Vehicle Delay Modeling at Signalized Intersections with Gene-Expression Programming

Bağdatlı, Muhammed Emin Cihangir

doi:10.1061/jtepbs.0000423

Cited by 15 publications

(18 citation statements)

References 42 publications

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“…The results generated by the developed models were compared with the results of other studies in the literature to see the performance of the developed models from a different perspective. In Table 6, a comparison of the models developed by Murat (6), Bagdatli (2), and the models obtained from this study is presented. The delay estimations obtained from the artificial neural networks and neuro fuzzy models developed by Murat (6), the gene expression programming estimation model developed by Bagdatli (2), and the delay estimations generated by the models developed in this study are shown in comparison in Table 4.…”

Section: Resultsmentioning

confidence: 99%

“…Kimber and Hollis (21) and Kimber and Daly (22) paid attention to the queue lengths of vehicles to estimate the vehicle delays. They emphasized the importance of ''headway'' Source: Adapted from Bagdatli (2).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The estimation models obtained through analytical approaches were able to estimate the delay successfully in undersaturated traffic flows, but they were unsuccessful in oversaturated traffic flows. It has been observed that the delay estimation models obtained by utilizing artificial intelligence approaches achieve successful results in undersaturated and oversaturated traffic flows (2)(3)(4)(5)(6)(7)(8)(9)(10). However, the number of artificial intelligence techniques utilized for vehicle delay estimation is limited in the literature.…”

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confidence: 99%

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Vehicle Delay Estimation at Signalized Intersections Using Machine Learning Algorithms

Bağdatlı

Dokuz

2021

Transportation Research Record: Journal of the Transportation R

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Accurate determination of average vehicle delays is significant for effective management of a signalized intersection. The vehicle delays can be determined by field studies, however, this approach is costly and time consuming. Analytical methods which are commonly utilized to estimate delay cannot generate accurate estimates, especially in oversaturated traffic flow conditions. Delay estimation models based on artificial intelligence have been presented in the literature in recent years to estimate the delay more accurately. However, the number of artificial/heuristic intelligence techniques utilized for vehicle delay estimation is limited in the literature. In this study, estimation models are developed using four different machine learning methods—support vector regression (SVR), random forest (RF), k nearest neighbor (kNN), and extreme gradient boosting (XGBoost)—that have not previously been applied in the literature for vehicle delay estimation at signalized intersections. The models were tested with data collected from 12 signalized intersections located in Ankara, the capital of Turkey, and the performance of the models was revealed. The models were furthermore compared with successful delay models from the literature. The developed models, in particular the RF and XGBoost models, showed high performance in estimating the delay at signalized intersections under different traffic conditions. The results indicate that the delay estimation models based on the RF and XGBoost techniques can significantly contribute to both the literature and practice.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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confidence: 99%

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Vehicle Delay Estimation at Signalized Intersections Using Machine Learning Algorithms

Bağdatlı

Dokuz

2021

Transportation Research Record: Journal of the Transportation R

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“…In this paper, we compared the analytical results of our proposed intersection management method with the results of the two most widespread and efficient methods: signalized intersections and roundabouts. A roundabout is a perfect method for low traffic density [15], they have less conflict points than a signalized intersection [16], whereas a signalized intersection can handle high traffic flow better, but the effect of a signalized intersection on traffic delay is particularly obvious where vehicles are forced to a complete stop [17].…”

Section: Introductionmentioning

confidence: 99%

Intersection Control and Delay Optimization for Autonomous Vehicles Flows Only as Well as Mixed Flows with Ordinary Vehicles

Baz

Qurashi

2020

Vehicles

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The rapidly improving autonomous vehicle (AV) technology will have a significant impact on traffic safety and efficiency. This study introduces a game-theory-based priority control algorithm for autonomous vehicles to improve intersection safety and efficiency with mixed traffic. By using vehicle-to-infrastructure (V2I) communications, this model allows an AV to exchange information with the roadside units (RSU) to support the decision making of whether an ordinary vehicle (OV) or an AV should pass the intersection first. The safety of vehicles is taken in different stages of decisions to assure collision-free intersection operations. Two different mathematical models have been developed, where model one is for an AV/AV situation and model two is when an AV meets an OV. A simulation model was developed to implement the algorithm and compare the performance of each model with the conventional traffic control at a four-legged signalized intersection and at a roundabout. Three levels of traffic volume and speed combinations were tested in the simulation. The results show significant reductions in delay for both cases; for case (I), AV/AV model, a 65% reduction compared to a roundabout and 84% compared to a four-legged signalized intersection, and for case (II), AV/OV model, the reduction is 30% and 89%, respectively.

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“…Kelleci Kardeşler Kavşağı yaklaşım kolu bazlı trafik hacimleri Tablo 2' de verilen saatlik trafik hacimlerinden Denizli ve Antalya yaklaşım kollarının ana akımları, Baraj ve Kelleci Kardeşler yaklaşım kollarının ise yan akımları oluşturduğu söylenebilir. Yaklaşım kolu bazlı trafik hacimlerinin belirlenmesinin ardından, daha önceki çalışmalarda[24,25] belirtilen gecikme ölçüm yöntemi göz önünde bulundurularak her bir taşıtın (bu çalışma kapsamında toplam 4309 taşıt) kavşakta kaybettiği zaman ayrı ayrı belirlenmiştir. 4 farklı zaman periyodu için, ölçümler sonucunda elde edilen yaklaşım kolu ve kavşak bazlı ortalama gecikme değerleri Tablo 3' de sunulmaktadır.Tablo 3.Tablo 3 dikkatle incelendiğinde, Denizli ve Antalya yaklaşımlarındaki (ana akımlardaki) ortalama gecikmelerin Baraj ve Kelleci Kardeşler yaklaşımlarındaki (yan akımlardaki) ortalama gecikmelerden daha düşük olduğu görülmektedir.…”

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Sinyalize Dönel Kavşaklarda Diferansiyel Gelişim Algoritması ile Sinyal Süre Optimizasyonu

Çakıcı

Murat

2021

El-Cezeri Fen Ve Mühendislik Dergisi

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Özet: "Vakit" in "Nakit" ten daha değerli olduğunu her fırsatta dile getiren insanoğlu, günlük hayatta, zamanının büyük bir bölümünü de trafikte harcamaktadır. Özellikle sinyalizasyon sistemi ile kontrol edilen kavşaklarda meydana gelen yüksek düzeyli zaman kayıpları, yakıt tüketimleri, gürültü kirlilikleri vb. psikolojik, ekonomik ve çevresel problemleri de beraberinde getirebilmektedir. Karşılaşılabilecek olası problemler ise yalnızca doğru ve güçlü bir matematiksel altyapıya sahip olan denetim stratejileri ile en aza indirilebilmektedir. Bu çalışmada, sinyalize dönel kavşakların performanslarının arttırılması amacıyla, kavşaktaki ortalama taşıt gecikmesinin minimize edilmesini hedefleyen bir sinyal süresi optimizasyonu modeli oluşturulmuştur. Oluşturulan modelde, fazlara ait sinyal süreleri ve devre süresi Diferansiyel Gelişim Algoritması kullanılarak optimize edilmiştir. Çalışma kapsamında, modelin etkinliği, Denizli Kelleci Kardeşler Kavşağı'ndan 4 farklı zaman periyodunda toplanan gerçek trafik verileri kullanılarak VISSIM simülasyon programında test edilmiştir. Sonuç olarak, söz konusu model ile ortalama gecikmelerin, kavşaktaki mevcut duruma kıyasla yaklaşık %45' e kadar azaltılabileceği belirlenmiştir.

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Vehicle Delay Modeling at Signalized Intersections with Gene-Expression Programming

Cited by 15 publications

References 42 publications

Vehicle Delay Estimation at Signalized Intersections Using Machine Learning Algorithms

Vehicle Delay Estimation at Signalized Intersections Using Machine Learning Algorithms

Intersection Control and Delay Optimization for Autonomous Vehicles Flows Only as Well as Mixed Flows with Ordinary Vehicles

Sinyalize Dönel Kavşaklarda Diferansiyel Gelişim Algoritması ile Sinyal Süre Optimizasyonu

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