Trade-Offs Between Efficiency and Safety: AV Behavior Strategies in Uncontrolled Urban Traffic


Abdulrahman H. S., Albdairi M., ÖNER J., Almusawi A.

Nigerian Journal of Technological Development, cilt.23, sa.3, ss.100-113, 2026 (Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 23 Sayı: 3
  • Basım Tarihi: 2026
  • Doi Numarası: 10.63746/njtd.v23i3.3737
  • Dergi Adı: Nigerian Journal of Technological Development
  • Derginin Tarandığı İndeksler: Scopus
  • Sayfa Sayıları: ss.100-113
  • Anahtar Kelimeler: Autonomous Vehicles, Conflict Analysis, Travel Time, Urban Traffic Safety, Vehicle Delay
  • Uşak Üniversitesi Adresli: Evet

Özet

Autonomous vehicle behavior is not neutral: the same vehicle may be configured to drive cautiously or aggressively, with different consequences for efficiency and safety. In uncontrolled networks, where priority is resolved through gap acceptance rather than signals, these effects remain poorly established. This study examines how cautious, normal and aggressive autonomous vehicle behaviors affect traffic performance and surrogate safety in such a network. A 702,024 m² area of Kirkuk, Iraq, was modeled in PTV VISSIM as a synthetic, uncalibrated experiment. Thirteen scenarios were simulated: an all-human baseline and twelve combinations of three behavioral profiles with penetration rates of 25%, 50%, 75% and 100%. Behaviors were represented using Wiedemann 99 parameters, and unsignalized intersections as conflict areas. A demand of 2,000 vehicles/h was applied at four entry points, with ten runs per scenario. Seven movements were evaluated for travel time, vehicle delay and stop delay, and trajectories processed using the Surrogate Safety Assessment Model. In the modeled network, travel time decreased by up to 22%. Conflicts increased for all behaviors: aggressive produced the most crossing conflicts, cautious the most rear-end, lane-change and total conflicts, while mean time to collision rose. These findings suggest human-oriented surrogate safety measures may misrepresent autonomous car-following.