Skip to main content
SpringerLink
Log in

Applications of Deep Learning in Intelligent Transportation Systems

  • Original Paper
  • Published:
Journal of Big Data Analytics in Transportation Aims and scope Submit manuscript

Abstract

In recent years, Intelligent Transportation Systems (ITS) have seen efficient and faster development by implementing deep learning techniques in problem domains which were previously addressed using analytical or statistical solutions and also in some areas that were untouched. These improvements have facilitated traffic management and traffic planning, increased safety and security in transit roads, decreased costs of maintenance, optimized public transportation and ride-sharing company's performance, and advanced driver-less vehicle development to a new stage. This papers primary objective was to provide a review and comprehensive insight into the applications of deep learning models on intelligent transportation systems accompanied by presenting the progress of ITS research due to deep learning. First, different techniques of deep learning and their state-of-the-art are discussed, followed by an in-depth analysis and explanation of the current applications of these techniques in transportation systems. This enumeration of deep learning on ITS highlights its significance in the domain. The applications are furthermore categorized based on the gap they are trying to address. Finally, different embedded systems for deployment of these techniques are investigated and their advantages and weaknesses over each other are discussed. Based on this systematic review, credible benefits of deep learning models on ITS are demonstrated and directions for future research are discussed.

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
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Abedin MZ, Nath AC, Dhar P, Deb K, Hossain MS (2017) License plate recognition system based on contour properties and deep learning model. In: 2017 IEEE region 10 humanitarian technology conference (R10-HTC), IEEE, pp 590–593

  • Adu-Gyamfi YO, Asare SK, Sharma A, Titus T (2017) Automated vehicle recognition with deep convolutional neural networks. Transp Res Rec 2645:113–122

    Google Scholar 

  • Ali EM, Ahmed MM, Wulff SS (2019) Detection of critical safety events on freeways in clear and rainy weather using shrp2 naturalistic driving data: parametric and non-parametric techniques. Saf Sci 119:141–149

    Google Scholar 

  • Alkheder S, Taamneh M, Taamneh S (2017) Severity prediction of traffic accident using an artificial neural network. J Forecast 36:100–108

    MathSciNet  Google Scholar 

  • Arabi S, Haghighat A, Sharma A (2020) A deep-learning-based computer vision solution for construction vehicle detection. Comput Aided Civil Infrastruct Eng 35:753–767

    Google Scholar 

  • Arabi S, Shafei B, Phares BM (2018) Fatigue analysis of sign-support structures during transportation under road-induced excitations. Eng Struct 164:305–315

    Google Scholar 

  • Arabi S, Shafei B, Phares BM (2019) Investigation of fatigue in steel sign-support structures under diurnal temperature changes. J Constr Steel Res 153:286–297

    Google Scholar 

  • Arabi S, Shafei B, Phares BM (2017) Vulnerability assessment of sign support structures during transportation. Technical Report

  • Arcos-Garcia A, Alvarez-Garcia JA, Soria-Morillo LM (2018) Deep neural network for traffic sign recognition systems: an analysis of spatial transformers and stochastic optimisation methods. Neural Netw 99:158–165

    Google Scholar 

  • Arif M, Wang G, Chen S (2018) Deep learning with non-parametric regression model for traffic flow prediction. In: 2018 IEEE 16th intl conf on dependable, autonomic and secure computing, 16th intl conf on pervasive intelligence and computing, 4th intl conf on big data intelligence and computing and cyber science and technology congress (DASC/PiCom/DataCom/CyberSciTech), IEEE, pp 681–688

  • Arulkumaran K, Deisenroth MP, Brundage M, Bharath AA (2017) Deep reinforcement learning: a brief survey. IEEE Signal Process Mag 34:26–38

    Google Scholar 

  • Baldi P (2012) Autoencoders, unsupervised learning, and deep architectures. In: Proceedings of ICML workshop on unsupervised and transfer learning, pp 37–49.

  • Bang S, Park S, Kim H, Kim H (2019) Encoder–decoder network for pixel-level road crack detection in black-box images. Comput Aided Civil Infrastruct Eng 34:713–727

    Google Scholar 

  • Bao J, Liu P, Ukkusuri SV (2019) A spatiotemporal deep learning approach for citywide short-term crash risk prediction with multisource data. Accid Anal Prev 122:239–254

    Google Scholar 

  • Bastani O, Ioannou Y, Lampropoulos L, Vytiniotis D, Nori A, Criminisi A (2016) Measuring neural net robustness with constraints. Advances in neural information processing systems. MIT Press, Cambridge, pp 2613–2621

    Google Scholar 

  • Behrendt K, Novak L, Botros R (2017) A deep learning approach to traffic lights: detection, tracking, and classification. In: 2017 IEEE international conference on robotics and automation (ICRA), IEEE, pp 1370–1377.

  • Bengio Y, Goodfellow IJ, Courville A (2015) Deep learning. Nature 521:436–444

    MATH  Google Scholar 

  • Biggio B, Corona I, Maiorca D, Nelson B, Šrndić N, Laskov P, Giacinto G, Roli F (2013) Evasion attacks against machine learning at test time. In: Joint European conference on machine learning and knowledge discovery in databases, Springer, pp 387–402.

  • Bulan O, Kozitsky V, Ramesh P, Shreve M (2017) Segmentation-and annotation-free license plate recognition with deep localization and failure identification. IEEE Trans Intell Transp Syst 18:2351–2363

    Google Scholar 

  • Cai Y, Li D, Zhou X, Mou X (2018) Robust drivable road region detection for fixed-route autonomous vehicles using map-fusion images. Sensors 18:4158

    Google Scholar 

  • Cai Y, Sun X, Wang H, Chen L, Jiang H (2016) Night-time vehicle detection algorithm based on visual saliency and deep learning. J Sens 2016:1–7

    Google Scholar 

  • Carlini N, Wagner D (2017) Towards evaluating the robustness of neural networks. In: 2017 IEEE symposium on security and privacy (SP), IEEE, pp 39–57

  • Chakraborty P, Adu-Gyamfi YO, Poddar S, Ahsani V, Sharma A, Sarkar S (2018) Traffic congestion detection from camera images using deep convolution neural networks. Transp Res Rec 2672:222–231

    Google Scholar 

  • Chang J, Wang L, Meng G, Xiang S, Pan C (2018) Vision-based occlusion handling and vehicle classification for traffic surveillance systems. IEEE Intell Transp Syst Mag 10:80–92

    Google Scholar 

  • Chen L, Hu X, Xu T, Kuang H, Li Q (2017) Turn signal detection during nighttime by CNN detector and perceptual hashing tracking. IEEE Trans Intell Transp Syst 18:3303–3314

    Google Scholar 

  • Chen Q, Song X, Yamada H, Shibasaki R (2016) Learning deep representation from big and heterogeneous data for traffic accident inference. In: Thirtieth AAAI conference on artificial intelligence

  • Chen Y, Shu L, Wang L (2017) Traffic flow prediction with big data: A deep learning based time series model. In: 2017 IEEE conference on computer communications workshops (INFOCOM WKSHPS), IEEE, pp 1010–1011.

  • Chen C, Xiang H, Qiu T, Wang C, Zhou Y, Chang V (2018a) A rear-end collision prediction scheme based on deep learning in the internet of vehicles. J Parallel Distrib Comput 117:192–204

    Google Scholar 

  • Chen M, Yu X, Liu Y (2018b) PCNN: deep convolutional networks for short-term traffic congestion prediction. IEEE Trans Intell Transp Syst 19(11):3550–3559

    Google Scholar 

  • Chen B, Gong C, Yang J (2018c) Importance-aware semantic segmentation for autonomous vehicles. IEEE Trans Intell Transp Syst 20(1):137–148

    Google Scholar 

  • Cho K, Van Merrienboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint. https://arxiv.org/abs/1406.1078

  • Choi S, Yeo H, Kim J (2018) Network-wide vehicle trajectory prediction in urban traffic networks using deep learning. Transp Res Rec 2672:173–184

    Google Scholar 

  • Chung J, Sohn K (2017) Image-based learning to measure traffic density using a deep convolutional neural network. IEEE Trans Intell Transp Syst 19:1670–1675

    Google Scholar 

  • Cireşan D, Meier U, Schmidhuber J (2012) Multi-column deep neural networks for image classification. arXiv preprint. https://arxiv.org/abs/1202.2745

  • Collobert R, Weston J (2008) A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the 25th international conference on machine learning, ACM, pp 160–167.

  • Constantinescu G, Bhatti A, Phares B (2018) Effect of wind induced unsteady vortex shedding, diurnal temperature changes, and transit conditions on truss structures supporting large highway signs problem statement. Technical Report

  • Creswell A, White T, Dumoulin V, Arulkumaran K, Sengupta B, Bharath AA (2018) Generative adversarial networks: an overview. IEEE Signal Process Mag 35:53–65

    Google Scholar 

  • Cui Y, Meng C, He Q, Gao J (2018) Forecasting current and next trip purpose with social media data and google places. Transportation Research Part C: Emerging Technologies 97:159–174

    Google Scholar 

  • Cui Z, Ke R, Wang Y (2018) Deep bidirectional and unidirectional LSTM recurrent neural network for network-wide traffic speed prediction. arXiv preprint. https://arxiv.org/abs/1801.02143

  • Cui Z, Henrickson K, Ke R, Wang Y (2018) Traffic graph convolutional recurrent neural network: a deep learning framework for network-scale traffic learning and forecasting. arXiv preprint. https://arxiv.org/abs/1802.07007

  • Dabiri S, Heaslip K (2018) Inferring transportation modes from gps trajectories using a convolutional neural network. Transp Res Part C Emerg Technol 86:360–371

    Google Scholar 

  • Dai X, Fu R, Zhao E, Zhang Z, Lin Y, Wang F-Y, Li L (2019) Deeptrend 2.0: a light-weighted multi-scale traffic prediction model using detrending. Transp Res Part C Emerg Technol 103:142–157

    Google Scholar 

  • Dairi A, Harrou F, Senouci M, Sun Y (2018) Unsupervised obstacle detection in driving environments using deep-learning-based stereovision. Robot Autonom Syst 100:287–301

    Google Scholar 

  • Dairi A, Harrou F, Sun Y, Senouci M (2018) Obstacle detection for intelligent transportation systems using deep stacked autoencoder and k-nearest neighbor scheme. IEEE Sens J 18:5122–5132

    Google Scholar 

  • Daneshgaran F, Zacheo L, Stasio FD, Mondin M (2019) Use of deep learning for automatic detection of cracks in tunnels: prototype-2 developed in the 2017–2018 time period. Transp Res Rec 2673(9):44–50

    Google Scholar 

  • Das S, Dutta A, Dixon K, Minjares-Kyle L, Gillette G (2018) Using deep learning in severity analysis of at-fault motorcycle rider crashes. Transp Res Rec 2672:122–134

    Google Scholar 

  • Deng L, Hinton G, Kingsbury B (2013) New types of deep neural network learning for speech recognition and related applications: an overview. In: 2013 IEEE international conference on acoustics, speech and signal processing, IEEE, pp 8599–8603.

  • Ding F, Zhang Z, Zhou Y, Chen X, Ran B (2019) Large-scale full-coverage traffic speed estimation under extreme traffic conditions using a big data and deep learning approach: case study in china. J Transp Eng Part A Syst 145:05019001

    Google Scholar 

  • Doersch C (2016) Tutorial on variational autoencoders. arXiv preprint. https://arxiv.org/abs/1606.05908

  • Dominguez-Sanchez A, Cazorla M, Orts-Escolano S (2017) Pedestrian movement direction recognition using convolutional neural networks. IEEE Trans Intell Transp Syst 18:3540–3548

    Google Scholar 

  • Dong C, Shao C, Li J, Xiong Z (2018) An improved deep learning model for traffic crash prediction. J Adv Transp 2018:1–13

    Google Scholar 

  • Dong Z, Wu Y, Pei M, Jia Y (2015) Vehicle type classification using a semisupervised convolutional neural network. IEEE Trans Intell Transp Syst 16:2247–2256

    Google Scholar 

  • Dou Y, Fang Y, Hu C, Zheng R, Yan F (2018) Gated branch neural network for mandatory lane changing suggestion at the on-ramps of highway. IET Intel Transp Syst 13:48–54

    Google Scholar 

  • Dougherty M (1995) A review of neural networks applied to transport. Transp Res Part C Emerg Technol 3:247–260

    Google Scholar 

  • Du S, Li T, Gong X, Yang Y, Horng SJ (2017) Traffic flow forecasting based on hybrid deep learning framework. In: 2017 12th International conference on intelligent systems and knowledge engineering (ISKE), IEEE, pp 1–6.

  • Duan Y, Lv Y, Liu Y-L, Wang F-Y (2016) An efficient realization of deep learning for traffic data imputation. Transp Res Part C Emerg Technol 72:168–181

    Google Scholar 

  • E. Van der Pol, F. A. Oliehoek, Coordinated deep reinforcement learners for traffic light control, Proceedings of Learning, Inference and Control of Multi-Agent Systems (at NIPS 2016) (2016).

  • Elman JL (1990) Finding structure in time. Cognit Sci 14:179–211

    Google Scholar 

  • Esfandiari Y, Balu A, Ebrahimi K, Vaidya U, Elia N, Sarkar S (2019) A fast saddle-point dynamical system approach to robust deep learning. arXiv preprint. https://arxiv.org/abs/1910.08623

  • Fang J, Zhou Y, Yu Y, Du S (2016) Fine-grained vehicle model recognition using a coarse-to-fine convolutional neural network architecture. IEEE Trans Intell Transp Syst 18:1782–1792

    Google Scholar 

  • Fawcett T (2003) In vivo spam filtering: a challenge problem for kdd. ACM SIGKDD Explor Newsl 5:140–148

    Google Scholar 

  • Fouladgar M, Parchami M, Elmasri R, Ghaderi A (2017) Scalable deep traffic flow neural networks for urban traffic congestion prediction. In: 2017 International joint conference on neural networks (IJCNN), IEEE, pp 2251–2258.

  • Gang X, Kang W, Wang F, Zhu F, Lv Y, Dong X, Riekki J, Pirttikangas S (2015) Continuous travel time prediction for transit signal priority based on a deep network. In: 2015 IEEE 18th international conference on intelligent transportation systems, IEEE, pp 523–528.

  • Gao J, Shen Y, Liu J, Ito M, Shiratori N (2017) Adaptive traffic signal control: deep reinforcement learning algorithm with experience replay and target network. arXiv preprint. https://arxiv.org/abs/1705.02755

  • Genders W, Razavi S (2018) Evaluating reinforcement learning state representations for adaptive traffic signal control. Proc Comput Sci 130:26–33

    Google Scholar 

  • Gers FA, Schmidhuber E (2001) LSTM recurrent networks learn simple context-free and context-sensitive languages. IEEE Trans Neural Netw 12:1333–1340

    Google Scholar 

  • Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks. In: Proceedings of the fourteenth international conference on artificial intelligence and statistics, pp 315–323.

  • Gong Y, Abdel-Aty M, Cai Q, Rahman MS (2019) Decentralized network level adaptive signal control by multi-agent deep reinforcement learning. Transp Res Interdiscip Perspect 1:100020

    Google Scholar 

  • Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014a) Generative adversarial nets. Advances in neural information processing systems. MIT Press, Cambridge, pp 2672–2680

    Google Scholar 

  • Goodfellow I (2016) Nips 2016 tutorial: generative adversarial networks. arXiv preprint. https://arxiv.org/abs/1701.00160

  • Goodfellow IJ, Shlens J, Szegedy C (2014) Explaining and harnessing adversarial examples. arXiv preprint. https://arxiv.org/abs/1412.6572

  • Goudarzi S, Kama M, Anisi M, Soleymani S, Doctor F (2018) Self-organizing traffic flow prediction with an optimized deep belief network for internet of vehicles. Sensors 18:3459

    Google Scholar 

  • Graves A, Schmidhuber J (2005) Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw 18:602–610

    Google Scholar 

  • Gu Y, Lu W, Qin L, Li M, Shao Z (2019) Short-term prediction of lane-level traffic speeds: a fusion deep learning model. Transp Res Part C Emerg Technol 106:1–16

    Google Scholar 

  • Gu S, Rigazio L (2014) Towards deep neural network architectures robust to adversarial examples. arXiv preprint. https://arxiv.org/abs/1412.5068

  • Guo J, Liu Y, Wang Y, Yang K (2019) Deep learning based congestion prediction using probe trajectory data. In: 19th COTA international conference of transportation professionals

  • Gurghian A, Koduri T, Bailur SV, Carey KJ, Murali VN (2016) Deeplanes: end-to-end lane position estimation using deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 38–45.

  • Hao S, Lee D-H, Zhao D (2019) Sequence to sequence learning with attention mechanism for short-term passenger flow prediction in large-scale metro system. Transp Res Part C Emerg Technol 107:287–300

    Google Scholar 

  • Hardy W, Chen L, Hou S, Ye Y, Li X (2016) Dl4md: a deep learning framework for intelligent malware detection. In: Proceedings of the international conference on data mining (DMIN), The Steering Committee of The World Congress in Computer Science, computer engineering and applied computing (WorldComp), p 61.

  • Hasselt HV (2010) Double q-learning. Advances in neural information processing systems. MIT Press, Cambridge, pp 2613–2621

    Google Scholar 

  • El Hatri C, Boumhidi J (2018) Fuzzy deep learning based urban traffic incident detection. Cognit Syst Res 50:206–213

    Google Scholar 

  • He M, Luo H, Chang Z, Hui B (2017) Pedestrian detection with semantic regions of interest. Sensors 17:2699

    Google Scholar 

  • Hoang TM, Nguyen PH, Truong NQ, Lee YW, Park KR (2019) Deep retinanet-based detection and classification of road markings by visible light camera sensors. Sensors 19:281

    Google Scholar 

  • Hochreiter S, Schmidhuber J (1997) LSTM can solve hard long time lag problems. Advances in neural information processing systems. MIT Press, Cambridge, pp 473–479

    Google Scholar 

  • Home—transport research international documentation (2017). https://trid.trb.org/

  • Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci 79:2554–2558

    MathSciNet  MATH  Google Scholar 

  • Hosseini SA (2020) Data-driven framework for modeling deterioration of pavements in the state of Iowa. Graduate Theses and Dissertations

  • Hosseini SA, Smadi O (2020) How prediction accuracy can affect the decision-making process in pavement management system. https://doi.org/10.31224/osf.io/t28ue

  • Hosseini SA, Alhasan A, Smadi O (2020) Use of deep learning to study modelling deterioration of pavements a case study in Iowa. https://doi.org/10.31224/osf.io/edhvy

  • Hou Y, Edara P (2018) Network scale travel time prediction using deep learning. Transp Res Rec 2672:115–123

    Google Scholar 

  • Hu C, Bai X, Qi L, Chen P, Xue G, Mei L (2015) Vehicle color recognition with spatial pyramid deep learning. IEEE Trans Intell Transp Syst 16:2925–2934

    Google Scholar 

  • Hu Q, Wang H, Li T, Shen C (2017) Deep CNNs with spatially weighted pooling for fine-grained car recognition. IEEE Trans Intell Transp Syst 18:3147–3156

    Google Scholar 

  • Hu X, Xu X, Xiao Y, Chen H, He S, Qin J, Heng P-A (2018) Sinet: a scale-insensitive convolutional neural network for fast vehicle detection. IEEE Trans Intell Transp Syst 20:1010–1019

    Google Scholar 

  • Hu W, Zhuo Q, Zhang C, Li J (2017) Fast branch convolutional neural network for traffic sign recognition. IEEE Intell Transp Syst Mag 9:114–126

    Google Scholar 

  • Huang W, Song G, Hong H, Xie K (2014) Deep architecture for traffic flow prediction: deep belief networks with multitask learning. IEEE Trans Intell Transp Syst 15:2191–2201

    Google Scholar 

  • Huang Y, Wu R, Sun Y, Wang W, Ding X (2015) Vehicle logo recognition system based on convolutional neural networks with a pretraining strategy. IEEE Trans Intell Transp Syst 16:1951–1960

    Google Scholar 

  • Huang R, Hu J, Huo Y, Pei X (2019) Cooperative multi-intersection traffic signal control based on deep reinforcement learning. In: CICTP 2019, pp 2959–2970.

  • Hubel DH, Wiesel TN (1962) Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J Physiol 160:106–154

    Google Scholar 

  • Hussain KF, Afifi M, Moussa G (2018) A comprehensive study of the effect of spatial resolution and color of digital images on vehicle classification. IEEE Trans Intell Transp Syst 20(3):1181–1190

    Google Scholar 

  • Intel® Neural Compute Stick 2 Product Specifications (2020) Ark.Intel.Com. https://ark.intel.com/content/www/us/en/ark/products/140109/intel-neural-compute-stick-2.html

  • Isola P, Zhu J-Y, Zhou T, Efros AA (2017) Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1125–1134

  • Jetson AGX Xavier Developer Kit (2020) NVIDIA Developer. https://developer.nvidia.com/embedded/jetson-agx-xavier-developer-kit

  • Jetson Nano Developer Kit (2020) NVIDIA Developer. https://developer.nvidia.com/embedded/jetson-nano-developer-kit

  • Jetson TX2 - Elinux.Org (2020) Elinux.Org. https://elinux.org/Jetson_TX2

  • Jia Y, Wu J, Xu M (2017) Traffic flow prediction with rainfall impact using a deep learning method. J Adv Transp 207:1–10

    Google Scholar 

  • Jia Y, Wu J, Du Y (2016) Traffic speed prediction using deep learning method. In: 2016 IEEE 19th international conference on intelligent transportation systems (ITSC), IEEE, pp 1217–1222.

  • Jo D, Yu B, Jeon H, Sohn K (2018) Image-to-image learning to predict traffic speeds by considering area-wide spatio-temporal dependencies. IEEE Trans Veh Technol 68:1188–1197

    Google Scholar 

  • Jung J, Sohn K (2017) Deep-learning architecture to forecast destinations of bus passengers from entry-only smart-card data. IET Intel Transp Syst 11:334–339

    Google Scholar 

  • Ke J, Zheng H, Yang H, Chen XM (2017) Short-term forecasting of passenger demand under on-demand ride services: a spatiotemporal deep learning approach. Transp Res Part C Emerg Technol 85:591–608

    Google Scholar 

  • Ketkar N et al (2017) Deep learning with Python. Springer, Berlin

    Google Scholar 

  • Khajeh Hosseini M, Talebpour A (2019) Traffic prediction using time-space diagram: a convolutional neural network approach. Transp Res Rec 2673(7):425–435

    Google Scholar 

  • Kim T, Ghosh J (2016) Robust detection of non-motorized road users using deep learning on optical and lidar data. In: 2016 IEEE 19th international conference on intelligent transportation systems (ITSC), IEEE, pp 271–276.

  • Kim H-K, Park JH, Jung H-Y (2018a) An efficient color space for deep-learning based traffic light recognition. J Adv Transp 2018:2365414

    Google Scholar 

  • Kim I-H, Jeon H, Baek S-C, Hong W-H, Jung H-J (2018b) Application of crack identification techniques for an aging concrete bridge inspection using an unmanned aerial vehicle. Sensors 18:1881

    Google Scholar 

  • Kim EJ, Park HC, Ham SW, Kho SY, Kim DK (2019a) Extracting vehicle trajectories using unmanned aerial vehicles in congested traffic conditions. J Adv Transp 2019:9060797

    Google Scholar 

  • Kim TS, Lee WK, Sohn SY (2019b) Graph convolutional network approach applied to predict hourly bike-sharing demands considering spatial, temporal, and global effects. PLoS ONE 14:e0220782

    Google Scholar 

  • Kingma DP, Welling M (2013) Auto-encoding variational bayes. arXiv preprint. https://arxiv.org/abs/1312.6114

  • Koesdwiady A, Soua R, Karray F (2016) Improving traffic flow prediction with weather information in connected cars: a deep learning approach. IEEE Trans Veh Technol 65:9508–9517

    Google Scholar 

  • Le QV et al (2015) A tutorial on deep learning part 2: autoencoders, convolutional neural networks and recurrent neural networks. Google Brain, California, pp 1–20

    Google Scholar 

  • LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1:541–551

    Google Scholar 

  • LeCun Y, Bottou L, Bengio Y, Haffner P et al (1998) Gradient-based learning applied to document recognition. Proc IEEE 86:2278–2324

    Google Scholar 

  • LeCun Y, Kavukcuoglu K, Farabet C (2010) Convolutional networks and applications in vision. In: Proceedings of 2010 IEEE international symposium on circuits and systems, IEEE, pp 253–256.

  • Ledig C, Theis L, Huszar F, Caballero J, Cunningham A, Acosta A, Aitken A, Tejani A, Totz J, Wang Z et al. (2017) Photo-realistic ´ single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4681–4690

  • Lee HS, Kim K (2018) Simultaneous traffic sign detection and boundary estimation using convolutional neural network. IEEE Trans Intell Transp Syst 19:1652–1663

    Google Scholar 

  • Lee S, Xie K, Ngoduy D, Keyvan-Ekbatani M, Yang H (2018) A lane-based predictive model of downstream arrival rates in a queue estimation model using a long short-term memory network. Transp Res Proc 34:163–170

    Google Scholar 

  • Li J, Wang J (2017) Short term traffic flow prediction based on deep learning. In: CICTP 2019, pp 2457–2469.

  • Li C, Yang C (2016) The research on traffic sign recognition based on deep learning. In: 2016 16th international symposium on communications and information technologies (ISCIT), Qingdao, China, 26–28 September 2016. IEEE, pp 156–161

  • Li L, Lv Y, Wang F-Y (2016) Traffic signal timing via deep reinforcement learning. IEEE/CAA J Automatica Sinica 3:247–254

    MathSciNet  Google Scholar 

  • Li L, Qian B, Lian J, Zheng W, Zhou Y (2017) Traffic scene segmentation based on rgb-d image and deep learning. IEEE Trans Intell Transp Syst 19:1664–1669

    Google Scholar 

  • Li X, Liu Y, Zhao Z, Zhang Y, He L (2018a) A deep learning approach of vehicle multitarget detection from traffic video. J Adv Transp 2018:1–11

    Google Scholar 

  • Li Y, Song B, Kang X, Du X, Guizani M (2018b) Vehicle-type detection based on compressed sensing and deep learning in vehicular networks. Sensors 18:4500

    Google Scholar 

  • Li C, Chen Z, Wu QJ, Liu C (2018c) Deep saliency with channel-wise hierarchical feature responses for traffic sign detection. IEEE Trans Intell Transp Syst 20(7):2497–2509

    Google Scholar 

  • Liang X, Du X, Wang G, Han Z (2019) A deep reinforcement learning network for traffic light cycle control. IEEE Trans Veh Technol 68:1243–1253

    Google Scholar 

  • Liang X, Du X, Wang G, Han Z (2018) Deep reinforcement learning for traffic light control in vehicular networks. arXiv preprint. https://arxiv.org/abs/1803.11115

  • Liao S, Zhou L, Di X, Yuan B, Xiong J (2018) Large-scale short-term urban taxi demand forecasting using deep learning. In: Proceedings of the 23rd Asia and South Pacific design automation conference, IEEE Press, pp 428–433.

  • Lim K, Hong Y, Choi Y, Byun H (2017) Real-time traffic sign recognition based on a general purpose GPU and deep-learning. PLoS ONE 12:e0173317

    Google Scholar 

  • Lin L-J (1992) Self-improving reactive agents based on reinforcement learning, planning and teaching. Mach Learn 8:293–321

    Google Scholar 

  • Lin Y, Dai X, Li L, Wang F-Y (2018) Pattern sensitive prediction of traffic flow based on generative adversarial framework. IEEE Trans Intell Transp Syst 20:2395–2400

    Google Scholar 

  • Lin L, He Z, Peeta S (2018) Predicting station-level hourly demand in a large-scale bike-sharing network: a graph convolutional neural network approach. Transp Res Part C Emerg Technol 97:258–276

    Google Scholar 

  • Lin F, Lai Y, Lin L, Yuan Y (2016) A traffic sign recognition method based on deep visual feature. In: 2016 Progress in electromagnetic research symposium (PIERS), IEEE, pp 2247–2250

  • Lin Z, Yih M, Ota JM, Owens JD, Muyan-Özçelik P (2019) Benchmarking deep learning frameworks and investigating fpga deployment for traffic sign classification and detection. IEEE Trans Intell Veh 4(3):385–395

    Google Scholar 

  • Lipton ZC, Berkowitz J, Elkan C (2015) A critical review of recurrent neural networks for sequence learning. arXiv preprint. https://arxiv.org/abs/1506.00019

  • Liu L, Chen R-C (2017) A novel passenger flow prediction model using deep learning methods. Transportation Research Part C: Emerging Technologies 84:74–91

    Google Scholar 

  • Liu Y, Liu Z, Jia R (2019) Deeppf: a deep learning based architecture for metro passenger flow prediction. Transp Res Part C Emerg Technol 101:18–34

    Google Scholar 

  • Liu X, Liu W, Mei T, Ma H (2016) A deep learning-based approach to progressive vehicle re-identification for urban surveillance. In: European conference on computer vision, Springer, pp 869–884.

  • Liu Y, Wang Y, Yang X, Zhang L (2017) Short-term travel time prediction by deep learning: a comparison of different LSTM-DNN models. In: 2017 IEEE 20th international conference on intelligent transportation systems (ITSC), IEEE, pp 1–8.

  • Loce RP, Bernal EA, Wu W, Bala R (2013) Computer vision in roadway transportation systems: a survey. J Electron Imaging 22:041121

    Google Scholar 

  • Luo X, Shen R, Hu J, Deng J, Hu L, Guan Q (2017) A deep convolution neural network model for vehicle recognition and face recognition. Proc Comput Sci 107:715–720

    Google Scholar 

  • Lv Y, Duan Y, Kang W, Li Z, Wang F-Y (2014) Traffic flow prediction with big data: a deep learning approach. IEEE Trans Intell Transp Syst 16:865–873

    Google Scholar 

  • 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 17:818

    Google Scholar 

  • Ma X, Yu H, Wang Y, Wang Y (2015) Large-scale transportation network congestion evolution prediction using deep learning theory. PLoS ONE 10:e0119044

    Google Scholar 

  • Ma X, Zhang J, Du B, Ding C, Sun L (2018) Parallel architecture of convolutional bi-directional lstm neural networks for network-wide metro ridership prediction. IEEE Trans Intell Transp Syst 20:2278–2288

    Google Scholar 

  • Madry A, Makelov A, Schmidt L, Tsipras D, Vladu A (2017) Towards deep learning models resistant to adversarial attacks. arXiv preprint. https://arxiv.org/abs/1706.06083

  • Maŕın-Reyes PA, Bergamini L, Lorenzo-Navarro J, Palazzi A, Calderara S, Cucchiara R (2018) Unsupervised vehicle re-identification using triplet networks. In: 2018 IEEE/CVF conference on computer vision and pattern recognition workshops (CVPRW), IEEE, pp 166–1665.

  • Mirza M, Osindero S (2014) Conditional generative adversarial nets. arXiv preprint. https://arxiv.org/abs/1411.1784

  • Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller M, Fidjeland AK, Ostrovski G et al (2015) Human-level control through deep reinforcement learning. Nature 518:529

    Google Scholar 

  • Moher D, Liberati A, Tetzlaff J, Altman DG (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med 151:264–269

    Google Scholar 

  • Moll J, Arnold P, Malzer M, Krozer V, Pozdniakov D, Salman R, Rediske S, Scholz M, Friedmann H, Nuber A (2018) Radar-based structural health monitoring of wind turbine blades: the case of damage detection. Struct Health Monit 17:815–822

    Google Scholar 

  • Moosavi-Dezfooli S-M, Fawzi A, Fawzi O, Frossard P (2017) Universal adversarial perturbations. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1765–1773.

  • Mousavi SS, Schukat M, Howley E (2017) Traffic light control using deep policy-gradient and value-function-based reinforcement learning. IET Intel Transp Syst 11:417–423

    Google Scholar 

  • Muresan M, Fu L, Pan G (2019) Adaptive traffic signal control with deep reinforcement learning an exploratory investigation. arXiv preprint. https://arxiv.org/abs/1901.00960

  • Najafabadi MM, Villanustre F, Khoshgoftaar TM, Seliya N, Wald R, Muharemagic E (2015) Deep learning applications and challenges in big data analytics. J Big Data 2:1

    Google Scholar 

  • Nam D, Kim H, Cho J, Jayakrishnan R (2017) A model based on deep learning for predicting travel mode choice. In: Proceedings of the transportation research board 96th annual meeting transportation research board, Washington, DC, USA, pp 8–12.

  • Natarajan S, Annamraju AK, Baradkar CS (2018) Traffic sign recognition using weighted multi-convolutional neural network. IET Intel Transp Syst 12:1396–1405

    Google Scholar 

  • Nezafat RV, Sahin O, Cetin M (2019) Transfer learning using deep neural networks for classification of truck body types based on side-fire lidar data. J Big Data Anal Transp 1:71–82

    Google Scholar 

  • Nguyen H, Kieu L-M, Wen T, Cai C (2018) Deep learning methods in transportation domain: a review. IET Intel Transp Syst 12:998–1004

    Google Scholar 

  • Nguyen VD, Van Nguyen H, Tran DT, Lee SJ, Jeon JW (2016) Learning framework for robust obstacle detection, recognition, and tracking. IEEE Trans Intell Transp Syst 18:1633–1646

    Google Scholar 

  • Nguyen H, Bentley C, Kieu LM, Fu Y, Cai C (2019) Deep learning system for travel speed predictions on multiple arterial road segments. Transp Res Rec 2673(4):145–157

    Google Scholar 

  • Nhat-Duc H, Nguyen Q-L, Tran V-D (2018) Automatic recognition of asphalt pavement cracks using metaheuristic optimized edge detection algorithms and convolution neural network. Autom Constr 94:203–213

    Google Scholar 

  • Ni X, Wang H, Che C, Hong J, Sun Z (2019) Civil aviation safety evaluation based on deep belief network and principal component analysis. Saf Sci 112:90–95

    Google Scholar 

  • de Oliveira D, Wehrmeister M (2018) Using deep learning and low-cost rgb and thermal cameras to detect pedestrians in aerial images captured by multirotor uav. Sensors 18:2244

    Google Scholar 

  • Pamula T (2018) Road traffic conditions classification based on multilevel filtering of image content using convolutional neural networks. IEEE Intell Transp Syst Mag 10:11–21

    Google Scholar 

  • Pan G, Fu L, Thakali L (2017) Development of a global road safety performance function using deep neural networks. Int J Transp Sci Technol 6:159–173

    Google Scholar 

  • Pan G, Fu L, Thakali L, Muresan M, Yu M (2018) An improved deep belief network model for road safety analyses. arXiv preprint. https://arxiv.org/abs/1812.07410

  • Papernot N, McDaniel P, Goodfellow I, Jha S, Celik ZB, Swami A (2016) Practical black-box attacks against deep learning systems using adversarial examples. arXiv preprint. https://arxiv.org/abs/1602.026971

  • Papernot N, McDaniel P, Wu X, Jha S, Swami A (2016) Distillation as a defense to adversarial perturbations against deep neural networks. In: 2016 IEEE symposium on security and privacy (SP), IEEE, pp 582–597

  • Park J, Min K, Kim H, Lee W, Cho G, Huh K (2018) Road surface classification using a deep ensemble network with sensor feature selection. Sensors 18:4342

    Google Scholar 

  • Polson NG, Sokolov VO (2017) Deep learning for short-term traffic flow prediction. Transp Res Part C Emerg Technol 79:1–17

    Google Scholar 

  • Puarungroj W, Boonsirisumpun N (2018) Thai license plate recognition based on deep learning. Proc Comput Sci 135:214–221

    Google Scholar 

  • Qian R, Zhang B, Yue Y, Wang Z, Coenen F (2015) Robust chinese traffic sign detection and recognition with deep convolutional neural network. In: 2015 11th international conference on natural computation (ICNC), IEEE, pp 791–796.

  • Radford A, Metz L, Chintala S (2015) Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint. https://arxiv.org/abs/1511.06434

  • Rahman M, Islam M, Calhoun J, Chowdhury M (2019) Real-time pedestrian detection approach with an efficient data communication bandwidth strategy. Transp Res Rec 2673(6):129–139

    Google Scholar 

  • Raspberry (2020) Raspberrypi.Org. https://www.raspberrypi.org/products/raspberry-pi-4-model-b/

  • Ren H, Song Y, Liu J, Hu Y, Lei J (2017) A deep learning approach to the prediction of short-term traffic accident risk. arXiv preprint. https://arxiv.org/abs/1710.09543

  • Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L (2015) ImageNet large scale visual recognition challenge. Int J Comput Vis 115:211–252

    MathSciNet  Google Scholar 

  • Saadi I, Wong M, Farooq B, Teller J, Cools M (2017) An investigation into machine learning approaches for forecasting spatio-temporal demand in ride-hailing service. arXiv preprint. https://arxiv.org/abs/1703.02433

  • Sameen M, Pradhan B (2017) Severity prediction of traffic accidents with recurrent neural networks. Appl Sci 7:476

    Google Scholar 

  • Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117

    Google Scholar 

  • Shaham U, Yamada Y, Negahban S (2018) Understanding adversarial training: increasing local stability of supervised models through robust optimization. Neurocomputing 307:195–204

    Google Scholar 

  • Shen Y, Xiao T, Li H, Yi S, Wang X (2017) Learning deep neural networks for vehicle re-id with visual-spatio-temporal path proposals. In: Proceedings of the IEEE international conference on computer vision, pp 1900–1909.

  • Shi D, Ding J, Errapotu SM, Yue H, Xu W, Zhou X, Pan M (2018) Deep q-network based route scheduling for transportation network company vehicles. In: 2018 IEEE global communications conference (GLOBECOM), IEEE, pp 1–7

  • Shin H-C, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers RM (2016) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35:1285–1298

    Google Scholar 

  • Shustanov A, Yakimov P (2017) CNN design for real-time traffic sign recognition. Proc Eng 201:718–725

    Google Scholar 

  • Silver D (2015) UCL course on RL, lecture notes: reinforcement learning. https://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html. Accessed 9 Sep 2019

  • Simoncini M, Taccari L, Sambo F, Bravi L, Salti S, Lori A (2018) Vehicle classification from low-frequency GPS data with recurrent neural networks. Transp Res Part C Emerg Technol 91:176–191

    Google Scholar 

  • Singh D, Mohan CK (2018) Deep spatio-temporal representation for detection of road accidents using stacked autoencoder. IEEE Trans Intell Transp Syst 20(3):879–887

    Google Scholar 

  • Siripanpornchana C, Panichpapiboon S, Chaovalit P (2016) Travel-time prediction with deep learning. In: 2016 IEEE region 10 conference (TENCON), IEEE, p. 1859–1862

  • Soon FC, Khaw HY, Chuah JH, Kanesan J (2018a) Hyper-parameters optimisation of deep CNN architecture for vehicle logo recognition. IET Intel Syst 12:939–946

    Google Scholar 

  • Soon FC, Khaw HY, Chuah JH, Kanesan J (2018b) Pcanet-based convolutional neural network architecture for a vehicle model recognition system. IEEE Trans Intell Transp Syst 20(2):749–759

    Google Scholar 

  • Suhao L, Jinzhao L, Guoquan L, Tong B, Huiqian W, Yu P (2018) Vehicle type detection based on deep learning in traffic scene. Proc Comput Sci 131:564–572

    Google Scholar 

  • Sussman JS (2008) Perspectives on intelligent transportation systems (ITS). Springer, Berlin

    Google Scholar 

  • Suzuki T, Kataoka H, Aoki Y, Satoh Y (2018) Anticipating traffic accidents with adaptive loss and large-scale incident db. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3521–3529.

  • Szegedy C, Zaremba W, Sutskever I, Bruna J, Erhan D, Goodfellow I, Fergus R (2013) Intriguing properties of neural networks. arXiv preprint. https://arxiv.org/abs/1312.6199

  • Tan C, Eswaran C (2008) Reconstruction of handwritten digit images using autoencoder neural networks. In: 2008 Canadian conference on electrical and computer engineering, IEEE, pp 000465–000470.

  • Tan KL, Esfandiari Y, Lee XY, Sarkar AS (2020) Robustifying reinforcement learning agents via action space adversarial training. arXiv preprint. https://arxiv.org/abs/2007.07176

  • Tang T, Zhou S, Deng Z, Zou H, Lei L (2017) Vehicle detection in aerial images based on region convolutional neural networks and hard negative example mining. Sensors 17:336

    Google Scholar 

  • Tang Z, Wang G, Xiao H, Zheng A, Hwang J-N (2018) Single-camera and inter-camera vehicle tracking and 3d speed estimation based on fusion of visual and semantic features. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 108–115.

  • Tapu R, Mocanu B, Zaharia T (2017) Deep-see: Joint object detection, tracking and recognition with application to visually impaired navigational assistance. Sensors 17:2473

    Google Scholar 

  • Tawfeek MH, El-Basyouny K (2019) Estimating traffic volume on minor roads at rural stop-controlled intersections using deep learning. Transp Res Rec 2673(4):108–116

    Google Scholar 

  • Tesauro G (1995) Temporal difference learning and td-gammon. Commun ACM 38:58–68

    Google Scholar 

  • Theofilatos A, Chen C, Antoniou C (2019) Comparing machine learning and deep learning methods for real-time crash prediction. Transp Res Rec 2673(8):169–178

    Google Scholar 

  • Tian Y, Pan L (2015) Predicting short-term traffic flow by long short-term memory recurrent neural network. In: 2015 IEEE international conference on smart city/SocialCom/SustainCom (SmartCity), IEEE, pp 153–158

  • Vincent P, Larochelle H, Bengio Y, Manzagol P-A (2008) Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th international conference on machine learning, ACM, pp 1096–1103.

  • Wan C-H, Hwang M-C (2018) Value-based deep reinforcement learning for adaptive isolated intersection signal control. IET Intel Transp Syst 12:1005–1010

    Google Scholar 

  • Wan Z, Jiang C, Fahad M, Ni Z, Guo Y, He H (2018) Robot-assisted pedestrian regulation based on deep reinforcement learning. IEEE Trans Cybern 50:1669–1682

    Google Scholar 

  • Wang Z, Schaul T, Hessel M, Van Hasselt H, Lanctot M, De Freitas N (2015) Dueling network architectures for deep reinforcement learning. arXiv preprint. https://arxiv.org/abs/1511.06581

  • Wang J, Gu Q, Wu J, Liu G, Xiong Z (2016a) Traffic speed prediction and congestion source exploration: a deep learning method. In: 2016 IEEE 16th international conference on data mining (ICDM), IEEE, pp 499–508.

  • Wang H, Cai Y, Chen X, Chen L (2016b) Night-time vehicle sensing in far infrared image with deep learning. J Sens 2016:3403451

    Google Scholar 

  • Wang JG, Zhou L, Pan Y, Lee S, Song Z, Han BS, Saputra VB (2016c) Appearance-based brake-lights recognition using deep learning and vehicle detection. In: 2016 IEEE intelligent vehicles symposium (IV), IEEE, pp 815–820

  • Wang J, Zheng H, Huang Y, Ding X (2017a) Vehicle type recognition in surveillance images from labeled web-nature data using deep transfer learning. IEEE Trans Intell Transp Syst 1–10.

  • Wang Q, Gao J, Yuan Y (2017b) A joint convolutional neural networks and context transfer for street scenes labeling. IEEE Trans Intell Transp Syst 19:1457–1470

    Google Scholar 

  • Wang K, Zhang A, Li JQ, Fei Y, Chen C, Li B (2017c) Deep learning for asphalt pavement cracking recognition using convolutional neural network. In: Proceedings of international conference airfield highway pavements, pp 166–177.

  • Wang Y, Zhang D, Liu Y, Dai B, Lee LH (2018a) Enhancing transportation systems via deep learning: a survey. Transp Res Part C Emerg Technol 99:144–163

    Google Scholar 

  • Wang Y, Wang C, Zhang H (2018b) Ship classification in high-resolution sar images using deep learning of small datasets. Sensors 18:2929

    Google Scholar 

  • Wang H, Yu Y, Cai Y, Chen L, Chen X (2018c) A vehicle recognition algorithm based on deep transfer learning with a multiple feature subspace distribution. Sensors 18:4109

    Google Scholar 

  • Wang J, Chen R, He Z (2019) Traffic speed prediction for urban transportation network: a path based deep learning approach. Transp Res Part C Emerg Technol 100:372–385

    Google Scholar 

  • Weber M, Wolf P, Zollner JM (2016) Deeptlr: A single deep convolutional network for detection and classification of traffic lights. In: 2016 IEEE intelligent vehicles symposium (IV), IEEE, pp 342–348

  • Wei H, Zheng G, Yao H, Li Z (2018) Intellilight: a reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 2496–2505.

  • Wen J, Zhao J, Jaillet P (2017) Rebalancing shared mobility-on-demand systems: a reinforcement learning approach. In: 2017 IEEE 20th international conference on intelligent transportation systems (ITSC), IEEE, pp 220–225.

  • Williams RJ, Zipser D (1989) Experimental analysis of the real-time recurrent learning algorithm. Connect Sci 1:87–111

    Google Scholar 

  • Wu Y, Tan H, Qin L, Ran B, Jiang Z (2018) A hybrid deep learning based traffic flow prediction method and its understanding. Transp Res Part C Emerg Technol 90:166–180

    Google Scholar 

  • Wu C-W, Liu C-T, Chiang C-E, Tu W-C, Chien S-Y (2018) Vehicle re-identification with the space-time prior. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 121–128.

  • Xiang X, Lv N, Guo X, Wang S, El Saddik A (2018) Engineering vehicles detection based on modified faster R-CNN for power grid surveillance. Sensors 18:2258

    Google Scholar 

  • Xie L, Ahmad T, Jin L, Liu Y, Zhang S (2018) A new CNN-based method for multi-directional car license plate detection. IEEE Trans Intell Transp Syst 19:507–517

    Google Scholar 

  • Xu C, Ji J, Liu P (2018) The station-free sharing bike demand forecasting with a deep learning approach and large-scale datasets. Trans Res Part C Emerg Technol 95:47–60

    Google Scholar 

  • Yamashita R, Nishio M, Do RKG, Togashi K (2018) Convolutional neural networks: an overview and application in radiology. Insights Imaging 9:611–629

    Google Scholar 

  • Yan Z, Feng Y, Cheng C, Fu J, Zhou X, Yuan J (2017) Extensive exploration of comprehensive vehicle attributes using D-CNN with weighted multi-attribute strategy. IET Intel Transp Syst 12:186–193

    Google Scholar 

  • Yang Y, Li D, Duan Z (2017) Chinese vehicle license plate recognition using kernel-based extreme learning machine with deep convolutional features. IET Intel Transport Syst 12:213–219

    Google Scholar 

  • Yang Y, Luo H, Xu H, Wu F (2015) Towards real-time traffic sign detection and classification. IEEE Trans Intell Transp Syst 17:2022–2031

    Google Scholar 

  • Yang G, Wang Y, Yu H, Ren Y, Xie J (2018a) Short-term traffic state prediction based on the spatiotemporal features of critical road sections. Sensors 18:2287

    Google Scholar 

  • Yang H, Xie K, Ozbay K, Ma Y, Wang Z (2018b) Use of deep learning to predict daily usage of bike sharing systems. Transp Res Rec 2672(36):92–102

    Google Scholar 

  • Yao Y, Tian B, Wang F-Y (2016) Coupled multivehicle detection and classification with prior objectness measure. IEEE Trans Veh Technol 66:1975–1984

    Google Scholar 

  • Yao H, Tang X, Wei H, Zheng G, Yu Y, Li Z (2018) Modeling spatial-temporal dynamics for traffic prediction. arXiv preprint. https://arxiv.org/abs/1803.01254

  • Yao H, Wu F, Ke J, Tang X, Jia Y, Lu S, Gong P, Ye J, Li Z (2018) Deep multi-view spatial-temporal network for taxi demand prediction. In: Thirty-second AAAI conference on artificial intelligence

  • Ye YY, Hao XL, Chen HJ (2018) Lane detection method based on lane structural analysis and CNNs. IET Intel Transp Syst 12:513–520

    Google Scholar 

  • Ye T, Wang B, Song P, Li J (2018) Automatic railway traffic object detection system using feature fusion refine neural network under shunting mode. Sensors 18:1916

    Google Scholar 

  • You C, Wen C, Wang C, Li J, Habib A (2018) Joint 2-D–3-D traffic sign landmark data set for geo-localization using mobile laser scanning data. IEEE Trans Intell Transp Syst 20(7):2550–2565

    Google Scholar 

  • Yu B, Guo Z, Asian S, Wang H, Chen G (2019) Flight delay prediction for commercial air transport: a deep learning approach. Transp Res Part E Logist Transp Rev 125:203–221

    Google Scholar 

  • Yu S, Wu Y, Li W, Song Z, Zeng W (2017) A model for fine-grained vehicle classification based on deep learning. Neurocomputing 257:97–103

    Google Scholar 

  • Yu R, Li Y, Shahabi C, Demiryurek U, Liu Y (2017) Deep learning: a generic approach for extreme condition traffic forecasting. In: Proceedings of the 2017 SIAM international conference on data mining, SIAM, pp 777–785.

  • Yu B, Yin H, Zhu Z (2017) Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. arXiv preprint. https://arxiv.org/abs/1709.04875

  • Yuan Y, Xiong Z, Wang Q (2016) An incremental framework for video-based traffic sign detection, tracking, and recognition. IEEE Trans Intell Transp Syst 18:1918–1929

    Google Scholar 

  • Yuan Z, Zhou X, Yang T, Tamerius J, Mantilla R (2017) Predicting traffic accidents through heterogeneous urban data: a case study. In: Proceedings of the 6th international workshop on urban computing (UrbComp 2017), Halifax, NS, Canada, volume 14

  • Yuan Z, Zhou X, Yang T (2018) Hetero-convLSTM: A deep learning approach to traffic accident prediction on heterogeneous spatiotemporal data. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 984–992.

  • Zang D, Chai Z, Zhang J, Zhang D, Cheng J (2015) Vehicle license plate recognition using visual attention model and deep learning. J Electron Imaging 24:033001

    Google Scholar 

  • Zeng Y, Xu X, Shen D, Fang Y, Xiao Z (2016) Traffic sign recognition using kernel extreme learning machines with deep perceptual features. IEEE Trans Intell Transp Syst 18:1647–1653

    Google Scholar 

  • Zhang X, Cheng L, Li B, Hu H-M (2018) Too far to see? not really!—pedestrian detection with scale-aware localization policy. IEEE Trans Image Process 27:3703–3715

    MathSciNet  MATH  Google Scholar 

  • Zhang K, Cheng H, Zhang B (2018) Unified approach to pavement crack and sealed crack detection using preclassification based on transfer learning. J Comput Civil Eng 32:04018001

    Google Scholar 

  • Zhang Z, He Q, Gao J, Ni M (2018) A deep learning approach for detecting traffic accidents from social media data. Transp Res Part C Emerg Technol 86:580–596

    Google Scholar 

  • Zhang D, Kabuka MR (2018) Combining weather condition data to predict traffic flow: a gru-based deep learning approach. IET Intel Transp Syst 12:578–585

    Google Scholar 

  • Zhang Z, Li M, Lin X, Wang Y, He F (2019) Multistep speed prediction on traffic networks: a deep learning approach considering spatio-temporal dependencies. Transp Res Part C Emerg Technol 105:297–322

    Google Scholar 

  • Zhang A, Wang KC, Fei Y, Liu Y, Tao S, Chen C, Li JQ, Li B (2018) Deep learning–based fully automated pavement crack detection on 3d asphalt surfaces with an improved cracknet. J Comput Civil Eng 32:04018041

    Google Scholar 

  • Zhang W, Wang Z, Liu X, Sun H, Zhou J, Liu Y, Gong W (2018) Deep learning-based real-time fine-grained pedestrian recognition using stream processing. IET Intel Transp Syst 12:602–609

    Google Scholar 

  • Zhang X, Yang W, Tang X, Wang Y (2018) Lateral distance detection model based on convolutional neural network. IET Intel Transport Syst 13:31–39

    Google Scholar 

  • Zhang L, Zhang G, Liang Z, Ozioko EF (2018) Multi-features taxi destination prediction with frequency domain processing. PLoS ONE 13:e0194629

    Google Scholar 

  • Zhang J, Zheng Y, Qi D (2017) Deep spatio-temporal residual networks for citywide crowd flows prediction. In: Thirty-first AAAI conference on artificial intelligence, IEEE

  • Zhao Z, Chen W, Wu X, Chen PC, Liu J (2017) LSTM network: a deep learning approach for short-term traffic forecast. IET Intell Transp Syst 11:68–75

    Google Scholar 

  • Zhao X, Gu Y, Chen L, Shao Z (2019) Urban short-term traffic flow prediction based on stacked autoencoder. In: 19th COTA international conference of transportation professionals

  • Zheng Y, Ozcan K, Velipasalar S (2017) A codebook of brightness transfer functions for improved target re-identification across nonoverlapping camera views. In: 2017 IEEE global conference on signal and information processing (GlobalSIP), IEEE, pp 166–170.

  • Zhong J, Lei T, Yao G (2017) Robust vehicle detection in aerial images based on cascaded convolutional neural networks. Sensors 17:2720

    Google Scholar 

  • Zhou F, Li J, Li X, Li Z, Cao Y (2019) Freight car target detection in a complex background based on convolutional neural networks. Proc Inst Mech Eng Part F J Rail Rapid Transit 233:298–311

    Google Scholar 

  • Zhou Y, Liu L, Shao L, Mellor M (2018) Fast automatic vehicle annotation for urban traffic surveillance. IEEE Trans Intell Transp Syst 19:1973–1984

    Google Scholar 

  • Zhu L, Yu FR, Wang Y, Ning B, Tang T (2018a) Big data analytics in intelligent transportation systems: a survey. IEEE Trans Intell Transp Syst 20(1):383–398

    Google Scholar 

  • Zhu H, Yang X, Wang Y (2018b) Prediction of daily entrance and exit passenger flow of rail transit stations by deep learning method. J Adv Transp 2018:1–11

    Google Scholar 

  • Zhuang Y, Ke R, Wang Y (2018) Innovative method for traffic data imputation based on convolutional neural network. IET Intell Transp Syst 13:605–613

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Iowa State University, Ames, IA, USA

    Arya Ketabchi Haghighat, Varsha Ravichandra-Mouli, Pranamesh Chakraborty, Yasaman Esfandiari, Saeed Arabi & Anuj Sharma

Authors
  1. Arya Ketabchi Haghighat
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Varsha Ravichandra-Mouli
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pranamesh Chakraborty
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yasaman Esfandiari
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Saeed Arabi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Anuj Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Arya Ketabchi Haghighat.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Haghighat, A.K., Ravichandra-Mouli, V., Chakraborty, P. et al. Applications of Deep Learning in Intelligent Transportation Systems. J. Big Data Anal. Transp. 2, 115–145 (2020). https://doi.org/10.1007/s42421-020-00020-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42421-020-00020-1

Keywords

Search

Navigation