Speed frequency distribution in air pollutants' emissions estimate from road traffic

Trozzi, C.; Vaccaro, R.; Crocetti, S.

doi:10.1016/0048-9697(96)05208-4

Cited by 17 publications

(13 citation statements)

References 1 publication

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“…The creation of microtrips is generally based on arbitrarily defined criteria, such as average speed or the beginning or end of a particular facility type. Many researchers have shown that significantly different emission rates can result at the same average speed (e.g., Trozzi et al 1996, Joumard et al 1995, Hansen et al 1995, Andre and Pronello 1997, and the facility type criterion has little physical association to the driving contained within the segments. In short, the approach is not robust and doesn't reflect the stochastic nature of the data.…”

Section: Lin and Niemeier Estimating Regional Air Quality Vehicle Emimentioning

confidence: 99%

Estimating Regional Air Quality Vehicle Emission Inventories: Constructing Robust Driving Cycles

Lin

Niemeier

2003

Transportation Science

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M obile emission inventories are constructed by multiplying a pollutant emission factor by a travel activity (e.g., number of trips, vehicle miles traveled, etc.). To create emission rates, vehicles are tested on dynamometers using driving cycles, or speed-time traces. The process currently used to create the driving cycles is deterministic. However, if we examine the data and data collection techniques closely, it is clear that an observed speed, v t , represents one of the many possible values that true speed, V t , may take on at a given time t. With an ordered set of random variables {V t } and associated probability distributions, driving cycles should be defined by a stochastic process. In this study, we propose a new approach for constructing driving cycles using Markov process theory. The new approach not only provides an important statistical foundation for drive cycle estimation, it also overcomes several key limitations of the current driving cycle construction methodologies. For example, we use a maximum likelihood estimation (MLE) partitioning algorithm that enables us to associate a segment with a specific modal operating condition, (e.g., cruise, idle, acceleration, or deceleration), which, in turn, preserves finely resolved driving variability. We apply the new method to the data used to construct EPA's new regulatory facility-specific driving cycles. Comparisons with these cycles indicate relatively similar global results (e.g., average speeds) under uncongested conditions. However, the new cycles tend to contain a higher frequency of small scale acceleration and deceleration modal events than are represented in the EPA cycles. For congested conditions, in addition to greater frequencies of acceleration and deceleration modal events, the new cycles tend to have higher speeds and harder accelerations. Overall, the improvements in the new method represent significant advances in the development of stochastic driving cycle construction methods.

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Section: Lin and Niemeier Estimating Regional Air Quality Vehicle Emimentioning

confidence: 99%

Estimating Regional Air Quality Vehicle Emission Inventories: Constructing Robust Driving Cycles

Lin

Niemeier

2003

Transportation Science

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“…This assumption is supported by IMAGINE (2006), where different speed distributions are related to different traffic conditions. The development of mathematical tools focused on the modelling of the speed distribution in a traffic flow is widely reported in the scientific literature (Castro et al 2008;Dey et al 2006;Fitzpatrick et al 2000;Trozzi et al 1996). In general, speed distribution is necessary for many traffic engineering applications.…”

Section: Introductionmentioning

confidence: 99%

Distribution of Traffic Speed in Different Traffic Conditions: An Empirical Study in Budapest

Zefreh

Török

2020

Transport

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Fundamental diagram, a graphical representation of the relationship among traffic flow, speed, and density, has been the foundation of traffic flow theory and transportation engineering for many years. Underlying a fundamental diagram is the relation between traffic speed and density, which serves as the basis to understand system dynamics. Empirical observations of the traffic speed versus traffic density show a wide-scattering of traffic speeds over a certain level of density, which would form a speed distribution over a certain level of density. The main aim of the current research is to study on the distribution of traffic speed in different traffic conditions in the urban roads since the distribution of traffic speed is necessary for many traffic engineering applications including generating traffic in micro-simulation systems. To do so, the traffic stream is videotaped at various locations in the city of Budapest (Hungary). The recorded videos were analysed by traffic engineering experts and different traffic conditions were extracted from these recorded videos based on the predefined scenarios. Then their relevant speeds in that time interval were estimated with the so-called “g-estimator method” using the outputs of the available loop detectors among the videotaped locations. Then different parametric candidate distributions have been fitted to the speeds by Maximum Likelihood Estimation (MLE) method. Having fitted different parametric distributions to speed data, they were compared by three goodness-of-fit tests along with two penalized criteria (Akaike Information Criterion – AIC and Bayesian Information Criterion – BIC) in order to overcome the over-fitting problems. The results showed that the speed of traffic flow follows exponential, normal, lognormal, gamma, beta and chisquare distribution in the condition that traffic flow followed over-saturated congestion, under saturated flow, free flow, congestion, accelerated flow and decelerated flow respectively.

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“…What if in deterministic speed-density models a certain level of density will be incorporated with one or more part of different speed distributions related to that specific traffic condition?! The development of mathematical tools focused on the modelling of the speed distribution in a traffic flow is widely reported in the scientific literature [21][22][23][24]. Many papers concern this problem since vehicles speed distribution is an important input parameter in lots of issues, such as kinematical traffic simulation model, road design, speed limit evaluation, road traffic noise prediction, traffic safety evaluation, bicycle performance evaluation, analysis of pedestrian walking, road transport emission estimation [25][26][27][28][29][30][31][32][33], [46] etc.…”

Section: Methodsmentioning

confidence: 99%

Assessing the Possibility of Presenting a Semi-Stochastic Speed-Density Function

2016

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Abstract. Underlying a fundamental diagram is a relation between traffic speed and density which roughly corresponds to drivers' speed choices under varying car-following distances. Stochastic and deterministic models are mainly two different categories of speed-density models. The advantages of deterministic models are their mathematical simplicity and analytical tractability though their results will show just the average parameters. Although the stochastic models may represent more accurate results taking uncertainty into account, they are often hard to use and analytically not tractable. The aim of this paper is to investigate the possibility of presenting a model which is neither completely deterministic nor completely stochastic but easy to use and understand which incorporates different traffic conditions and speed distributions. Monte Carlo Method has been used to generate different speed distributions based on different traffic conditions and consequently generating their relevant densities. Surveying the relation between the mentioned speed distributions and the obtained densities kept the chance of presenting a model which is neither completely deterministic nor completely stochastic but easy to use and understand which incorporates different traffic conditions and speed distributions.

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Speed frequency distribution in air pollutants' emissions estimate from road traffic

Cited by 17 publications

References 1 publication

Estimating Regional Air Quality Vehicle Emission Inventories: Constructing Robust Driving Cycles

Estimating Regional Air Quality Vehicle Emission Inventories: Constructing Robust Driving Cycles

Distribution of Traffic Speed in Different Traffic Conditions: An Empirical Study in Budapest

Assessing the Possibility of Presenting a Semi-Stochastic Speed-Density Function

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