Using GPS Technology to Relate Macroscopic and Microscopic Traffic Parameters

1996 ◽  
Vol 1520 (1) ◽  
pp. 89-96 ◽  
Author(s):  
Matthew J. Barth ◽  
Eric Johnston ◽  
Ramakrishna R. Tadi
Keyword(s):  
Traffic Congestion ◽  
Vehicle Emissions ◽  
Velocity Variation ◽  
Flow Density ◽  
Emission Model ◽  
Freeway Traffic ◽  
Traffic Sensors ◽  
Statistical Measures ◽  
Speed Flow ◽  
Emission Models

Traffic congestion on today's freeways is a serious problem, causing significant delays for both passengers and goods. Freeway traffic congestion also results in increased vehicle emissions; however, this increase has not been quantified using current vehicle emission models. Current models use emission factors based on driving cycles that do not properly represent freeway driving characteristics. This paper presents a new methodology for relating the macroscopic speed, flow, and density parameters measured by traffic sensors with statistics of microscopic driving traces under different levels of congestion. This approach can be used to better estimate freeway emissions when combined with an appropriate modal emissions model. Preliminary experimentation has been carried out with a vehicle equipped with global positioning system (GPS) instrumentation, allowing for precise localization in both space and time. With the GPS, second-by-second velocity traces are acquired and matched with simultaneously measured freeway traffic data obtained by embedded traffic sensors. Statistical measures of velocity variation are derived from the velocity traces and are functionally related to the macroscopic traffic parameters of speed, flow, and density. Given a known distribution of vehicle types, models, and model years, vehicle emissions can be related to these statistical measures of velocity variation using a modal emission model, and, thus given speed-flow-density measures of freeway traffic, localized emissions estimates can be made.

scholarly journals Travel Time and Congestion Assessment Along Ujala Circle (S.G. Highway Ahmedabad)

2020 ◽  
Vol 9 (5) ◽  
pp. 995-1004
Keyword(s):  
Travel Time ◽  
Traffic Congestion ◽  
Calculated Data ◽  
Noise Pollution ◽  
Flow Density ◽  
Time Loss ◽  
Environment Effects ◽  
Speed Flow ◽  
Density Relationship ◽  
Data Graph

The approximately 25201084 (2018) vehicular citizenry in Ahmedabad city so it is very necessary to negotiate its movements and infirmity effects like as air and noise pollution & other environment effects, traffic congestion, time loss, and so on. S.G Highway has 44.5 km long stretch of Ahmedabad, an oldest and most substantial stretch which started from Ujala circle. For declamation the traffic congestion, classified volume count survey (by Video Graphic method), travel time and delay survey (by Moving Observer Method), and for speed-flow-density relationship Spot speed study at Ujala circle during Friday has been accomplished and analyzed. From the calculated data, graph of flow v/s density, speed v/s density and speed v/s flow relationship has been prosecute with the R2 value of each relationship. This analysis proved that current traffic situation at Ujala circle is highly congested, which leads to the higher travel time taken by vehicle compare to free flow condition. From all analyzed data, alternate invigorating measures are prospected. Based on those alternative invigorating measures best alternative is choose as well as bring off simulation and validation of Model in the VISSIM software, also design in AutoCAD for better illustration of issue.

scholarly journals Freeway ramp metering based on PSO-PID control

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260977
Author(s):  
Junjun Wei ◽  
Kejun Long ◽  
Jian Gu ◽  
Zhengchuan Zhou ◽  
Shun Li
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Pid Controller ◽  
Ramp Metering ◽  
Flow Density ◽  
Freeway Traffic ◽  
Traffic Efficiency ◽  
Nonlinear Processing ◽  
Actual Profile

Ramp metering on freeway is one of the effective methods to alleviate traffic congestion. This paper advances the field of freeway ramp metering by introducing an application to the on-ramp, capitalizing on the macro traffic follow theory and improved the freeway traffic flow. The Particle Swarm Optimization (PSO) based on Proportional Integral Derivative (PID) controller is further developed to single ramp metering as well as to optimize the PID parameters. The approach is applied to a case study of the Changyi Freeway(G5513) in Hunan, China. The simulation is conducted by applying the actual profile traffic data to PID controller to adjust the entering traffic flow on the freeway on-ramp. The results show that the PSO-PID controller tends to converge in about 80 minutes, and the density tends to be stable after 240 iterations. The system has smaller oscillation, more accurate adjustment of ramp regulation rate, and more ideal expected traffic flow density. The traffic congestion on mainline is effectively slowed down, traffic efficiency is improved, and travel time and cost are reduced. The nonlinear processing ability of PSO-PID controller overcomes the defects of the traditional manual closing ramp, and can be successfully applied in the field of intelligent ramp metering.

scholarly journals Evaluation of a plot-scale methane emission model using eddy covariance observations and footprint modelling

2014 ◽  
Vol 11 (17) ◽  
pp. 4651-4664 ◽  
Author(s):  
A. Budishchev ◽  
Y. Mi ◽  
J. van Huissteden ◽  
L. Belelli-Marchesini ◽  
G. Schaepman-Strub ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Methane Emission ◽  
Average Method ◽  
Temporal Dynamics ◽  
Weighted Average ◽  
Ambient Atmosphere ◽  
Emission Model ◽  
Closed Chamber ◽  
Weighted Average Method ◽  
Emission Models

Abstract. Most plot-scale methane emission models – of which many have been developed in the recent past – are validated using data collected with the closed-chamber technique. This method, however, suffers from a low spatial representativeness and a poor temporal resolution. Also, during a chamber-flux measurement the air within a chamber is separated from the ambient atmosphere, which negates the influence of wind on emissions. Additionally, some methane models are validated by upscaling fluxes based on the area-weighted averages of modelled fluxes, and by comparing those to the eddy covariance (EC) flux. This technique is rather inaccurate, as the area of upscaling might be different from the EC tower footprint, therefore introducing significant mismatch. In this study, we present an approach to validate plot-scale methane models with EC observations using the footprint-weighted average method. Our results show that the fluxes obtained by the footprint-weighted average method are of the same magnitude as the EC flux. More importantly, the temporal dynamics of the EC flux on a daily timescale are also captured (r2 = 0.7). In contrast, using the area-weighted average method yielded a low (r2 = 0.14) correlation with the EC measurements. This shows that the footprint-weighted average method is preferable when validating methane emission models with EC fluxes for areas with a heterogeneous and irregular vegetation pattern.

scholarly journals Microstructure representation of snow in coupled snowpack and microwave emission models

2016 ◽  
Author(s):  
Melody Sandells ◽  
Richard Essery ◽  
Nick Rutter ◽  
Leanne Wake ◽  
Leena Leppänen ◽  
...  
Keyword(s):  
Brightness Temperature ◽  
Microstructure Evolution ◽  
Microwave Emission ◽  
Model Systems ◽  
Research Centre ◽  
Emission Model ◽  
Mean Error ◽  
Scale Factors ◽  
Wide Range ◽  
Emission Models

Abstract. This is the first study to encompass a wide range of coupled snow evolution and microwave emission models in a common modelling framework in order to generalise the link between snowpack microstructure predicted by the snow evolution models and microstructure required to reproduce observations of brightness temperature as simulated by snow emission models. Brightness temperatures at 18.7 and 36.5 GHz were simulated by 1323 ensemble members, formed from 63 Jules Investigation Model snowpack simulations, three microstructure evolution functions and seven microwave emission model configurations. Two years of meteorological data from the Sodankylä Arctic Research Centre, Finland were used to drive the model over the 2011–2012 and 2012–2013 winter periods. Comparisons between simulated snow grain diameters and field measurements with an IceCube instrument showed that the evolution functions from SNTHERM simulated snow grain diameters that were too large (mean error 0.12 to 0.16 mm), whereas MOSES and SNICAR microstructure evolution functions simulated grain diameters that were too small (mean error −0.16 to −0.24 mm for MOSES, and −0.14 to −0.18 mm for SNICAR). No model (HUT, MEMLS or DMRT-ML) provided a consistently good fit across all frequencies and polarizations. The smallest absolute values of mean bias in brightness temperature over a season for a particular frequency and polarization ranged from 0.9 to 7.2 K. Optimal scaling factors for the snow microstructure were presented to compare compatibility between snowpack model microstructure and emission model microstructure. Scale factors ranged between 0.3 for the SNTHERM-Empirical MEMLS model combination (2011–2012), and 5.0 or greater when considering non-sticky particles in DMRT-ML in conjunction with MOSES or SNICAR microstructure (2012–2013). Differences in scale factors between microstructure models were generally greater than the differences between microwave emission models, suggesting that more accurate simulations in coupled snowpack-microwave model systems will be achieved primarily through improvements in the snowpack microstructure representation, followed by improvements in the emission models. Other snowpack parameterisations in the snowpack model, mainly densification, led to a mean brightness temperature difference of 11 K when the JIM ensemble was applied to the MOSES microstructure and empirical MEMLS emission model for the 2011–2012 season. Consistency between snowpack microstructure and microwave emission models, and the choice of snowpack densification algorithms should be considered in the design of snow mass retrieval systems and microwave data assimilation systems.

scholarly journals Traffic Flow Density Model and Dynamic Traffic Congestion Model Simulation Based on Practice Case with Vehicle Network and System Traffic Intelligent Communication

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1172 ◽  
Author(s):  
Eduard Zadobrischi ◽  
Lucian-Mihai Cosovanu ◽  
Mihai Dimian
Keyword(s):  
Traffic Congestion ◽  
Model Simulation ◽  
Communication Technologies ◽  
Transport Systems ◽  
Flow Density ◽  
Factors Affecting ◽  
Dedicated Short Range Communications ◽  
Vehicle To Vehicle ◽  
Vehicle To Infrastructure ◽  
Intelligent Communication

The massive increase in the number of vehicles has set a precedent in terms of congestion, being one of the important factors affecting the flow of traffic, but there are also effects on the world economy. The studies carried out so far try to highlight solutions that will streamline the traffic, as society revolves around transportation and its symmetry. Current research highlights that the increased density of vehicles could be remedied by dedicated short-range communications (DSRC) systems through communications of the type vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I) or vehicle-to-everything (V2X). We can say that wireless communication technologies have the potential to significantly change the efficiency and road safety, thus improving the efficiency of transport systems. An important factor is to comply with the requirements imposed on the use of vehicle safety and transport applications. Therefore, this paper focuses on several simulations on the basis of symmetry models, implemented in practical cases in order to streamline vehicle density and reduce traffic congestion. The scenarios aim at both the communication of the vehicles with each other and their prioritization by the infrastructure, so we can have a report on the efficiency of the proposed models.

scholarly journals Microscopic Traffic Simulation Calibration Level for Reliable Estimation of Vehicle Emissions

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Jinsoo Kim ◽  
Jae Hun Kim ◽  
Gunwoo Lee ◽  
Hyun-ju Shin ◽  
Jahng Hyon Park
Keyword(s):  
Vehicle Emissions ◽  
Traffic Simulation ◽  
Calibration Procedure ◽  
Traffic Data ◽  
Microscopic Traffic Simulation ◽  
Micro Scale ◽  
Reliable Estimation ◽  
Calibration Measurement ◽  
Emission Models ◽  
Calibrated Simulation

Vehicle emissions are largely determined by the details of driving behaviours. Accordingly, emissions are often estimated by integrating micro-scale emission models into traffic simulations. Under this approach, it is essential to replicate the actual traffic situation being considered in an emission evaluation using a proper calibration procedure. Most previous research with respect to traffic flow has primarily focused on adjusting the complex combinations of parameters evaluated in these models, but it is not guaranteed that the use of widely used calibration measures can lead more accurate emissions estimates. Accordingly, we propose a systematic guideline for calibration to ensure reliable micro-scale emissions estimates. A calibration procedure is thus established in this paper based on various measure of effect (MOE) compositions (i.e., calibration levels) consisting of aggregated traffic data to identify the level that most reliably estimates micro-scale emissions. Five calibration levels of progressively more detailed measurements are first defined, valid calibration levels are identified, and the reliable calibration level is finally selected based on the available traffic data. The effect of vehicle type (i.e., light vs. heavy vehicles) composition on the estimated emissions is also evaluated for a well-calibrated simulation. We expect that a highly reliable estimation of emissions is possible using this more detailed traffic simulation calibration measurement.

scholarly journals A Model Tree-Based Vehicle Emission Model at Freeway Toll Plazas

2020 ◽  
Vol 12 (21) ◽  
pp. 8959
Author(s):  
Yueru Xu ◽  
Chao Wang ◽  
Yuan Zheng ◽  
Zhuoqun Sun ◽  
Zhirui Ye
Keyword(s):  
Vehicle Emissions ◽  
Polynomial Regression ◽  
Mean Squared Error ◽  
Percentage Error ◽  
Vehicle Emission ◽  
Emission Model ◽  
Toll Plazas ◽  
Model Tree ◽  
Proposed Model ◽  
Toll Collection

With the increased concern over sustainable development, many efforts have been made to alleviate air quality deterioration. Freeway toll plazas can cause serious pollution, due to the increased emissions caused by stop-and-go operations. Different toll collections and different fuel types obviously influence the vehicle emissions at freeway toll plazas. Therefore, this paper proposes a model tree-based vehicle emission model by considering these factors. On-road emissions data and vehicle operation data were obtained from two different freeway toll plazas. The statistical analysis indicates that different methods of toll collection and fuel types have significant impacts on vehicle emissions at freeway toll plazas. The performance of the proposed model was compared with a polynomial regression method. Based on the results, the mean absolute percentage error (MAPE), root mean squared error (RMSE), and mean absolute error (MAE) of the proposed model were all smaller, while the R-squared value increased from 0.714 to 0.833. Finally, the variations of vehicle emissions at different locations of freeway toll plazas were calculated and shown in heat maps. The results of this study can help better estimate the vehicle emissions and give advice to the development of electronic toll collection (ETC) lanes and relevant policies at freeway toll plazas.

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