AUTOMATED REAL-TIME TRAFFIC FORECASTING SYSTEM
DOI:
https://doi.org/10.33042/2522-1809-2022-4-171-76-81Keywords:
automaticregulation of auto-traffic, controlled intersection, sensor network, Internet of Things, geolocation, identification, self-organization of clustersAbstract
Based on the analysis of the vehicles total number growth rates, which exceed the rates of expansion and optimization of the transport infrastructure, the need for the introduction of real-time traffic forecasting and control systems is shown. The factors that make it possible to detect the probability of potentially dangerous situations on the road, such as traffic jams, accidents and lack of parking spaces, respectively, in certain urban areas, based on the data of sensor networks and surveillance cameras combined within the global system of the Internet of Things, have been determined. It is proposed to build a sensor network based on magnetic sensors, which allows for high-precision geolocation with refinement of the received data by using ultrasonic sensors and optical monitoring tools, while identification is carried out by reading RFID tags. It is shown that the task of optimal organization of the relay system includes the determination of the features of the city infrastructure and statistical indicators of the city's traffic flows, and for a multi-level communication system, protocols are determined depending on the distance between nodes, requirements for the level of data protection, data transmission speed, minimum radio signal amplitude, as well as restrictions on the power supply of a separate node. The presented topology of the relay network includes the organization of sensor nodes into clusters, transmission from the main node of the cluster to the gateway node, and from the gateway nodes to the base station. On the basis of the specified model, a scheme for building cluster self-organization algorithms can be presented by forming clusters in real time according to the topology of the cluster tree, which allows simplifying the data transfer subsystem and reducing the time of processing input data. The developed scheme for analyzing the traffic flow at the intersection and the availability of parking spaces can be used in the development of methodological recommendations for the implementation of the "Smart City" concept and the creation of software applications that provide drivers with information about the state of traffic and predicted changes within a certain time interval.
References
Ma, J., Song, L., & Li, Y. (2017). Cost efficiency for Economical Mobile Data Traffic Management from users’ perspective. IEEE Transactions on Wireless Communications, 16 (1), 362–375. https://doi.org/10.1109/twc.2016.2623615.
Lamghari Elidrissi, H., Nait-Sidi-Moh, A., & Tajer, A. (2020). Knapsack problem-based control approach for Traffic Signal Management at urban intersections: Increasing smooth traffic flows and reducing environmental impact. Ecological Complexity, 44, 100878. https://doi.org/10.1016/j.ecocom.2020.100878.
Andreeva-Mori A., & Onji M. (2022) Impact of departure time prediction errors on optimal traffic flow management. ATC/ATM Simulation and Analysis, AIAA 2022-3834 https://doi.org/10.2514/6.2022-3834.vid.
Liu, H. (2020). Prediction model of traffic flow driven based on single data in Smart Traffic Systems. Smart Cities: Big Data Prediction Methods and Applications, 159–194. https://doi.org/10.1007/978-981-15-2837-8_6.
Olayode, I. O., Tartibu, L. K., Okwu, M. O., & Severino, A. (2021). Comparative traffic flow prediction of a heuristic Ann Model and a hybrid ANN PSO model in the traffic flow modeling of vehicles at a four-way signalized road intersection. Sustainability, 13 (19), 10704. https://doi.org/10.3390/su131910704.
Zitouni, R., Petit, J., Djoudi, A., & George, L. (2019). IOT-based urban traffic-light control: Modelling, Prototyping and evaluation of MQTT protocol. 2019 International Conference on Internet of Things (IThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData). https://doi.org/10.1109/ithings/greencom/cpscom/ smartdata.2019.00051.
Petit, J., Zitouni, R., & George, L. (2018). Prototyping of urban traffic-light control in IOT. 2018 IEEE International Smart Cities Conference (ISC2). https://doi.org/10.1109/isc2. 2018.8656717.
Dhar, P., & Gupta, P. (2016). Intelligent Parking Cloud Services based on IOT using MQTT protocol. 2016 International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT). https://doi.org/10.1109/icacdot.2016.7877546.
Rani, P. (2018). Improved traffic prediction by applying KNN and euclidean distance Arima (KE-Arima) approach. International Journal of Computer Applications, 182(3), 23–29. https://doi.org/10.5120/ijca2018917488.
Karlaftis, M. G., & Vlahogianni, E. I. (2011). Statistical methods versus neural networks in transportation research: Differences, similarities and some insights. Transportation Research Part C: Emerging Technologies, 19(3), 387–399. https://doi.org/10.1016/j.trc.2010.10.004.
Song, H., & Min, O. (2018). Statistical Traffic Generation Methods for urban traffic simulation. 2018 20th International Conference on Advanced Communication Technology (ICACT). https://doi.org/10.23919/icact.2018.8323712.
Westgate, B. S., Woodard, D. B., Matteson, D. S., & Henderson, S. G. (2013). Travel time estimation for ambulances using Bayesian Data Augmentation. The Annals of Applied Statistics, 7(2). https://doi.org/10.1214/13-aoas626.
Yan, H., & Yu, D.-J. (2017). Short-term traffic condition prediction of urban road network based on improved SVM. 2017 International Smart Cities Conference (ISC2). https://doi.org/10.1109/isc2.2017.8090856.
Ranjan, N., Bhandari, S., Zhao, H. P., Kim, H., & Khan, P. (2020). City-wide traffic congestion prediction based on CNN, LSTM and transpose CNN. IEEE Access, 8, 81606–81620. https://doi.org/10.1109/access.2020.2991462.
Narmadha, S., & Vijayakumar, V. (2021). Spatio-temporal vehicle traffic flow prediction using multivariate CNN and LSTM model. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2021.04.249.
Zhao, Z., Li, Z., Li, F., & Liu, Y. (2021). CNN-LSTM based traffic prediction using spatial-temporal features. Journal of Physics: Conference Series, 2037 (1), 012065. https://doi.org/10.1088/1742-6596/2037/1/012065.
Iwasaki, Y., Takehara, H., Miyata, T., Kuramoto, T., Kitajima, T., & Setoguchi, M. (2018). Automatic measurement of road traffic volumes and vehicle trajectories using an object detection algorithm Yolo. Proceedings of The 6th Virtual Multidisciplinary Conference. https://doi.org/10.18638/quaesti.2018.6. 1.387.
Asmara, R. A., Syahputro, B., Supriyanto, D., & Handayani, A. N. (2020). Prediction of traffic density using YOLO object detection and implemented in Raspberry pi 3b + and Intel NCS 2. 2020 4th International Conference on Vocational Education and Training (ICOVET). https://doi.org/10.1109/ icovet50258.2020.9230145.
Zhu, J., Li, X., Jin, P., Xu, Q., Sun, Z., & Song, X. (2020). Mme-Yolo: Multi-sensor multi-level enhanced YOLO for robust vehicle detection in traffic surveillance. Sensors, 21(1), 27. https://doi.org/10.3390/s21010027.
Wan, F., & Li, M. (2020). RFID based Intelligent Traffic Information Acquisition System. International Journal of RF Technologies, 11(1), 45–58. https://doi.org/10.3233/rft-190191.
Triantafyllou, A., Sarigiannidis, P., & Lagkas, T. D. (2018). Network protocols, schemes, and mechanisms for internet of things (IOT): Features, open challenges, and Trends. Wireless Communications and Mobile Computing, 2018, 1–24. https://doi.org/10.1155/2018/5349894.
Farman, H., Javed, H., Ahmad, J., Jan, B., & Zeeshan, M. (2016). Grid-based hybrid network deployment approach for Energy Efficient Wireless Sensor Networks. Journal of Sensors, 2016, 1–14. https://doi.org/10.1155/2016/2326917.
Downloads
Published
How to Cite
Issue
Section
License
The authors who publish in this collection agree with the following terms:
• The authors reserve the right to authorship of their work and give the magazine the right to first publish this work under the terms of license CC BY-NC-ND 4.0 (with the Designation of Authorship - Non-Commercial - Without Derivatives 4.0 International), which allows others to freely distribute the published work with a mandatory reference to the authors of the original work and the first publication of the work in this magazine.
• Authors have the right to make independent extra-exclusive work agreements in the form in which they were published by this magazine (for example, posting work in an electronic repository of an institution or publishing as part of a monograph), provided that the link to the first publication of the work in this journal is maintained. .
• Journal policy allows and encourages the publication of manuscripts on the Internet (for example, in institutions' repositories or on personal websites), both before the publication of this manuscript and during its editorial work, as it contributes to the emergence of productive scientific discussion and positively affects the efficiency and dynamics of the citation of the published work (see The Effect of Open Access).
