delay minimization
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Author(s):  
Yen-Hsiang Chen ◽  
Yao Cheng ◽  
Gang-Len Chang

Despite the abundance of studies on signal progression for arterial roads, most existing models for bandwidth maximization cannot concurrently ensure that the resulting delays will be at a desirable level, especially for urban arterials accommodating high turning volume at some major intersections or constrained by limited turning bay length. Extending from those models that aim to address delay minimization in the progression design, this study provides two enhanced progression maximization models for arterials with high turning volumes. The first model aims to select the signal plan that can produce the lowest total signal delays for all movements from the set of non-inferior offsets produced by MAXBAND. Failing to address the impact of potential turning bay spillback at some critical intersections under such a design may significantly degrade the quality of through progression and increase the overall delay. For this reason, the second model proposed in this study offers the flexibility to trade the progression bandwidths within a pre-specified level for the target delay reduction, especially for turning traffic. The evaluation results from both numerical analyses and simulation experiments have shown that both proposed models can produce the desirable level of performance when compared with the two benchmark models, MAXBAND and TRANSYT 16. The second model yielded the lowest average network delay of 117.2 seconds per vehicle (s/veh), compared with 121.7 s/veh with TRANSYT. Moreover, even its average delay of 141.8 s/veh for through vehicles is comparable with that of 141.2 s/veh by MAXBAND, which is designed mainly to benefit through-traffic flows.


2021 ◽  
Vol 2087 (1) ◽  
pp. 012074
Author(s):  
Bingsen Xia ◽  
Yuanchun Tang

Abstract the paper introduces IRS to assist offloading, and the propagation Environment can be intelligently changed by changing the reflection unit of the IRS, This article proposes an IRS-assisted MEC power distribution Internet of Things system, and studies the gain effect of IRS in the MEC system. In this system, the single antenna equipment can choose to unload a small part of its computing task to the edge computing node of the distribution Internet of things through the multi antenna access point with the help of IRS. In this paper, the delay minimization problem of the whole system is established, the DNQ reinforcement learning algorithm is used to solve the problem, which can effectively change the coverage of smart substations.


2021 ◽  
Vol 18 (6) ◽  
pp. 1-11
Author(s):  
Chengyue Lu ◽  
Zihan Wang ◽  
Wenbo Ding ◽  
Gang Li ◽  
Sicong Liu ◽  
...  

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