Biomonitoring of air quality in the Bodoquena microregion, Mato Grosso Do Sul: mutagenic and morphoanatomical alterations in Tradescantia pallida (rose) D.R. Hunt var. purpurea

The objective of this study was to assess air quality in relation to vehicular traffic flow in cities located at different elevations in the Bodoquena microregion, state of Mato Grosso do Sul, Brazil. To do so, a micronucleus test was carried out using the TRAD-MCN bioassay on young Tradescantia buds collected from February to November 2018 in seven cities of the microregion with different traffic flow intensities. Meteorological parameters were evaluated, and vehicular traffic was counted to determine traffic flow in each city. With data from the Shuttle Radar Topography Mission (SRTM) and processing in Esri ArcGIS® software version 10.5.1, the regions was mapped based on an Elevation Model. Morphoanatomical analyses were performed according to standard methodology. Measurements were taken of thickness, length and width of tissues and structures, including the upper and lower cuticle, upper and lower epidermis, hypodermis and mesophyll. The greatest traffic flow was found in the cities of Bodoquena, Guia Lopes da Laguna, Jardim, and Porto Murtinho, with the period from 5:00 to 6:00 p.m. showing the highest traffic flow. The greatest frequency of mutagenic alterations was found in the city of Guia Lopes da Laguna, although the results did not differ significantly from Bonito, Caracol, and Jardim. Throughout the biomonitoring, the summer and autumn seasons showed the greatest micronuclei frequencies in all evaluated cities. Variations in the tissue/structure thickness was observed across cities and seasons, but with a decrease in thickness during autumn. In general, the tissues/structures were smaller for the cities of Nioaque and Porto Murtinho, while the anatomical and morphological characteristics of leaf length and thickness showed no differences among cities. We found limited correlation between micronuclei frequency and traffic flow, supporting the hypothesis that although mutagenic alterations are observed in T. pallida, in this microregion the changes are numerically lower when compared to other regions of the state. In light of the genotoxic and morphoanatomical factors assessed herein, the Bodoquena microregion appears to be well preserved in terms of air quality, presenting low micronuclei frequency and a limited reduction in tissues and leaf structures, regardless of the season.

Performance Evaluation of a Hybrid PSO Enhanced ANFIS Model in Prediction of Traffic Flow of Vehicles on Freeways: Traffic Data Evidence from South Africa

In the last few years, there has been a significant rise in the number of private vehicles ownership, migration of people from rural areas to urban cities, and the rise in the number of under-maintained freeways; all these have added to the perennial problem of traffic congestion. Traffic flow prediction has been recognized as the solution in alleviating and reducing the problem of traffic congestion. In this research, we developed an adaptive neuro-fuzzy inference system trained by particle swarm optimization (ANFIS-PSO) by performing an evaluative performance of the model through traffic flow modelling of vehicles on five freeways (N1,N3,N12,N14 and N17) using South Africa Transportation System as a case study. Six hundred and fifty (650) traffic data were collected using inductive loop detectors and video cameras from the five freeways. The traffic data used for developing these models comprises traffic volume, traffic density, speed of vehicles, time, and different types of vehicles. The traffic data were divided into 70% and 30% for the training and validation of the model. The model results show a positively correlated optimal performance between the inputs and the output with a regression value R2 of 0.9978 and 0.9860 for the training and testing. The result of this research shows that the soft computing model ANFIS-PSO used in this research can model vehicular traffic flow on freeways. Furthermore, the evidence from this research suggests that the on-peak and off-peak hours are significant determinants of vehicular traffic flow on freeways. The modelling approach developed in this research will assist urban planners in developing practical ways to tackle traffic congestion and assist motorists and pedestrians in travel behaviour decision-making. Finally, the approach used in this study will assist transportation engineers in making constructive and safety dependent guidelines for drivers and pedestrians on freeways.

Characteristics of vehicular traffic flow at nonsignalized intersection: Simulation study by Lárraga and Alvarez-Icaza model

This paper develops Lárraga and Alvarez-Icaza (LAI) cellular automata model which is based on safe human reaction. Vehicles are moving at two single-lane streets with periodic boundary condition and pass from intersection point via yielding mechanism avoiding collision. The model characteristics and fundamental diagrams have been obtained by Monte Carlo simulations. Our results suggest that LAI model with yielding mechanism is able to reproduce the realistic acceleration and deceleration capabilities, which are desired parameters. Moreover, the plateau region in total current illustrates the hardness of the yielding mechanism and limited speed strategy, which is applied near the intersection point.

TrafficWave: Generative Deep Learning Architecture for Vehicular Traffic Flow Prediction

Vehicular traffic flow prediction for a specific day of the week in a specific time span is valuable information. Local police can use this information to preventively control the traffic in more critical areas and improve the viability by decreasing, also, the number of accidents. In this paper, a novel generative deep learning architecture for time series analysis, inspired by the Google DeepMind’ Wavenet network, called TrafficWave, is proposed and applied to traffic prediction problem. The technique is compared with the most performing state-of-the-art approaches: stacked auto encoders, long–short term memory and gated recurrent unit. Results show that the proposed system performs a valuable MAPE error rate reduction when compared with other state of art techniques.