Calibration of Microscopic Traffic Flow Simulation Models Using a Memetic Algorithm with Solis and Wets Local Search Chaining (MA-SW-Chains)

Author(s):  
Carlos Cobos ◽  
Carlos Daza ◽  
Cristhian Martínez ◽  
Martha Mendoza ◽  
Carlos Gaviria ◽  
...  
2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Alexander Paz ◽  
Naveen Veeramisti ◽  
Romesh Khaddar ◽  
Hanns de la Fuente-Mella ◽  
Luiza Modorcea

This study proposes an architecture for an interactive motion-based traffic simulation environment. In order to enhance modeling realism involving actual human beings, the proposed architecture integrates multiple types of simulation, including: (i) motion-based driving simulation, (ii) pedestrian simulation, (iii) motorcycling and bicycling simulation, and (iv) traffic flow simulation. The architecture has been designed to enable the simulation of the entire network; as a result, the actual driver, pedestrian, and bike rider can navigate anywhere in the system. In addition, the background traffic interacts with the actual human beings. This is accomplished by using a hybrid mesomicroscopic traffic flow simulation modeling approach. The mesoscopic traffic flow simulation model loads the results of a user equilibrium traffic assignment solution and propagates the corresponding traffic through the entire system. The microscopic traffic flow simulation model provides background traffic around the vicinities where actual human beings are navigating the system. The two traffic flow simulation models interact continuously to update system conditions based on the interactions between actual humans and the fully simulated entities. Implementation efforts are currently in progress and some preliminary tests of individual components have been conducted. The implementation of the proposed architecture faces significant challenges ranging from multiplatform and multilanguage integration to multievent communication and coordination.


IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 103124-103140 ◽  
Author(s):  
Carlos Cobos ◽  
Alexander Paz ◽  
Julio Luna ◽  
Cristian Erazo ◽  
Martha Mendoza

2004 ◽  
Vol 57 (1) ◽  
pp. 53-71 ◽  
Author(s):  
A. N. Ince ◽  
E. Topuz

This paper outlines the design of a Vessel Traffic Management and Information System (VTMIS) for the Turkish Straits and taking this as an example shows how modelling and simulation may aid safe and efficient navigation of vessels through waterways which are narrow and winding with changing currents and are therefore difficult to navigate and prone to accidents. Ship Handling and Vessel Traffic Flow simulation models and Hyrographic Prediction model are described and the simulation trials conducted under different traffic and environment conditions are discussed to show the role that these prediction and simulation programmes can play in preventing marine casualities in different waterways, which may result in loss of human lifes and property and contamination of the environment.


2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Alexander Paz ◽  
Kul Shrestha ◽  
Cristian Arteaga ◽  
Douglas Baker

This study proposes a methodology for the calibration of microscopic traffic flow simulation models by enabling simultaneous selection of traffic links and associated parameters. The analyst selects any number and combination of links and model parameters for calibration. Most calibration methods consider the entire network and use ad hoc approaches without enabling a specific selection of location and associated parameters. In practice, only a subset of links and parameters is used for calibration based on several factors such as expert knowledge of the system or constraints imposed by local governance. In this study, the calibration problem for the simultaneous selection of links and parameters was formulated using a mathematical programming approach. The proposed methodology is capable of calibrating model parameters considering multiple time periods and performance measures simultaneously. Traffic volume and speed are the performance measures used in this study, and the methodology is developed without considering the characteristics of a specific traffic flow model. A genetic algorithm was implemented to find a solution to the proposed mathematical program. In the experiments, two traffic models were calibrated: the first set of experiments included selection of links only, while all associated parameters were considered for calibration. The second set of experiments considered simultaneous selection of links and parameters. The implications of these experiments indicate that the models were calibrated successfully subject to selection of a minimum number of links. As expected, the more links and parameters that are used for calibration, the more time it takes to find a solution, but the overall results are better. All parameter values were reasonable and within constraints after successful calibration.


2021 ◽  
Author(s):  
Christian Siebke ◽  
◽  
Maximilian Bäumler ◽  
Madlen Ringhand ◽  
Marcus Mai ◽  
...  

As part of the AutoDrive project, OpenPASS is used to develop a cognitive-stochastic traffic flow simulation for urban intersection scenarios described in deliverable D1.14. The deliverable D4.20 is about the design of the modules for the stochastic traffic simulation. This initially includes an examination of the existing traffic simulations described in chapter 2. Subsequently, the underlying tasks of the driver when crossing an intersection are explained. The main part contains the design of the cognitive structure of the road user (chapter 4.2) and the development of the cognitive behaviour modules (chapter 4.3).


2011 ◽  
Vol 2011 ◽  
pp. 1-7
Author(s):  
Hikaru Shimizu ◽  
Sho Nishiyama ◽  
Yukiko Wakita ◽  
Eisuke Kita

A driver usually controls the vehicle according to only the information from the nearest leader vehicle. If the information from the other leader vehicles is also available, the driver can control the vehicle more adequately. The aim of this study is to discuss the effectiveness of the information from the other leader vehicles than the nearest one for the traffic flow. For this purpose, the traffic flow is modeled by using the Chandler-type multi-vehicle-following model. This model changes the vehicle acceleration rate according to the velocity differences between the vehicle and its multileader vehicles. After the model stability analysis, the traffic flow simulation is performed. The results reveal that the stable region of the model parameters expands according to the increase of the number of the leader vehicles.


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