Prediction of Vehicular Traffic Flow using Levenberg-Marquardt Artificial Neural Network Model: Italy Road Transportation System

In the last decades, the Italian road transport system has been characterized by severe and consistent traffic congestion and in particular Rome is one of the Italian cities most affected by this problem. In this study, a LevenbergMarquardt (LM) artificial neural network heuristic model was used to predict the traffic flow of non-autonomous vehicles. Traffic datasets were collected using both inductive loop detectors and video cameras as acquisition systems and selecting some parameters including vehicle speed, time of day, traffic volume and number of vehicles. The model showed a training, test and regression value (R2) of 0.99892, 0.99615 and 0.99714 respectively. The results of this research add to the growing body of literature on traffic flow modelling and help urban planners and traffic managers in terms of the traffic control and the provision of convenient travel routes for pedestrians and motorists.

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.

Make More Connections: Urban Traffic Flow Forecasting with Spatiotemporal Adaptive Gated Graph Convolution Network

Urban traffic flow forecasting is a critical issue in intelligent transportation systems. Due to the complexity and uncertainty of urban road conditions, how to capture the dynamic spatiotemporal correlation and make accurate predictions is very challenging. In most of existing works, urban road network is often modeled as a fixed graph based on local proximity. However, such modeling is not sufficient to describe the dynamics of the road network and capture the global contextual information. In this paper, we consider constructing the road network as a dynamic weighted graph through attention mechanism. Furthermore, we propose to seek both spatial neighbors and semantic neighbors to make more connections between road nodes. We propose a novel Spatiotemporal Adaptive Gated Graph Convolution Network ( STAG-GCN ) to predict traffic conditions for several time steps ahead. STAG-GCN mainly consists of two major components: (1) multivariate self-attention Temporal Convolution Network ( TCN ) is utilized to capture local and long-range temporal dependencies across recent, daily-periodic and weekly-periodic observations; (2) mix-hop AG-GCN extracts selective spatial and semantic dependencies within multi-layer stacking through adaptive graph gating mechanism and mix-hop propagation mechanism. The output of different components are weighted fused to generate the final prediction results. Extensive experiments on two real-world large scale urban traffic dataset have verified the effectiveness, and the multi-step forecasting performance of our proposed models outperforms the state-of-the-art baselines.

Deep Spatio-temporal Adaptive 3D Convolutional Neural Networks for Traffic Flow Prediction

Traffic flow prediction is the upstream problem of path planning, intelligent transportation system, and other tasks. Many studies have been carried out on the traffic flow prediction of the spatio-temporal network, but the effects of spatio-temporal flexibility (historical data of the same type of time intervals in the same location will change flexibly) and spatio-temporal correlation (different road conditions have different effects at different times) have not been considered at the same time. We propose the Deep Spatio-temporal Adaptive 3D Convolution Neural Network (ST-A3DNet), which is a new scheme to solve both spatio-temporal correlation and flexibility, and consider spatio-temporal complexity (complex external factors, such as weather and holidays). Different from other traffic forecasting models, ST-A3DNet captures the spatio-temporal relationship at the same time through the Adaptive 3D convolution module, assigns different weights flexibly according to the influence of historical data, and obtains the impact of external factors on the flow through the ex-mask module. Considering the holidays and weather conditions, we train our model for experiments in Xi’an and Chengdu. We evaluate the ST-A3DNet and the results show that we have better results than the other 11 baselines.

A Roadway Safety Sustainable Approach: Modeling for Real-Time Traffic Crash with Limited Data and Its Reliability Verification

With the development of freeway system informatization, it is easier to obtain the traffic flow data of freeway, which are widely used to study the relationship between traffic flow state and traffic safety. However, as the development degree of the freeway system is different in different regions, the sample size of traffic data collected in some regions is insufficient, and the precision of data is relatively low. In order to study the influence of limited data on the real-time freeway traffic crash risk modeling, three data sets including high precision data, small sample data, and low precision data were considered. Firstly, Bayesian Logistic regression was used to identify and predict the risk of three data sets. Secondly, based on the Bayesian updating method, the migration test towards high and low precision data sets was established. Finally, the applicability of machine learning and statistical methods to low precision data set was compared. The results show that the prediction performance of Bayesian Logistic regression improves with the increasing of sample size. Bayesian Logistic regression can identify various significant risk factors when data sets are of different precision. Comparatively, the prediction performance of the support vector machine is better than that of Bayesian Logistic. In addition, Bayesian updating method can improve the prediction performance of the transplanted model.