Average Speed Model Research Articles

Modeling Speed Mean and Variance for Different Enforcement Conditions on Multilane Highways

Two major characteristics that can impact the frequency and severity of traffic crashes are average speed and speed variance. This paper investigated these characteristics on 49 multilane highway segments in Jordan and developed prediction models for three different conditions: free (no enforcement), camera, and police enforcement. Speed data were collected during off-peak periods with fair weather conditions. Multiple linear regressions and nonlinear regressions were used to develop speed variance and average speed models. For the free condition, speed variance mainly depends on the difference between design speed and speed limit (DS-SL), with the speed variance decreasing as the difference reduces, while the average speed was positively correlated with the design speed. Camera enforcement causes speed variance and average speed to be positively correlated and linearly dependent with the speed limit. A quadratic relationship was found between speed variance during police enforcement and DS-SL, while the average speed during police enforcement depended on the speed limit and design speed. These modeling results, along with analysis of the collected data, can help operating agencies and roadway designers determine how various enforcement strategies affect average speed and speed variance and better set speed limits on existing and future roadways to improve safety.

Emergency Evacuation Based on Cellular Automata

With the increasing number or scale of large-scale assembly activities, emergency evacuation has become increasingly important. In order to understand the evacuation behavior of people, this paper uses European distance, simulation and other methods to optimize the research and design of evacuation in emergency. The main contents of this paper are as follows: First, based on the principle and update rules of cellular automata, determine the three key factors of gender, age and emotional intensity, and the evacuation model based on cellular automata, and then use MATLAB software to simulate the change of people flow under different anxiety conditions, and output relevant data and visual images. Finally, the differences of the experimental results caused by the key factors are discussed. Secondly, study the evacuation rate change curve, as well as the key factors and their effects. Based on the average speed and average flow model of the system, simulate and output the corresponding visual images. Thirdly, in order to study and determine the best simulated evacuation route and the dynamic process of personnel evacuation in the case of different door widths, based on the evacuation route selection model with the shortest time to reach the two doors, the best pedestrian evacuation route is selected according to the shortest time rule. Fourth, in order to study the impact of reduced visibility in the hall on the whole process. In this paper, the regional discretization method is used to establish the perception range model under visibility and analyze the factor changes in the whole process.

Full-waveform centroid moment tensor inversion of passive seismic data acquired at the reservoir scale

SUMMARY Passive seismic inversion at the reservoir scale offers the advantages of low cost, negligible environmental impact and the ability to probe a target area with low-frequency energy not afforded by even the most modern active-source seismic technology. In order to build starting models suitable for full-waveform wave speed tomography, characterization of earthquake sources is an indispensable first step. We present a workflow for the centroid moment tensor (CMT) inversion of seismic events identified in a passive seismic data set acquired by a large and dense array of three-component broad-band seismic sensors in a mountainous setting in the Himalayan foothills. The data set comprised 256 instruments operating for 2×4 months over an area of 8000 km2. An initial 3-D wave speed model was determined for the region via the analysis of first-arriving traveltime picks. Of the 2607 identified seismic events that were well recorded at frequencies between 0.2–50 Hz, 86 with magnitudes 1.3 ≤ M ≤ 3.0 initially had their CMT focal mechanisms determined by a waveform fitting procedure built on a Green’s function approach in a 1-D layered average wave speed model, for stations within an offset of 10 km, in the frequency range 0.2–1.4 Hz. Here, we obtain updated CMT mechanisms for the 86 events in that catalogue via multicomponent full-waveform inversion in the 3-D wave speed model. Our workflow includes automated data- and model-driven data selection using a combination of different metrics derived from signal-to-noise considerations and waveform-fitting criteria, and relies upon spectral-element simulations of elastic wave propagation in the 3-D wave speed model, honouring topography. Starting from the initial CMT solutions, we seek improvement to the data fit within the frequency band 0.5–2.5 Hz by minimizing the waveform difference between observed and synthetic data, while accommodating wave speed-model errors by allowing for small time-shifts. We balance uneven data coverage and tune their contributions via data-space weighting functions. We quantify the improvements to the data fit in terms of different metrics. We summarize the changes to the CMT solutions, and present and analyse the resulting catalogue for the region, including their breakdown into double-couple and non-double couple components, and their relation to mapped faults.

Evaluation of a mechanical drag coefficient formulation in the complex urban area of Beijing

The constant building drag coefficient was replaced by a mechanical drag coefficient formulation in a multilayer urban canopy system coupled with the Rapid-refresh Multiscale Analysis and Prediction System to improve the wind speed prediction performance in complex urban areas. Although this formulation has been assessed in idealized urban areas by computational fluid dynamic simulations, the applicability of this formulation has not been sufficiently evaluated, especially in complex urban areas with inhomogeneously distributed tall buildings. The main objective of this study is to evaluate the performance of the coupled system in the Beijing urban area. The results show that the average wind speed model performance is improved by 40% in summer and 36% in winter by introducing a drag coefficient into the regional forecast model.

COMPARATIVE ANALYSIS OF TRAVEL COSTS ON TOLL ROADS AND NON-TOLL ROADS FOR CITY CENTER ACCESS

The Mamminasata Urban Area which is centered in Makassar City has a fivesection crossroads at the north gate of the city, where two of which, the Sutami toll and Perintis Kemerdekaan non-toll road sections lead to the city center while the gate is accessed by 18 districts and all provinces in Sulawesi. As alternative roads, the two is generally described as follows: The distance of the gate to the city by the toll road is 17 km while that of by Jalan Perintis Kemerdekaan is 24 Km or 7 km longer than the toll road. The toll road is smooth during the day while the Perintis Kemerdekaan non-toll road section is congested at the peak hours in the morning, afternoon and evening with the potential resulting in far different costs and travel time. The results of the survey indicate 2: 5 in average ratio suggesting that 5 vehicles enter the city center through Perintis Kemerdekaan Non-Toll Road while 2 vehicles opt for Sutami Toll Road. This study was aimed at (1) comparing the Vehicle Operating Costs (VOC) from and to the city center through toll roads or non-toll roads, and (2) analyzing the tendency of the number of travellers to choose through toll roads and non-toll roads based on their orientation to the unit of cost to incur. The mathematical and statistical analysis process was done by considering all components of fixed and variable costs. The analysis also considers the use of the elemental mode of driving mode, the average speed model and the trans-log and Cobb-Douglas models. The results obtained illustrate that the value of vehicle operating costs derived from the two road sections amount to Rp. 25,462 and Rp. 25,500 suggesting insignificant difference. Despite similar travel cost between the two, the toll road is considered more beneficial in terms of the value of time and energy for motorists travelling to the city center of Makassar.

Build Time Estimation for Fused Filament Fabrication via Average Printing Speed.

Build time is a key issue in additive manufacturing, but even nowadays, its accurate estimation is challenging. This work proposes a build time estimation method for fused filament fabrication (FFF) based on an average printing speed model. It captures the printer kinematics by fitting printing speed measurements for different interpolation segment lengths and changes of direction along the printing path. Unlike analytical approaches, printer users do not need to know the printer kinematics parameters such as maximum speed and acceleration or how the printer movement is programmed to obtain an accurate estimation. To build the proposed model, few measurements are needed. Two approaches are proposed: a fitting procedure via linear and power approximations, and a Coons patch. The procedure was applied to three desktop FFF printers, and different infill patterns and part shapes were tested. The proposed method provides a robust and accurate estimation with a maximum relative error below 8.5%.

Improving the accuracy of emission inventories with a machine-learning approach and investigating transferability across cities

ABSTRACT This study presents a novel method for integrating the output of a microscopic emission modeling approach with a regional traffic assignment model in order to achieve an accurate greenhouse gas (GHG, in CO2-eq) emission estimate for transportation in large metropolitan regions. The CLustEr-based Validated Emission Recalculation (CLEVER) method makes use of instantaneous speed data and link-based traffic characteristics in order to refine on-road GHG inventories. The CLEVER approach first clusters road links based on aggregate traffic characteristics, then assigns representative emission factors (EFs), calibrated using the output of microscopic emission modeling. In this paper, cluster parameters including number and feature vector were calibrated with different sets of roads within the Greater Toronto Area (GTA), while assessing the spatial transferability of the algorithm. Using calibrated cluster sets, morning peak GHG emissions in the GTA were estimated to be 2,692 tons, which is lower than the estimate generated by a traditional, average speed approach (3,254 tons). Link-level comparison between CLEVER and the average speed approach demonstrates that GHG emissions for uncongested links were overestimated by the average speed model. In contrast, at intersections and ramps with more congested links and interrupted traffic flow, the average speed model underestimated GHG emissions. This proposed approach is able to capture variations in traffic conditions compared to the traditional average speed approach, without the need to conduct traffic simulation. Implications: A reliable traffic emissions estimate is necessary to evaluate transportation policies. Currently, accuracy and transferability are major limitations in modeling regional emissions. This paper develops a hybrid modeling approach (CLEVER) to bridge between computational efficiency and estimation accuracy. Using a k-means clustering algorithm with street-level traffic data, CLEVER generates representative emission factors for each cluster. The approach was validated against the baseline (output of a microscopic emission model), demonstrating transferability across different cities .

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

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.

Development of a hybrid modelling approach for the generation of an urban on-road transportation emission inventory

The development of accurate emission inventories at an urban scale is of utmost importance for cities in light of climate change commitments and the need to identify the emission reduction potential of various strategies. Emission inventories for on-road transportation are sensitive to the network models used to generate traffic activity data. For large networks (cities or regions), average-speed models have been relied upon extensively in research and practice, primarily due to their computational attractiveness. Nevertheless, these models are myopic to traffic states and driving cycles and therefore lack in accuracy. The aim of this study is to improve the quality of regional on-road emission inventories without resorting to computationally-intensive traffic microsimulation of an entire region. For this purpose, macroscopic, mesoscopic, and microscopic emission models are applied and compared, using average speed, average speed and its standard deviation, and instantaneous speeds. We also propose a hybrid approach called the CLustEr-based Validated Emission Re-calculation (CLEVER), which bridges between the microscopic and mesoscopic approaches. CLEVER defines unsupervised traffic conditions using a combination of mesoscopic traffic characteristics for selected road segments, and identifies a representative emission factor (EF) for each condition based on the microscopic driving cycle of the sample. Regional emissions can then be estimated by classifying segments in the regional network into these conditions, and applying corresponding EFs. The results of the CLEVER method are compared with the results of microsimulation and of mesoscopic approaches revealing a robust methodology that improves the emission inventory while reducing computational burden.

Investigating the link between carbon dioxide emissions and transport-related social exclusion in rural Northern Ireland

Carbon dioxide (CO2) is considered the most harmful of the greenhouse gases. Despite policy efforts, transport is the only sector experiencing an increase in the level of CO2 emissions and thereby possesses a major threat to sustainable development. In contrast, a reduced level of mobility has been associated with an increasing risk of being socially excluded. However, despite being the two key elements in transport policy, little effort has so far been made to investigate the links between CO2 emissions and social exclusion. This research contributes to this gap by analysing data from 157 weekly activity-travel diaries collected in rural Northern Ireland. CO2 emission levels were calculated using average speed models for different modes of transport. Regression analyses were then conducted to identify the socio-spatial patterns associated with these CO2 emissions, mode choice behaviour, and patterns of participation in activities. This research found that despite emitting a higher level of CO2, groups in rural areas possess the risk of being socially excluded due to their higher levels of mobility.

An averaging approach to estimate urban traffic speed using large-scale origin-destination data

Knowledge about the driving condition can be exploited by the energy management system of plug-in hybrid electric vehicle (PHEV) to optimise its fuel economy. However, estimating urban traffic speed can be a challenging problem, since the complexity of the road network requires a large amount of sensing coverage. GPS equipped vehicles have been used to address this problem, despite the drawback of high transmission cost due to the periodic locations reporting during the entire trip. This paper presents an alternative method to estimate urban traffic speed when only the trip distance, origin, destination and trip time are known, without the detailed information about the vehicle trajectory. Taxicab fleet data is used due to its wide area-coverage and large data availability. One month (15 million) observations of taxi origin-destination data in New York City is processed using cluster computers to create a traffic speed model of Manhattan at different times of day. The developed average traffic speed model is able to accurately predict the recorded trip duration from the taxicab data, and therefore, can be used to estimate energy consumption and optimise PHEV control.

Development of a corrected average speed model for calculating carbon dioxide emissions per link unit on urban roads

The purpose of our study is to develop a “corrected average emission model,” i.e., an improved average speed model that accurately calculates CO2 emissions on the road. When emissions from the central roads of a city are calculated, the existing average speed model only reflects the driving behavior of a vehicle that accelerates and decelerates due to signals and traffic. Therefore, we verified the accuracy of the average speed model, analyzed the causes of errors based on the instantaneous model utilizing second-by-second data from driving in a city center, and then developed a corrected model that can improve the accuracy. We collected GPS data from probe vehicles, and calculated and analyzed the average emissions and instantaneous emissions per link unit. Our results showed that the average speed model underestimated CO2 emissions with an increase in acceleration and idle time for a speed range of 20km/h and below, which is the speed range for traffic congestion. Based on these results, we analyzed the relationship between average emissions and instantaneous emissions according to the average speed per link unit, and we developed a model that performed better with an improved accuracy of calculated CO2 emissions for 20km/h and below.

Assessing the impact of bus technology on greenhouse gas emissions along a major corridor: A lifecycle analysis

This paper evaluates the impact of alternative bus transit technologies including compressed natural gas, biodiesel, and diesel-electric hybrid on greenhouse gas emissions along a busy transit corridor using a lifecycle analysis approach. In addition, we compare the operational emissions of buses running on these technologies using an instantaneous speed and an average speed model. Our results indicate that operational emissions make-up the largest portion of lifecycle emissions. When comparing instantaneous and average speed emissions we find that both methods produce consistent results for diesel, however, the average speed method underestimates biodiesel emissions by 21% and overestimates compressed natural gas emissions by 16%. Bus technologies ranked in increasing order of lifecycle greenhouse gas emissions are: hybrid, compressed natural gas, biodiesel, and conventional diesel.

Real-time Modeling and Control Objective Analysis of Motorway Emissions

In this paper a real-time macroscopic modeling framework of road traffic emissions is suggested based on an average-speed emission model. A model function is created to describe the spatiotemporal distribution of motorway traffic emissions using loop detector data only which is suitable for emission dispersion and immission modeling. The proposed model is built using macroscopic traffic variables, and derivation is shown both on microscopic and macroscopic levels, in the latter case showing relationship with traffic performance functions as well. The suggested model function is validated in a VISSIM/MatLab simulation environment, applying COPERT IV average speed model in the proposed framework, compared to a microscopic emission model (Versit+Micro is used via the VISSIM add-on called EnViVer). Based on the suggested model, control objectives are stated for a multi-criteria model-based control. During the cost function analysis, pollutants are distinguished in terms of the areal extension of their effects and a diverse modeling approach is carried out for pollutants causing global and local effects. The cost function is analyzed for steady-state flow conditions.

SEISMOGRAM CONSTRUCTION TO FIT THE RECORDED B032593C EARTHQUAKE, JAPAN ON OBSERVATION STATION BFO, GERMANY

In this research the model of earth layers between earthquake's epicenter in Hokkaido Japan and observation station in Black Forest of Observatory (BFO), Germany is investigated. The earth model is 1-D that represents the average speed model. The earth model is obtained by seismogram comparison between data and synthetic seismogram in time domain and three components simultaneously. Synthetic Seismogram is calculated with the Green's function of the Earth by MINor Integration (GEMINI) program, where program's input is initially the earth model IASPEI91, PREMAN and also the Centroid Moment Tensor (CMT) solution of the earthquake. A Butterworth low-pass filter with corner frequency of 20 mHz is imposed to measured and synthetic seismogram. On seismogram comparison we can find unsystematic discrepancies, covering the travel time and waveform of all wave phases, namely on P, S, SS wave and surface wave of Rayleigh and Love. Solution to the above mentioned discrepancies needs correction to the earth structure, that covering the change of earth crust thickness, the gradient of b h and value of zero order coefficient inb h andbv in upper mantle, to get the fitting on the surface wave of Love and Rayleigh. Further correction to accomplish the discrepancies on body waves is conducted on layers beneath upper mantle down to depth of 630 km, where a little change at speed model of P and S wave is carried out. The number of oscillation amount especially on Love wave is influenced by earth crust depth earth. Good fitting is obtained at phase and amplitude of Love wave, but also at amplitude of some body wave too. This effect is not yet been exploited for the determination of moment tensor.

Do air pollution emissions and fuel consumption models for roadways include the effects of congestion in the roadway traffic flow?

Road transport emission and fuel consumption models are currently used extensively to predict levels of air pollution along roadway links and networks. This paper examines how, and to what extent, models which are currently used to predict emissions and fuel consumption from road traffic include the effects of congestion. A classification framework is presented in which a key factor, driving pattern, connects emissions to congestion. Prediction of the effects of different driving patterns in emission models is generally restricted to certain aspects of modelling, i.e. hot-running emissions of regulated pollutants. As a consequence, the effects of congestion are only partially incorporated in the predictions. The majority of emission models explicitly incorporate congestion in the modelling process, but for one important family of emission models, namely average speed models, this could not be determined directly. Re-examination of the (light-duty) driving patterns on which three average speed models (COPERT, MOBILE, EMFAC) are based, shows that it is likely that congestion is represented in these patterns. Since (hot-running) emission factors are based on these patterns, this implies that the emission factors used in these emission models also reflect different levels of congestion. Congestion is thus indirectly incorporated in these models. It is recommended, that, in order to get more accurate (local) emission predictions and to achieve correct application in particular situations, it is important to improve current average speed models by including a congestion algorithm, or alternatively, at least provide information on the level of congestion in the driving patterns on which these models are based and recommendations on what applications the models are suitable for.

유효면적과 평균속도를 고려한 TFT의 해석적 Drain 전류 모델

In this paper, we proposed an analytical model for TFT which has series of the polycrystalline structures. An average speed is defined as carrier speed by the electric field. The effective square is suggested as the area of grain without depletion for the changed grain size. First, physical parameters such as grain size, channel lenght and trap density, have been changed to prove the validity of the average speed model and the value of the effective square has been estimated through drain-source current.

Robust identification for multi-section freeway traffic models

Since it is difficult to fit measured parameters using the conventional traffic model, a new traffic density and average speed model is introduced in this paper. To determine traffic model structures accurately, a model identification method for uncertain nonlinear system is developed. To simplify uncertain nonlinear problem, this paper presents a new robust criterion to identify the multi-section traffic model structure of freeway efficiently. In the new model identification criterion, numerically efficient U-D factorization is used to avoid computing the determinant values of two complex matrices. By estimating the values of U-D factor of data matrix, both the upper and lower bounds of system uncertainties are described. Thus a model structure identification algorithm is proposed. Comparisons between identification outputs and simulation outputs of traffic states show that the traffic states can be accurately predicted by means of the new traffic models and the structure identification criterion.

Performance evaluation of vehicles emissions prediction models

Road traffic is a dominant source of urban air pollution. Therefore, it is necessary to quantify emission levels as accurately as possible to evaluate their impacts on the public health and the environment. Several models were developed to predict these emissions. These models can be grouped into three categories, namely, emission factors models, average speed models, and modal models. The prediction capability of most developed models is relatively poor. Therefore, there is a pressing need to improve the predictability of the existing models or to develop new ones with better accuracy. The main focus of this paper is to review different traffic emissions modeling efforts and to describe the effect of different factors on emission levels and modeling accuracy, so as to get reliable emission estimates. In addition, different models were evaluated for the prediction capability of certain emissions such as carbon monoxide (CO), nitrogen monoxide (NO), nitrogen dioxide (NO2) and hydrocarbons (HC). These models are mainly based on traffic volume, composition, and flow. The predicted values by one of the models were compared to measured values based on field surveys. The result of comparison indicated that there is a significant difference between the measured and predicted values. These differences ranged from 17% for NO2 to 72% in the case of CO, which suggests that the NO2 model has better predictability. This deviation in prediction may be attributed to the fact that prediction models ignored some of the parameters affecting vehicle emissions such as the type of fuel used, air–fuel ratio, engine compression ratio, spark timing, surrounding environment, wind effect, regional characteristics and high pollutants emitters effect.

Safety Effects of Speed Limit Changes: Use of Panel Models, Including Speed, Use, and Design Variables

This work estimates the total safety effects of speed limit changes on high-speed roadways by using traffic detector data and Highway Safety Information System data from 1993 to 1996. To gauge the total effects, the study applies a sequential modeling approach: average speed and speed variance models are first estimated on the basis of roadway design, use, and speed limit information. Then, crash counts (of varying severity) are estimated on the basis of the speed estimates, design, and use variables. The 4 years of data come from 63,937 homogeneous roadway segments along seven Interstates and 143 state highways in Washington State. A random effects negative binomial model was selected among several alternative panel and nonpanel models for count data. Results indicate that the average road segment in the data set can be expected to exhibit lower nonfatal crash rates up to a 55 mph (88 km/h) speed limit. In contrast, fatality rates appear unresponsive to speed limit changes. Fatal and nonfatal rates fall for design reasons, including wider shoulders and more gradual curves, which appear to be key design variables. However, fatal and nonfatal rates move differently when traffic levels rise, with nonfatal rates remaining unchanged and fatal rates falling.