Optimization of the method for collecting source data to calculate the length of the traffic light control cycle

Builenko, Valery; Pakhomova, A. A.; Pakhomov, Sergey

doi:10.1016/j.trpro.2018.12.048

Cited by 6 publications

(5 citation statements)

References 10 publications

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“…In the literature, several indicators are developed to assess the performance of traffic signal; these indicators include the delay time [3,7], the traffic capacity [3], the system throughput [3,7], the intersection safety [2,4], the GHG emission count [2], the queue length [5], the travel time [5,6], etc. However, all these indicators are driven by two fundamental measures: the delay time (i.e., the additional travel time experienced by users compared to free movement through the intersection) and intersection safety (i.e., events to do with any conflict situations or possible interactions between vehicle and vehicle or pedestrian movements at the intersection).…”

Section: Performance Indicatorsmentioning

confidence: 99%

“…In the literature, traffic signal control optimization is mainly geared towards minimizing the cycle length and improving measures that are directly or indirectly influenced by the cycle length. These measure include traffic capacity, travel time, system throughput, vehicle-to-vehicle conflict, and vehicle emissions [2][3][4][5][6][7]. All these measures mainly quantify vehicle-related performance.…”

Section: Introductionmentioning

confidence: 99%

“…All these measures mainly quantify vehicle-related performance. Pedestrian-related performance indicators have also been studied in the literature [4,8,9], but in most related works, interactions between pedestrians and vehicles were not explicitly considered.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of the Performance of Hybrid Traffic Signal Patterns and Conventional Alternatives When Accounting for Both Pedestrians and Vehicles

2022

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Traffic control systems are crucial for managing traffic flows. Their main function is to reduce interactions among users for safety reasons, while minimizing the travel times. Researchers often concentrate on the cycle length, whose impact on travel times is directly measurable. However, the choice of the signal pattern may also have a great potential to reduce travel times and unsafe situations. This potential is yet to be thoroughly investigated. In this work, we are interested in comparing different signal patterns in terms of the number of potential conflicts and delay time for both drivers and pedestrians. To this end, we first select three commonly adopted signal patterns, namely the Exclusive Pedestrian Phase (EPP), the Leading Through Interval (LTI) and the Two-Way Crossing (TWC). We then generalize existing methods for measuring user delay and safety for these three signal patterns. Moreover, we investigate a hypothetical hybrid pattern obtained by dynamically adapting the signal pattern to real-time data. The proposed methodology is applied to a case study considering an isolated intersection in Montreal, Canada. We perform computational experiments geared towards determining the best pattern according to ad hoc performance indicators and user flows. Results show that the EPP and LTI patterns generally perform better than TWC. EPP generally outperforms LTI when measuring the number of potential conflicts, while LTI outperforms EPP when considering delay times. Furthermore, the hypothetical hybrid pattern shows a positive but overall limited impact regarding both delay times and number of potential conflicts.

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Section: Performance Indicatorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of the Performance of Hybrid Traffic Signal Patterns and Conventional Alternatives When Accounting for Both Pedestrians and Vehicles

2022

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“…Pada akhirnya pengguna jalan merasa kehilangan waktunya dikarenakan beban waktu siklus pada lampu warna merah terlalu lama. Setelah merasa kehilangan waktu karena menunggu lampu hijau, pada ketika waktu lampu hijau tiba, pengguna jalan akan terburuburu dan saling menyalip [7], [8]. Hal inilah yang membuat kemacetan ketika setelah lampu hijau selesai, tetapi masih ada pengguna jalan yang tetap berusaha melewati lampu warna merah.…”

Section: Pendahuluanunclassified

“…b. Traffic responsive Pada traffic responsive berkaitan dengan operasi waktu siklus yang berubahubah sesuai kondisi arus lalu lintas [14], [8]. Pada karakteristik volume lalu lintas ada jumlah gerakan yang dihitung berdasarkan pengguna jalan baik itu yang lewat adalah mobil, motor maupun truck dalam kelompok campuran maupun khusus satu jenis kendaraan.…”

Section: B Simpang Bersinyalunclassified

Analisis Waktu Lampu Hijau, Kapasitas Kendaaran dan Derajat Jenuh di Simpang Empat Bersinyal

Komari¹,

Santoso²,

Tripariyanto³

2019

jatiunik. jurnal. ilm. teknik. manaj. industri.

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Congestion has a negative impact on motorists during certain hours. Therefore it is necessary to do an analysis related to traffic with the aim to find out the optimum time of green light, vehicle capacity and degree of saturation. Observations were carried out in the collection of data related to the cycle time of green light, yellow light and red light. Data was collected for 60 minutes at an intersection on Jalan Veteran (east), Jalan Veteran (west), Jalan Kawi and Jalan Penanggungan, Kota Kediri, using the Webster method. Data is processed in terms of saturation flow, traffic flow rate, lost time, optimum cycle time, effective green time, total effective green time, green light time for each phase, actual green time, capacity of each crossing arm and degree of saturation. The results of the study are in accordance with the objectives of the actual green time on the Veteran Road from the east by 10 seconds with a capacity of 389 (kend / hour), degree of saturation 0.93, Veteran Road from the west by 11 seconds with a capacity of 357 (vehicle / hour) , saturation degree 1,07, Jalan Kawi from the north for 8 seconds, with a capacity of 260 (kend / hour), saturation degree 1.21 and a Penanggungan from the south for 11 seconds, with a capacity of 357 (kend / hour), saturation degree 1.12.Keyword : Congestion, Webster, Capacity, Saturated degree Kemacetan menimbulkan dampak negatif bagi pengendara kendaraan di jam – jam tertentu. Oleh karena itu perlu dilakukan analisis terkait lalu lintas dengan tujuan untuk mengetahui waktu optimum lampu hijau, kapasitas kendaraan dan derajat jenuh. Dilakukan observasi yaitu pengambilan data terkait siklus waktu lampu hijau, lampu kuning dan lampu merah. Dilakukan pengambilan data selama 60 menit di persimpangan pada Jalan Veteran (timur), Jalan Veteran (barat), Jalan Kawi dan Jalan Penanggungan, Kota Kediri, menggunkan metode Webster. Data diolah terkait arus jenuh, tingkat arus lalu lintas, waktu hilang, waktu siklus optimum, waktu hijau efektif, waktu hijau efektif total, waktu lampu hijau tiap fase, waktu hijau aktual, kapasitas tiap lengan persimpangan dan derajat jenuh. Hasil penelitian sesuai dengan tujuan yaitu waktu hijau aktual yang pada Jalan Veteran dari arah timur sebesar 10 detik dengan kapasitas 389 (kend/jam), derajat jenuh 0,93, Jalan Veteran dari arah barat sebesar 11 detik dengan kapasitas 357 (kend/jam), derajat jenuh 1,07, Jalan Kawi dari arah utara sebesar 8 detik, dengan kapasitas 260 (kend/jam), derajat jenuh 1,21 dan Penanggungan dari arah selatan sebesar 11 detik, dengan kapasitas 357 (kend/jam), derajat jenuh 1,12. Kata Kunci: Kemacetan, Webster, Kapasitas, Derajat jenuh

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A distributed deep reinforcement learning method for traffic light control

Liu

Ding²

2022

Neurocomputing

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Optimization of the method for collecting source data to calculate the length of the traffic light control cycle

Cited by 6 publications

References 10 publications

Comparison of the Performance of Hybrid Traffic Signal Patterns and Conventional Alternatives When Accounting for Both Pedestrians and Vehicles

Comparison of the Performance of Hybrid Traffic Signal Patterns and Conventional Alternatives When Accounting for Both Pedestrians and Vehicles

Analisis Waktu Lampu Hijau, Kapasitas Kendaaran dan Derajat Jenuh di Simpang Empat Bersinyal

A distributed deep reinforcement learning method for traffic light control

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