on data-driven K-nearest neighbour nonparametric regression. J Phys Conf Ser. https://doi.org/10.1088/1742-6596/1213/5/052070

    Article  Google Scholar 

  12. Chen X, Chen R (2019) A review on traffic prediction methods for intelligent transportation system in smart cities. In: Proceedings of the 2019 12th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2019. https://doi.org/10.1109/CISP-BMEI48845.2019.8965742

  13. Cheng S, Lu F, Peng P, Wu S (2018) Short-term traffic forecasting: an adaptive ST-KNN model that considers spatial heterogeneity. Comput Environ Urban Syst. https://doi.org/10.1016/j.compenvurbsys.2018.05.009

    Article  Google Scholar 

  14. Cherkaoui B, Beni-Hssane A, el Fissaoui M, Erritali M (2019) Road traffic congestion detection in VANET networks. Procedia Comput Sci. https://doi.org/10.1016/j.procs.2019.04.165

    Article  Google Scholar 

  15. Chhabra A (2018) Road traffic prediction using KNN and optimized multilayer perceptron

  16. Cho J, Yi H, Jung H, Bui K-HN (2020) An image generation approach for traffic density classification at large-scale road network. J Inf Telecommun. https://doi.org/10.1080/24751839.2020.1847507

    Article  Google Scholar 

  17. Chung J, Sohn K (2018) Image-based learning to measure traffic density using a deep convolutional neural network. IEEE Trans Intell Transp Syst. https://doi.org/10.1109/TITS.2017.2732029

    Article  Google Scholar 

  18. Costanzo A (n.d.) Using GPS data to monitor road traffic flows in a metropolitan area: methodology and case study

  19. Cover TM, Hart PE (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory. https://doi.org/10.1109/TIT.1967.1053964

    Article  Google Scholar 

  20. de Souza AM, Brennand CARL, Yokoyama RS, Donato EA, Madeira ERM, Villas LA (2017) Traffic management systems: a classification, review, challenges, and future perspectives. Int J Distrib Sens Netw. https://doi.org/10.1177/1550147716683612

    Article  Google Scholar 

  21. Du S, Li T, Gong X, Horng SJ (2020) A hybrid method for traffic flow forecasting using multimodal deep learning. Int J Comput Intell Syst. https://doi.org/10.2991/ijcis.d.200120.001

    Article  Google Scholar 

  22. Eamthanakul B, Ketcham M, Chumuang N (2017) The traffic congestion investigating system by image processing from CCTV camera. In: 2nd joint international conference on digital arts, media and technology 2017: digital economy for sustainable growth, ICDAMT 2017. https://doi.org/10.1109/ICDAMT.2017.7904969

  23. Faghri A, Hamad K (2002) Application of GPS in traffic management systems. GPS Solut. https://doi.org/10.1007/PL00012899

    Article  Google Scholar 

  24. Fedorov A, Nikolskaia K, Ivanov S, Shepelev V, Minbaleev A (2019) Traffic flow estimation with data from a video surveillance camera. J Big Data. https://doi.org/10.1186/s40537-019-0234-z

    Article  Google Scholar 

  25. Gong X, Wang F (2002) Three improvements on KNN-NPR for traffic flow forecasting. In: IEEE conference on intelligent transportation systems, proceedings, ITSC, 2002-January. https://doi.org/10.1109/ITSC.2002.1041310

  26. Gregurić M, Vujić M, Alexopoulos C, Miletić M (2020) Application of deep reinforcement learning in traffic signal control: An overview and impact of open traffic data. Appl Sci (Switzerland). https://doi.org/10.3390/app10114011

    Article  Google Scholar 

  27. Gu J, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, Liu T, Wang X, Wang G, Cai J, Chen T (2018) Recent advances in convolutional neural networks. Pattern Recognit. https://doi.org/10.1016/j.patcog.2017.10.013

    Article  Google Scholar 

  28. Guo J, Liu Y, Yang Q, Wang Y, Fang S (2021) GPS-based citywide traffic congestion forecasting using CNN-RNN and C3D hybrid model. Transp A Transp Sci. https://doi.org/10.1080/23249935.2020.1745927

    Article  Google Scholar 

  29. Han D, Chen J, Sun J (2019) A parallel spatiotemporal deep learning network for highway traffic flow forecasting. Int J Distrib Sens Netw. https://doi.org/10.1177/1550147719832792

    Article  Google Scholar 

  30. Harrou F, Zeroual A, Sun Y (2020) Traffic congestion monitoring using an improved kNN strategy. Meas J Int Meas Confed 156:75. https://doi.org/10.1016/j.measurement.2020.107534

    Article  Google Scholar 

  31. Harrou F, Zeroual A, Sun Y (2020) Traffic congestion monitoring using an improved kNN strategy. Meas J Int Meas Confed. https://doi.org/10.1016/j.measurement.2020.107534

    Article  Google Scholar 

  32. Hippolitus J, Victor N, Bogavalli R, Premkumar A, Sharathi S (2020) Traffic signal control using machine learning. https://doi.org/10.24247/ijmperdjun20201040

  33. Ibna Rahman F (n.d.) Short term traffic flow prediction using machine learning-KNN, SVM and ANN with weather information. https://doi.org/10.7708/ijtte.2020.10(3)

  34. Ikiriwatte AK, Perera DDR, Samarakoon SMMC, Dissanayake DMWCB, Rupasignhe PL (2019) Traffic density estimation and traffic control using convolutional neural network. In: 2019 international conference on advancements in computing, ICAC 2019. https://doi.org/10.1109/ICAC49085.2019.9103369

  35. Jia Y, Wu J, Du Y, Qi G (2014) Impacts of rainfall weather on urban traffic in Beijing: analysis and modeling

  36. Jouppi NP, Young C, Patil N, Patterson D, Agrawal G, Bajwa R, Bates S, Bhatia S, Boden N, Borchers A, Boyle R, Cantin P, Chao C, Clark C, Coriell J, Daley M, Dau M, Dean J, Gelb B (2017) In-datacenter performance analysis of a tensor processing unit. ACM SIGARCH Comput Archit News. https://doi.org/10.1145/3140659.3080246

    Article  Google Scholar 

  37. Kala R (2016) 5—introduction to planning. In: Kala R (ed) On-road intelligent vehicles. Butterworth-Heinemann, Oxford, pp 83–108. https://doi.org/10.1016/B978-0-12-803729-4.00005-2

    Chapter  Google Scholar 

  38. Khan A, Sohail A, Zahoora U, Qureshi AS (2020) A survey of the recent architectures of deep convolutional neural networks. Artif Intell Rev. https://doi.org/10.1007/s10462-020-09825-6

    Article  Google Scholar 

  39. Kim Y, Wang P, Zhu Y, Mihaylova L (2018) A capsule network for traffic speed prediction in complex road networks. In: 2018 symposium on sensor data fusion: trends, solutions, applications, SDF 2018. https://doi.org/10.1109/SDF.2018.8547068

  40. Kuang L, Yan H, Zhu Y, Tu S, Fan X (2019) Predicting duration of traffic accidents based on cost-sensitive Bayesian network and weighted K-nearest neighbor. J Intell Transp Syst Technol Plan Oper. https://doi.org/10.1080/15472450.2018.1536978

    Article  Google Scholar 

  41. Torbjörn L, Lundgren JT (2002) A decision support methodology for strategic traffic management. In: Gendreau P, Marcotte M (eds) Transportation and network analysis: current trends: miscellanea in Honor of Michael Florian. Springer, New York, pp 147–164. https://doi.org/10.1007/978-1-4757-6871-8_10

    Chapter  Google Scholar 

  42. Lee HS, Kim K (2018) Simultaneous traffic sign detection and boundary estimation using convolutional neural network. IEEE Trans Intell Transp Syst. https://doi.org/10.1109/TITS.2018.2801560

    Article  Google Scholar 

  43. Li Y, Chai S, Ma Z, Wang G (2021) A hybrid deep learning framework for long-term traffic flow prediction. IEEE Access. https://doi.org/10.1109/ACCESS.2021.3050836

    Article  Google Scholar 

  44. Liu Z, Du W, Yan D, Chai G, Guo J (2018) Short-term traffic flow forecasting based on combination of K-nearest neighbor and support vector regression. J Highw Transp Res Dev (Engl Ed). https://doi.org/10.1061/jhtrcq.0000615

    Article  Google Scholar 

  45. Liu Z, Guo J, Cao J, Wei YUN, Huang WEI (2018) A hybrid short-term traffic flow forecasting method based on neural networks combined with K-nearest neighbor. Promet Traffic Traffico. https://doi.org/10.7307/ptt.v30i4.2651

    Article  Google Scholar 

  46. Luo X, Li D, Yang Y, Zhang S (2019) Spatiotemporal traffic flow prediction with KNN and LSTM. J Adv Transp. https://doi.org/10.1155/2019/4145353

    Article  Google Scholar 

  47. Ma X, Dai Z, He Z, Ma J, Wang Y, Wang Y (2017) Learning traffic as images: a deep convolutional neural network for large-scale transportation network speed prediction. Sensors (Switzerland). https://doi.org/10.3390/s17040818

    Article  Google Scholar 

  48. Mandal V, Mussah AR, Jin P, Adu-Gyamfi Y (2020) Artificial intelligence-enabled traffic monitoring system. Sustainability (Switzerland). https://doi.org/10.3390/su12219177

    Article  Google Scholar 

  49. Menaka R, Ashadevi B, Kartheeswari TK (2018) Leveraging route saver based on location service in carpooling system using K-NN algorithm. Int J Adv Comput Electron Eng 3(8):56

    Google Scholar 

  50. Miglani A, Kumar N (2019) Deep learning models for traffic flow prediction in autonomous vehicles: a review, solutions, and challenges. Veh Commun. https://doi.org/10.1016/j.vehcom.2019.100184

    Article  Google Scholar 

  51. Mudliar K, Patel V, Ghane S, Naik A (2017) Using AI and machine learning techniques for traffic signal control management—review. Int J Eng Res. https://doi.org/10.17577/IJERTV6IS110065

    Article  Google Scholar 

  52. Nama M, Nath A, Bechra N, Bhatia J, Tanwar S, Chaturvedi M, Sadoun B (2021) Machine learning-based traffic scheduling techniques for intelligent transportation system: opportunities and challenges. Int J Commun Syst 34(9):5. https://doi.org/10.1002/dac.4814

    Article  Google Scholar 

  53. Nisha S, Ratna S (n.d.) Survey on various intelligent traffic management schemes to minimize congestion for emergency vehicles. www.ijstm.com

  54. Ozkurt C, Camci F (2009) Automatic traffic density estimation and vehicle classification for traffic surveillance systems using neural networks. Math Comput Appl. https://doi.org/10.3390/mca14030187

    Article  Google Scholar 

  55. Pang X, Wang C, Huang G (2016) A short-term traffic flow forecasting method based on a three-layer K-nearest neighbor non-parametric regression algorithm. J Transp Technol. https://doi.org/10.4236/jtts.2016.64020

    Article  Google Scholar 

  56. Pashupatimath Shilpa Madhavanavar SG (n.d.) Different techniques used in traffic control system: an introduction. www.ijert.org

  57. Priyadharshini P, Mahesh N, Student ME (n.d.) Less traffic and maintaining high accuracy of query result in MANET using KNN algorithm. www.ijert.org

  58. Qu W, Li J, Yang L, Li D, Liu S, Zhao Q, Qi Y (2020) Short-term intersection traffic flow forecasting. Sustainability (Switzerland). https://doi.org/10.3390/su12198158

    Article  Google Scholar 

  59. Quiros ARF, Bedruz RA, Uy AC, Abad A, Bandala A, Dadios EP, Fernando A, la Salle D (2017) A kNN-based approach for the machine vision of character recognition of license plate numbers. In: IEEE region 10 annual international conference, proceedings/TENCON, 2017-December. https://doi.org/10.1109/TENCON.2017.8228018

  60. Raj J, Bahuleyan H, Vanajakshi LD (2016) Application of data mining techniques for traffic density estimation and prediction. Transp Res Procedia. https://doi.org/10.1016/j.trpro.2016.11.102

    Article  Google Scholar 

  61. Rawat W, Wang Z (2017) Deep convolutional neural networks for image classification: a comprehensive review. Neural Comput. https://doi.org/10.1162/NECO_a_00990

    Article  Google Scholar 

  62. Shahgholian M, Gharavian D (2018) Advanced traffic management systems: an overview and a development strategy

  63. Shustanov A, Yakimov P (2017) CNN design for real-time traffic sign recognition. Procedia Eng. https://doi.org/10.1016/j.proeng.2017.09.594

    Article  Google Scholar 

  64. Tak S, Kim S, Jang K, Yeo H (2014) Real-time travel time prediction using multi-level k-nearest neighbor algorithm and data fusion method. In: Computing in civil and building engineering—proceedings of the 2014 international conference on computing in civil and building engineering. https://doi.org/10.1061/9780784413616.231

  65. Tedjopurnomo DA, Bao Z, Zheng B, Choudhury F, Qin AK (2020) A survey on modern deep neural network for traffic prediction: trends, methods and challenges. IEEE Trans Knowl Data Eng. https://doi.org/10.1109/tkde.2020.3001195

    Article  Google Scholar 

  66. Vlahogianni EI, Golias JC, Karlaftis MG (2004) Short-term traffic forecasting: overview of objectives and methods. Transp Rev. https://doi.org/10.1080/0144164042000195072

    Article  Google Scholar 

  67. Wang J, Wang B, Zhou S, Li C (2018) Prediction model for traffic congestion based on the deep learning of convolutional neural network. In: CICTP 2017: transportation reform and change—equity, inclusiveness, sharing, and innovation—proceedings of the 17th COTA international conference of transportation professionals, 2018-January. https://doi.org/10.1061/9780784480915.262

  68. Wang S, Xie X, Huang K, Zeng J, Cai Z (2019) Deep reinforcement learning-based traffic signal control using high-resolution event-based data. Entropy. https://doi.org/10.3390/e21080744

    Article  Google Scholar 

  69. Wang X, An K, Tang L, Chen X (2015) Short term prediction of freeway exiting volume based on SVM and KNN. Int J Transp Sci Technol. https://doi.org/10.1260/2046-0430.4.3.337

    Article  Google Scholar 

  70. Wang Z, Ji S, Yu B (2019) Short-term traffic volume forecasting with asymmetric loss based on enhanced KNN method. Math Probl Eng. https://doi.org/10.1155/2019/4589437

    Article  Google Scholar 

  71. Wu Y, Liu Y, Li J, Liu H, Hu X (2013) Traffic sign detection based on convolutional neural networks. In: Proceedings of the international joint conference on neural networks. https://doi.org/10.1109/IJCNN.2013.6706811

  72. Xiao X (2008) Chapter 8—Technical challenges. In: Xiao X (ed) Technical, commercial and regulatory challenges of QoS. Morgan Kaufmann, Burlington, pp 113–136. https://doi.org/10.1016/B978-0-12-373693-2.00008-2

    Chapter  Google Scholar 

  73. Xu D, Wang Y, Peng P, Beilun S, Deng Z, Guo H (2020) Real-time road traffic state prediction based on kernel-KNN. Transp Transp Sci. https://doi.org/10.1080/23249935.2018.1491073

    Article  Google Scholar 

  74. Yadav A, More V, Shinde N, Nerurkar M, Sakhare N (2019) Adaptive traffic management system using IoT and machine learning. Int J Sci Res Sci Eng Technol. https://doi.org/10.32628/ijsrset196146

    Article  Google Scholar 

  75. Yang D, Li S, Peng Z, Wang P, Wang J, Yang H (2019) MF-CNN: traffic flow prediction using convolutional neural network and multi-features fusion. IEICE Trans Inf Syst. https://doi.org/10.1587/transinf.2018EDP7330

    Article  Google Scholar 

  76. Yu B, Song X, Guan F, Yang Z, Yao B (2016) k-nearest neighbor model for multiple-time-step prediction of short-term traffic condition. J Transp Eng. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000816

    Article  Google Scholar 

  77. Yu H, Wu Z, Wang S, Wang Y, Ma X (2017) Spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks. Sensors (Switzerland). https://doi.org/10.3390/s17071501

    Article  Google Scholar 

  78. Yuan H, Li G (2021) A survey of traffic prediction: from spatio-temporal data to intelligent transportation. Data Sci Eng. https://doi.org/10.1007/s41019-020-00151-z

    Article  Google Scholar 

  79. Zhang K, Batterman S (2013) Air pollution and health risks due to vehicle traffic. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2013.01.074

    Article  Google Scholar 

  80. Zhang L, Liu Q, Yang W, Wei N, Dong D (2013) An improved K-nearest neighbor model for short-term traffic flow prediction. Procedia Soc Behav Sci. https://doi.org/10.1016/j.sbspro.2

    Article  Google Scholar 

Download references

Acknowledgements

The authors are grateful to VGEC, Government Engineering College, Gandhinagar and Department of Chemical Engineering, School of Technology, Pandit Deendayal Energy University for the permission to publish this research.

Funding

Not applicable.

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Vishwakarma Government Engineering College, Ahmedabad, Gujarat, India

    Yash Modi

  2. Department of Computer Engineering, Government Engineering College, Gandhinagar, Gujarat, India

    Ridham Teli & Konark Shah

  3. Department of Electronics and Communication Engineering, Government Engineering College, Gandhinagar, Gujarat, India

    Akshat Mehta

  4. Department of Chemical Engineering School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat, India

    Manan Shah

Authors
  1. Yash Modi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ridham Teli
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Akshat Mehta
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Konark Shah
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Manan Shah
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All the authors make a substantial contribution to this manuscript. YM, RT, AM, KS and MS participated in drafting the manuscript. YM, RT and MS wrote the main manuscript. All the authors discussed the results and implication on the manuscript at all stages.

Corresponding author

Correspondence to Manan Shah.

Ethics declarations

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Modi, Y., Teli, R., Mehta, A. et al. A comprehensive review on intelligent traffic management using machine learning algorithms. Innov. Infrastruct. Solut. 7, 128 (2022). https://doi.org/10.1007/s41062-021-00718-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s41062-021-00718-3

Keywords

Search

Navigation