A High-Resolution Cellular Automata Traffic Simulation Model with Application in a Freeway Traffic Information System

2004 ◽  
Vol 19 (5) ◽  
pp. 338-350 ◽  
Author(s):  
Sigurur F. Hafstein ◽  
Roland Chrobok ◽  
Andreas Pottmeier ◽  
Michael Schreckenberg ◽  
Florian C. Mazur
Keyword(s):  
Information System ◽  
High Resolution ◽  
Cellular Automata ◽  
Simulation Model ◽  
Traffic Simulation ◽  
Traffic Information ◽  
Freeway Traffic

An overview of a simulation study of the Highway 401 freeway traffic management system

1994 ◽  
Vol 21 (3) ◽  
pp. 439-454 ◽  
Author(s):  
Bruce Hellinga ◽  
Michel Van Aerde
Keyword(s):  
Simulation Model ◽  
Traffic Management ◽  
Management System ◽  
Model Calibration ◽  
Traffic Simulation ◽  
Time Of Day ◽  
Calibration Model ◽  
Integration Model ◽  
Freeway Traffic ◽  
Traffic Management System

This paper discusses the application of the network traffic simulation model INTEGRATION to a 35-km section of Highway 401 in Toronto, Canada. Results for the eastbound direction from 4 a.m. to 12 noon are presented. Existing freeway conditions are quantified using data from the COMPASS freeway traffic management system and from a floating car travel time survey. Variations that exist in observed link flows and trip travel durations over time of day and day of week are examined. The extent to which COMPASS data meets the data requirements of the INTEGRATION model is examined. Since the current COMPASS system encompassed less than 50% of the network analyzed, complications arise in accurately estimating the prevailing time-varying origin–destination demands, as well as in comprehensively validating the simulation model's results. The present level of model calibration results in a correlation coefficient of estimated and observed link flows of 97.23%. This level of discrepancy is generally within the natural day-to-day variations that are inherent within the system. However, travel times estimated by the simulation model tend to be underestimated, particularly for the express lanes. Further model calibration, to improve the model's results, is deferred until more of the network is covered by COMPASS. Key words: traffic simulation, COMPASS, model calibration, model validation, speed–flow relationship.

Research on Pedestrian Stereo Traffic Simulation Model Based on Cellular Automata

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Tao Liu ◽  
WeiHua Zhang ◽  
RanRan Liu ◽  
HuanPing Pang ◽  
WenJuan Huang ◽  
...  
Keyword(s):  
Cellular Automata ◽  
Simulation Model ◽  
Traffic Simulation ◽  
Model Based

A Realistic Two-Lanes Traffic Simulation Model Based on Cellular Automata

2014 ◽  
Author(s):  
Hector Guzman ◽  
Maria Larraga ◽  
Luis Alvarez-Icaza ◽  
Fernando Huerta
Keyword(s):  
Cellular Automata ◽  
Simulation Model ◽  
Traffic Simulation ◽  
Model Based

PENGEMBANGAN ”REAL TIME TRAFFIC INFORMATION SYSTEM” BAGI PENGGUNA JALAN

2013 ◽  
Vol 12 (3) ◽  
Author(s):  
Rusmadi Suyuti
Keyword(s):  
Information System ◽  
Real Time ◽  
Travel Demand ◽  
Urban Traffic ◽  
Road User ◽  
Traffic Information ◽  
Estimation Methods ◽  
Traffic Counts ◽  
Real Time Traffic ◽  
Demand Models

Traffic information condition is a very useful  information for road user because road user can choose his best route for each trip from his origin to his destination. The final goal for this research is to develop real time traffic information system for road user using real time traffic volume. Main input for developing real time traffic information system is an origin-destination (O-D) matrix to represent the travel pattern. However, O-D matrices obtained through a large scale survey such as home or road side interviews, tend to be costly, labour intensive and time disruptive to trip makers. Therefore, the alternative of using traffic counts to estimate O-D matrices is particularly attractive. Models of transport demand have been used for many years to synthesize O-D matrices in study areas. A typical example of the approach is the gravity model; its functional form, plus the appropriate values for the parameters involved, is employed to produce acceptable matrices representing trip making behaviour for many trip purposes and time periods. The work reported in this paper has combined the advantages of acceptable travel demand models with the low cost and availability of traffic counts. Two types of demand models have been used: gravity (GR) and gravity-opportunity (GO) models. Four estimation methods have been analysed and tested to calibrate the transport demand models from traffic counts, namely: Non-Linear-Least-Squares (NLLS), Maximum-Likelihood (ML), Maximum-Entropy (ME) and Bayes-Inference (BI). The Bandung’s Urban Traffic Movement survey has been used to test the developed method. Based on several statistical tests, the estimation methods are found to perform satisfactorily since each calibrated model reproduced the observed matrix fairly closely. The tests were carried out using two assignment techniques, all-or-nothing and equilibrium assignment.  

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 High-Resolution Traffic Sensing with Probe Autonomous Vehicles: A Data-Driven Approach

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 464
Author(s):  
Wei Ma ◽  
Sean Qian
Keyword(s):  
High Resolution ◽  
Autonomous Vehicles ◽  
Penetration Rate ◽  
Data Driven ◽  
Traffic Information ◽  
Flow Density ◽  
Massive Data ◽  
Market Penetration ◽  
High Resolution Data ◽  
Data Driven Approach

Recent decades have witnessed the breakthrough of autonomous vehicles (AVs), and the sensing capabilities of AVs have been dramatically improved. Various sensors installed on AVs will be collecting massive data and perceiving the surrounding traffic continuously. In fact, a fleet of AVs can serve as floating (or probe) sensors, which can be utilized to infer traffic information while cruising around the roadway networks. Unlike conventional traffic sensing methods relying on fixed location sensors or moving sensors that acquire only the information of their carrying vehicle, this paper leverages data from AVs carrying sensors for not only the information of the AVs, but also the characteristics of the surrounding traffic. A high-resolution data-driven traffic sensing framework is proposed, which estimates the fundamental traffic state characteristics, namely, flow, density and speed in high spatio-temporal resolutions and of each lane on a general road, and it is developed under different levels of AV perception capabilities and for any AV market penetration rate. Experimental results show that the proposed method achieves high accuracy even with a low AV market penetration rate. This study would help policymakers and private sectors (e.g., Waymo) to understand the values of massive data collected by AVs in traffic operation and management.

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