Traffic Simulation in Urban Networks Using Stochastic Cell Transmission Model

2017 ◽  
Vol 33 (4) ◽  
pp. 826-842 ◽  
Author(s):  
Rafik Hadfi ◽  
Sho Tokuda ◽  
Takayuki Ito
Keyword(s):  
Traffic Simulation ◽  
Transmission Model ◽  
Cell Transmission Model ◽  
Urban Networks ◽  
Cell Transmission

Modelling Heterogeneous and Undisciplined Traffic Flow using Cell Transmission Model

2020 ◽  
Vol 02 (01) ◽  
pp. 01-05
Author(s):  
Afzal Ahmed ◽  
Mir Shabbar Ali ◽  
Toor Ansari
Keyword(s):  
Traffic Flow ◽  
Traffic Simulation ◽  
Flow Diagram ◽  
Transmission Model ◽  
Flow Density ◽  
Cell Transmission Model ◽  
Flow Models ◽  
Simulation And Optimization ◽  
Cell Transmission ◽  
Traffic Flow Models

This research calibrates Cell Transmission Model (CTM) for heterogeneous and non-lane disciplined traffic, as observed in Pakistan and some other developing countries by constructing a flow-density fundamental traffic flow diagram. Currently, most of the traffic simulation packages used for such heterogonous and non-lane-disciplined traffic are not calibrated for local traffic conditions and most of the traffic flow models are developed for comparatively less heterogeneous and lane-disciplined traffic. The flow-density fundamental traffic flow diagram is developed based on extensive field data collected from Karachi, Pakistan. The calibrated CTM model is validated by using actual data from another road and it was concluded that CTM is capable of modelling heterogeneous and non-lane disciplined traffic and performed very reasonably. The calibrated CTM will be a useful input for the application of traffic simulation and optimization packages such as TRANSYT, SIGMIX, DISCO, and CTMSIM.

Intelligent control of complex traffic conditions based on machine learning

2021 ◽  
pp. 1-12
Author(s):  
Zhe Li
Keyword(s):  
Machine Learning ◽  
Simulation Model ◽  
Traffic Simulation ◽  
Learning Algorithms ◽  
Control Model ◽  
Machine Learning Algorithms ◽  
Transmission Model ◽  
Cell Transmission Model ◽  
Traffic Conditions ◽  
Simulation Control

 In order to improve the simulation effect of complex traffic conditions, based on machine learning algorithms, this paper builds a simulation model. Starting from the macroscopic traffic flow LWR theory, this paper introduces the process of establishing the original CTM mathematical model, and combines it with machine learning algorithms to improve it, and establishes the variable cell transmission model VCTM ordinary transmission, split transmission, and combined transmission mathematical expressions. Moreover, this paper establishes a road network simulation model to calibrate related simulation parameters. In addition, this paper combines the actual needs of complex traffic conditions analysis to construct a complex traffic simulation control model based on machine learning, and designs a hybrid microscopic traffic simulation system architecture to simulate all relevant factors of complex road conditions. Finally, this paper designs experiments to verify the performance of the simulation model. The research results show that the simulation control model of complex traffic conditions constructed in this paper has certain practical effects.

scholarly journals An Optimization Model of Multi-Intersection Signal Control for Trunk Road under Collaborative Information

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Xun Li ◽  
Zhengfan Zhao ◽  
Li Liu ◽  
Yao Liu ◽  
Pengfei Li
Keyword(s):  
Optimization Model ◽  
Delay Time ◽  
Traffic Simulation ◽  
Simulation Software ◽  
Transmission Model ◽  
Signal Control ◽  
Cell Transmission Model ◽  
Average Delay ◽  
Main Trunk ◽  
Average Delay Time

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.

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