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Potential risk and efficiency analysis of decision-making dilemmas in connected dual-vehicle interactions at uncontrolled intersections

Author

Listed:
  • Yang, Miaomiao
  • Bao, Qiong
  • Shen, Yongjun
  • Qu, Qikai
  • Zhang, Rui
  • Han, Tianyuan
  • Zhang, Huansong

Abstract

In dual-vehicle interactive scenarios, drivers often encounter decision-making dilemmas, even with connected information, thus posing significant risk challenges to traffic operations. This study explored the impact of such dilemmas on decision-making and traffic operations for drivers involved in straight-going (SG) and left-turning (LT) vehicles at uncontrolled intersections. Employing driving simulation technology, a decision-making experiment incorporating preemptive (P) and yielding (Y) strategies was conducted with 72 S G and 72 L T drivers under connected information provision. By analyzing joint decisions (P-P, Y-P, and Y-Y) made by drivers on both sides, we investigated implications for traffic operational safety and efficiency. Furthermore, recognizing the diversity in individual characteristics and time pressure, we developed a random parameters multinomial logit model to examine the relationship between potential traffic operational performance (both safety and efficiency) and various contributing factors. Moreover, we calculated the marginal effects of these contributing factors. By assessing the probability change of Y-P joint decisions balancing safety and efficiency, we identified reference values for each contributing factor that promote effective interactive operations. The findings suggest that: (1) Regarding interactive dilemmas conditions, Conditions where the SG vehicle had higher speed and farther distance than the LT vehicle were more likely to result in heterogeneous Y-P joint decisions, in which one driver chooses to yield and the other party passes quickly, thereby improving traffic performance. (2) Time pressure increased the likelihood of heterogeneous Y-P joint decisions but also increased P-P joint decisions. (3) heterogeneous individual attributes did not always lead to higher heterogeneous decisions. Overall, this study provides insights into factors influencing joint decisions and traffic operational performance under connected information provision, considering individual heterogeneity and time pressure, aiming to the advancement of more efficient and safer vehicle-connected transportation systems.

Suggested Citation

  • Yang, Miaomiao & Bao, Qiong & Shen, Yongjun & Qu, Qikai & Zhang, Rui & Han, Tianyuan & Zhang, Huansong, 2024. "Potential risk and efficiency analysis of decision-making dilemmas in connected dual-vehicle interactions at uncontrolled intersections," Technology in Society, Elsevier, vol. 79(C).
    • Handle: RePEc:eee:teinso:v:79:y:2024:i:c:s0160791x24002823
    DOI: 10.1016/j.techsoc.2024.102734
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    References listed on IDEAS

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    1. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
    2. Bhat, Chandra R., 2001. "Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 677-693, August.
    3. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, Enero.
    4. Bhat, Chandra R., 2003. "Simulation estimation of mixed discrete choice models using randomized and scrambled Halton sequences," Transportation Research Part B: Methodological, Elsevier, vol. 37(9), pages 837-855, November.
    5. repec:plo:pone00:0238728 is not listed on IDEAS
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