Development of vehicle speed forecasting method for intelligent highway transport system
DOI:
https://doi.org/10.15587/1729-4061.2019.174255Keywords:
traffic flow, highway, cellular automaton, sliding window, relative speed, intelligent transport systemAbstract
Interaction of vehicles on an intercity highway is considered. The vehicle control model here is idealized, close to the 4th generation automated intelligent transport system. Each vehicle has the desired motion program, independent of the driver's motives, which is justified by minimum resource consumption and compliance with the desired schedule. The diversity of programs affects their unwanted change. The aim was to identify the dependence of the actual vehicle speed on traffic flow parameters. The main task was to reveal a direct parameter for changing the motion program. The use of simulation models based on cellular automata is substantiated. A new cellular automaton, which is a sliding window with a reference point, which is the observer vehicle is developed. The number of objects in the field increases periodically and is constant. All cells on the left and right of the reference point of the automaton form the information field, or the total length of the automaton. The automaton height, which depends on the type of highway, is modeled. The rules of objects movement in the automaton grid at each iteration are finite, established and similar to the Schreckenberg automaton, except for randomization, which is minimized in this model. Such an automaton reflects relative speeds of vehicles relative to the observer vehicle, as well as the ability to reproduce accelerations. At each iteration, the change in vehicle speeds is calculated. The simulation algorithm is programmed in the Delphi language. Simulation of the vehicle movement on the E-471 international highway is performed. On the 20 km section of this route, traffic flows with different density and speed distribution are modeled. The quadratic correlation dependences of the forced change in the desired speed of the observer vehicle on changes in the average speed of the flow vehicles are revealed. The degree of agreement between the theoretical dependence and empirical data is very high. On the basis of the dependencies obtained, the choice of the direct diagnostic parameter of the traffic flow is justified.
References
- Hashchuk, P., Pelo, R. (2018). Optimal laws of gear shift in automotive transmissions. Econtechmod, 7 (2), 59–69.
- Danchuk, V., Bakulich, O., Svatko, V. (2017). An Improvement in ant Algorithm Method for Optimizing a Transport Route with Regard to Traffic Flow. Procedia Engineering, 187, 425–434. doi: https://doi.org/10.1016/j.proeng.2017.04.396
- Willemsen, D. et. al. (2018). Requirements Review from EU projects. D2.1 of H2020 project ENSEMBLE. Available at: https://platooningensemble.eu/storage/uploads/documents/2019/02/11/ENSEMBLE-Deliverable-2.1-StateOfTheArt_EUProjects_disclaimer.pdf
- Kulbashnaya, N., Soroka, K. (2016). Development of a model of a driver’s choice of speed considering the road conditions. Eastern-European Journal of Enterprise Technologies, 3 (2 (81)), 32–38. doi: https://doi.org/10.15587/1729-4061.2016.71489
- Kuzhel, V. P., Kashkanov, A. A., Kashkanov, V. A. (2010). Metodyka zmenshennia nevyznachenosti v zadachakh avtotekhnichnoi ekspertyzy DTP pry identyfikatsiyi dalnosti vydymosti dorozhnikh obiektiv v temnu poru doby. Vinnytsia: VNTU, 200.
- Volkov, V. P., Grytsuk, I. V., Grytsuk, Yu. V., Shurko, H. K., Volkov, Yu. V. (2017). The formation features of method of usage of classification of operation conditions of the vehicles in informational terms of ITS. Bulletin of NTU "KhPI". Series: Transport machine building, 14 (1236), 10–20.
- Hegyi, A. (2004). Model Predictive Control for Integrating Traffic Control Measures. TRAIL Thesis Series T2004/2. The Netherlands TRAIL Research School, 232.
- Semenov, V. V. (2004). Matematicheskoe modelirovanie dinamiki transportnyh potokov megapolisa. Мoscow, 44. Available at: http://spkurdyumov.ru/uploads/2013/08/Semenov.pdf
- Protsyshyn, О. (2014). Research of instantaneous velocity in traffic flow. Naukovi notatky, 45, 448–452.
- Ioannou, P. A., Chien, C. C. (1993). Autonomous intelligent cruise control. IEEE Transactions on Vehicular Technology, 42 (4), 657–672. doi: https://doi.org/10.1109/25.260745
- Englund, C., Estrada, J., Jaaskelainen, J., Misener, J., Satyavolu, S., Serna, F., Sundararajan, S. (2017). Enabling Technologies for Road Vehicle Automation. Lecture Notes in Mobility, 177–185. doi: https://doi.org/10.1007/978-3-319-60934-8_15
- Sakhno, V. P., Zharov, K. S. (2012). Do vyznachennia yizdovykh tsykliv ta pozdovzhnikh profiliv dorih. Avtoshliakhovyk Ukrainy, 1 (225), 7–11.
- Kovalenko, L. O. (2013). Analiz umov ta bezpeky rukhu na avtomobilnykh dorohakh z urakhuvanniam informatsiynykh pokaznykiv dorozhnoho seredovyshcha. Avtomobilni dorohy i dorozhne budivnytstvo, 88, 294–301.
- Karpinskyi, Yu. O., Liashchenko, A. A., Drozdivskyi, O. P. (2007). Heoinformatsiyne zabezpechennia navihatsiyi nazemnoho transportu. Nauka ta innovatsiyi, 3 (1), 43–57.
- Srour, F., Newton, D. (2006). Freight-Specific Data Derived from Intelligent Transportation Systems: Potential Uses in Planning Freight Improvement Projects. Transportation Research Record: Journal of the Transportation Research Board, 1957, 66–74. doi: https://doi.org/10.3141/1957-10
- Hallenbeck, M., McCormack, E., Nee, J., Wright, D. (2003). Freight Data from Intelligent Transportation System Devices. WA-RD 566.1. Washington State Department of Transportation, 117. Available at: https://www.wsdot.wa.gov/research/reports/fullreports/566.1.pdf
- Lashenyh, O., Turpak, S., Gritcay, S., Vasileva, L., Ostroglyad, E. (2016). Development of mathematical models for planning the duration of shunting operations. Eastern-European Journal of Enterprise Technologies, 5 (3 (83)), 40–46. doi: https://doi.org/10.15587/1729-4061.2016.80752
- Wu, N., Brilon, W. Cellular automata for highway traffic flow simulation. Traffic and Mobility. Available at: http://homepage.rub.de/ning.wu/pdf/ca_14isttt.pdf
- Clarridge, A., Salomaa, K. (2010). Analysis of a cellular automaton model for car traffic with a slow-to-stop rule. Theoretical Computer Science, 411 (38-39), 3507–3515. doi: https://doi.org/10.1016/j.tcs.2010.05.027
- Mozaffari, L., Mozaffari, A., Azad, N. L. (2015). Vehicle speed prediction via a sliding-window time series analysis and an evolutionary least learning machine: A case study on San Francisco urban roads. Engineering Science and Technology, an International Journal, 18 (2), 150–162. doi: https://doi.org/10.1016/j.jestch.2014.11.002
- Young, K., Regan, M. (2002). Intelligent speed adaptation: A review. Proceedings of the Australasian Road Safety Research Policing and Education Conference, 445–450. Available at: https://acrs.org.au/files/arsrpe/RS020049.PDF
- Bekmagambetov, M. M., Kochetkov, A. V. Analiz sovremennyh programmnyh sredstv transportnogo modelirovaniya. Zhurnal avtomobil'nyh inzhenerov, 6 (77), 25–34. Available at: http://www.aae-press.ru/f/77/25.pdf
- Zaharov, Yu. I., Karnauh, E. S. (2014). Osnovnye sovremennye instrumenty imitatsionnogo modelirovaniya transportnyh potokov. Visnyk PDABA, 1 (190), 46–51. Available at: http://visnyk.pgasa.dp.ua/article/download/39889/36019
- Ivanov, V. O. (2008). Rozpodilena systema imitatsiynoho modeliuvannia dorozhnoho rukhu. Visnyk NTUU «KPI». Informatyka, upravlinnia ta obchysliuvalna tekhnika, 48, 41–45.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2019 Georgii Prokudin, Myroslav Oliskevych, Alexey Chupaylenko, Olexiy Dudnik
This work is licensed under a Creative Commons Attribution 4.0 International License.
The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.
A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.