Floating Car Data–Based Short-Term Travel Time Forecasting with Deep Recurrent Neural Networks Incorporating Weather Data
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 6
Abstract
The prediction of future traffic conditions represents a main building block for traffic management. With the advent of multiple traffic and environmental sensors, diverse data for predictions are available and models may incorporate not only traffic data but also additional aspects such as local weather conditions. A review of the state-of-the art methods in short-term traffic forecasting presented in this paper reveals that machine learning (ML) algorithms from the field of deep learning are occasionally used for forecasts based on historical traffic data, but not for traffic predictions including exogenous factors. Weather conditions represent an exogenous factor, which, for example, may affect travel times. This paper investigates how short-term travel time predictions may be improved by applying deep recurrent neural networks that incorporate weather data. Therefore, two hypotheses are formulated. Hypothesis 1 tests the prediction quality of the recurrent neural network (RNN) models, long short-term memory (LSTM), and gated recurrent unit (GRU) compared to the autoregressive moving average (ARMA) prediction method. The respective results indicate that the RNN models using historical traffic data and weather data show significant improvement compared to the ARMA model using only historical traffic data. Hypothesis 2 tests the prediction quality of LSTM and GRU compared to ML-based forecast models already in place in the field of traffic predictions, namely k-nearest neighbor (kNN), support vector regression (SVR), and neural networks (NN). In this context, the LSTM and GRU models using historical traffic data and weather data show significant improvement compared to the models kNN, SVR, and NN that also consider weather data. Despite the results presented in this work, there is still further potential for improvement. Thus, further research focusing on hyperparameter tuning of the RNN algorithms and the optimized selection of (additional) input variables with significant influence on travel times can contribute to further improvements of the forecast quality.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. Some or all data, models, or code used during the study were provided by a third party. Direct requests for these materials may be made to the provider as indicated in the Acknowledgments.
Acknowledgments
This research was supported by the State of Upper Austria within the project ITS Upper Austria. The authors want to thank the RISC Software GmbH for providing both Floating Car Data and the necessary weather data for the experiments conducted in this work.
References
Cai, P., Y. Wang, G. Lu, P. Chen, C. Ding, and J. Sun. 2016. “A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting.” Transp. Res. Part C: Emerging Technol. 62 (Jan): 21–34. https://doi.org/10.1016/j.trc.2015.11.002.
Chang, H., Y. Lee, B. Yoon, and S. Baek. 2012. “Dynamic near-term traffic flow prediction: System-oriented approach based on past experiences.” IET Intell. Transp. Syst. 6 (3): 292–305. https://doi.org/10.1049/iet-its.2011.0123.
Chatfield, C. 2001. Time-series forecasting. Boca Raton, FL: Chapman & Hall/CRC.
Chen, C., Y. Wang, L. Li, J. Hu, and Z. Zhang. 2012. “The retrieval of intra-day trend and its influence on traffic prediction.” Transp. Res. Part C: Emerging Technol. 22 (Jun): 103–118. https://doi.org/10.1016/j.trc.2011.12.006.
Chen, D., X. Yan, X. Liu, S. Li, L. Wang, and X. Tian. 2021. “A multiscale-grid-based stacked bidirectional GRU neural network model for predicting traffic speeds of urban expressways.” IEEE Access 9 (Oct): 1321–1337. https://doi.org/10.1109/ACCESS.2020.3034551.
Cheng, S., F. Lu, P. Peng, and S. Wu. 2018. “Short-term traffic forecasting: An adaptive ST-KNN model that considers spatial heterogeneity.” Comput. Environ. Urban Syst. 71 (Sep): 186–198. https://doi.org/10.1016/j.compenvurbsys.2018.05.009.
ChikkaKrishna, N. K., P. Rachakonda, and T. Tallam. 2022. “Short-term traffic prediction using Fb-PROPHET and neural-PROPHET.” In Proc., 2022 IEEE Delhi Section Conf. (DELCON), 1–4. New York: IEEE.
Chu, Q., G. Li, R. Zhou, and Z. Ping. 2021. “Traffic flow prediction model based on LSTM with Finnish dataset.” In Proc., 6th Int. Conf. on Intelligent Computing and Signal Processing (ICSP), 389–392. New York: IEEE. https://doi.org/10.1109/ICSP51882.2021.9408888.
Dai, G., C. Ma, and X. Xu. 2019. “Short-term traffic flow prediction method for urban road sections based on space–time analysis and GRU.” IEEE Access 7 (Sep): 143025–143035. https://doi.org/10.1109/ACCESS.2019.2941280.
Deng, M., and S. Qu. 2016. “Road short-term travel time prediction method based on flow spatial distribution and the relations.” In Mathematical problems in engineering, 2016, 1–14. London: Hindawi. https://doi.org/10.1155/2016/7626875.
Dia, H. 2001. “An object-oriented neural network approach to short-term traffic forecasting.” Eur. J. Oper. Res. 131 (2): 253–261. https://doi.org/10.1016/S0377-2217(00)00125-9.
Dodge, Y. 2008. “Time series.” In The concise encyclopedia of statistics, edited by Y. Dodge, 536–539. Berlin: Springer.
Fan, W., and Z. Gurmu. 2015. “Dynamic travel time prediction models for buses using only GPS data.” Int. J. Transp. Sci. Technol. 4 (4): 353–366. https://doi.org/10.1016/S2046-0430(16)30168-X.
Fang, W., W. Cai, B. Fan, J. Yan, and T. Zhou. 2021. “Kalman-LSTM model for short-term traffic flow forecasting.” In Proc., 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conf. (IAEAC), 1604–1608. New York: IEEE.
Feng, X., X. Ling, H. Zheng, Z. Chen, and Y. Xu. 2019. “Adaptive multi-kernel SVM with spatial–temporal correlation for short-term traffic flow prediction.” IEEE Trans. Intell. Transp. Syst. 20 (6): 2001–2013. https://doi.org/10.1109/TITS.2018.2854913.
Fu, R., Z. Zhang, and L. Li. 2016. “Using LSTM and GRU neural network methods for traffic flow prediction.” In Proc., 31st Youth Academic Annual Conf. of Chinese Association of Automation (YAC), 324–328. New York: IEEE. https://doi.org/10.1109/YAC.2016.7804912.
Gao, Y., C. Zhou, J. Rong, Y. Wang, and S. Liu. 2022. “Short-term traffic speed forecasting using a deep learning method based on multitemporal traffic flow volume.” IEEE Access 10 (Aug): 82384–82395. https://doi.org/10.1109/ACCESS.2022.3195353.
Guo, F. 2013. “Short-term traffic prediction under normal and abnormal conditions.” Dissertation, Dept. of Civil and Environmental Engineering, Imperial College London.
Guo, F., J. W. Polak, and R. Krishnan. 2018. “Predictor fusion for short-term traffic forecasting.” Transp. Res. Part C: Emerging Technol. 92 (Jul): 90–100. https://doi.org/10.1016/j.trc.2018.04.025.
Haputhanthri, D., and A. Wijayasiri. 2021. “Short-term traffic forecasting using LSTM-based deep learning models.” In Proc., 2021 Moratuwa Engineering Research Conference (MERCon), 602–607. New York: IEEE. https://doi.org/10.1109/MERCon52712.2021.9525670.
Hong, W.-C., Y. Dong, F. Zheng, and C.-Y. Lai. 2011. “Forecasting urban traffic flow by SVR with continuous ACO.” Appl. Math. Modell. 35 (3): 1282–1291. https://doi.org/10.1016/j.apm.2010.09.005.
Huang, Y., M. Chen, Z. Liang, M. Qin, Z. Guo, and C. Zhong. 2021. “Short-term urban traffic prediction based on deep learning: A systematic map.” In Proc., 2021 IEEE Int. Conf. on Emergency Science and Information Technology (ICESIT), 19–26. New York: IEEE.
Jakteerangkool, C., and V. Muangsin. 2020. “Short-term travel time prediction from GPS trace data using recurrent neural networks.” In Proc., 2020 Asia Conf. on Computers and Communications (ACCC), 62–66. New York: IEEE. https://doi.org/10.1109/ACCC51160.2020.9347930.
Jiang, H., Y. Zou, S. Zhang, J. Tang, and Y. Wang. 2016. “Short-term speed prediction using remote microwave sensor data: Machine learning versus statistical model.” In Mathematical problems in engineering, 2016, 1–13. London: Hindawi. https://doi.org/10.1155/2016/9236156.
Kavoosi, M., M. A. Dulebenets, O. F. Abioye, J. Pasha, H. Wang, and H. Chi. 2019. “An augmented self-adaptive parameter control in evolutionary computation: A case study for the berth scheduling problem.” Adv. Eng. Inf. 42 (Oct): 100972. https://doi.org/10.1016/j.aei.2019.100972.
Klosa, D., A. Mallek, and C. Buskens. 2021. “Short-term traffic flow forecast using regression analysis and graph convolutional neural networks.” In Proc., 2021 IEEE 23rd Int. Conf. on High Performance Computing & Communications; 7th Int Conf on Data Science & Systems; 19th Int. Conf. on Smart City; 7th Int. Conf. on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys), 1413–1418. New York: IEEE.
Krishna, B. R., M. H. Reddy, P. S. Vaishnavi, and S. V. Reddy. 2022. “Traffic flow forecast using time series analysis based on machine learning.” In Proc., 2022 6th Int. Conf. on Computing Methodologies and Communication (ICCMC), 943–947. New York: IEEE.
Li, C.-S., and M.-C. Chen. 2013. “Identifying important variables for predicting travel time of freeway with non-recurrent congestion with neural networks.” Neural Comput. Appl. 23 (6): 1611–1629. https://doi.org/10.1007/s00521-012-1114-z.
Li, K., C. Zhai, and J. Xu. 2017. “Short-term traffic flow prediction using a methodology based on ARIMA and RBF-ANN.” In Proc., 2017 Chinese Automation Congress (CAC), 2804–2807. New York: IEEE. https://doi.org/10.1109/CAC.2017.8243253.
Lippi, M., M. Bertini, and P. Frasconi. 2013. “Short-term traffic flow forecasting: An experimental comparison of time-series analysis and supervised learning.” IEEE Trans. Intell. Transp. Syst. 14 (2): 871–882. https://doi.org/10.1109/TITS.2013.2247040.
Liu, L. 2021. “A short-term traffic flow prediction method based on SVR.” In Proc., 2021 2nd Int. Conf. on Urban Engineering and Management Science (ICUEMS), 1–4. New York: IEEE. https://doi.org/10.1109/ICUEMS52408.2021.00008.
Liu, Y., Y. Wang, X. Yang, and L. Zhang. 2017. “Short-term travel time prediction by deep learning: A comparison of different LSTM-DNN models.” In Proc., 2017 IEEE 20th Int. Conf. on Intelligent Transportation Systems (ITSC), 1–8. New York: IEEE.
Luo, X., D. Li, Y. Yang, and S. Zhang. 2019. “Spatiotemporal traffic flow prediction with KNN and LSTM.” J. Adv. Transp. 2019 (Feb): 1–10. https://doi.org/10.1155/2019/4145353.
Ma, D., X. Song, and P. Li. 2021. “Daily traffic flow forecasting through a contextual convolutional recurrent neural network modeling inter- and intra-day traffic patterns.” IEEE Trans. Intell. Transp. Syst. 22 (5): 2627–2636. https://doi.org/10.1109/TITS.2020.2973279.
Ma, X., Z. Tao, Y. Wang, H. Yu, and Y. Wang. 2015. “Long short-term memory neural network for traffic speed prediction using remote microwave sensor data.” Transp. Res. Part C: Emerging Technol. 54 (May): 187–197. https://doi.org/10.1016/j.trc.2015.03.014.
Mailund, T. 2017. Beginning data science in R: Data analysis, visualization, and modelling for the data scientist. Berkeley, CA: Apress.
Meng, X., H. Fu, L. Peng, G. Liu, Y. Yu, Z. Wang, and E. Chen. 2022. “D-LSTM: Short-term road traffic speed prediction model based on GPS positioning data.” IEEE Trans. Intell. Transp. Syst. 23 (3): 2021–2030. https://doi.org/10.1109/TITS.2020.3030546.
Myung, J., D.-K. Kim, S.-Y. Kho, and C.-H. Park. 2011. “Travel time prediction using k nearest neighbor method with combined data from vehicle detector system and automatic toll collection system.” Transp. Res. Rec. 2256 (1): 51–59. https://doi.org/10.3141/2256-07.
Narayanan, A. K., C. Pranesh, S. C. Nagavarapu, B. A. Kumar, and J. Dauwels. 2019. “Data-driven models for short-term travel time prediction.” In Proc., 2019 IEEE Intelligent Transportation Systems Conf.– ITSC, 1941–1946. New York: IEEE.
Oh, C., and S. Park. 2011. “Investigating the effects of daily travel time patterns on short-term prediction.” KSCE J. Civ. Eng. 15 (7): 1263–1272. https://doi.org/10.1007/s12205-011-1123-y.
Petersen, N. C., F. Rodrigues, and F. C. Pereira. 2019. “Multi-output bus travel time prediction with convolutional LSTM neural network.” Expert Syst. Appl. 120 (Apr): 426–435. https://doi.org/10.1016/j.eswa.2018.11.028.
Poonia, P., and V. K. Jain. 2020. “Short-term traffic flow prediction: Using LSTM.” In Proc., 2020 Int. Conf. on Emerging Trends in Communication, Control and Computing (ICONC3), 1–4. New York: IEEE. https://doi.org/10.1109/ICONC345789.2020.9117329.
Praveen Kumar, B., and K. Hariharan. 2020. “Multivariate time series traffic forecast with long short term memory based deep learning model.” In Proc., 2020 Int. Conf. on Power, Instrumentation, Control and Computing (PICC), 1–5. New York: IEEE. https://doi.org/10.1109/PICC51425.2020.9362455.
Qiao, W., A. Haghani, and M. Hamedi. 2012. “Short-term travel time prediction considering the effects of weather.” Transp. Res. Rec. 2308 (1): 61–72. https://doi.org/10.3141/2308-07.
Rui-min, L., J. Jian-gang, and T. Jin. 2013. “Performance evaluation of short-term travel time prediction model on urban arterials.” In Proc., 2013 5th Int. Conf. on Measuring Technology and Mechatronics Automation (ICMTMA 2013), 792–795. New York: IEEE. https://doi.org/10.1109/ICMTMA.2013.197.
Sadeghi-Niaraki, A., P. Mirshafiei, M. Shakeri, and S.-M. Choi. 2020. “Short-term traffic flow prediction using the modified Elman recurrent neural network optimized through a genetic algorithm.” IEEE Access 8 (Nov): 217526–217540. https://doi.org/10.1109/ACCESS.2020.3039410.
Salari, N., S. Liu, and Z.-J. M. Shen. 2022. “Real-time delivery time forecasting and promising in online retailing: When will your package arrive?” Manuf. Serv. Oper. Manage. 24 (3): 1421–1436. https://doi.org/10.1287/msom.2022.1081.
Samuel, A. 1959. “Some studies in machine learning using the game of checkers.” IBM J. 3 (3): 210–229. https://doi.org/10.1147/rd.33.0210.
Shin, Y., and Y. Yoon. 2022. “Incorporating dynamicity of transportation network with multi-weight traffic graph convolutional network for traffic forecasting.” IEEE Trans. Intell. Transp. Syst. 23 (3): 2082–2092. https://doi.org/10.1109/TITS.2020.3031331.
Sun, S., R. Huang, and Y. Gao. 2012. “Network-scale traffic modeling and forecasting with graphical lasso and neural networks.” J. Transp. Eng. 138 (11): 1358–1367. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000435.
Taghipour, H., A. B. Parsa, and A. Mohammadian. 2020. “A dynamic approach to predict travel time in real time using data driven techniques and comprehensive data sources.” Transp. Eng. 2 (Dec): 1–9. https://doi.org/10.1016/j.treng.2020.100025.
Tang, K., S. Chen, Y. Cao, X. Li, D. Zang, J. Sun, and Y. Ji. 2020. “Short-term travel speed prediction for urban expressways: Hybrid convolutional neural network models.” IEEE Trans. Intell. Transp. Syst. 23 (3): 1829–1840. https://doi.org/10.1109/TITS.2020.3027628.
Tao, Y., P. Sun, and A. Boukerche. 2020. “A delay-based deep learning approach for urban traffic volume prediction.” In Proc., 2020 IEEE Int. Conf. on Communications (ICC), 1–6. New York: IEEE.
Thakuriah, P., and N. Tilahun. 2013. “Incorporating weather information into real-time speed estimates: Comparison of alternative models.” J. Transp. Eng. 139 (4): 379–389. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000506.
Tian, Y., and L. Pan. 2015. “Predicting short-term traffic flow by long short-term memory recurrent neural network.” In Proc., IEEE Int. Conf. on Smart City/SocialCom/SustainCom (SmartCity), 153–158. New York: IEEE.
Tian, Y., K. Zhang, J. Li, X. Lin, and B. Yang. 2018. “LSTM-based traffic flow prediction with missing data.” Neurocomputing 318 (Nov): 297–305. https://doi.org/10.1016/j.neucom.2018.08.067.
Tian, Z., and D. He. 2022. “Short-term traffic flow prediction based on GMO-DELM.” In Proc., 2022 3rd Int. Conf. on Geology, Mapping and Remote Sensing (ICGMRS), 260–264. New York: IEEE. https://doi.org/10.1109/ICGMRS55602.2022.9849312.
Tsai, M.-J., H.-Y. Chen, Z. Cui, and Y. Wang. 2021. “Multivariate long and short term LSTM-based network for traffic forecasting under interference: Experiments during COVID-19.” In Proc., 2021 IEEE Int. Intelligent Transportation Systems Conf. (ITSC), 2169–2174. New York: IEEE.
Tsirigotis, L., E. I. Vlahogianni, and M. G. Karlaftis. 2012. “Does information on weather affect the performance of short-term traffic forecasting models?” Int. J. ITS Res. 10 (1): 1–10. https://doi.org/10.1007/s13177-011-0037-x.
van Arem, B., H. R. Kirby, M. J. van der Vlist, and J. C. Whittaker. 1997. “Recent advances and applications in the field of short-term traffic forecasting.” Int. J. Forecasting 13 (1): 1–12. https://doi.org/10.1016/S0169-2070(96)00695-4.
Vlahogianni, E., and M. Karlaftis. 2011. “Temporal aggregation in traffic data: Implications for statistical characteristics and model choice.” Transp. Lett. 3 (1): 37–49. https://doi.org/10.3328/TL.2011.03.01.37-49.
Vlahogianni, E., M. Karlaftis, and J. Golias. 2014. “Short-term traffic forecasting: Where we are and where we’re going.” Transp. Res. Part C Emerging Technol. 43 (1): 3–19. https://doi.org/10.1016/j.trc.2014.01.005.
Wang, J., and Q. Shi. 2013. “Short-term traffic speed forecasting hybrid model based on chaos–wavelet analysis-support vector machine theory.” Transp. Res. Part C: Emerging Technol. 27 (Aug): 219–232. https://doi.org/10.1016/j.trc.2012.08.004.
Wang, X. 2021. “SVR short-term traffic flow forecasting model based on spatial-temporal feature selection.” In Proc., 2021 7th Annual Int. Conf. on Network and Information Systems for Computers (ICNISC), 270–275. New York: IEEE. https://doi.org/10.1109/ICNISC54316.2021.00056.
Wei, C.-H., and Y. Lee. 2007. “Development of freeway travel time forecasting models by integrating different sources of traffic data.” IEEE Trans. Veh. Technol. 56 (6): 3682–3694. https://doi.org/10.1109/TVT.2007.901965.
Xie, Y., Y. Zhang, and Z. Ye. 2007. “Short-Term traffic volume forecasting using Kalman filter with discrete wavelet decomposition.” Comput.-Aided Civ. Eng. 22 (5): 326–334. https://doi.org/10.1111/j.1467-8667.2007.00489.x.
Yang, L., Q. Yang, Y. Li, and Y. Feng. 2019. “K-nearest neighbor model based short-term traffic flow prediction method.” In Proc., 2019 18th Int. Symp. on Distributed Computing and Applications for Business Engineering and Science (DCABES), 27–30. New York: IEEE. https://doi.org/10.1109/DCABES48411.2019.00014.
Yang, S., and S. Qian. 2019. “Understanding and predicting travel time with Spatio-temporal features of network traffic flow, weather and incidents.” IEEE Intell. Transp. Syst. Mag. 11 (3): 12–28. https://doi.org/10.1109/MITS.2019.2919615.
Yao, B., C. Chen, Q. Cao, L. Jin, M. Zhang, H. Zhu, and B. Yu. 2017. “Short-term traffic speed prediction for an urban corridor.” Comput.-Aided Civ. Infrastruct. Eng. 32 (2): 154–169. https://doi.org/10.1111/mice.12221.
Ye, Q., W. Y. Szeto, and S. C. Wong. 2012. “Short-term traffic speed forecasting based on data recorded at irregular intervals.” IEEE Trans. Intell. Transp. Syst. 13 (4): 1727–1737. https://doi.org/10.1109/TITS.2012.2203122.
Yong-chuan, Z., Z. Xiao-qing, Z. li-ting, and C. Zhen-ting. 2011. “Traffic congestion detection based on GPS floating-car data.” Procedia Eng. 15 (Jan): 5541–5546. https://doi.org/10.1016/j.proeng.2011.08.1028.
Yu, L., J. Zhao, Y. Gao, and W. Lin. 2019. “Short-term traffic flow prediction based on deep learning network.” In Proc., 2019 Int. Conf. on Robots & Intelligent System (ICRIS), 466–469. New York: IEEE. https://doi.org/10.1109/ICRIS.2019.00122.
Zhang, L., Q. Liu, W. Yang, N. Wei, and D. Dong. 2013. “An improved K-nearest neighbor model for short-term traffic flow prediction.” Procedia–Soc. Behav. Sci. 96 (Aug): 653–662. https://doi.org/10.1016/j.sbspro.2013.08.076.
Zhang, T., and G. Guo. 2022. “Graph attention LSTM: A spatiotemporal approach for traffic flow forecasting.” IEEE Intell. Transp. Syst. Mag. 14 (2): 190–196. https://doi.org/10.1109/MITS.2020.2990165.
Zhang, Y., and Z. Ye. 2008. “Short-term traffic flow forecasting using fuzzy logic system methods.” J. Intell. Transp. Syst. 12 (3): 102–112. https://doi.org/10.1080/15472450802262281.
Zhang, Z., P. Chen, Y. Wang, and Y. Guizhen. 2017. “A hybrid deep learning approach for urban expressway travel time prediction considering spatial-temporal features.” In Proc., 2017 IEEE 20th Int. Conf. on Intelligent Transportation Systems (ITSC), 795–800. New York: IEEE. https://doi.org/10.1109/ITSC.2017.8317889.
Zhao, Z., W. Chen, X. Wu, P. C. Y. Chen, and J. Liu. 2017. “LSTM network: A deep learning approach for short-term traffic forecast.” IET Intel. Transport Syst. 11 (2): 68–75. https://doi.org/10.1049/iet-its.2016.0208.
Zhaowei, Q., L. Haitao, L. Zhihui, and Z. Tao. 2022. “Short-term traffic flow forecasting method with M-B-LSTM hybrid network.” IEEE Trans. Intell. Transp. Syst. 23 (1): 225–235. https://doi.org/10.1109/TITS.2020.3009725.
Zheng, F., and H. van Zuylen. 2013. “Urban link travel time estimation based on sparse probe vehicle data.” Transp. Res. Part C: Emerging Technol. 31 (Mar): 145–157. https://doi.org/10.1016/j.trc.2012.04.007.
Zheng, H., F. Lin, X. Feng, and Y. Chen. 2021. “A hybrid deep learning model with attention-based Conv-LSTM networks for short-term traffic flow prediction.” IEEE Trans. Intell. Transp. Syst. 22 (11): 6910–6920. https://doi.org/10.1109/TITS.2020.2997352.
Zhu, J. Z., J. X. Cao, and Y. Zhu. 2014. “Traffic volume forecasting based on radial basis function neural network with the consideration of traffic flows at the adjacent intersections.” Transp. Res. Part C Emerging Technol. 47 (Oct): 139–154. https://doi.org/10.1016/j.trc.2014.06.011.
Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 6 • June 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Aug 10, 2022
Accepted: Dec 29, 2022
Published online: Mar 21, 2023
Published in print: Jun 1, 2023
Discussion open until: Aug 21, 2023
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Meiye Li, Jianhua Guo, Xiaobin Zhong, Real-Time Traffic Flow Uncertainty Quantification Based on Nonparametric Probability Density Function Estimation, Journal of Transportation Engineering, Part A: Systems, 10.1061/JTEPBS.TEENG-8539, 150, 11, (2024).
- Ayad Ghany Ismaeel, Krishnadas Janardhanan, Manishankar Sankar, Yuvaraj Natarajan, Sarmad Nozad Mahmood, Sameer Alani, Akram H. Shather, Traffic Pattern Classification in Smart Cities Using Deep Recurrent Neural Network, Sustainability, 10.3390/su151914522, 15, 19, (14522), (2023).