Abstract
Son dönemlerde,
yapay zekada konularında yaşanan gelişmeler sonucunda, sürücüsüz araç
teknolojileri ortaya çıkmıştır. Yakın gelecekte bu araçların daha fazla günlük
trafikte yer alması beklenmektedir. Sürücüsüz araçlar birbirleriyle iletişim
kurabilmeleri sayesinde çok daha düşük tepki süresine sahiptirler ve bu nedenle
birbirlerini daha yakından takip edebilmektedir. Bu özellikleri sayesinde tüm
trafik sürücüsüz araçlardan meydana geldiğinde yolların kapasitesinin önemli
ölçüde artması ve trafik kazalarında azalma görülmesi beklenmektedir. Ancak, bu
etkiler, sürücülü ve sürücüsüz araçların bir arada olduğu karma trafik
koşullarında, karmaşıklaşmaktadır. Araştırmalar sürücüsüz araçların karma
trafik koşullarında trafik akım özellikleri üzerinde olumsuz bir etki yaratacağını
göstermektedir. Bu çalışmada, sürücüsüz araçların trafik ağı üzerindeki etkisi,
trafik talebinin ve sürücüsüz araçların yüzdelerinin farklı olduğu, 15 farklı
senaryoda incelenmiştir. İstanbul’daki Turgut Özal Caddesi bu senaryoların
sınandığı yer olarak seçilmiş ve bir ince boyut benzetim modeli
oluşturulmuştur. Sonrasında trafik değişkenleri
incelenerek, bu değişkenleri tahmin edecek bir yapay sinir ağı modeli
oluşturulmuştur. Oluşturulan model ortalama akım, hız ve ivme değerlerini,
ortalama takip süresi, takip uzunluğu ve doluluğa göre daha iyi tahmin
edebilmektedir.
References
- [1]. Hoeger, Reiner et. al. Highly Automated Vehicles for Intelligent Transport, HAVEit, 2011.
- [2]. Milakis, Dimitris, Bart Van Arem, and Bert Van Wee. Policy and Society Related İmplications Of Automated Driving: A Review of Literature and Directions for Future Research. Journal of Intelligent Transportation Systems. 2017, 21, 4, 324-348.
- [3]. Abraham, Zianga, Identifying the optimal highway driving conditions for the integration of manned and autonomous vehicles, Department of Mechanical Engineering, Massachusetts Institute of Technology, Massachusetts, USA, 2015.
- [4]. Gökaşar, Ilgın. Şerit Kontrol Sistemleri: D 100 Karayolu, İstanbul Örneği, İMO Teknik Dergi. 2016, 134, 7635-7657.
- [5]. Dresner, Kurt; Peter Stone. A Multiagent Approach to Autonomous Intersection Management. Journal of Artificial Intelligence Research. 2008, 31, 591-656.
- [6]. Philip E. Ross. A Cloud-Connected Car Is a Hackable Car, Worries Microsoft. IEEE Spectrum, USA, 2014.
- [7]. David Shepardson. Study: Self-driving cars to jolt market by 2035. The Detroit News, USA, 2013.
- [8]. Lee Gomes. Hidden Obstacles for Google's Self-Driving Cars. MIT Technology Review, USA, 2014.
- [9]. Donald Light. A Scenario: The End of Auto Insurance. Technical report, Cent, 2012.
- [10]. Chunka Mui. Will The Google Car Force A Choice Between Lives And Jobs? Forbes, USA, 2013.
- [11]. Fagnant, Daniel J; Kara Kockelman. Preparing A Nation for Autonomous Vehicles: Opportunities, Barriers and Policy Recommendations. Transportation Research Part A: Policy and Practice. 2015, 77, 167-181.
- [12]. Roncoli, Claudio; Papageorgiou, Markos; Papamichail, Ioannis. Traffic Flow Optimisation İn Presence of Vehicle Automation and Communication Systems–Part II: Optimal Control for Multi-Lane Motorways. Transportation Research Part C: Emerging Technologies. 2015, 57, 260-275.
- [13]. Chang, Tang-Hsien; Lai, I-Shyen. Analysis of Characteristics of Mixed Traffic Flow of Autopilot Vehicles and Manual Vehicles, Transportation Research Part C: Emerging Technologies. 1997, 5, 6, 333-348.
- [14]. Chien, Cheng-Chih; Zhang, Youping; Ioannou, Petros A. Traffic Density Control for Automated Highway Systems. Automatica. 1997, 33, 7, 1273-1285.[15]. Letter, Clark; Elefteriadou, Lily. Efficient Control of Fully Automated Connected Vehicles at Freeway Merge Segments. Transportation Research Part C: Emerging Technologies. 2017, 80, 190-205.
- [16]. Baluja, Shumeet. Evolution of An Artificial Neural Network Based Autonomous Land Vehicle Controller. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics). 1996, 26, 3, 450-463.
- [17]. Pomerleau, Dean A. Efficient Training of Artificial Neural Networks for Autonomous Navigation. Neural Computation. 1991, 3, 1, 88-97.
Abstract
In
recent decades, autonomous vehicles are introduced as a result of significant
developments in artificial intelligence. In near feature, it is possible that
more and more autonomous cars will be a part of the daily traffic. Autonomous vehicles
can communicate with other vehicles, so they have much lower response time than
the human drivers, thus, autonomous vehicles can be operated with lower
headway. This feature is expected to significantly increase the capacity of
roads when completely autonomous vehicles are operated in traffic and to reduce
traffic accidents. However, the effect is more complicated in combined traffic
conditions where autonomous and human-driven vehicles are present. Studies have
shown that autonomous vehicles adversely affect traffic flow characteristics in
combined traffic conditions. In this study, the effects of autonomous vehicles
on traffic network is evaluated using 15 scenarios including different traffic
demand levels and autonomous vehicle composition on a microsimulation model of
a network in Turgut Özal (Millet) Caddesi in Istanbul. Then, the effect of the
traffic parameters is analyzed and predicted using Artificial Neural Networks. Artificial
Neural Network model is capable of estimating the average flow, speed and
acceleration with a higher accuracy than average headway, gap and occupancy.
References
- [1]. Hoeger, Reiner et. al. Highly Automated Vehicles for Intelligent Transport, HAVEit, 2011.
- [2]. Milakis, Dimitris, Bart Van Arem, and Bert Van Wee. Policy and Society Related İmplications Of Automated Driving: A Review of Literature and Directions for Future Research. Journal of Intelligent Transportation Systems. 2017, 21, 4, 324-348.
- [3]. Abraham, Zianga, Identifying the optimal highway driving conditions for the integration of manned and autonomous vehicles, Department of Mechanical Engineering, Massachusetts Institute of Technology, Massachusetts, USA, 2015.
- [4]. Gökaşar, Ilgın. Şerit Kontrol Sistemleri: D 100 Karayolu, İstanbul Örneği, İMO Teknik Dergi. 2016, 134, 7635-7657.
- [5]. Dresner, Kurt; Peter Stone. A Multiagent Approach to Autonomous Intersection Management. Journal of Artificial Intelligence Research. 2008, 31, 591-656.
- [6]. Philip E. Ross. A Cloud-Connected Car Is a Hackable Car, Worries Microsoft. IEEE Spectrum, USA, 2014.
- [7]. David Shepardson. Study: Self-driving cars to jolt market by 2035. The Detroit News, USA, 2013.
- [8]. Lee Gomes. Hidden Obstacles for Google's Self-Driving Cars. MIT Technology Review, USA, 2014.
- [9]. Donald Light. A Scenario: The End of Auto Insurance. Technical report, Cent, 2012.
- [10]. Chunka Mui. Will The Google Car Force A Choice Between Lives And Jobs? Forbes, USA, 2013.
- [11]. Fagnant, Daniel J; Kara Kockelman. Preparing A Nation for Autonomous Vehicles: Opportunities, Barriers and Policy Recommendations. Transportation Research Part A: Policy and Practice. 2015, 77, 167-181.
- [12]. Roncoli, Claudio; Papageorgiou, Markos; Papamichail, Ioannis. Traffic Flow Optimisation İn Presence of Vehicle Automation and Communication Systems–Part II: Optimal Control for Multi-Lane Motorways. Transportation Research Part C: Emerging Technologies. 2015, 57, 260-275.
- [13]. Chang, Tang-Hsien; Lai, I-Shyen. Analysis of Characteristics of Mixed Traffic Flow of Autopilot Vehicles and Manual Vehicles, Transportation Research Part C: Emerging Technologies. 1997, 5, 6, 333-348.
- [14]. Chien, Cheng-Chih; Zhang, Youping; Ioannou, Petros A. Traffic Density Control for Automated Highway Systems. Automatica. 1997, 33, 7, 1273-1285.[15]. Letter, Clark; Elefteriadou, Lily. Efficient Control of Fully Automated Connected Vehicles at Freeway Merge Segments. Transportation Research Part C: Emerging Technologies. 2017, 80, 190-205.
- [16]. Baluja, Shumeet. Evolution of An Artificial Neural Network Based Autonomous Land Vehicle Controller. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics). 1996, 26, 3, 450-463.
- [17]. Pomerleau, Dean A. Efficient Training of Artificial Neural Networks for Autonomous Navigation. Neural Computation. 1991, 3, 1, 88-97.
Details
| Primary Language | Turkish |
|---|---|
| Journal Section | Articles |
| Authors | |
| Publication Date | October 30, 2018 |
| Submission Date | October 11, 2018 |
| Acceptance Date | October 15, 2018 |
| Published in Issue | Year 2018 Volume: 1 Issue: 2 |