Revisión del estado del arte (no sistemática) sobre el uso de algoritmos genéticos en la calibración de modelos de micro simulación vehicular

Este trabajo propone una revisión del estado del arte entre 2011 hasta la actualidad (2022), sobre el uso de Algoritmos Genéticos (AG) en la calibración de modelos de micro simulación vehicular. La calibración no es más que el proceso de optimización de modelos bajo la comparación de parámetros observados y reales. Se seleccionó a los AG debido a su gran robustez y capacidad de trabajo con grandes cantidades de datos. Se seleccionaron un total de 19 artículos de fuentes de investigación reconocidas como: IEEE Xplore, ScienceDirect, Springer Link y Scopus, respetando todos los criterios de selección y filtrado para únicamente trabajar con aquellos que aporten una actualización adecuada del tema. Los resultados muestran que gracias a la actualización de este tema se pudo constatar que el uso de los AG en la calibración de modelos de micro simulación vehicular tiene el potencial para mejorar y acelerar el proceso de calibración, lo cual ayudara a investigaciones y futuras publicaciones.This paper proposes a state-of-the-art review from 2011 to the present (2022) on the use of genetic algorithms (GA) to calibrate vehicle microsimulation models. Calibration is nothing more than the optimization process of models under comparing experimental and real parameters. Genetic algorithms (GA) were selected because of their excellent robustness and ability to work with large amounts of data. A total of 19 articles were selected from recognized research sources such as IEE Xplore, ScienceDirect, Springer Link, and Scopus, respecting all the selection and filtering criteria to work only with those that provide an adequate update of the topic. The results show that thanks to the update on this topic, it was possible to verify that the use of GA (Genetic Algorithms) in the calibration of vehicle microsimulation models can improve and accelerate the calibration process, which will help future research and publications

CALIBRATION OF MICROSCOPIC TRAFFIC SIMULATION OF URBAN ROAD NETWORK INCLUDING MINI-ROUNDABOUTS AND UNSIGNALIZED INTERSECTION USING OPEN-SOURCE SIMULATION TOOL

Microscopic traffic simulation models offer an effective way to analyze and assess different transportation systems thanks to their efficiency and reliability. As traffic management issues become more prevalent, notably in urban areas, simulation tools enable a significant opportunity to replicate real-world conditions before implementation. Therefore, the calibration of traffic simulation models plays a substantial role in obtaining accurate and confidential results. Nowadays, urban regions are facing the challenge of restricted space for developing traffic solutions. As a consequence of environmental restrictions, the use of mini-roundabouts rather than larger roundabouts is increasing. Based on the given literature review, it is seen that not much attention was given to the complex modeling and calibration of microsimulation models of mini-roundabouts and unsignalized intersections. The objective of this study is to offer the calibration of microscopic traffic simulation of urban road network, including closely located mini-roundabouts and unsignalized intersection. To this end, an open-source tool called SUMO (Simulation of Urban Mobility) was utilized as a simulation environment in this study. The necessary data for developing a microsimulation model in SUMO was gathered using a videography technique. The traffic count data and speed were considered performance measures between field observations and simulation outputs. The routeSampler tool of SUMO, which has recently emerged in the literature, was used to match traffic count data and the corresponding time interval for traffic volume data calibration. The calibration of car-following model parameters using a trial-and-error approach was employed based on mean absolute percent error (MAPE) between simulated speeds and field-measured speeds. According to the findings of the study, the simulation model fulfilled the calibration aims of the FHWA guideline and is suitable for further research

Calibration of Microscopic Traffic Flow Models Enabling Simultaneous Selection of Specific Links and Parameters

This study proposes a methodology for the calibration of microscopic traffic flow simulation models by enabling simultaneous selection of traffic links and associated parameters. That is, any number and combination of links and model parameters can be selected for calibration. Most calibration approaches consider the entire network without enabling a specific selection of location and associated parameters. In practice, only a subset of links and parameters are used for calibration based on a number of factors such as expert local knowledge of the system. 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 model parameters, taking into consideration multiple performance measures and time periods simultaneously. The performance measures used in this study were volume and speed. The development of the methodology is independent of the characteristics of a specific traffic flow model. A genetic algorithm was implemented to determine the solution to the proposed mathematical program for the calibration of microscopic traffic flow models. 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. Results showed that the models were calibrated successfully subject to selection of a minimum number of links. All parameter values were reasonable and within constraints after successful calibration

Specification of Mixed Logit Models Using an Optimization Approach

Mixed logit models are a widely-used tool for studying discrete outcome problems. Modeling development entails answering three important questions that highly affect the quality of the specification: (i) what variables are considered in the analysis? (ii) what are going to be the coefficients for these variables? and (iii) what density function these coefficients will follow? The literature provides guidance; however, a strong statistical background and an ad hoc search process are required to obtain the best model specification. Knowledge of the problem context and data is required. Given a dataset including discrete outcomes and associated characteristics the problem to be addressed in this thesis is to investigate to what extend a relatively simple metaheuristic such as Simulated Annealing, can determine the best model specification for a mixed logit model and answer the above questions. A mathematical programing formulation is proposed and simulated annealing is implemented to find solutions for the proposed formulation. Three experiments were performed to test the effectiveness of the proposed algorithm. A comparison with existing model specifications for the same datasets was performed. The results suggest that the proposed algorithm is able to find an adequate model specification in terms of goodness of fit thereby reducing involvement of the analyst

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field

Identification of Factors Contributing to Traffic Crashes by Analysis of Text Narratives

The fatalities, injuries, and property damage that result from traffic crashes impose a significant burden on society. Current research and practice in traffic safety rely on analysis of quantitative data from crash reports to understand crash severity contributors and develop countermeasures. Despite advances from this effort, quantitative crash data suffers from drawbacks, such as the limited ability to capture all the information relevant to the crashes and the potential errors introduced during data collection. Crash narratives can help address these limitations, as they contain detailed descriptions of the context and sequence of events of the crash. However, the unstructured nature of text data within narratives has challenged exploration of crash narratives. In response, this dissertation aims to develop an analysis framework and methods to enable the extraction of insights from crash narratives and thus improve our level of understanding of traffic crashes to a new level. The methodological development of this dissertation is split into three objectives. The first objective is to devise an approach for extraction of severity contributing insights from crash narratives by investigating interpretable machine learning and text mining techniques. The second objective is to enable an enhanced identification of crash severity contributors in the form of meaningful phrases by integrating recent advancements in Natural Language Processing (NLP). The third objective is to develop an approach for semantic search of information of interest in crash narratives. The obtained results indicate that the developed approaches enable the extraction of valuable insights from crash narratives to 1) uncover factors that quantitative may not reveal, 2) confirm results from classic statistical analysis on crash data, and 3) fix inconsistencies in quantitative data. The outcomes of this dissertation add substantial value to traffic safety, as the developed approaches allow analysts to exploit the rich information in crash narratives for a more comprehensive and accurate diagnosis of traffic crashes

Multi-objective memetic algorithm based on NSGA-II and simulated annealing for calibrating CORSIM micro-simulation models of vehicular traffic flow

This paper proposes a multi-objective memetic algorithm based on NSGA-II and Simulated Annealing (SA), NSGA-II-SA, for calibration of microscopic vehicular traffic flow simulation models. The NSGA-II algorithm performs a scan in the search space and obtains the Pareto front which is optimized locally with SA. The best solution of the obtained front is selected. Two CORSIM models were calibrated with the proposed NSGA-II-SA whose performance is compared with two alternative state-of-the-art algorithms, a single-objective genetic algorithm which uses simulated annealing (GASA) and a simultaneous perturbation stochastic approximation algorithm (SPSA). The results illustrate the superiority of the NSGA-II-SA algorithm in terms of both runtime and convergence