A note on Artificial Intelligence techniques and Dissipativity-based Approach in Traffic Signal Control For an Over-Saturated Intersection

Motawej, Fadi; Bouyekhf, Rachid; Moudni, Abdellah El

doi:10.3182/20110828-6-it-1002.01901

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…Thus, the parameters of effective green times and green split of every phase can be calculated by (7) and (8).…”

Section: The Arranging Methods Of Green Splitmentioning

confidence: 99%

“…The research indicated that, vehicle delays can be reduced by 13% when considering only vehicle delays and by 5% when simultaneously considering vehicle and pedestrian delays. Motawej F. et al (2011) [8] constructed a discrete timing model for describing the change of queuing length at signal intersections. Based on the dissipative system theory, this model realizes the increasing of green time.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Setting Method of Signal Timing Parameters at Over-saturated Intersection

Cao¹,

Wang²,

Zuo³

2015

Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science

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In order to adapt to traffic operation situation of over-saturated signalized intersection, the setting methods of optimal cycle and green split of every phase are studied. Selecting Xinan Rd. and Wuyi Rd. intersection as study object, traffic investigation was carried out. Considering the two factors of the average delay and capacity at oversaturated intersection comprehensively, the optimization method of signal cycle length was proposed. Considering the traffic volume and queue length, setting method of the green splits of every phase was also proposed. Using the measured traffic data, signal timing scheme of the investigated intersection was not only designed, but also compared with the existing situation. The research indicates that, using this method, the average delay will be increased at oversaturated intersection, and the capacity can also be increased. This method is beneficial to reduce vehicles queuing at this kind of intersection.

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“…Thus, the parameters of effective green times and green split of every phase can be calculated by (7) and (8).…”

Section: The Arranging Methods Of Green Splitmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Setting Method of Signal Timing Parameters at Over-saturated Intersection

Cao¹,

Wang²,

Zuo³

2015

Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science

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show abstract

“…TrafficTalk proposed in this paper achieves better MAPE (i.e., 10.4%). Motawej et al applied the same method along with the dissipativitybased control to build a time-series model to perform the estimation [30]. In their methods, the lagged traffic flow data are used as the input for predicting the real-time traffic flow.…”

Section: Related Workmentioning

confidence: 99%

Prediction of Queue Dissipation Time for Mixed Traffic Flows With Deep Learning

Chen

Lin

Yeh

et al. 2022

IEEE Open J. Intell. Transp. Syst.

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Queue dissipation has been extensively studied about traffic signalization, work zone operations, and ramp metering. Various methods for estimating the intersection's queue length and dissipation time have been reported in the literature, including the use of car-following models with simulation, vehicle trajectories from GPS, shock-wave theory, statistical estimation from traffic flow patterns, and artificial neural networks (ANN). However, most of such methods cannot account for the impacts of interactions between different vehicle types and their spatial distributions in the queue length on the initial discharge time and the resulting total dissipation duration. As such, this study presents a system, named TrafficTalk, that applies a deep learning-based method to reliably capture the queue characteristics of mixed traffic flows, and produce a robust estimate of the dissipating duration for the design of the optimal signal plan. The proposed TrafficTalk, featuring the effectiveness in transforming video-imaged traffic conditions into vehicle density maps, has proved its performance under extensive field evaluations. For instance, compared with the benchmark model, XGBoost in the literature, it has reduced the MAPE from 25.8% to 10.4%., and from 31.3% to 10.4% if the queue discharging stream comprises motorcycles. INDEX TERMS Deep learning (DL), traffic queue dissipation time, traffic queue pattern, mixed traffic flows, object detection, traffic signal countdown timer (TSCT).

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“…In the era of advancement, traffic network optimization is becoming a more complex and challenging task in developing countries because of the rapid development of urbanization construction. The gigantic amount of vehicles on the limited road network is resulting in a slow movement, congestion and traffic jams [1]. The expansion of road infrastructure with the increasing number of vehicles is a simple method to resolve this problem.…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Based Traffic Signal Optimization under Fully Actuated Control

Rani¹,

Shaw²

2019

IJITEE

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This paper presents a multi-agent based distributed traffic control model to optimize the traffic signal for multiple intersections. Previous works in the area of traffic signal control suffer from a number of inadequacies, including the use of fixed cycle length, centralized mode of operations and dependency on historical data. Considering these, the aim of this work is to control the traffic signal timings by adjusting the phase sequence in order to minimize the delay in traffic at the intersections. To model the traffic network, a three-tier multi-agent system has been adopted in distributed mode. In addition, a fully actuated signal control algorithm is designed and it utilizes state-space equations to formulate the queue length at the green light phase and red light phase. The proposed model is simulated with SUMO simulator and a comparative analysis has been made between adaptive control method, multi-agent method based on collective learning and multi-agent based fully actuated control method on a similar platform. The results spectacle the proposed traffic control model outperforms that of other existing control methods in all condition; hence it can be deployed to control the tremendous traffic on the road network and to optimize the traffic signal in more effective manner.

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A note on Artificial Intelligence techniques and Dissipativity-based Approach in Traffic Signal Control For an Over-Saturated Intersection

Cited by 5 publications

References 9 publications

The Setting Method of Signal Timing Parameters at Over-saturated Intersection

The Setting Method of Signal Timing Parameters at Over-saturated Intersection

Prediction of Queue Dissipation Time for Mixed Traffic Flows With Deep Learning

Multi-Agent Based Traffic Signal Optimization under Fully Actuated Control

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