Implementation of Artificial Intelligence in Traffic Management in the United States
Keywords:
artificial intelligence, machine learning, deep learning, traffic management, congestion prediction, GPS trajectory data, Tom-Tom Traffic Index, decision trees, PythonAbstract
This paper investigates the application and deployment of artificial intelligence (AI) in enhancing traffic management within the U.S., focusing mainly on predicting future traffic demand using machine learning and deep learning models. Utilizing datasets from the Tom-Tom Traffic Index and the Python programming language for data processing, the study aims to mitigate traffic congestion through accurate traffic prediction. The study specifically examines Baltimore, Maryland (used as a proxy for major U.S. cities) to assess the efficiency of AI technologies on traffic levels and provides a comparative analysis of machine learning and deep learning algorithms (decision tree, random forest, logistic regression, and deep learning neural network). The results revealed that decision tree models surpass other algorithms with an 85% accuracy rate in congestion prediction. The study contemplates the technical aspects of traffic management systems and addresses the practical implications for city planning and the overarching goals of reducing congestion and facilitating transportation logistics. The paper offers valuable insights to transportation planners, logistics managers, and academic researchers.
References
A. Downs, “Traffic: Why It’s Getting Worse, What Government Can Do,” Jan. 2004. Accessed: Dec. 02, 2023. [Online]. Available: https://www.brookings.edu/articles/traffic-why-its-getting-worse-what-government-can-do
J. MacDonald, “The Science of Traffic,” JSTOR Daily, Dec. 2017, [Online]. Available: https://daily.jstor.org/the-science-of-traffic/
Federal Highway Administration, “Traffic Congestion and Reliability: Trends and Advanced Strategies for Congestion Mitigation,” Mar. 2020. Accessed: Dec. 02, 2023. [Online]. Available: https://ops.fhwa.dot.gov/congestion_report/executive_summary.htm
D. Schrank and B. Eisele, “Urban Mobility Report,” Mobility Division - Texas a&M Transportation Institute, 2021. https://mobility.tamu.edu/umr/ (accessed Dec. 02, 2023).
E. M. Dogo, T. Makaba, O. J. Afolabi, and A. C. Ajibo, “Combating Road Traffic Congestion with Big Data: A Bibliometric Review and Analysis of Scientific Research,” in EAI/Springer Innovations in Communication and Computing, 2021, pp. 43–86. doi: 10.1007/978-3-030-66042-0_4.
I. Thomson and A. Bull, “Urban Traffic Congestion: Its Economic and Social Causes and Consequences,” CEPAL Review, no. 76, pp. 105–116, Apr. 2002.
M. Cogoni, “Estimating Peak-Hour Traffic Congestion Patterns For Interacting Agents On Urban Networks,” arXiv.org, Jul. 22, 2022. https://arxiv.org/abs/2207.11219
H. Karimi, B. Ghadirifaraz, S. N. S. Boushehri, S.-M. Hosseininasab, and N. Rafiei, “Reducing traffic congestion and increasing sustainability in special urban areas through one-way traffic reconfiguration,” Transportation, vol. 49, no. 1, pp. 37–60, Jan. 2021, doi: 10.1007/s11116-020-10162-4.
D. Q. Nguyen-Phuoc, W. Young, G. Currie, and C. De Gruyter, “Traffic congestion relief associated with public transport: state-of-the-art,” Public Transport, vol. 12, no. 2, pp. 455–481, Feb. 2020, doi: 10.1007/s12469-020-00231-3.
American Psychological Association, “Road rage: What makes some people more prone to anger behind the wheel,” American Psychological Association, 2014. Accessed: Dec. 02, 2023. [Online]. Available: https://www.apa.org/topics/anger/road-rage
Y. Chen, P. Yan, Z. Zheng, and D. Chen, “Identifying Traffic Bottleneck in Urban Road Networks via Causal Inference,” in Lecture Notes in Computer Science, 2021, pp. 372–383. doi: 10.1007/978-3-030-68884-4_31.
G. P. R. Filho et al., “Enhancing intelligence in traffic management systems to aid in vehicle traffic congestion problems in smart cities,” Ad Hoc Networks, vol. 107, p. 102265, Oct. 2020, doi: 10.1016/j.adhoc.2020.102265.
A. Iqbal, Z. U. Rehman, S. Ali, K. Ullah, and U. Ghani, “Road traffic accident analysis and identification of black spot locations on highway,” Civil Engineering Journal, vol. 6, no. 12, pp. 2448–2456, Dec. 2020, doi: 10.28991/cej-2020-03091629.
M. Al-Turki, A. Jamal, H. M. Al-Ahmadi, M. A. Al-Sughaiyer, and M. Zahid, “On the Potential Impacts of Smart Traffic Control for Delay, Fuel Energy Consumption, and Emissions: An NSGA-II-Based Optimization Case Study from Dhahran, Saudi Arabia,” Sustainability, vol. 12, no. 18, p. 7394, Sep. 2020, doi: 10.3390/su12187394.
M. S. Kumar, K. Kumar, and P. Das, “Study on road traffic congestion: A review,” in CRC Press eBooks, 2021, pp. 230–240. doi: 10.1201/9781003193838-43.
Y. J. Yasin, M. Grivna, and F. M. Abu?Zidan, “Global impact of COVID-19 pandemic on road traffic collisions,” World Journal of Emergency Surgery, vol. 16, no. 1, Sep. 2021, doi: 10.1186/s13017-021-00395-8.
R. Fisa, M. Musukuma, M. Sampa, P. Musonda, and T. Young, “Effects of interventions for preventing road traffic crashes: an overview of systematic reviews,” BMC Public Health, vol. 22, no. 1, Mar. 2022, doi: 10.1186/s12889-021-12253-y.
M. J. Giummarra, B. Beck, and B. Gabbe, “Classification of road traffic injury collision characteristics using text mining analysis: Implications for road injury prevention,” PLOS ONE, vol. 16, no. 1, p. e0245636, Jan. 2021, doi: 10.1371/journal.pone.0245636.
K. K. C. Hung et al., “Impacts of injury severity on long-term outcomes following motor vehicle crashes,” BMC Public Health, vol. 21, no. 1, Mar. 2021, doi: 10.1186/s12889-021-10638-7.
C. Chantith, C. K. Permpoonwiwat, and B. Hamaide, “Measure of productivity loss due to road traffic accidents in Thailand,” IATSS Research, vol. 45, no. 1, pp. 131–136, Apr. 2021, doi: 10.1016/j.iatssr.2020.07.001.
N. Drop and D. Garli?ska, “Evaluation of intelligent transport systems used in urban agglomerations and intercity roads by professional truck drivers,” Sustainability, vol. 13, no. 5, p. 2935, Mar. 2021, doi: 10.3390/su13052935.
D. West and J. Allen, “How artificial intelligence is transforming the world,” Brookings, Apr. 24, 2018. https://www.brookings.edu/articles/how-artificial-intelligence-is-transforming-the-world/ (accessed Dec. 02, 2023).
C. Janiesch, P. Zschech, and K. Heinrich, “Machine learning and deep learning,” Electronic Markets, vol. 31, no. 3, pp. 685–695, Apr. 2021, doi: 10.1007/s12525-021-00475-2.
B. Mahesh, “Machine Learning Algorithms -A Review,” ResearchGate, Jan. 2019, doi: 10.21275/ART20203995.
I. H. Sarker, “Machine Learning: Algorithms, Real-World Applications and Research Directions,” SN Computer Science, vol. 2, no. 3, Mar. 2021, doi: 10.1007/s42979-021-00592-x.
I. H. Sarker, “Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions,” SN Computer Science, vol. 2, no. 6, Aug. 2021, doi: 10.1007/s42979-021-00815-1.
W. S. Gan, “Deep Learning and Artificial Neural Networks,” in Springer eBooks, 2020, pp. 69–79. doi: 10.1007/978-981-10-5550-8_13.
F. Dashdamirov, A. Aliyev, T. Verdiyev, and U. Javadli, “Improving Intersection Traffic Management Solutions by Means of Simulation: Case Study,” in Studies in fuzziness and soft computing, 2023, pp. 279–286. doi: 10.1007/978-3-031-23476-7_25.
A. Sukhadia, K. Upadhyay, M. Gundeti, S. Shah, and M. Shah, “Optimization of Smart Traffic Governance System Using Artificial Intelligence,” Augmented Human Research, vol. 5, no. 1, Mar. 2020, doi: 10.1007/s41133-020-00035-x.
Y. Modi, R. Teli, A. Mehta, K. Shah, and M. Shah, “A comprehensive review on intelligent traffic management using machine learning algorithms,” Innovative Infrastructure Solutions, vol. 7, no. 1, Dec. 2021, doi: 10.1007/s41062-021-00718-3.
M. Greguri?, M. Vuji?, C. Alexopoulos, and M. Mileti?, “Application of Deep Reinforcement Learning in Traffic Signal Control: An Overview and Impact of Open Traffic Data,” Applied Sciences, vol. 10, no. 11, p. 4011, Jun. 2020, doi: 10.3390/app10114011.
S. Saha, “A TRAFFIC MANAGEMENT SYSTEM APPROACH WITH THE IMPLEMENTATION OF ARTIFICIAL INTELLIGENCE ALGORITHMS,” International Journal of Engineering Applied Sciences and Technology, vol. 4, no. 6, pp. 1–6, Oct. 2019.
F. Habtemichael and M. Cetin, “Short-term traffic flow rate forecasting based on identifying similar traffic patterns,” Transportation Research Part C: Emerging Technologies, vol. 66, pp. 61–78, May 2016, doi: 10.1016/j.trc.2015.08.017.
X. Fan et al., “Deep learning for intelligent traffic sensing and prediction: recent advances and future challenges,” CCF Transactions on Pervasive Computing and Interaction, vol. 2, no. 4, pp. 240–260, Sep. 2020, doi: 10.1007/s42486-020-00039-x.
N. A. M. Razali, N. Shamsaimon, K. K. Ishak, S. Ramli, M. F. M. Amran, and S. Sukardi, “Gap, techniques and evaluation: traffic flow prediction using machine learning and deep learning,” Journal of Big Data, vol. 8, no. 1, Dec. 2021, doi: 10.1186/s40537-021-00542-7.
H. K. An et al., “Optimized Intersection Signal Timing: An Intelligent Approach-Based Study for Sustainable Models,” Sustainability, vol. 14, no. 18, p. 11422, Sep. 2022, doi: 10.3390/su141811422.
Z. Ma, T. Cui, W. J. Deng, F. Jiang, and L. Zhang, “Adaptive Optimization of Traffic Signal Timing via Deep Reinforcement Learning,” Journal of Advanced Transportation, vol. 2021, pp. 1–14, Nov. 2021, doi: 10.1155/2021/6616702.
Y. Modi, R. Teli, A. Mehta, K. Shah, and M. Shah, “A comprehensive review on intelligent traffic management using machine learning algorithms,” Innovative Infrastructure Solutions, vol. 7, no. 1, Dec. 2021, doi: 10.1007/s41062-021-00718-3.
Y. Feng, J. Fang, B. Chen, and Y. Shao, “An incremental learning based convolutional neural network model for Large-Scale and Short-Term traffic flow,” International Journal of Machine Learning and Computing, vol. 11, no. 2, pp. 143–151, Mar. 2021, doi: 10.18178/ijmlc.2021.11.2.1027.
A. S. Iliyasu, I. Abba, B. S. Iliyasu, and A. S. Muhammad, “A review of deep learning techniques for encrypted traffic Classification,” Computational Intelligence and Machine Learning, vol. 3, no. 2, pp. 15–21, Oct. 2022, doi: 10.36647/ciml/03.02.a003.
F. Kong, J. Li, B. Jiang, T. Zhang, and H. Song, “Big data?driven machine learning?enabled traffic flow prediction,” Transactions on Emerging Telecommunications Technologies, vol. 30, no. 9, Jul. 2018, doi: 10.1002/ett.3482.
X. Ma, H. Yu, Y. Wang, and Y. Wang, “Large-Scale transportation network congestion evolution prediction using deep learning theory,” PLOS ONE, vol. 10, no. 3, p. e0119044, Mar. 2015, doi: 10.1371/journal.pone.0119044.
S. Rajagopal, S. A. Angayarkanni, R. R. Y V., and A. S. Sadiq, “Traffic flow forecasting using natural selection based hybrid Bald Eagle Search—Grey Wolf optimization algorithm,” PLOS ONE, vol. 17, no. 9, p. e0275104, Sep. 2022, doi: 10.1371/journal.pone.0275104.
F. U. Islam, G. Liu, W. Liu, and Q. M. U. Haq, “A deep learning?based framework to identify and characterise heterogeneous secure network traffic,” Iet Information Security, vol. 17, no. 2, pp. 294–308, Oct. 2022, doi: 10.1049/ise2.12095.
?. K?l?ç and G. Ayd?n, “Traffic Lights Detection and Recognition with New Benchmark Datasets Using Deep Learning and TensorFlow Object Detection API,” Traitement Du Signal, vol. 39, no. 5, pp. 1673–1683, Nov. 2022, doi: 10.18280/ts.390525.
A. A. Alkhatib, K. A. Maria, S. AlZu’bi, and E. A. Maria, “Novel system for road traffic optimisation in large cities,” IET Smart Cities, vol. 4, no. 2, pp. 143–155, Jun. 2022, doi: 10.1049/smc2.12032.
Y. Fang, Q. Yang, L. Zheng, X. Zhou, and B. Peng, “A Deep Cycle limit Learning Machine method for urban Expressway traffic incident detection,” Mathematical Problems in Engineering, vol. 2020, pp. 1–11, Jul. 2020, doi: 10.1155/2020/5965089.
F. A. Shakil, S. M. Hossain, R. Hossain, and S. Momen, “Prediction of road accidents using data mining techniques,” in Algorithms for intelligent systems, 2021, pp. 25–35. doi: 10.1007/978-981-16-4103-9_3.
R. M. AlZoman and M. J. F. Alenazi, “A comparative study of traffic classification techniques for smart city networks,” Sensors, vol. 21, no. 14, p. 4677, Jul. 2021, doi: 10.3390/s21144677.
L. Peng, Q. Wang, S.-Q. Zhou, and Z. Tian, “Research on the evolution law of incidental traffic congestion based on CTM,” Journal of Physics, vol. 2277, no. 1, p. 012007, Jun. 2022, doi: 10.1088/1742-6596/2277/1/012007.
N. M. Balamurugan, M. Adimoolam, M. H. Alsharif, and P. Uthansakul, “A novel method for improved network traffic prediction using enhanced deep reinforcement learning algorithm,” Sensors, vol. 22, no. 13, p. 5006, Jul. 2022, doi: 10.3390/s22135006.
C. Li, C. Dong, K. Niu, and Z. Zhang, “Mobile service traffic classification based on joint deep learning with attention mechanism,” IEEE Access, vol. 9, pp. 74729–74738, Jan. 2021, doi: 10.1109/access.2021.3081504.
F. Liu, X. Ma, X. An, and G. Liang, “Urban Traffic Flow Prediction Model with CPSO/SSVM Algorithm under the Edge Computing Framework,” Wireless Communications and Mobile Computing, vol. 2020, pp. 1–11, Sep. 2020, doi: 10.1155/2020/8871998.
M. S. B. Othman, S. L. Keoh, and G. Tan, “Efficient journey planning and congestion prediction through deep learning,” IEEE Xplore, Nov. 2017, doi: 10.1109/isc2.2017.8090805.
Y. Shi, Z. G. Wang, and Y. Zhong, “Research on Dwell Point Strategy of Compact Intelligent Parking System under Dynamic Demand,” Journal of Physics, vol. 2583, no. 1, p. 012005, Sep. 2023, doi: 10.1088/1742-6596/2583/1/012005.
W. Sirikul, N. Buawangpong, R. Sapbamrer, and P. Siviroj, “Mortality-Risk Prediction Model from Road-Traffic Injury in Drunk Drivers: Machine Learning Approach,” International Journal of Environmental Research and Public Health, vol. 18, no. 19, p. 10540, Oct. 2021, doi: 10.3390/ijerph181910540.
N. Zafar and I. U. Haq, “Traffic congestion prediction based on Estimated Time of Arrival,” PLOS ONE, vol. 15, no. 12, p. e0238200, Dec. 2020, doi: 10.1371/journal.pone.0238200.
J. Gan, L. Li, D. Zhang, Z. Yi, and Q. Xiang, “An alternative method for traffic accident severity prediction: using deep forests algorithm,” Journal of Advanced Transportation, vol. 2020, pp. 1–13, Dec. 2020, doi: 10.1155/2020/1257627.
A. H. A. Hussain et al., “Urban Traffic Flow Estimation System based on Gated Recurrent Unit Deep learning Methodology for Internet of Vehicles,” IEEE Access, vol. 11, pp. 58516–58531, Jan. 2023, doi: 10.1109/access.2023.3270395.
A. Paul and S. Mitra, “Deep reinforcement learning based cooperative control of traffic signal for multi?intersection network in intelligent transportation system using edge computing,” Transactions on Emerging Telecommunications Technologies, vol. 33, no. 11, Jul. 2022, doi: 10.1002/ett.4588.
S. Zhang, Y. Yao, J. Hu, Y. Zhao, S. Li, and J. Hu, “Deep autoencoder neural networks for Short-Term Traffic Congestion prediction of transportation networks,” Sensors, vol. 19, no. 10, p. 2229, May 2019, doi: 10.3390/s19102229.
A. Jamal, M. T. Rahman, H. M. Al-Ahmadi, and U. Mansoor, “The dilemma of road safety in the eastern province of Saudi Arabia: Consequences and prevention strategies,” International Journal of Environmental Research and Public Health, vol. 17, no. 1, p. 157, Dec. 2019, doi: 10.3390/ijerph17010157.
F. Pérez, G. E. R. Ortiz, E. R. Muñiz, F. J. O. Sacarello, J. E. Kang, and D. Rodríguez-Román, “Predicting trip cancellations and No-Shows in paratransit operations,” Transportation Research Record, vol. 2674, no. 8, pp. 774–784, Jun. 2020, doi: 10.1177/0361198120924661.
T. Bandaragoda et al., “Artificial intelligence based commuter behaviour profiling framework using Internet of things for real-time decision-making,” Neural Computing and Applications, vol. 32, no. 20, pp. 16057–16071, Feb. 2020, doi: 10.1007/s00521-020-04736-7.
D. Nallaperuma et al., “Online Incremental Machine Learning platform for Big Data-Driven smart traffic management,” IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 12, pp. 4679–4690, Dec. 2019, doi: 10.1109/tits.2019.2924883.
A. Stathopoulos, L. Dimitriou, and T. Tsekeris, “Fuzzy modeling approach for combined forecasting of urban traffic flow,” Computer-Aided Civil and Infrastructure Engineering, vol. 23, no. 7, pp. 521–535, Aug. 2008, doi: 10.1111/j.1467-8667.2008.00558.x.
A. Boukerche, Y. Tao, and P. Sun, “Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems,” Computer Networks, vol. 182, p. 107484, Dec. 2020, doi: 10.1016/j.comnet.2020.107484.
S. S. Kolekar, S. Gite, B. Pradhan, and K. Kotecha, “Behavior Prediction of traffic actors for intelligent vehicle using Artificial Intelligence Techniques: a review,” IEEE Access, vol. 9, pp. 135034–135058, Jan. 2021, doi: 10.1109/access.2021.3116303.
I. O. Olayode, L. K. Tartibu, M. O. Okwu, and A. Severino, “Comparative traffic flow prediction of a heuristic ANN model and a hybrid ANN-PSO model in the traffic flow modelling of vehicles at a Four-Way signalized road intersection,” Sustainability, vol. 13, no. 19, p. 10704, Sep. 2021, doi: 10.3390/su131910704.
S. C. S. Chen, H. W. S. Chen, and J. W. H. Wen, “Artificial intelligence based traffic control for edge computing assisted vehicle networks,” Journal of Internet Technology, vol. 23, no. 5, pp. 989–996, Sep. 2022, doi: 10.53106/160792642022092305007.
X. Yang, Z. Pu, and L. Zhang, “Designing data visualization using artificial intelligence for urban intelligent transportation scenarios,” Mobile Information Systems, vol. 2022, pp. 1–11, Aug. 2022, doi: 10.1155/2022/3967267.
A. Ata, M. A. Khan, S. Abbas, G. Ahmad, and A. Fatima, “MODELLING SMART ROAD TRAFFIC CONGESTION CONTROL SYSTEM USING MACHINE LEARNING TECHNIQUES,” Neural Network World, vol. 29, no. 2, pp. 99–110, Jan. 2019, doi: 10.14311/nnw.2019.29.008.
Z. Li and D. Li, “Data visualization and interaction of Urban Traffic Logistics Management System using WebGIS,” Computational Intelligence and Neuroscience, vol. 2022, pp. 1–12, Jun. 2022, doi: 10.1155/2022/9347247.
A. Srivastava, Aryan, and M. Khurana, “A survey paper on traffic Prediction using Machine learning,” International Journal for Research in Applied Science and Engineering Technology, vol. 11, no. 5, pp. 44–48, May 2023, doi: 10.22214/ijraset.2023.51390.
M. E. C. Ba?datl? and A. ?. Dokuz, “Vehicle delay estimation at signalized intersections using machine learning algorithms,” Transportation Research Record, vol. 2675, no. 9, pp. 110–126, Aug. 2021, doi: 10.1177/03611981211036874.
P. M. Kumar, U. D. G, G. Manogaran, R. Sundarasekar, N. Chilamkurti, and R. Varatharajan, “Ant colony optimization algorithm with Internet of Vehicles for intelligent traffic control system,” Computer Networks, vol. 144, pp. 154–162, Oct. 2018, doi: 10.1016/j.comnet.2018.07.001.
A. Stathopoulos, M. G. Karlaftis, and L. Dimitriou, “Fuzzy Rule-Based System approach to combining traffic count forecasts,” Transportation Research Record, vol. 2183, no. 1, pp. 120–128, Jan. 2010, doi: 10.3141/2183-13.
W. Xu, Z. Dong, S. Wu, and N. Hao, “A Control Method of Traffic flow Based on Region Coordination,” Advances in Intelligent Systems Research, Jan. 2015, doi: 10.2991/meici-15.2015.257.
P. Chhatpar, N. Doolani, S. Shahani, and Priya, “Machine Learning Solutions to Vehicular Traffic Congestion,” IEEE Xplore, Nov. 2018, doi: 10.1109/ICSCET.2018.8537260.
M. Babar, M. H. Roos, and P. H. Nguyen, “Machine Learning for Agile and Self-Adaptive Congestion Management in Active Distribution Networks,” IEEE Xplore, Aug. 2019, doi: 10.1109/eeeic.2019.8783624.
S. Khatri et al., “Machine learning models and techniques for VANET based traffic management: Implementation issues and challenges,” Peer-to-Peer Networking and Applications, vol. 14, no. 3, pp. 1778–1805, Sep. 2020, doi: 10.1007/s12083-020-00993-4.
E. Mangortey, M.-H. Bleu-Laine, T. G. Puranik, O. Fischer, and D. N. Mavris, “Machine learning approach to the analysis of traffic management initiatives,” Journal of Air Transportation, vol. 29, no. 2, pp. 56–68, Apr. 2021, doi: 10.2514/1.d0212.
S. J. Kamble and M. R. Kounte, “Machine Learning Approach on Traffic Congestion Monitoring System in Internet of Vehicles,” Procedia Computer Science, vol. 171, pp. 2235–2241, Jan. 2020, doi: 10.1016/j.procs.2020.04.241.
F. Baker, “The technology that could end traffic jams,” BBC Future, Dec. 12, 2018. Accessed: Dec. 02, 2023. [Online]. Available: https://www.bbc.com/future/article/20181212-can-artificial-intelligence-end-traffic-jams
B. R. Sharma, S. Kumar, P. Tiwari, P. Yadav, and M. I. Nezhurina, “ANN based short-term traffic flow forecasting in undivided two lane highway,” Journal of Big Data, vol. 5, no. 1, Dec. 2018, doi: 10.1186/s40537-018-0157-0.
E. I. Vlahogianni, J. Del Ser, ?. ????????????, and I. Laña, “Model Free Identification of Traffic Conditions Using Unmanned Aerial Vehicles and Deep Learning,” Journal of Big Data Analytics in Transportation, vol. 3, no. 1, pp. 1–13, Mar. 2021, doi: 10.1007/s42421-021-00038-z.
M. Kiran and A. Verma, “Review of Studies on Mixed Traffic Flow: Perspective of Developing Economies,” Transportation in Developing Economies, vol. 2, no. 1, Feb. 2016, doi: 10.1007/s40890-016-0010-0.
G. Asaithambi, V. Kanagaraj, and T. Toledo, “Driving Behaviors: Models and Challenges for Non-Lane Based Mixed Traffic,” Transportation in Developing Economies, vol. 2, no. 2, Aug. 2016, doi: 10.1007/s40890-016-0025-6.
J. Guo, W. Huang, and B. M. Williams, “Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification,” Transportation Research Part C: Emerging Technologies, vol. 43, pp. 50–64, Jun. 2014, doi: 10.1016/j.trc.2014.02.006.
M. Nama et al., “Machine learning?based traffic scheduling techniques for intelligent transportation system: Opportunities and challenges,” International Journal of Communication Systems, vol. 34, no. 9, Apr. 2021, doi: 10.1002/dac.4814.
M. A. Khamis and W. Gomaa, “Adaptive multi-objective reinforcement learning with hybrid exploration for traffic signal control based on cooperative multi-agent framework,” Engineering Applications of Artificial Intelligence, vol. 29, pp. 134–151, Mar. 2014, doi: 10.1016/j.engappai.2014.01.007.
A. E. Sallab, M. Abdou, E. Pérot, and S. Yogamani, “Deep Reinforcement Learning framework for Autonomous Driving,” IS&T International Symposium on Electronic Imaging Science and Technology, vol. 29, no. 19, pp. 70–76, Jan. 2017, doi: 10.2352/issn.2470-1173.2017.19.avm-023.
R. Boutaba et al., “A comprehensive survey on machine learning for networking: evolution, applications and research opportunities,” Journal of Internet Services and Applications, vol. 9, no. 1, Jun. 2018, doi: 10.1186/s13174-018-0087-2.
S. Madjiheurem and L. Toni, “Representation Learning on Graphs: A Reinforcement Learning Application,” Proceedings of Machine Learning Research (PMLR), 2019. https://proceedings.mlr.press/v89/madjiheurem19a.html (accessed Dec. 02, 2023).
Z. Li, C. Xu, and G. Zhang, “A Deep Reinforcement Learning Approach for Traffic Signal Control Optimization,” arXiv.org, Jul. 13, 2021. https://arxiv.org/abs/2107.06115 (accessed Dec. 02, 2023).
A. A. Rahi, “Machine Learning Approaches for Traffic Flow Forecasting,” Oct. 23, 2019. https://uhra.herts.ac.uk/handle/2299/22590
W. Alajali, W. Zhou, S. Wen, and Y. Wang, “Intersection Traffic Prediction Using Decision Tree Models,” Symmetry, vol. 10, no. 9, p. 386, Sep. 2018, doi: 10.3390/sym10090386.
O. Günlük, J. Kalagnanam, M. Li, M. Menickelly, and K. Scheinberg, “Optimal decision trees for categorical data via integer programming,” Journal of Global Optimization, vol. 81, no. 1, pp. 233–260, Mar. 2021, doi: 10.1007/s10898-021-01009-y.
S. George and A. K. Santra, “Traffic Prediction Using Multifaceted Techniques: A Survey,” Wireless Personal Communications, vol. 115, no. 2, pp. 1047–1106, Jul. 2020, doi: 10.1007/s11277-020-07612-8.
J. Dai, Y. Chen, Y. Chen, and A. Meng, “An analysis of Network Traffic Identification based on Decision Tree,” IEEE Conference Publication | IEEE Xplore, Sep. 02, 2021. https://ieeexplore.ieee.org/document/9525567 (accessed Dec. 03, 2023).
A. Louati, H. Louati, and Z. Li, “Deep learning and case-based reasoning for predictive and adaptive traffic emergency management,” The Journal of Supercomputing, vol. 77, no. 5, pp. 4389–4418, Oct. 2020, doi: 10.1007/s11227-020-03435-3.
V. Varghese, M. Chikaraishi, and J. Urata, “Deep Learning in Transport Studies: A Meta-analysis on the Prediction Accuracy,” Journal of Big Data Analytics in Transportation, vol. 2, no. 3, pp. 199–220, Dec. 2020, doi: 10.1007/s42421-020-00030-z.
U. Jilani, M. Asif, M. Y. I. Zia, M. Rashid, S. Shams, and P. Otero, “A Systematic Review on Urban Road Traffic Congestion,” Wireless Personal Communications, Aug. 2023, doi: 10.1007/s11277-023-10700-0.
S. P. Singh, A. A. Khan, R. Souissi, and S. A. Yusuf, “Leveraging Neo4j and deep learning for traffic congestion simulation & optimization,” arXiv (Cornell University), Mar. 2023, doi: 10.48550/arxiv.2304.00192.
N. Petrovska, A. Stevanovic, and B. Furht, “Innovative Web Applications for Analyzing Traffic Operations,” SpringerLink, 2016, doi: 10.1007/978-3-319-33319-9.
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