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Accessing the Influences of Weather and Environment Factors on Traffic Speed of Freeway

  • Danni Cao
  • Jianjun WuEmail author
  • Ziling Zeng
Conference paper
  • 48 Downloads
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 185)

Abstract

Traffic speed has been traditionally used as a measure of traffic performance. Predicting the traffic speed is fundamental for efficient traffic management and control strategy. This study explores the influences of freeway attributes, weather, and air condition on traffic speed. A quantitative model is also introduced to predict the traffic speed as per the identified influencing factors. Empirical data of traffic flow and potential influencing factors are collected from multiple sources for analysis and model calibration. The principal component analysis is firstly conducted to select the significant variables influencing the traffic speed. Afterward, a multiple linear regression model is calibrated to quantitatively model the impacts of different factors and investigate their weights. The results show that the attributes of freeway, the humidity of the area, the temperature, the horizontal visibility, the station maker, the air quality, and the PM quality have influences on the traffic speed. Among all of the variables, the weight of the existence of toll station is highest, indicating the largest influence on the traffic speed.

Keywords

Traffic speed Freeway Variables selection Model regression 

References

  1. 1.
    Li, X., Ghiasi, A., Xu, Z., Qu, X.: A piecewise trajectory optimization model for connected automated vehicles: exact optimization algorithm and queue propagation analysis. Transp. Res. Part B Methodol. 118, 429–456 (2018)CrossRefGoogle Scholar
  2. 2.
    Qu, X., Zhou, M., Yu, Y., Lin, C.T., Wang, X.: Jointly dampening traffic oscillations and improving energy consumption with electric, connected and automated vehicles: a reinforcement learning based approach. Appl. Energ. 257, 114030 (2019)CrossRefGoogle Scholar
  3. 3.
    Zhou, M., Yu, Y., Qu, X.: Development of an efficient driving strategy for connected and automated vehicles at signalized intersections: a reinforcement learning approach. IEEE Trans. Intell. Transp. Syst. 21, 433–443 (2019)CrossRefGoogle Scholar
  4. 4.
    Zhou, M., Qu, X., Li, X.: A recurrent neural network based microscopic car following model to predict traffic oscillation. Transp. Res. Part C. 84, 245–264 (2017)CrossRefGoogle Scholar
  5. 5.
    Kuang, Y., Qu, X., Wang, S.: A tree-structured crash surrogate measure for freeways. Accid. Anal. Prev. 77, 137–148 (2015)CrossRefGoogle Scholar
  6. 6.
    Qu, X., Meng, Q., Li, S.: Ship collision risk assessment for the Singapore Strait. Accid. Anal. Prev. 43, 2030–2036 (2011)CrossRefGoogle Scholar
  7. 7.
    Xu, C., Yang, Y., Jin, S., Qu, Z., Hou, L.: Potential risk and its influencing factors for separated bicycle paths. Accid. Anal. Prev. 87, 59–67 (2016)CrossRefGoogle Scholar
  8. 8.
    Gao, K., Tu, H., Sun, L., Sze, N. N., Song, Z., Shi, H.: Impacts of reduced visibility under hazy weather condition on collision risk and car-following behavior: Implications for traffic control and management. Int. J. Sustain. Transp. 1–8 (2019)Google Scholar
  9. 9.
    Oh, C., Oh, J., Ritchie, S., Chang, M.: Real-time Estimation of Freeway Accident Likelihood, 80th Annual Meeting of the Transportation Research Board. Washington, D.C, Washington, DC (2001)Google Scholar
  10. 10.
    Wang, X., Song, Y., Yu, R., Schultz, G.G.: Safety modeling of suburban arterials in Shanghai, China. Accid. Anal. Prev. 70, 215–224 (2014)CrossRefGoogle Scholar
  11. 11.
    Finch, D.J., Kompfner, P., Lockwood, C.R., Maycock, G.: Speed, speed limits and crashes. Project record S211G/RB/project report PR 58. Transport research laboratory TRL, Crowthorne, Berkshire. (1994)Google Scholar
  12. 12.
    Ahmed, M.M., Abdel-Aty, M.A.: The viability of using automatic vehicle identification data for real-time crash prediction. IEEE Trans. Intell. Transp. Syst. 13, 459–468 (2012)CrossRefGoogle Scholar
  13. 13.
    Joo, S., Oh, C., Lee, S., Lee, G.: Assessing the impact of traffic crashes on near freeway air quality. Transp. Res. Part D Transp. Environ. 57, 64–73 (2017)CrossRefGoogle Scholar
  14. 14.
    Rahman, A., Lownes, N.E.: Analysis of rainfall impacts on platooned vehicle spacing and speed. Transp. Res. Part F Traffic Psychol. Behav. 15, 395–403 (2012)CrossRefGoogle Scholar
  15. 15.
    Wang, Y., Luo, J.: Study of rainfall impacts on freeway traffic flow characteristics. Transp. Res. Procedia 25, 1533–1543 (2017)CrossRefGoogle Scholar
  16. 16.
    Qu, X., Zhang, J., Wang, S.: On the stochastic fundamental diagram for freeway traffic: model development, analytical properties, validation, and extensive applications. Transp. Res. Part B 104, 256–271 (2017)CrossRefGoogle Scholar
  17. 17.
    Qu, X., Wang, S., Zhang, J.: On the fundamental diagram for freeway traffic: a novel calibration approach for single-regime models. Transp. Res. Part B 73, 91–102 (2015)CrossRefGoogle Scholar
  18. 18.
    Xu, Z., Wei, T., Easa, S., Zhao, X., Qu, X.: Modeling relationship between truck fuel consumption and driving behavior using data from internet of vehicles. Compu. Aided Civil Infrastruct. Eng. 33(3), 209–219 (2018)CrossRefGoogle Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.State Key Laboratory of Rail Traffic Control and SafetyBeijing Jiaotong UniversityBeijingChina
  2. 2.Architecture and Civil EngineeringChalmers University of TechnologyGoteborgSweden

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