Abstract
Analyzed the mathematical models of car movement according to urban space and the human factor. Presented the mathematical model, which is considered the most productive due using the moment that depend on the motivational forces of a person, but due to the lack of information on characteristics of road factors for movement by urban conditions, its practical use is difficult. According to the results of experimental research, the signal values objects of movement in urban conditions were established and the signal values of objects of the movement for suburban conditions were formalized. These signal values are used to calculating the information characteristics of the driver’s perception in the model for calculating vehicle speeds and traffic flow. The research of the signal values of the objects of perception by drivers allows calculating the absolute organization and the current entropy of objects of perception by drivers who determines the behavior of movement by car subsequently. Information characteristics are used in models for estimate and prognostication of the ecological state in the city streets and highways, prognostication of the evolution in the ergonomic system “driver – vehicle – transport network – environment”.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ahangari, S., Jeihani, M., Anam Ardeshiri, M., Rahman, M., Dehzangi, A.: Enhancing the performance of a model to predict driving distraction with the random forest classifier. J. Transport. Res. Board 2675(11), 612–622 (2021). https://doi.org/10.1177/03611981211018695
Evtiukov, S., Karelina, M., Terentyev, A.: A method for multi-criteria evaluation of the complex safety characteristic of a road vehicle. Transport. Res. Procedia 36, 149–156 (2018). https://doi.org/10.1016/j.trpro.2018.12.057
Gavrilov, E.: Systematology Transport, vol. 5: Ergonomics. Knowledge of Ukraine, Kharkiv (2008)
Kang, X., Namgung, M., Fujiwara, A., Kim, W., Wang, W.: Analysis of vehicle maneuverability and driving characteristics on a curved road condition. KSCE J. Civ. Eng. 23(1), 420–432 (2019). https://doi.org/10.1007/s12205-018-1803-y
Shaju, A., Kumar Pandey, A.: Modelling transient response using PAC 2002-based tyre model. Veh. Syst. Dyn. 60(1), 20–46 (2022). https://doi.org/10.1080/00423114.2020.1802048
Gruppelaar, V., van Paassen, M.M., Mulder, M., Abbink, D.A.: A perceptually inspired driver model for speed control in curves. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1257–1262. IEEE, Miyazaki (2018). https://doi.org/10.1109/SMC.2018.00220
Mahmoud, N., Abdel-Aty, M., Cai, Q.: Factors contributing to operating speeds on arterial roads by context classifications. J. Transp. Eng., Part A: Syst. 147(8), 04021040 (2021). https://doi.org/10.1061/JTEPBS.0000548
Chakraborty, M., Gates, T.: Assessing safety performance on urban and suburban roadways of lower functional classification: a comparison of minor arterial and collector roadway segments. engrXiv Preprint (2021). https://doi.org/10.31224/osf.io/wgpn7
Yao, Z., Wang, Y., Liu, B., Zhao, B., Jiang, Y.: Fuel consumption and transportation emissions evaluation of mixed traffic flow with connected automated vehicles and human-driven vehicles on expressway. Energy 230, 120766 (2021). https://doi.org/10.1016/j.energy.2021.120766
Biondi, F., Alvarez, I., Jeong, K.A.: Human–vehicle cooperation in automated driving: a multidisciplinary review and appraisal. Int. J. Hum.-Comput. Inter. 35(11), 932–946 (2019). https://doi.org/10.1080/10447318.2018.1561792
Afanasieva, I., Galkin, A.: Assessing the information flows and established their effects on the results of driver’s activity. Arch. Transp. 45(1), 7–23 (2018). https://doi.org/10.5604/01.3001.0012.0938
Galkin, A., Davidich, N., Melenchuk, T., Kush, Y., Davidich, Y., Lobashov, O.: Modelling truck’s transportation speed on the route considering driver’s state. Transport. Res. Procedia 30, 207–215 (2018). https://doi.org/10.1016/j.trpro.2018.09.023
Prasolenko, O., Burko, D., Tolmachov, I., Gyulyev, N., Galkin, A., Lobashov, O.: Creating safer routing for urban freight transportation. Transport. Res. Procedia 39, 417–427 (2019). https://doi.org/10.1016/j.trpro.2019.06.044
González-Hernández, B., et al.: The driver’s visual perception research to analyze pedestrian safety at twilight. Transport. Res. Procedia 45, 827–834 (2020). https://doi.org/10.1016/j.trpro.2020.02.087
Lynnyk, I., Vakulenko, K., Lezhneva, E.: Analysis of the air quality in considering the impact of the atmospheric emission from the urban road traffic. In: Macioszek, E., Sierpiński, G. (eds.) Research Methods in Modern Urban Transportation Systems and Networks. LNNS, vol. 207, pp. 13–27. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-71708-7_2
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Lynnyk, I., Chepurna, S., Vakulenko, K., Kulbashna, N. (2023). Informational Characteristics of Objects to the Driver’s Perception Field in Urban and Suburban Conditions. In: Arsenyeva, O., Romanova, T., Sukhonos, M., Tsegelnyk, Y. (eds) Smart Technologies in Urban Engineering. STUE 2022. Lecture Notes in Networks and Systems, vol 536. Springer, Cham. https://doi.org/10.1007/978-3-031-20141-7_62
Download citation
DOI: https://doi.org/10.1007/978-3-031-20141-7_62
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-20140-0
Online ISBN: 978-3-031-20141-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)