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

Samaras, Christos; Tsokolis, Dimitrios; Toffolo, Silvana; Magra, Giorgio; Ntziachristos, Leónidas; Samaras, Zissis

doi:10.1016/j.trd.2019.08.029

Cited by 42 publications

(20 citation statements)

References 30 publications

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“…According to [ 21 ], the emissions of a vehicle must be evaluated under normal driving conditions, which excludes laboratory tests using standardized driving cycles, while computerized models for estimating emissions require databases with characteristics of: vehicle fleet, fuels, environmental conditions and geographic location [ 8 ]. Currently, the Cuenca Mobility Company (EMOV-EP) estimates the emissions inventory based on the MOBILE6-Mexico model [ 22 ], which considers only vehicles manufactured in the USA without including those manufactured in the European Union and Asia [ 23 ] and which also considers types of fuels, environmental and traffic conditions different from those of the city of Cuenca in its database.…”

Section: Methodsmentioning

confidence: 99%

“…Several stochastic models determine the influence of various variables on fuelconsumption during road driving, such as speed and the presence of road features and Sensors 2021, 21, 6344 2 of 16 characteristics that determine stop/start sequences, like stop signals, speed reducers, driving curves and topography. These situations affect eco-driving [7], whilst in urban driving [8], it has been determined that fuel consumption is strongly influenced by vehicular congestion.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Pollutant Emissions in Real Driving Conditions Based on Data from OBD and Machine Learning

Rivera-Campoverde

Muñoz-Sanz

Arenas-Ramírez

2021

Sensors

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This article proposes a methodology for the estimation of emissions in real driving conditions, based on board diagnostics data and machine learning, since it has been detected that there are no models for estimating pollutants without large measurement campaigns. For this purpose, driving data are obtained by means of a data logger and emissions through a portable emissions measurement system in a real driving emissions test. The data obtained are used to train artificial neural networks that estimate emissions, having previously estimated the relative importance of variables through random forest techniques. Then, by the application of the K-means algorithm, labels are obtained to implement a classification tree and thereby determine the selected gear by the driver. These models were loaded with a data set generated covering 1218.19 km of driving. The results generated were compared to the ones obtained by applying the international vehicle emissions model and with the results of the real driving emissions test, showing evidence of similar results. The main contribution of this article is that the generated model is stronger in different traffic conditions and presents good results at the speed interval with small differences at low average driving speeds because more than half of the vehicle’s trip occurs in urban areas, in completely random driving conditions. These results can be useful for the estimation of emission factors with potential application in vehicular homologation processes and the estimation of vehicular emission inventories.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation of Pollutant Emissions in Real Driving Conditions Based on Data from OBD and Machine Learning

Rivera-Campoverde

Muñoz-Sanz

Arenas-Ramírez

2021

Sensors

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“…Chen et al (2017) proposed a mesoscopic fuel consumption estimation model using free-flow speed data as the model parameter and showed that this solution can achieve higher accuracy when compared with similar macroscopic and mesoscopic fuel estimation models. Samaras et al (2019) also deal with the calculation of fuel consumption for different traffic conditions, including free-flow traffic. When there was no measurement data about free-flow speed, a simple model for estimating free-flow speed was formulated and applied in order to calculate the fuel consumption.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Prediction of Vehicles Speed in Free-Flow Traffic

Maczyński

Brzozowski

Ryguła

2021

Transport and Telecommunication Journal

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Speed is a crucial factor in the frequency and severity of road accidents. Light and heavy vehicles speed in free-flow traffic at six locations on Poland’s national road network was analyzed. The results were used to formulate two models predicting the mean speed in free-flow traffic for both light and heavy vehicles. The first one is a multiple linear regression model, the second is based on an artificial neural network with a radial type of neuron function. A set of the following input parameters is used: average hourly traffic, the percentage of vehicles in free-flow traffic, geometric parameters of the road section (lane and hard shoulder width), type of day and time (hour). The ANN model was found to be more effective for predicting the mean free-flow speed of vehicles. Assuming a 5% acceptable error of indication, the ANN model predicted the mean free-flow speed correctly in 84% of cases for light and 89% for heavy vehicles.

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“…Secondly, it is hard to solve the problem in a real-time manner when using optimization algorithms, e.g. Samaras et al (2019); Zong et al (2010); Anjum et al (2019). The searching space grows exponentially when more routing edges are added into the map.…”

Section: Introductionmentioning

confidence: 99%