Reduction of Freeway Accidents through Using of Intelligent Vehicular Networks
Publish place: international conference on civil engineering, architecture and Urban Sustainable Development
Publish Year: 1392
نوع سند: مقاله کنفرانسی
زبان: English
View: 901
This Paper With 13 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
ICCAU01_3140
تاریخ نمایه سازی: 29 تیر 1393
Abstract:
Increasing population growth on the one hand and the growth of transportation industry on the other have had numerous positive and negative impacts in societies. This issue has led to an increase in the volume of traffic demand in road networks and has hence increased the number of road accidents. 30% of accidents in the world, and 20% in Iran, have been of the rear-end type, in which human error has the greatest role. The use of VANETs as a module of intelligent transportation systems (ITS) could play a considerable role in reducing human error. This study offers a new method for investigating the role of vehicular ad-hoc networks (VANETs) in reducing the number of rear-end accidents in freeways by reducing drivers’ reaction time. In so doing, by using the micro data of the traffic flow in the I-80 freeway of the NGSIM project, drivers’ behavior in a specific time span was analyzed; and by using Urgent Deceleration Index (UDM), the safety of drivers at each moment was investigated. The results of data analysis reveal that the use of VANET communication networks can reduce the percentage of vehicles’ exposure to danger in the car-following process from 68% to 15% by reducing drivers’ reaction time.
Keywords:
Authors
Seyed Ehsan Jafari Nasab
MSc. Transportation Engineering, Faculty of Transportation, University of Isfahan,
Nasser Pourmoallem
Ph.D. of Transportation & Traffic Eng., Faculty of Transportation, University of Isfahan
Seyed Saber Naseralavi
Ph.D. of Road & Transportation Eng., Shahid Bahonar University of Kerman
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :