Short-Term Trend Forecast of Different Traffic Pollutants in Minnesota Based on Spot Velocity Conversion
Bibliographic Data
| ID | 15480725 |
|---|---|
| Authors | Xiaojian Hu (0000-0002-4275-1158, Southeast University, corresponding author), Dan Xu (0009-0002-3944-6192, Southeast University), Qian Wan (0000-0002-0453-1330, China Design Group (China)) |
| Year | 2018 |
| Volume | 15 |
| Issue | 9 |
| Pages | 1925-1925 |
| Publication date | 2018-09-04 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph15091925 |
| PMID | 30181505 |
| OpenAlex | W2892163554 |
| Language | EN |
| References cited | 3 |
Because traffic pollution is a global problem, the prediction of traffic emissions and the analysis of their influencing factors is the key to adopting management and control measures to reduce traffic emissions. Hence, the evaluation of the actual level of traffic emissions has gained more interest. The Computer Program to calculate Emissions from Road Transport model (COPERT) is being downloaded by 100 users per month and is being used in a large number of applications. This paper uses this model to calculate short-term vehicle emissions. The difference from the traditional research was that the input velocity parameter was not the empirical value, but through reasonable conversion of the spot velocity at one point, obtained by the roadside detector, which provided new ideas for predicting traffic emissions by the COPERT model. The hybrid Autoregressive Integrated Moving Average (ARIMA) Model was used to predict spot mean velocity, after converted it to the predicted interval velocity averaged for some period, input the conversion results and other parameters into the COPERT IV model to forecast short-term vehicle emissions. Six common emissions (CO, NO X , CO2, SO2, PM 10 , NMVOC) of four types of vehicles (PC, LDV, HDV, BUS) were discussed. As a result, PM 10 emission estimates increased sharply during late peak hours (from 15:30 p.m.−18:00 p.m.), HDV contributed most of NO X and SO2, accounting for 39% and 45% respectively. Based on this prediction method, the average traffic emissions on the freeway reached a minimum when interval mean velocity reduced to 40 km/h−60 km/h. This paper establishes a bridge between the emissions and velocity of traffic flow and provides new ideas for forecasting traffic emissions. It is further inferred that the implementation of dynamic velocity guidance and vehicle differential management has a controlling effect that improves on road traffic pollution emissions
Autoregressive integrated moving average · Meteorology · NOx · Physics · Pollutant · Statistics · Term (time · Time series · Environmental Science · Mathematics · Traffic Prediction and Management Techniques · Transportation Planning and Optimization · Vehicle emissions and performance
| Highly cited | No |
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