Influence of driving patterns on vehicle emissions: A case study for Latin American cities

Rodriguez, R.; Virgüez, Edgar; Rodríguez, Paula; Behrentz, Eduardo

doi:10.1016/j.trd.2015.12.008

Cited by 41 publications

(26 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Recall, that of these differences, only the VSP values between the EM and EF driver groups were statistically different, suggesting the VSP differences along road segment 2E2 alone cannot account for differences in the observed emissions. It should also be noted here that using VSPbased emission algorithms developed by Rodríguez et al (2016) for similar vehicle types produced emission rates generally within an order of magnitude of the values reported in Tables 1-3. Table 2 shows the VSP values and pollutant emission rates for the higher speed, uphill road segment 4N1.…”

Section: Figure 5 Heresupporting

confidence: 71%

“…The VSP approach has subsequently been used to categorize mobile source emissions in numerous studies (e.g. Wang and Fu, 2010;Zhai et al, 2011;Yao et al, 2013;Rodríguez et al, 2016;Zhai et al 2017) and has been incorporated into the most recent USEPA mobile source emissions model, Multi-scale mOtor Vehicle and equipment Emission System or more simply, MOtor Vehicle Emissions Simulator (MOVES) (Koupal et al, 2003). While many of the studies referenced above have identified driver-to-driver variability as a significant impact on tailpipe pollutant emission, the variability of on-road emissions from segregated, common driver types is not as well researched.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On-the-road testing of the effects of driver’s experience, gender, speed, and road grade on car emissions

Khader

Martin

2019

Journal of the Air & Waste Management Association

View full text Add to dashboard Cite

Section: Figure 5 Heresupporting

confidence: 71%

Section: Introductionmentioning

confidence: 99%

On-the-road testing of the effects of driver’s experience, gender, speed, and road grade on car emissions

Khader

Martin

2019

Journal of the Air & Waste Management Association

View full text Add to dashboard Cite

“…Consequently, this accelerates climate change as well as impacts of global warming in the cities [37]. According to Rodriguez et al [38], driving behaviour can limit the annual emission of CO 2 by 12%, CO and HC by 13% each, and NO x emission by 24%. Likewise, the effect of vehicle type and quality of fuel in mitigating climate change was investigated [39].…”

Section: Emissions and Driving Behaviourmentioning

confidence: 99%

“…[33] Emission of NOx increases with increase of rapid acceleration [35] Traffic emission was found to increase with over speeding [38] Reducing rapid acceleration was found to decrease annual emission of CO 2 by 12%, CO, and HC by 13% each as well as NOx by 24%.…”

Section: Frequency Aggregation Of Measurement Techniques In Driving Bmentioning

confidence: 99%

Driving Behaviour and Sustainable Mobility—Policies and Approaches Revisited

et al. 2018

View full text Add to dashboard Cite

Climate change is receiving increasing attention in recent years. The transportation sector contributes substantially to increased fuel consumption, greenhouse gas (GHG) emissions, and poor air quality, which imposes a serious respiratory health hazard. Road transport has made a significant contribution to this effect. Consequently, many countries have attempted to mitigate climate change using various strategies. This study analysed and compared the number of policies and other approaches necessary to achieve reduced fuel consumption and carbon emission. Frequency aggregation indicates that the mitigation policies associated with driving behaviours adopted to curtail this consumption and decrease hazardous emissions, as well as a safety enhancement. Furthermore, car-sharing/carpooling was the least investigated approach to establish its influence on mitigation of climate change. Additionally, the influence of such driving behaviours as acceleration/deceleration and the compliance to speed limits on each approach was discussed. Other driving behaviours, such as gear shifting, compliance to traffic laws, choice of route, and idling and braking style, were also discussed. Likewise, the influence of aggression, anxiety, and motivation on driving behaviour of motorists was highlighted. The research determined that driving behaviours can lead to new adaptive driving behaviours and, thus, cause a significant decrease of vehicle fuel consumption and CO 2 emissions.

show abstract

“…Since 1999, a great number of studies have applied the conventional VSP equation and coefficients for LDV to estimate the driving power of passenger cars regardless of vehicle model and technology. VSP has been utilized to estimate driving power to construct fuel-consumption and -emission models, and conduct further analysis on the influential factors or contributors to vehicle fuel consumption [1,3,[7][8][9][10][11][12][13][14][15][16][17][18][19]. Nevertheless, before implementing the VSP equation and the LDV coefficients, none of them has performed validation to confirm prediction accuracy of whether the equation and coefficients estimated by the vehicles from the last 20 years can still accurately capture a vehicle or studied vehicle fleet.…”

Section: Introductionmentioning

confidence: 99%

Development of Hybrid-Vehicle Energy-Consumption Model for Transportation Applications—Part I: Driving-Power Equation Development and Coefficient Calibration

et al. 2020

View full text Add to dashboard Cite

This study is the first of a two-part paper. The overall study presents a new methodology to improve the accuracy of hybrid vehicles’ energy-consumption model over conventional transportation modeling methods. The first paper attempts to improve an equation for vehicles’ driving-power estimation to be more realistic and specific for a particular vehicle model or fleet. The second paper adopts the driving-power equation to estimate the requested driving power. Then, the data are utilized to construct the hybrid-vehicle energy-consumption model, namely, the traction-force–speed-based energy-consumption model (TFS model). The main concept of the first paper is to utilize the power-split hybrid powertrain’s accessible on-board diagnostics (OBD) dataset, and its dynamic model to estimate the total propulsion power. Then, propulsion power was applied as the main parameter for driving-power equation development and vehicle-specific coefficient calibration. For coefficient calibration, this study implemented the stepwise multiple regression method to select and calibrate an optimal set of coefficients. Results showed that conventional driving-power equations Vehicle-Specific Power (VSP) LDV 1999 and VSP Prius3Spec provide low prediction fidelity, especially under high-speed (>80 km/h) and heavy-load driving (≥50 kW). In contrast, D r v P w P r i u s 3 , proposed in this study, effectively improved prediction to become more accurate and reliable through all driving conditions and speed ranges. It dramatically helped to reduce prediction discrepancy over the conventional equations at heavy-load driving, from an R-square of 0.79 and 0.78 to 0.96. D r v P w P r i u s 3 also the prediction error at high-speed driving from the maximal error of approximately −20 to −5 kW. This study also discovered that aerodynamics and rolling resistance were the primary factors that caused the prediction error of conventional VSP equations. In addition, results in this study showed that both of the approaches used to establish the P P T d r v and D r v P w P r i u s 3 equations were valid for a power-split hybrid vehicle’s driving-power estimation. For the coefficient-calibration part, the stepwise and multiple regression method is low-cost and simple, allowing to calibrate an appropriate set of optimal coefficients for a specific vehicle model or fleet.

show abstract

Influence of driving patterns on vehicle emissions: A case study for Latin American cities

Cited by 41 publications

References 24 publications

On-the-road testing of the effects of driver’s experience, gender, speed, and road grade on car emissions

On-the-road testing of the effects of driver’s experience, gender, speed, and road grade on car emissions

Driving Behaviour and Sustainable Mobility—Policies and Approaches Revisited

Development of Hybrid-Vehicle Energy-Consumption Model for Transportation Applications—Part I: Driving-Power Equation Development and Coefficient Calibration

Contact Info

Product

Resources

About