scholarly journals The Design of Signal Control Software and Intersection Traffic Simulation

Author(s):  
Hongke Xu ◽  
Xiaoqing Hou ◽  
Rixing Zhu
2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Xun Li ◽  
Zhengfan Zhao ◽  
Li Liu ◽  
Yao Liu ◽  
Pengfei Li

We proposed a signal control optimization model for urban main trunk line intersections. Four-phase intersection was analyzed and modeled based on the Cell Transmission Model (CTM). CTM and signal control model in our study had both been improved for multi-intersections by three-phase theory and information-exchanging. To achieve a real-time application, an improved genetic algorithm (GA) was proposed finally, the DISCO traffic simulation software was used for numerical simulation experiment, and comparisons with the standard GA and CTM were reported in this paper. Experimental results indicate that our searching time is less than that of SGA by 38%, and our method needs only 1/3 iteration time of SGA. According to our DISCO traffic simulation processing, compared with SGA, if the input traffic flow is changed from free phase to synchronized phase, for example, less than 900 vel/h, the delay time can reduce to 87.99% by our method, and the minimum delay time is 77.76% of existing method. Furthermore, if input traffic volume is increased to 1200 vel/h or more at the synchronized phase, the summary and minimum values of average delay time are reduced to 81.16% and 75.83%, respectively, and the average delay time is reduced to 17.72 seconds.


ORiON ◽  
2019 ◽  
Vol 35 (1) ◽  
pp. 57-87
Author(s):  
SJ Movius ◽  
JH Van Vuuren

Fixed-time control and vehicle-actuated control are two distinct types of traffic signal control. The latter control method involves switching traffic signals based on detected traffic flows and thus offers more flexibility (appropriate for lighter traffic conditions) than the former, which relies solely on cyclic, predetermined signal phases that are better suited for heavier traffic conditions. The notion of self-organisation has relatively recently been proposed as an alternative approach towards improving traffic signal control, particularly under light traffic conditions, due to its flexible nature and its potential to result in emergent behaviour. The effectiveness of five existing self-organising traffic signal control strategies from the literature and a fixed-control strategy are compared in this paper within a newly designed agent-based, microscopic traffic simulation model. Various shortcomings of three of these algorithms are identified and algorithmic improvements are suggested to remedy these deficiencies. The relative performance improvements resulting from these algorithmic modifications are then quantified by their implementation in the aforementioned traffic simulation model. Finally, a new self-organising algorithm is proposed that is particularly effective under lighter traffic conditions.


Author(s):  
Sunil Taori ◽  
Ajay K. Rathi

This paper documents a comparative analysis of the NETSIM, NETFLO I, and NETFLO II traffic simulation models when simulating traffic networks with fixed-time signal control. The objective was to find out whether the results of simulating the same traffic network with the three models were compatible. The three models employ different approaches to simulate traffic flow on urban street networks. Four different scenarios with varying network configurations and intersection geometries were simulated for three volume levels. The average speed and delay measures of effectiveness generated by the three models were compared for each scenario. Analysis of variance techniques were used for statistical analyses of the simulation output data. The execution speeds of the models were also compared. The quickness of simulation will be very important in the ITS applications, real-time traffic adaptive systems, and so forth. The results of this study indicated that the estimated measures of effectiveness from the three models were statistically significantly different at each level of comparison. The speed values generated by NETSIM were found to be the lowest; NETFLO II values were highest. NETFLO I values in all cases were between NETFLO II and NETSIM values. In most cases, NETFLO I values were closer to NETFLO II than to NETSIM values. Identical results were obtained from the analysis of delay values, with NETSIM delay estimates always being highest and NETFLO II values lowest. The program execution time for NETSIM and NETFLO I increased significantly as the volume level increased, whereas it practically did not change for NETFLO II. The execution times for NETSIM were found to be about 20 to 45 times higher than for NETFLO II. Even NETFLO I was found to be about 5 to 10 times faster than NETSIM.


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