Develop dynamic model for predicting traffic CO emissions in urban areas

Abstract

The greater the use of energy in the transportation sectors, the higher the emission of carbon monoxide (CO), and hence inevitable harm to environment and human health. In this concern, measuring and predicting of CO emission from transportation sector-especially large cities-is important as it constitute 90 % of all CO emission. Many urban cities in developing world have not properly experienced such measurements or predictions. In this paper, for the first time, field measurements of traffic characteristics data and corresponding CO concentration have been performed for developing a model for predicting CO emissions from transportation sector for New Borg El Arab (NBC), Egypt. The performance of Swiss-German Handbook Emission Factors for Road Transport (HBEFA v3.1) model has been assessed for predicting the CO concentration at roadside in the study area. Results indicated that HBEFA v3.1 underestimate emission figures. The developed CO dynamic emission model involves the traffic flow characteristics with roadside CO concentrations. Acceptable representation of measured CO concentration has been shown by the developed dynamic CO emission model which introduces R (2) = 0.77, mean biases and frictional biases of -0.27 mg m(-3) and 0.09, respectively. A comparison between predicted CO concentrations using HBEFA v3.1 and the promoted dynamic model indicate that HBEFA v3.1 estimates CO emission concentrations in the study area with a mean error and frictional biases 159.26 and 233.33 %, respectively, higher than those of the developed model.

Road Emissions in London: Insights from Geographically Detailed Classification and Regression Modelling

“…HBEFA has been used by [8] to estimate nitrogen oxides (NO x ) in Madrid and [29] to estimate carbon monoxide (CO), carbon dioxide (CO 2 ) and NO x emissions with both studies concluding that the model overestimates emissions. By contrast, [30] testing HBEFA on CO concludes that the model underestimates emissions. The Assessment and Reliability of Transport Emissions Models and Inventory Systems (ARTEMIS) is another traffic situation model [31,32] consisting of a collection of sub-models [33].…”

Learning Traffic Network Embeddings for Predicting Congestion Propagation

“…Hence, the optimized hyper-parameter combination is not restricted to the existing Orthogonal Array table. Specifically, 10 hyperparameters are optimized whose values are set as follows (based on the Taguchi L32 type B orthogonal array): the number of hops of local neighborhood in LPE module h (2,4), the vector dimension K 1 of the (1,2,4,8) dimension K 2 in NMF (1,2,4,8), dimension c in GCN (8,16,24,32), K GCN in GCN (1,2,4,8), number of neurons in each LSTM layer (8,16,32,64), number of neurons in each FCN layer (8,16,32,64), number of neurons in each attention layer (8,16,32,64), the learning rate (0.0001, 0.0005, 0.001, 0.005), the batch size (20,40,60,80). After tuning, the initial parameters are set to h = 4, K 1 = 4, K 2 = 1, c = 8, K GCN =, 16, 16, 16, 0.0001, 40, respectively.…”

“…T Raffic congestions, especially in urban areas, is becoming increasingly severe due to the rapid growth in the number of vehicles and travel demands. This adversely impacts the quality of life and economic productivity in metropolitan areas [1], [2] due to the increase in fuel consumption, travel cost, road accidents, and carbon emissions. Generally, traffic congestions exhibit specific propagation patterns based on their spatiotemporal significance [3].…”

Assessment Approach of Life Cycle of Vehicles Tyres on Egyptian Road Network

“…In general Mass and people transportation operation is highly contributor to environmental problems involving noise, emissions, and also climate changes (Elkafoury et al, 2015). One highly emitting transportation related stage is the cars production.…”