This study presents the result of a traffic simulation analysis based on Floating Car Data and a noise emission assessment to show the impact of mobility restriction for COVID-19 containment on urban vehicular traffic
and road noise pollution on the road network of Rome,
Italy The adoption of strong and severe measures to contain the spreading of Coronavirus during March-April 2020
generated a significant reduction in private vehicle trips
in the city of Rome (-646% during the lockdown) Traffic
volumes, obtained through a simulation approach, were
used as input parameters for a noise emission assessment
conducted using the CNOSSOS-EU method, and an overall
noise emissions reduction on the entire road network was
found, even if its extent varied between road types

/pdf/analysing-urban-traffic-volumes-and-mapping-noise-emissions-bfiisp90th.pdf

Analysing urban traffic volumes and mapping noise emissions in Rome (Italy) in the context of containment measures for the COVID-19 disease

The development of real-time road condition systems will better monitor road network operation status. However, the weak point of all these systems is their need for comprehensive and reliable data. For traffic data acquisition, two sources are currently available: 1) floating vehicles and 2) remote traffic microwave sensors (RTMS). The former consists of the use of mobile probe vehicles as mobile sensors, and the latter consists of a set of fixed point detectors installed in the roads. First, the structure of a three-layer BP neural network is designed to achieve the fusion of the floating car data (FCD) and the fixed detector data (FDD) efficiently. Second, in order to improve the accuracy of traffic speed estimation, a multi-source data fusion model that combines information from floating vehicles and microwave sensors, and that, by using GA-PSO-BP neural network is proposed. The proposed model has combined GA and PSO ingeniously. The hybrid model can not only overcome the difficulties of the traditional fusion model of its estimation inaccuracy, but also compensate the insufficiency of the traditional BP algorithm. Finally, this system has been tested and implemented on actual roads, and the simulation results show the accuracy of data has reached 98%.

Data Fusion for Multi-Source Sensors Using GA-PSO-BP Neural Network

This study aims to demonstrate a new approach for estimating fuel consumption from vehicles circulating on urban roads with different levels of congestion. A microscopic dynamic traffic model (AIMSUN) was coupled with an instantaneous emissions model (CRUISE) and their output was used to refine the average speed model (COPERT) functions. The integrated methodology was applied in an urban corridor in Turin using two validated vehicle models of Euro 5 passenger cars. Three traffic conditions were examined, corresponding to free-flow, normal and congested traffic. New average speed – fuel consumption functions were developed for free and congested traffic, which successfully estimated the differences in fuel consumption when moving from normal to other conditions. The results showed that under congested conditions the fuel consumption can increase by more than 18% in some cases, indicating the significance of incorporating similar congestion algorithms in macro emission models. Moreover, if a more comprehensive method does not exist, the standard COPERT functions could be used to estimate fuel consumption under congested traffic conditions.

Enhancing average speed emission models to account for congestion impacts in traffic network link-based simulations

https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0253868&type=printable

Human trajectory prediction and generation using LSTM models and GANs

Traffic data mining applying machine learning algorithms is necessary to analyse and understand the road traffic flow in busy cities. Also, it is very essential for making smart cities. Mining traffic data helps us to reduce travel delays and improve the city life. Currently, many cities in the developed countries use different sensors to collect the real-time traffic data and apply machine learning algorithms on the traffic data to improve the traffic condition. In this paper, we have collected the real-time traffic data from the city of Porto, Portugal, and applied five regression models: Linear Regression, Sequential Minimal Optimisation (SMO) Regression, Multilayer Perceptron, M5P model tree and Random Forest to predict/forecast the traffic flow of Porto city. Also, we have tested the performance of these regression models. The experimental results show that the M5P regression tree outperforms the other regression models.

The Prediction of Traffic Flow with Regression Analysis

Real time data collection in traffic engineering is crucial for better traffic corridor control and management In the literature, many data collection methods have been used such as; magnetic loops, road tube counters, piezo sensors, radars, Bluetooth etc to estimate the link occupancy, average speed or density of a corridor More recently, Floating Car Data (FCD) has become another important traffic data source and has an increasing usage due to its lower cost and higher coverage despite its reliability problems FCD obtained from GPS equipped vehicles moving in the traffic can provide speed or travel speed data for many segments for even 1-min intervals in real-time Though not totally diverse providing more than one of the traffic flow parameters, measuring the effectiveness of this extensive data source in detecting some critical urban traffic states is the ultimate goal of this study As a case study, 1-min interval FCD for an urban arterial in Ankara has been collected during the morning peak hour for 2 months Average speed values were transformed into a qualitative 4-scale state parameter based on the Level of Service (LOS) definitions for urban roads Pattern searches over consecutive segment states using different search length (ie 2 segments, 3 segments, etc) showed that FCD is capable to detect recurrent congestion or bottleneck locations, and even have an idea about the length of queue formed before the bottlenecks

Detection of Urban Traffic Patterns from Floating Car Data (FCD)

Commercial floating car data (FCD) is being increasingly used as a traffic data source due to its lower cost despite concerns about its reliability. This paper focuses on the evaluation of FCD spee...

Quality of floating car data (FCD) as a surrogate measure for urban arterial speed

Siting Charging Stations and Identifying Safe and Convenient Routes for Environmentally Sustainable E-Scooter Systems

Urban rail transit (URT) systems are particularly favoured by planners due to their potential in attracting car users. However, a URT investment must be complemented by land-use characteristics at macroscale (whole city) and design parameters at microscale (vicinity of the station). Despite the common referencing to key concepts (i.e. density, diversity and connectivity), the variability in their definitions and scales causes ambiguity in the determination of their quantified impact on URT ridership. Furthermore, their impact may be different in developing countries, where more mesoscale (corridor-based) effects are expected in the early stages of the URT network. This study aimed to evaluate mathematically the impact of eight selected station design and public transit supply variables on ridership, based on data collected at 14 stations of two existing rail system corridors, both metro (M1) and light rail transit (A1) lines in Ankara, Turkey. Principal component analysis (PCA) indicated three major groups of parameters: (1) land use, (2) public transit supply and (3) connectivity around stations. In single-variable regression models, density and diversity were not found to be statistically significant factors despite the commonly accepted conceptual relation to ridership. A multiple regression model with bus frequency and density (R2 = 0.902) explained the ridership in Ankara URT systems more significantly at its early development stage. There was a significant difference between developed factor relations for both M1 and A1 lines, suggesting that mesolevel impacts should be considered in the evaluation of URT systems.

Evaluating the Relation Between Station Area Design Parameters and Transit Usage for Urban Rail Systems in Ankara, Turkey

Reducing carbon footprint, especially from private cars, is an inevitable goal of sustainable transportation programs. Middle East Technical University (METU) in Ankara, Turkey, has a fairly large campus, for which carbon emissions are estimated to guide development of sustainable campus transportation demand management (TDM) policies. Focus is on reducing private car usage and increasing non-motorized and shared ride modes in a network. On the other hand, vehicle emissions with today’s engine technology are mostly governed by two major factors: Vehicle-km-travelled (VKT) and congestion, which slows down traffic and forces inefficiency in the engine performance. From engineering perspective, in order to evaluate real emission reduction potential of TDM scenarios, it is important to quantify its impact on these two factors and how much, which is the main focus of this study. For numerical examples, METU campus private car travel demand was used to calculate the VKT and speed levels for campus links by performing a network assignment in PTV-VISUM. The daily assignment was carried in three parts, as a morning and an evening peak, and an off-peak assignment, for a base case and 8 different scenarios.  Emission component due to congestion was determined separately, as a percentage. The results showed that METU campus emissions were primarily function of the VKT, thus, could not be reduced by congestion management alone. They could be reduced by 30% if commuters would shift to the metro service supported by a strong in-campus shuttle services.

/pdf/best-option-for-reducing-on-campus-private-car-based-co2-4z0ttcguo8.pdf

Oruc Altintasi

Papers

Detection of Urban Traffic Patterns from Floating Car Data (FCD)

Quality of floating car data (FCD) as a surrogate measure for urban arterial speed

Siting Charging Stations and Identifying Safe and Convenient Routes for Environmentally Sustainable E-Scooter Systems

Evaluating the Relation Between Station Area Design Parameters and Transit Usage for Urban Rail Systems in Ankara, Turkey

Best Option For Reducing On-Campus Private Car-Based CO2 Emissions: Reducing VKT Or Congestion?