A Study on the Model of Traffic Flow and Vehicle Exhaust Emission

Abstract

The increase of traffic flow in cities causes traffic congestion and accidents as well as air pollution. Traffic problems have attracted the interest of many researchers from the perspective of theory and engineering. In order to provide a simple and practical method for measuring the exhaust emission and assessing the effect of pollution control, a model is based on the relationship between traffic flow and vehicle exhaust emission under a certain level of road capacity constraints. In the proposed model, the hydrocarbons (HC), carbon monoxide (CO), and nitrogen oxides (NO x ) are considered as the indexes of total exhaust emission, and the speed is used as an intermediate variable. To verify the rationality and practicality of the model, a case study for Beijing, China, is provided in which the effects of taxi fare regulation and the specific vehicle emission reduction policy are analyzed.

A Dynamic Risk Assessment Methodology for Maintenance Decision Support

“…However, prior to incorporating the input distributions to the MC simulation model, the posterior functions are first derived using the hierarchical Bayesian approach. This is achieved by first assigning prior distribution functions to the risk metric represented in Eqns (6) and (7). The assigned prior functions are combined with the observed evidences based on the empirical data sets.…”

“…By contrast, the Lognormal model least replicates the observed TTFs owing to the relatively higher DIC value and significantly lower p-value as compared with the two-parameter Weibull distribution. Following a similar approach, alternative prior models were also assigned to the severity or cost metrics earlier on denoted by Eqns (6) and (7). For the spare part cost, for instance, the Lognormal prior, it was determined as the best model based on its DIC value.…”

“…In the failure prioritization step, whereof the posterior functions were applied as input to the simulation model, the expected failure cost was simulated by sampling in each simulation run, 10 000 independent MC samples. Thereafter, a total of 100 simulation replications were implemented from which the expected failure cost was generated as per Eqns (6) and (7). Figure 3 visualizes the histogram overlay plots for the 100 MC simulation replications for the expected failure cost of the turbocharger vibration failure mode.…”

“…Additionally, incorporating the posterior functions as input into the MC simulation is viewed as an important step of further taking into account the uncertainties associated with the input posterior distribution functions. This is achieved through sampling independent values from the input distributions, and iteratively, aggregating these values as per the formulation of the expected failure costs earlier on defined in Eqns (6) and (7). The MC simulation is widely used for risk analysis, and particularly, propagating uncertainty 42 associated with the input risk metrics distribution functions.…”

“…Apart from failure‐related costs, asset failure is associated with salient yet intangible risks. Examples of such risks include potential injury to persons in the vicinity of the failed equipment, for example broken‐off shrapnel or projectile; industrial accidents; potential environmental damage, for instance spillage of pollutants to the atmosphere; or societal disruptions, for example power outages owing to equipment breakdown. Thus, to mitigate such tangible (or quantifiable costs) and intangible negative impacts associated with the asset or equipment failure, maintenance practitioners are confronted with the challenge of selecting and implementing effective maintenance strategies.…”