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Environmental effects of driving behaviour and congestion related to passenger cars

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Abstract

Using Vito's on-board measuring system the influence of track, driving behaviour and traffic conditions on fuel consumption and emissions were studied for a small test fleet of passenger cars. City traffic resulted in the highest fuel consumption and emissions. Fuel consumption was about two times higher than for ring roads, which generally gave the lowest values. This was even more pronounced for emissions. Depending on road type and technology, fuel consumption increased with up to 40% for aggressive driving compared to normal driving. Again, this was more pronounced for emissions, with increases up to a factor 8. Driving behaviour had a greater influence on petrol-fuelled than on diesel-fuelled cars.Traffic condition also has a major effect on fuel consumption and emissions. For city driving intense traffic increased fuel consumption by 20–45%. The increase in fuel consumption and emissions during rush hours were the highest on ring roads, with increases between 10 and 200%. In absolute terms, a surplus of up to 5 l fuel per 100 km was measured. More environment-friendly route option requires the use of ring roads and motorways during rush hours instead of short cuts.

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... Table (4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15): Model Parameters (TRMLFL of CO 2 (D) ) 83 ...
... Table (4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16): Goodness of Fit indicators (LRMLFI of CO (D) ) 84 ...
... Table (4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17): Omnibus Test (LRMLFI of CO (D) ) 84 (D) ) 85 ...
Thesis
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The main objective of this research is to study factors that effect on the vehicles emissions on Egyptian roads. Vehicle emission models were investigated using the application of (SPSS) computer program Version (26). The models were calibrated using vehicles emission records collected during the study for the period (November 2017). Data recorded for eight vehicles, emission data were classified according the fuel type to three categories (Diesel, Natural Gas and Petrol Vehicles), to conduct a comparative analysis of various statistical modeling techniques such as "Linear Regression with Link Function of Identity, Linear Regression with Link Function of Log, Gamma Regression with Link Function of Log and Tweedie Regression with Link Function of Log" which classified to generalized linear regression models to predict vehicle emission rates as a function of the independent variables. The study based on collecting data of the travel-related factors, highway characteristics and vehicle characteristics in addition to the effect of climate for the three different vehicles categories, also vehicles emission measurements (CO2 [g/s], CO [mg/s], HC [mg/s], and NOX [mg/s]) used in this study were obtained from Egyptian Environmental Affairs Agency (EEAA) recorded for the period (November 2017), Six independent variables were selected in this research (vehicle speed, profile grade, ambient temperature, ambient pressure, ambient relative humidity and numbers of rotation per minute for vehicle engine) which affect directly on vehicle emissions from transportation on the different vehicles categories then a comparison of these results obtained from the (SPSS) mathematical model.
... Other studies also explicitly mention the importance of acceleration behavior, which is a natural result of different preferences for velocity changes, in differentiating driving styles (see e.g., Müller et al., 2013;Reiser, Zellbeck, Härtle, & Klaiß, 2008). Many studies have divided the concept of driving style into three styles: comfortable, dynamic, and everyday driving (Langari & Won, 2005;Murphey et al., 2009;Vlieger et al., 2000). This implies that the perceived comfort should be maximal for comfortable driving and minimal in dynamic driving. ...
... Many studies are based on questionnaire data (Møller & Haustein, 2013;Reason et al., 1990;Taubman-Ben-Ari et al., 2004). Others have used objective data but have exclusively focused on speed and/or acceleration (e.g., Doshi & Trivedi, 2010;Vlieger et al., 2000). Moreover, further studies have used multiple metrics but relied on algorithms which summarized data of road types without regard for the individual maneuvers driven (Ericsson, 2000). ...
... The only proposed metric which was not able to discriminate between any driving styles is standard lane deviation in the maneuver following. This study not only supports the assumption that not only speed and acceleration are important parameters (see Doshi & Trivedi, 2010;Vlieger et al., 2000), further, the study also shows the necessity to use a maneuver-based approach instead of overall means of metrics over whole trips (see Ericsson, 2000). ...
Thesis
Over the last years, driving automation has increasingly moved into focus in human factors research. A large body of research focusses on situations in which the human driver needs to regain control. However, little research has so far been conducted on how SAE level 3+ automated driving should be designed with focus on occupant comfort. This thesis aims at identifying a comfortable driving style for automated vehicles. As a basis, it was necessary to pinpoint driving metrics, which vary between driving styles and can be manipulated in order to design a comfortable driving style. Hence, Study 1 was conducted, in which drivers (N = 24) manually drove on a highway or on urban and rural roads with certain driving styles. Results show relevant metrics (i.e., lateral and longitudinal acceleration, lateral and longitudinal jerk, quickness, and headway distance in seconds) and that these metrics vary across maneuvers and thus, a maneuver-specific analysis is recommended. As these metrics are derived from manual data, it remained unclear after Study 1, in which range the metric values should vary for comfortable automated driving. Therefore, as a second step, the main metrics were varied and the subsequent combinations implemented in an automated vehicle as well as in a dynamic simulator with two different configurations. The combinations were then subject to ratings by 72 participants. Results show that the metrics and values found in Study 1, are able to elicit a range of comfort ratings in automated driving. It was also found, that acceleration is a key variable in experiencing comfort. However, it is not the sole predictor. Additionally, as higher levels of automated driving with larger velocities are still bound to considerable constraints for on-road testing, the second study was also used to validate a dynamic driving simulator to allow comfort during automated driving to be studied. In comparison to ratings on a test track, the dynamic simulator setting with longitudinal orientation is able to show both relative and absolute validity of comfort ratings. In the third and final step, different approaches to automated maneuvers were rated by participants (N = 72) regarding the comfort they experienced. A lane change, an acceleration, and a deceleration maneuver were chosen as test maneuvers. The lateral or longitudinal acceleration was varied in each of these maneuvers. Results, again, show comfort ratings are maneuver specific. On one hand, symmetrical and early-onset lane change maneuvers and symmetrical acceleration maneuvers were preferred. However, symmetrical deceleration maneuvers and deceleration maneuvers with a slower acceleration decrease evoke the highest comfort ratings. These ratings made it possible to offer guidelines for the design of automated driving styles. Furthermore, dependence on a number of personality traits was analyzed. Results suggest the general preference for certain driving styles to be unaffected by personality. However, it seems, participants with certain personality types are less particular about their preference for certain driving styles. Summed up, comfortable automated driving is – under the investigated circumstances – characterized by maneuvers with sufficient headway distance and smooth applications of small acceleration and small jerk. These should, even so, still provide sufficient motion feedback. Surrounding traffic seems to play an important role through urgency and should be considered for on-road implementation. Differences in personality did not seem to play a crucial role.
... Similarly, other authors evaluated the exhaust emissions and fuel consumption on Tempo-30-zoned streets [36,37]. However, it was reported that the speed reduction and fuel consumption and pollutant emissions is also dependent on other factors, such as the drivers' behavior and resulting from traffic jams [38], speed control schemes [39,40], shifting gears on the way through the TCMs [41], In European countries, as expected, urban areas are the location where the majority of pedestrian fatalities occur (73%), increasing the relative percentage of the pedestrian death to the total deaths up to 38% (as previously said, pedestrian fatalities represent the 20% of the total). In Poland and in Spain, the proportion of pedestrians killed in urban areas represents 64.3% and 64.8%, respectively [3]. ...
... Similarly, other authors evaluated the exhaust emissions and fuel consumption on Tempo-30-zoned streets [36,37]. However, it was reported that the speed reduction and fuel consumption and pollutant emissions is also dependent on other factors, such as the drivers' behavior and resulting from traffic jams [38], speed control schemes [39,40], shifting gears on the way through the TCMs [41], etc. For example, Wang et al. [42] indicated the acceleration should be incorporated, instead of mean speed of the vehicle, for estimating emission. ...
Article
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Traffic calming measures (TCMs) are implemented in urban areas to reduce vehicles’ speed and, generally speaking, results are obtained. However, speed is still a problem in rural roads crossing small villages without a bypass and with short-length urban areas, since drivers do not normally reduce their speed for that short segment. Hence, various TCM can be installed. It is necessary to maintain a calm area in these short segments to improve road safety, especially for pedestrian aiming to cross the road, and to save combustible by avoiding a constant increase-decrease of speed. Four villages were selected to evaluate the efficiency of radar speed cameras and panels indicating vehicle’s speed. Results showed that the presence of radar speed cameras reduces the speed in the direction they can fine, but with a lower effect in the non-fining direction. Additionally, a positive effect was observed in the fining direction in other points, such as pedestrian crossings. Nevertheless, the effect does not last long and speed cameras may be considered as punctual measures. If the TCMs are placed far from the start of the village they are not respected. Hence, it is recommended to place them near the real start of the build-up area. Lastly, it was verified that longer urban areas make overall speed decrease. However, when drivers feel that they are arriving to the end of the urban area, due to the inexistence of buildings, they start speeding up.
... For example, Beusen et al. [9] summarized the main rules of fuel-efficient driving, e.g., maintain a steady speed by anticipating traffic flow and shut down the engine for longer stops. Then, good driving behavior can be recommended to drivers in order to decrease fuel consumption [8], [10], [11], [12], [13], [14], [15]. Our work differs from previous work because i) our FCM is locally constructed by the smartphone instead of by the remote server/cloud through uploading a large number of samples, ii) our FCM is personalized which is corresponding to a driver-vehicle pair rather than a vehicle, and iii) we train the FCM based on OBD data stream in real-time to adjust the model according to various driving conditions. ...
... In this paper, the total fuel consumption calculation is based on both the time-based method and the distance-based method. Another factor to compute the total fuel consumption is the traffic condition [14], [16]. GreenGPS [17] uses a set of static street parameters (e.g., the number of traffic lights and the number of stop signs), a set of dynamic street parameters (e.g., the average speed on the street or the average congestion level), and the vehicle parameters (e.g., weight and frontal area). ...
Article
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Traffic congestion reduces the working efficiency of taxis, thus lowering the income of taxi drivers. In most cases, taxi drivers will choose the least time-consuming route, which is usually the shortest in distance to the destination. However, during rush hours when traffic flow is heavy, the shortest route may not be the most time-efficient. A detour sometimes can save time for drivers but that may highly increase the fare for passengers. In order to optimize the balance between fare and profit, this paper proposes a framework, called ProfitMax, which will recommend a fixed price and a more profitable route by assigning the most suitable taxi for online taxi-hailing. The taxi's profit of completing an order is calculated by the income per minute. We use drivers' personal driving habit to estimate the basic cost of an order and then recommend the most profitable route. ProfitMax trains the personal fuel-consumption model and estimates the fuel consumption on drivers' smart devices. We use a real taxiing data set, including one-month taxis' GPS trajectories and car-OBD (on-board-diagnose) readings, for the performance evaluation. Experimental results show that ProfitMax estimated the fuel consumption more accurate than baselines and can also save more than 10% fuel.
... In fact, driving behavior and traffic conditions have a major impact on the traction power demand and consequently on the EMS. Several recent studies attempt to precisely establish such a relationship between driving conditions and energy consumption for different types of vehicle powertrains [24,36]. ...
... For this simulation we set the conditions SoC 0 = 0.25, SoC f = 0. 24. We observe that both the macro and DDP solutions satisfy the final SoC correctly, although the SoC trajectories are different. ...
Thesis
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The focus of this PhD thesis is to design an optimal Energy Management System (EMS) for a Hybrid Electric Vehicle (HEV) following traffic constraints.In the current state of the art, EMS are typically divided between real-time designs relying on local optimization methods, and global optimization that is only suitable for off-line use due to computational constraints.The starting point of the thesis is that in terms of energy consumption, the stochastic aspect of the traffic conditions can be accurately modelled thanks to (speed,acceleration) probability distributions.In order to reduce the data size of the model, we use clustering techniques based on the Wasserstein distance, the corresponding barycenters being computed by either a Sinkhorn or Stochastic Alternate Gradient method.Thanks to this stochastic traffic model, an off-line optimization can be performed to determine the optimal control (electric motor torque) that minimizes the fuel consumption of the HEV over a certain road segment.Then, a bi-level algorithm takes advantage of this information to optimize the consumption over a whole travel, the upper level optimization being deterministic and therefore fast enough for real-time implementation.We illustrate the relevance of the traffic model and the bi-level optimization, using both traffic data generated by a simulator, as well as some actual traffic data recorded near Lyon (France).Finally, we investigate the extension of the bi-level algorithm to the eco-routing problem, using an augmented graph to track the state of charge information over the road network.
... In addition, according to the findings in Zeeman and Booysen [33], Eboli et al. [34], and Eboli et al. [35], speed or road condition should be considered for characterizing drivers' behavior and the acceleration threshold is found to be reduced with an increase in speed. Also, the threshold range for positive acceleration on city roads in Brussels, as defined in Vlieger et al. [36], is 0.85~1.10 m/s 2 ...
... Additionally, according to the findings in Zeeman and Booysen [33], Eboli et al. [34], and Eboli et al. [35], speed or road condition should be considered for characterizing drivers' behavior and the acceleration threshold is found to be reduced with an increase in speed. e threshold of rapid acceleration or hard braking is set at 0.12 g/0.1 g/0.0875 g here for abrupt acceleration or hard braking (in absolute value) in three different kinds of routes, which is in line with the threshold described in Vlieger et al. [36], Boonmee and Tangamchit [31], and Paefgen et al. [30]. However, the threshold is acknowledged to be 0.3 g by Mortimer et al. [24] and Anderson & Baldock [25], 0.4 g in the euroFOT project [27], and 0.5 g by Fazeen et al. [16]. is could be attributed to the limited dataset (only 1.5 h to 2 h driving data) for each participant used in this test when compared with other larger datasets (i.e., the euroFOT project). ...
Article
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This paper presents an investigation of the relationship between driver risk and factors indicating vehicle’s speed and driver’s acceleration behavior. The main objective is to examine whether GPS data and derivative indicator can be used to identify risky drivers by means of factor analysis. In doing so, a real road driving experiment is conducted to collect data. Fifty drivers are asked to drive along a route which includes both rural highways and urban roads. The trajectories are recorded by GPS devices to calculate speed and derive acceleration measures. Driver’s behavior is also recorded by cameras and analyzed by another group of volunteers to determine whether the driver is risky or not. The drivers are then classified into five groups with different levels of risk based on the scores obtained through factor analysis. The results are verified by the volunteer's categorization and further evaluated by symbolic aggregate approximation. A binary logistic regression model is established ultimately for predicting high-risk drivers. The potential applications of this study include developing quantitative measures to identify risky drivers, especially for auto-insurance companies with usage-based insurance (UBI) applications, bus companies, and transport enterprises.
... Estimating energy consumption of the vehicles is a great challenge in the objective of improving global transportation efficiency, since this information is used in energy management, eco-routing, eco-driving, traffic management, ... Traffic congestion has a major impact on the driving behavior, and thus plays a key role in the level of fuel consumption [3]. ...
... which happens with probability 1 since vehicles never stop indefinitely.3 in practice integration is done by the Euler scheme. ...
Article
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A new approach to estimate traffic energy consumption via traffic data aggregation in (speed and acceleration) probability distributions is proposed. The aggregation is done on each segment composing the road network. In order to reduce data occupancy, clustering techniques are used to obtain meaningful classes of traffic conditions. Different times of the day with similar speed patterns and traffic behavior are thus grouped together in a single cluster. Different energy consumption models based on the aggregated data are proposed to estimate the energy consumption of the vehicles in the road network. For validation purposes, a microscopic traffic simulator is used to generate the data and compare the estimated energy consumption to the measured one. A thorough sensitivity analysis with respect to the parameters of the proposed method (i.e., number of clusters and size of the distributions support) is also conducted in simulation. Finally, a real-life scenario using floating car data is analyzed to evaluate the applicability and the robustness of the proposed method.
... The gear shift perfection, vehicle velocity, road grade and rolling resistance coefficient (route-choice) were human-controllable factors, from which the fuel economy could reach the optimal value. As for the driver behaviors, the fuel consumption factor increased up to 40% for aggressive behavior compared with normal driving in Ref. [59]; additionally, the driver behavior caused 45% fuel reduction in Ref. [60]. Road-surface characteristic, e.g. ...
... HC and CO emission factors decreased with vehicle velocity, however, it increased generally for NO x . The overall emission factor tendency was similar to the Ref. [59]. The optimal velocity region for exhaust emissions, under the compromise of the NO x emission with HC and CO emission factors, was 70 km/h-110 km/h, which was covered by the optimal fuel economy region. ...
Article
Large amounts of fossil fuels are consumed by motor vehicles annually, and hazardous exhaust emissions from the motor vehicles have caused serious problems to environment and human health. Eco-driving can effectively improve the fuel economy and decrease the exhaust emissions, which makes it vital to analyze the fuel consumption and exhaust emissions at given driving cycle, and investigate their sensitivities to eco-driving factors. In this paper, the fuel consumption and exhaust emissions of a Euro-6 compliant light-duty diesel vehicle were tested in Worldwide Harmonized Light Vehicles Test Cycles on a chassis dynamometer; further, the sensitivities of the eco-driving factors that influence the fuel economy and exhaust emissions were analyzed using validated vehicle model. For the vehicle model simulation, the effect of the coolant temperature on fuel consumption and exhaust emission only considered its effect on lubricating oil viscosity. The results showed that vehicle acceleration and velocity dominates the fuel consumption rates in Worldwide Harmonized Light Vehicles Test Cycles, where more than 50% of the exhaust emissions was emitted in the first 300 s; also, fuel economy and exhaust emission factors showed a significant dependency on the road grade, coolant temperature, vehicle velocity and mass. For the driver-controllable factors, high vehicle velocity and low road grade (via route-choice) were recommended to achieve low fuel consumption and exhaust emission.
... Traffic-related factors such as traffic signaling and congestion affect acceleration, braking, and constant speed behaviors. In this sense, an average increase of 26 percent is observed for fuel requirements of vehicles exposed to traffic congestion, and it can be as high as 40 percent (De Vlieger et al., 2000;Zacharof et al., 2016). Recent studies showed that integrated systems, including traffic lights and vehicle communication technologies, may achieve a fuel-saving up to 22 percent (Tielert et al., 2010). ...
Article
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This research study evaluates kinematic fuel consumption factors of haul trucks employed simultaneously in a multi-route operation network under stochastic payload and precipitation conditions. First, a discrete-event simulation algorithm was introduced, and significant parameters available in a material haulage system were correlated with time and location-based fuel usage behavior. Then, the model was validated with a large-scale cement production network covering two separate mines and one processing plant where fifteen different routes and twenty-nine trucks were available. The simulation results showed that precipitation conditions might lead to a variation in fuel consumption by 15–25 percent. Besides, the same-capacity trucks employed in the clay mine were detected to consume 40 percent more fuel in loaded travel than the limestone mine trucks due to the higher frequency of uphill loaded travels. The clay mine trucks also released 1.48 kg/km carbon dioxide in a complete production cycle, which is 17.5 percent more comparatively.
... Judging the driver's driving style by the speed of the vehicle, this is a better way to address driving problems. Kim and Choi (2013) report thresholds for aggressive and extremely aggressive accelerations in urban driving environments, while De Vlieger et al. (2000) did similar work for calm driving, normal driving, and aggressive driving. Simons-Morton et al. (2012) advanced the characterization of risky driving by observing the elevated gravitational force (Gforce) events that are captured when longitudinal or lateral accelerations exceed certain thresholds. ...
Article
Purpose Basic safety message (BSM) is a core subset of standard protocols for connected vehicle system to transmit related safety information via vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). Although some safety prototypes of connected vehicle have been proposed with effective strategies, few of them are fully evaluated in terms of the significance of BSM messages on performance of safety applications when in emergency. Design/methodology/approach To address this problem, a data fusion method is proposed to capture the vehicle crash risk by extracting critical information from raw BSMs data, such as driver volition, vehicle speed, hard accelerations and braking. Thereafter, a classification model based on information-entropy and variable precision rough set (VPRS) is used for assessing the instantaneous driving safety by fusing the BSMs data from field test, and predicting the vehicle crash risk level with the driver emergency maneuvers in the next short term. Findings The findings and implications are discussed for developing an improved warning and driving assistant system by using BSMs messages. Originality/value The findings of this study are relevant to incorporation of alerts, warnings and control assists in V2V applications of connected vehicles. Such applications can help drivers identify situations where surrounding drivers are volatile, and they may avoid dangers by taking defensive actions.
... However, the speed choice depends partly on the roadway conditions, including surrounding traffic, obstacles, pavement and speed limits [4,5]. Researchers provide several acceleration cut-off points as the thresholds for identifying aggressive driving behaviors [6,7]. To assess variations in driving behaviors under different road contexts, varying acceleration thresholds, given different speeds for identifying anomalous driving behaviors were proposed [8,9]. ...
Article
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The main objective of this vision paper is to present the project “DICA-VE: Driving Information in a Connected and Autonomous Vehicle Environment: Impacts on Safety and Emissions”, which aims to develop an integrated methodology to assess driving behavior volatility and develop warnings to reduce road conflicts and pollutants/noise emissions in a vehicle environment. A particular attention will be given to the interaction of motor vehicles with vulnerable road users (pedestrians and cyclists). The essence of assessing driving volatility aims the capture of the existence of strong accelerations and aggressive maneuvers. A fundamental understanding of instantaneous driving decisions (through a deep characterization of individual driver decision mechanisms, distinguishing normal from anomalous) is needed to develop a framework for optimizing these impacts. Thus, the research questions are: 1) Which strategies are adopted by each driver when he/she performs short-term driving decisions and how can these intentions be mapped, in a certain road network?; 2) How is driver’s volatility affected by the proximity of other road users, namely pedestrians or cyclists?; 3) How can driving volatility information be integrated into a platform to alert road users about potential dangers in the road infrastructure and prevent the occurrence of crash situations?; 4) How can anomalous driving variability be reduced in autonomous cars, in order to prevent road crashes and have a performance with a minimum degree of emissions? This paper brings a literature review on this topic and an evaluation of methods that can be used to assess driving behavior patterns and their influence on road safety, pollutant and noise emissions.
... Impact of aggressive driving compared to the normal driving is ~ 50% (comparison of the average FC gap from the last bin and the average of two middle bins). Previous studies that used similar a pos definitions found also up to 50% increase in FC related to aggressive driving [35,38,39]. ...
Article
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Background Divergence in fuel consumption (FC) between the type-approval tests and real-world driving trips, known also as the FC gap, is a well-known issue and Europe is preparing the field for tackling it. The present study focuses on the monitoring of the FC of a single vehicle throughout 1 year with 20 different drivers and almost 14,000 km driven with the aim to analyze and quantify the true intrinsic variability in the FC gap coming from environmental and traffic conditions and driving factors. In addition, the regression model has been developed to evaluate the importance of these different factors on the FC gap’s variability. Results The 1-year FC gap measured in this study was 29% while driver’s averages were in the range from 16 to 106%. The regression model developed had R2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^{2}$$\end{document} equal to 90.4 meaning that more than 90% of the FC gap’s variance can be explained with this model and factors measured in this study. The results of the model showed that among all factors analyzed the highest contribution in the FC gap’s variance is coming from the average vehicle speed (16.6%), followed by the road grade (13.4%), and trip distance (10.1%). Indeed, the highest FC gaps are measured when the average vehicle speeds were below 20 km/h, the average distance-weighted road grades above 1%, and the trip distances below 5 km. In addition, the impact of driver factors is not negligible (25%) and the highest FC gap is measured for the trips where average positive acceleration was higher than 0.7 m/s² (indicating aggressive driving) and the electric power demand higher than 800 W. Conclusions The future lifetime on-board fuel consumption reporting is a crucial instrument that will allow the monitoring of the evolution of the FC gap and ensuring that it does not increase over time. The analysis presented in this study is a basis for setting up a more detailed and refined prediction model, which could assist the European Commission in closely monitoring the gap and the underlying factors generating it.
... In this way, acceleration peaks are removed from the measured signal. The blue and orange lines show the thresholds of normal and aggressive driving based on the values presented by de Vlieger [35]. In the shown sequence, the main focus was on city driving and the maximum speed was limited to 50 km/h. ...
Article
Due to current progresses in the field of driver assistance systems and the continuously growing electrification of vehicle drive trains, the evaluation of driver behavior has become an important part in the development process of modern cars. Findings from driver analyses are used for the creation of individual profiles, which can be permanently adapted due to ongoing data processing. A benefit of data-based dynamic control systems lies in the possibility to individually configure the vehicle behavior for a specific driver, which can contribute to increasing customer acceptance and satisfaction. In this way, an optimization of the control behavior between driver and vehicle and the resulting mutual system learning and -adjustment hold great potential for improvements in driving behavior, safety and energy consumption. The submitted paper deals with the analysis of different methods and measurement systems for the identification and classification of driver profiles as well as with their potential to optimize both vehicle driving behavior and energy consumption on the example of a hybrid drive train. A literature research results in a number of different approaches of evaluation, which are analyzed, linked and adapted in the publication. As a result, an evaluation of the connection between different methods of driver profile determination is given. Data collection and interviews have been performed during twenty test drives on a defined route profile with different measurement systems and methods. The acquired data form the basis for a comparison and an analysis of a comprehensive driving style classification. Subsequently, a framework for computer-aided investigations of the influences of driver behavior on the control of drive trains is established by use of an existed simulation model of a hybrid drive train. Finally, a driver model is implemented based on the learnings out of analyzing the measurements and surveys. The evaluation of the measurement campaigns delivers detailed information about vehicle longitudinal acceleration behavior in different driving scenarios. This information is used to classify the individual driving styles into the types calm, normal and aggressive. This driving style-related information can be integrated into the control strategy of a hybrid power train to support operation strategy optimization regarding both driver satisfaction and reduction of energy-, respectively fuel consumption.
... 10 11 Table 4 shows the result of pairwise comparisons conducted between each of the distributions with rainfall and 12 the distribution without rainfall (i.e., dry) as the reference. This provides the maximum absolute difference (D) 13 with their p-value, (> ). The null hypotheses were that two data samples came from the same distribution. ...
Article
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This research was an investigation of changes in driving behavior that occurs in response to rainfall intensity, especially focusing on risky behaviors. This was done using driving records of 620 taxis in Seoul (South Korea). We utilized driving volatility as a quantitative measure of driving behavior. This parameter indicates the variability of vehicle movement as indicated by vehicular acceleration and jerk. The result verified that, as the rainfall intensity increases, driving patterns deviate more from those without rainfall. From these changes, a measure of aggressiveness was derived considering these behavioral differences under different rainfall conditions. In particular, volatile and risky driving decisions with respect to jerk occur more frequently as rainfall intensity increases. This implies that changes in acceleration (e.g., acceleration after deceleration, deceleration after acceleration) are prevalent in rainy days. Furthermore, using crashes and law violation information about taxis, this research verified that higher volatility is related to a higher likelihood of crashes and law violations. The contributions of this study are that it quantifies the aggressiveness of drivers as a reflection of changing driving behavior under different rainfall conditions, and verifies the volatility index by relating to crashes and traffic law violations of individual drivers.
... The downstream SCR can apply the upstream doc-converted NO 2 to improve the rapidity of the SCR response and then improve the low-temperature conversion efficiency of SCR [38], [39], [40]. The ASC can effectively reduce excessive ammonia injection, and the conversion efficiency is generally greater than 90% [40], [41], [42]. This technical scheme is mainly applied to heavy-duty vehicles that combine urban and high-speed sections [43], [44], [45]. ...
Article
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With a continuous increase in vehicle ownership, vehicle emissions have become one of the main sources of air pollution in China. Serious air pollution is a great threat to human health and life. The China VI regulations, which adopt strict particulate matter (PM) and nitrogen oxide (NOx) limitations, have been implemented. As one of the best technologies for measuring vehicle emissions, portable emission measurement systems (PEMSs) have gained attention. In this work, the characteristics of China VI heavy-duty vehicle emissions were investigated with a PEMS. The effects of road conditions on NOx, hydrocarbon (HC), carbon monoxide (CO), PM and particle number (PN) pollutants were analyzed based on distance-based emission factors. The results show that the gaseous pollutant levels of NOx, HC and CO for China VI vehicles are much lower than those for China V vehicles. The average distance-based emission factors of NOx CO and HC decreased by 88%, 98%, and 62.7%, respectively. Based on the power-based window method, the PEMS test results for NOx, HC and CO are 460 mg·(kWh)-1, 192 mg·(kWh)-1 and 37.5 mg·(kWh)-1, respectively. According to the PEMS tests, approximately 88%–95% of the particles are in the 10–100 nm class based on three typical operational modes, which represent approximately 0.08%–0.13% of the total particle mass. These heavy-duty vehicles can satisfy the requirements of the PEMS China VI standards. This study emphasizes the importance of obtaining real-world measurements of heavy-duty vehicles to improve the accuracy of emission factors in the development of emission inventories in China.
... De Vlieger et al. [25] and Jackson et al. [26] reported that CO 2 , CO, HC, NO x , and PN emissions are all systematically related to road type. The authors observed that the emissions for restricted access roads (like expressways) are significantly different from the emissions for unrestricted access roads (like arterial roads). ...
Article
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Emission models are important tools for traffic emission and air quality estimates. Existing instantaneous emission models employ the steady-state “engine emissions map” to estimate emissions for individual vehicles. However, vehicle emissions vary significantly, even under the same driving conditions. Variability in the emissions at a specific driving condition depends on various influencing factors. It is important to gain insight into the effects of these factors, to enable detailed modeling of individual vehicle emissions. This study employs a portable emissions measurement system (PEMS), to collect vehicle emissions including the corresponding parameters of engine condition, vehicle activity, catalyst temperature, geography, and meteorology, to analyze the variability in emission rates as a function of those factors, across different vehicle specific power (VSP) categories. We observe that carbon dioxide, carbon monoxide, nitrogen oxides, and particle number emissions are strongly correlated with engine parameters (engine speed, torque, load, and air-fuel ratio) and vehicle activity parameters (vehicle speed and acceleration). In the same VSP bin, emissions per second on highways and ramps are higher than those on arterial roads, and the emissions when the vehicle is traveling downhill tend to be higher than the emissions during uphill traveling, because of higher observed speeds and accelerations. Morning emissions are higher than afternoon emissions, due to lower temperatures.
... Moreover, congested traffic during busy hours causes subsequent aggressive driving. Multiple RDE studies also reported similarly increased driving aggressiveness during urban driving (De Vlieger et al., 2000;Gallus et al., 2017;Larsson and Ericsson, 2009;Luján et al., 2020;Mera et al., 2019;Szumska and Jurecki, 2020;Varella et al., 2019). ...
Article
Light-duty diesel vehicles are a significant contributor to urban air pollution. This study aimed to investigate the variation in driving style and emissions based on traffic conditions, route features and route familiarity using 30 drivers. Driving styles were assessed using acceleration, relative positive acceleration and velocity × positive acceleration and it was found that approximately 42% of drivers were aggressive on both trips. The difference between the highest and lowest emitting drivers was observed to be a factor of 5.9 for nitrogen oxides (NOx) and 1.56 for carbon dioxide (CO2). A linear relationship between cumulative NOx emissions and cumulative CO2 emissions was found at the individual driver level. The study also detected several emission hotspots resulting from certain route features. This study will contribute to: 1) understand how driver behaviour, traffic conditions, route familiarity and route features contribute to emissions; 2) develop predictive emission models; and 3) optimise route characteristics.
... Traffic operation have a significant impact on fuel consumption and emissions. Fuel consumption increases in the presence of traffic congestion, which has been found in previous studies (29)(30)(31). However, the research on the large-scale urban road network is still insufficient. ...
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Numerous studies shown that particulate matter in the ambient environment has a significant impact on the health of the respiratory system. To understand the interrelationships between urban built environment, transportation operations and health, this study proposes an innovative approach that uses real-world GPS datasets to calculate energy consumption and emissions from transportation. The experiment used the traffic operation state in the Fourth Ring Road of Beijing as the research object and tested the impact of using the Regional speed optimization (RSO) strategy based on Macroscopic Fundamental Diagram (MFD) on energy consumption and emissions during peak hours. The impact of traffic emission on the health of roadside pedestrians is also considered. Changes in PM2.5 concentrations around four different built-up areas were calculated and compared. The computational experiments indicate that the PM2.5 pollutants exhausted by the traffic on the Ring Road during peak hours can reach up to 250 μg/m3, while the traffic emission on general roads near residential areas is only 50 μg/m3. Adopting Regional speed optimization can reduce the energy consumption of the road network by up to 18.8%. For roadside runners, the PM2.5 inhalation caused by night running in commercial and recreational areas is about 1.3-2.6 times that of night running in residential areas. Compared with morning or night running, the risk of respiratory disease caused by PM2.5 inhalation was about 10.3% higher than commuter running behavior. The research results provide a useful reference for energy conservation and emission reduction control strategies for different road types in cities and help existing cities to establish a traveler health evaluation system caused by traffic operation.
... Emissions also vary with respect to drivers' attitude, experience, gender, physical condition, and age. Aggressive driving increases emissions compared to normal driving (De Vlieger et al., 2000). Sierra Research found that most drivers spend about 2% of total driving time in aggressive mode, which contributes about 40% of total emissions (Samuel et al., 2002). ...
Article
The Intergovernmental Panel on Climate Change (IPCC) estimates that baseline global GHG emissions may increase 25–90% from 2000 to 2030, with carbon dioxide (CO2) emissions growing 40–110% over the same period. On-road vehicles are a major source of CO2 emissions in all the developed countries, and in many of the developing countries in the world. Similarly, several criteria air pollutants are associated with transportation, for example, carbon monoxide (CO), nitrogen oxides (NOx), and particulate matter (PM). Therefore, the need to accurately quantify transportation-related emissions from vehicles is essential. The new U.S. Environmental Protection Agency (EPA) mobile source emissions model, MOVES2010a (MOVES), can estimate vehicle emissions on a second-by-second basis, creating the opportunity to combine a microscopic traffic simulation model (such as VISSIM) with MOVES to obtain accurate results. This paper presents an examination of four different approaches to capture the environmental impacts of vehicular operations on a 10-mile stretch of Interstate 4 (I-4), an urban limited-access highway in Orlando, FL. First (at the most basic level), emissions were estimated for the entire 10-mile section “by hand” using one average traffic volume and average speed. Then three advanced levels of detail were studied using VISSIM/MOVES to analyze smaller links: average speeds and volumes (AVG), second-by-second link drive schedules (LDS), and second-by-second operating mode distributions (OPMODE). This paper analyzes how the various approaches affect predicted emissions of CO, NOx, PM2.5, PM10, and CO2. The results demonstrate that obtaining precise and comprehensive operating mode distributions on a second-by-second basis provides more accurate emission estimates. Specifically, emission rates are highly sensitive to stop-and-go traffic and the associated driving cycles of acceleration, deceleration, and idling. Using the AVG or LDS approach may overestimate or underestimate emissions, respectively, compared to an operating mode distribution approach. Implications: Transportation agencies and researchers in the past have estimated emissions using one average speed and volume on a long stretch of roadway. With MOVES, there is an opportunity for higher precision and accuracy. Integrating a microscopic traffic simulation model (such as VISSIM) with MOVES allows one to obtain precise and accurate emissions estimates. The proposed emission rate estimation process also can be extended to gridded emissions for ozone modeling, or to localized air quality dispersion modeling, where temporal and spatial resolution of emissions is essential to predict the concentration of pollutants near roadways.
... According to Faiz et al. (1996) and Chen et al. (2007) the emission levels depend heavily on traffic-flow characteristics, such as average flow speed, the frequency and intensity of vehicle acceleration and deceleration, the number of stops, and vehicle operating mode. De Vlieger et al. (2000) studies the environmental effects of driving behaviour and congestion by considering passenger cars. According to this study an intense traffic congestion can increase fuel consumption by 20 -45. ...
... Abou-Senna et al. explored the carbon dioxide emissions of motorized vehicles on limited access highways in a microscopic and stochastic environment using an optimal design approach and speed was found to have a significant impact [10]. Vlieger et al. studied the influence of driving behavior and traffic conditions on fuel consumption and emissions for a small test fleet of passenger cars; city traffic was found to have the highest fuel consumption and emissions [11]. Gu et al. investigated the traffic-related emission impacts of work zones using an urban freeway case study; a VISSIM test bed combined with the Environmental Protection Agency's MOVES emission model was used to estimate the total emissions assuming daytime and nighttime lane-closure scenarios [12]. ...
Article
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In order to effectively control carbon dioxide emissions of motorized vehicles, it is very important to measure their carbon dioxide emission factors. The objective of this paper was to develop measurement models for the carbon dioxide emission factors of passenger cars. Road systems of downtown areas of four typical Chinese counties were explored and 12 types of basic road networks were recognized and defined. With PTV Vissim, microscopic traffic simulation models were set up for every type of basic road network, average speeds of the simulated cars were collected, and carbon dioxide emissions were calculated using MOVES (Motor Vehicle Emission Simulator) software. For model development, the paper put forth two compound explanatory variables: the weighted average of segment lengths and the sum of critical ratios of volume to saturation flow rate. Six functional relationships for the variables were tested and the double exponential function was proven to be the most appropriate. Finally, for each of the 12 types of basic road networks, a measurement model for carbon dioxide emission factors was calibrated using the double exponential function for the variables. The measurement models can be used to estimate the carbon dioxide emissions of passenger cars concerning potential improvement schemes impacting traffic demand and/or traffic supply.
... In addition, drivers who did not own their vehicles were under pressure from their 'bosses' to pay a daily fee. They were forced to drive from 6 a.m. until after 8 p.m., which partly explains polluting practicesthat is practices which increased fuel consumption and particulate emissions (De Vlieger, De Keukeleere, & Kretzschmar, 2000): erratic driving, longer hours driving, reducing the engine rest periods or use of adulterated fuel. On the issue of pollution representations in the industry, taxi drivers possessed detailed knowledge of the consequences for air quality of vehicle defects and maintenance, such as air filter cleaning, engine type and fuel quality. ...
Article
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Ambient air pollution is currently a major environmental health hazard in many urban areas across the African continent. Driven by the increased concentration of human activities in cities, occupational exposure represents one of the main risk factors to disease burden. Consequently, ‘living with’ air pollution is a significant daily life public health issue. In this study, we focus on three specific occupational sites in Abidjan, Côte d’Ivoire, representing different major pollution sources – road traffic, wood fires and waste-burning fires. We explore in particular the social experiences of people exposed to chronic air pollution as well as the distribution of health risks across different occupational sources. We assessed the characterization of the ‘risk culture’ of workers, studying if it varies according to participants’ occupations and how it influenced their exposure. Our analysis combines a qualitative assessment of the ‘risk culture’ of air pollution with the development of a Risk Culture Indicator (RCI). We show firstly that the working conditions and occupational practices in each group shape specific representations of air pollution, varying levels and emphasis within understandings of risk as represented within the RCI scores. We also demonstrate that occupational status in each group plays a role in reducing exposure to air pollution, with those most vulnerable socio-economically remaining the most exposed. Finally, the findings suggest that risk culture is the combination of a tangible experience of air pollution and other risks encountered in daily life, technical mediations shaping that experience (objects and equipment), as well as existing power relationships. These considerations of risk culture should be considered as an integral part in assessment of health risks.
... Although the technology and the age of the vehicle are the main factors that contribute to vehicular emissions, other factors are likely to affect these emissions such as individual driver variability. Inherent differences in driving functions and driver-todriver behavioral inconsistencies considering parameters such as speed, acceleration, gear-shifting, and braking, have been shown to be variable and contribute to potential changes in characteristic emissions (Ahn et al. 2002;Austin et al. 1993;Brundell-Freij and Ericson 2005;De Vlieger, De Keukeleere, and Kretzschmar 2000;Ericsson 2000Ericsson , 2001Hung et al. 2006;Liu and Frey 2015;Wasielewski and Evans 1985). De Genova and Austin (1994), using two drivers, reported dramatic emissions variability on per-mile vehicle emissions. ...
Article
On-road vehicles have become a dominant source of air pollution and energy consumption in many parts of the world. As a result, estimating the amount of pollution from these vehicles and analyzing the factors affecting their emission is necessary to understand and manage ambient air quality. Traditionally, automobile emissions have been measured with dynamometer tests using representative driving cycles. A review of the related literature shows that there is a lack of real life, on-the-road testing of automobile emissions. Moreover, a few previous studies have directly discussed the impact of driver variability on emissions from the vehicles. This research analyzes the impacts of driver experience, gender, speed, and road grade on vehicle emissions through on-the-road testing experiment in Logan, Utah, USA during summer of 2016. The methodology of the research starts by selecting a representative car to perform the tests on. The next step was to choose test drivers representing four groups: young males, young females, experienced males, and experienced females. After that, the drivers were assigned a specified route that has different speed limits and grades. Emissions from the car exhaust (specifically carbon monoxide-CO, hydrocarbons-HC, and nitrogen oxides-NOx) in addition to the engines rotational speed (rpm), car speed, and exhaust temperature, were measured every second while driving on the specified route. Statistical analysis of the results shows that contrary to the common stereotypes, experienced drivers emitted 52% more HC and 49% more NOx than young drivers and female drivers, and male drivers emitted 14% more HC and 44% more NOx than female drivers. It also shows that CO emission is not significantly dependent on age, gender, nor driving conditions. Finally, driving through low-speed segments emits significantly higher HC (79%), while driving through uphill segments emits significantly higher (98%) NOx than driving through downhill segment. Implications: This study showed that there are significant differences in vehicular emissions among drivers from different genders and age. These differences should be taking into consideration in future emission modeling studies and regulatory scenarios.
... In fact, driving behavior and traffic conditions have a major impact on the traction power demand and consequently on the EMS. Several recent studies attempt to precisely establish such a relationship between driving conditions and energy consumption for different types of vehicle powertrains [7], [8]. ...
Article
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This work proposes a new approach for the optimal energy management of a hybrid electric vehicle (EV) considering traffic conditions. The method is based on a bilevel decomposition. At the microscopic level, the offline part computes cost maps due to a stochastic optimization that considers the influence of traffic, in terms of speed/acceleration probability distributions. At the online macroscopic level, a deterministic optimization computes the ideal state of charge at the end of each road segment using the computed cost maps. The optimal torque split can then be recovered according to the cost maps and this SoC target sequence. Since the high computational cost due to the uncertainty of traffic conditions has been managed offline, the online part should be fast enough for real-time implementation on board the vehicle. Errors due to discretization and computation in the proposed algorithm have been studied. Finally, we present numerical simulations using actual traffic data and compare the proposed bilevel method to the best possible consumption, obtained by a deterministic optimization with full knowledge of future traffic conditions, as well as to an established solution for energy management of a hybrid EV. The solutions show a reasonable overconsumption compared with deterministic optimization and manageable computational times for both the offline and the online part.
... Recent studies also revealed a road-grade relationship with speed and acceleration (Bachman 1998). De Vlieger (1997) and De Vlieger et al. (2000) used instrumented vehicles to demonstrate that aggressive driving resulted in a sharp increase in fuel consumption (12-40%) and emissions (1-8 times) compared to normal driving. For volatile organic components (VOCs) and NO x , aggressive driving increases emissions ranging from 15-400% to 20-150%, respectively. ...
Article
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Frequent road intersections in Dhaka–Chittagong National Highway (NH1), the major transport corridor of Bangladesh, significantly reduce the level of service of the corridor and eventually leads to inefficient fuel economy and excessive greenhouse gas (GHG) emission. In spite of upgrading NH1 into a four-lane highway, major road intersections reduce vehicle speed and increase congestion time and eventually burn fuel. Fuel expenses during this lost time cover no distance but increase vehicular emission within the vicinity of road and contribute to roadside temperature. Besides, the transport sector’s energy demand in Bangladesh is supported mostly by imported fuel that drains out foreign currency and inhibits GDP growth. Against the backdrop, the Government of Bangladesh is proposing to construct a four-lane expressway. The paper attempts to estimate the fuel loss savings, GHG emission reduction and economic benefit of constructing Dhaka–Chittagong Expressway. As the construction of the expressway paved a way for an increment of traffic growth by 10%, the study infers that the average lost time because of 36 intersections for a projected annual average daily traffic of 27,334 vehicles/day (in 2022). In addition to that, the fuel loss savings for various vehicle classes affect economic growth and the ensuing idling emission of EFI and MFI engines contributes to transport sector pollution. The study intends to expedite the fact that Dhaka–Chittagong Expressway would not only replace road interventions that reduce travel time cost, expenditures regarding vehicle operating and accident but also contributes cardinally to economic emancipation of the country. The estimated Benefit–Cost Ratio (BCR) was 1.23, net present value was 762.34 Million USD) and Economic Internal Rate of return was 18.27% of the proposed project.
... Thresholds between the extreme driving decisions and the majority of driving decisions need to be defined. Most previous studies used one or several cutoff points as the thresholds to define the normal or aggressive driving (De Vlieger et al., 2000;Ericsson, 2001;Kim & Choi, 2013). However, the driving behavior varies across different driving environments and only the driving decisions under similar conditions are comparable. ...
Article
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Connected and automated vehicles (CAVs) are expected to change the way we travel. Before both the vehicles and infrastructures are fully automated, users of CAVs are required to respond appropriately to any adverse on-road conditions or malfunction that may prevent the autonomous driving system from reliably sustaining the dynamic driving task performance. The objective of this study is to construct spatiotemporal driving volatility profiles to help CAVs or drivers identify the potential hazards in the existing transportation network and make proactive driving decisions. The volatility profiles are constructed based on the historical traffic dynamics, varying spatially and temporally in the network. For demonstration, this study exploited the Basic Safety Messages datasets from Safety Pilot Model Development program in Ann Arbor, Michigan. The driving volatility is a measure to reflect the variability of driving performance, which is often used to show a vehicle or driver’s performance on road. This study extends the concept to capture the driving dynamics as a performance of the transportation network. This study also matched the driving volatility to the spatial and temporal occurrence of historical traffic crashes. Modeling results showed the volatility is significantly related to safety outcomes; therefore, the driving volatility profiles can be compiled into the high definition (HD) maps to inform CAVs and drivers of potential on-road hazards and assisting in making proactive driving decisions. Further, the results offer implications for potential upgrades of the transportation infrastructure for full automation in the future.
... In fact, driving behavior and traffic conditions have a major impact on the traction power demand and consequently on the EMS. Several recent studies attempt to precisely establish such a relationship between driving conditions and energy consumption for different types of vehicle powertrains [7], [8]. ...
Preprint
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This work proposes a new approach to optimize the consumption of a hybrid electric vehicle taking into account the traffic conditions. The method is based on a bi-level decomposition in order to make the implementation suitable for online use. The offline lower level computes cost maps thanks to a stochastic optimization that considers the influence of traffic, in terms of speed/acceleration probability distributions. At the online upper level, a deterministic optimization computes the ideal state of charge at the end of each road segment, using the computed cost maps. Since the high computational cost due to the uncertainty of traffic conditions has been managed at the lower level, the upper level is fast enough to be used online in the vehicle. Errors due to discretization and computation in the proposed algorithm have been studied. Finally, we present numerical simulations using actual traffic data, and compare the proposed bi-level method to a deterministic optimization with perfect information about traffic conditions. The solutions show a reasonable over-consumption compared with deterministic optimization, and manageable computational times for both the offline and online parts.
... In table 2, according to the literature and the researches that was provided by Vlieger (2000) and My climate (2021) In Table 3, it is clear that vehicles produced carbon emissions more than expected and planned due to the high traffic density in this road. It was noticed that the readings that was collected from Testo 315-3 in each zone, is different than the calculated emission, this due to the differences in fuel type and purity used by different vehicles, vehicle type with different burning capacity (Toyota, Mercedes benz, BMW, Honda …etc), year of manufacture, vehicle accessories, driving behavior (Fontaras et al., 2017). ...
Article
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In the last two decades, the percentage volume of carbon emissions has increased from 280 to more than 380 parts per million in the atmosphere, the problem is that it is still increasing daily in which it caused many environmental hazard that has been seen the last couple of years. The end of this century, It is expected that the uncontrolled amount of emissions emitted to the atmosphere will increase the surface temperature of plant earth by 3.4ºC. Worldwide, the percentage of carbon emissions in the atmosphere and its effect on air quality has been the main concern of scientist and researchers in the past decade. Egypt, is one of the biggest emitters that surfer from atmospheric pollution, almost 24% of the atmosphere pollutants in Egypt is from the transportation sector due to the heavy use of fossil fuels. Reducing the roads carbon emissions through streets design and form is the main scope of this research. This research intend to control the amount of carbon emission released in air by vehicles through controlling vehicles speed and motion which is effected by the street design and form. The presented research analysis the relation between carbon emissions and streets condition and forms, through measuring the amount of CO2 and CO emission produced in one of the Egyptian roads from different types of vehicles in road with three different conditions. El-Shuhada Street has been chosen to be the study area of this research. The researchers used Testo 315-3 to measure the Carbon emissions in the street and to identify the relation between CO2 emission and street condition and form. The results reveled that straight routes with vehicale speeds ranged between 80 to 100 kn/h produces less carbon emissions then straight routes with street bumps and vehicle speed ranged between 26 to 19 km/h. Moreover, curved routes emitted more emission than straight routes
... Consequently, as portable emissions measurement system (PEMS) technology improves, an increasing number of researchers around the world are focusing on real-world driving emissions. Vlieger et al. 14 studied the effects of traffic conditions on the fuel consumption and RDE of passenger cars using an onboard emissions testing system. In a study by Gallus et al., 15 24 Tongji University, 25 and Shanghai Academy of Environmental Sciences 26,27 have obtained emissions from various vehicle types based on different technologies and under different traffic conditions. ...
Article
Vehicle emissions standards and regulations remain weak in high-altitude regions. In this study, vehicle emissions from both the New European Driving Cycle and the Worldwide harmonized Light-duty driving Test Cycle were analyzed by employing on-road test data collected from typical roads in a high-altitude city. On-road measurements were conducted on five light-duty vehicles using a portable emissions measurement system. The certification cycle parameters were synthesized from real-world driving data using the vehicle specific power methodology. The analysis revealed that under real-world driving conditions, all emissions were generally higher than the estimated values for both the New European Driving Cycle and Worldwide harmonized Light-duty driving Test Cycle. Concerning emissions standards, more CO, NO x , and hydrocarbons were emitted by China 3 vehicles than by China 4 vehicles, whereas the CO 2 emissions exhibited interesting trends with vehicle displacement and emissions standards. These results have potential implications for policymakers in regard to vehicle emissions management and control strategies aimed at emissions reduction, fleet inspection, and maintenance programs.
Article
Vehicle energy economy is affected by different driving styles of individual drivers. To improve energy economy of plug-in hybrid electric vehicles (PHEVs), it is of great importance to develop the driving style adaptive optimal control strategy. In fact, driving styles are often influenced and restricted by different driving cycles. Therefore, to recognize driving style more accurately, this paper decouples driving styles from driving cycles. Based on classification and identification of driving cycles, the accelerator pedal opening and its change rate in different driving cycles are analyzed and the fuzzy-logic recognizer is built to identify driving styles. Afterwards, the driving style adaptive optimal control strategy is realized by combining the recognized driving style with the equivalent consumption minimization strategy (ECMS) and adopting a hybrid particle swarm optimization-genetic algorithm (PSO-GA) to optimize the relationship between the driving style and the equivalence factor (EF). The effectiveness of proposed driving style adaptive control strategy is validated by real vehicle test, which indicates that, compared with the original ECMS, the proposed driving style recognition based adaptive optimal control strategy improves the energy economy by 3.69% in the New European Driving Cycle (NEDC). This adaptive optimal strategy provides guidance for incorporating driving style into PHEV energy management strategy to improve fuel economy.
Conference Paper
This paper focuses on exploring the most sensitive factors affecting bus fuel consumption quantitatively based on the CART (classification and regression tree) model. Firstly, the distribution of fuel consumption data is proved to agree with Gamma distribution after data processing, which introduces the threshold of high-level fuel consumption used in this research. Further analysis on the processed data shows that four factors of road feature like intersection density on the route and six driving behavior factors including rapid acceleration have significant impacts on fuel consumption. Then, the CART model is applied to three sub-datasets of different periods, and the outcomes show that fuel consumption is sensitive to the smoothness of driving during peak hours, the density of bus stops and the frequency of idling at off-peak times. Based on the CART model results, fuel-efficient strategies can be presented quantitatively from aspects of bus line planning, traffic management, and driving behavior training.
Article
Connected and automated vehicles (CAVs) are already part of the surface transportation system. In order for a CAV to operate safely, it needs information such as static data (high-resolution navigation maps) and real-time dynamics from various sensors, some of which exchange information with other vehicles or roadside units. High resolution navigation maps can integrate historical on-road driving performance data to help CAVs and drivers operating vehicles with low level automation make informed proactive decisions. This study proposes that navigation maps on CAVs come pre-installed with historical driving data and that they work together with real-time sensors to help CAVs plan maneuvers. Historical driving data offers insights about decisions made by drivers at locations along a route, e.g., where drivers often make sharp turns or where they accelerate and decelerate hard. A pre-installed record of historical driving decisions will support informed decision-making and proactively “warn” CAVs and drivers about potential hazards. This study explores location-based driving volatility as a key to improving safety through CAVs. Location-based volatility is a measure of historical driving performance, defined as the percentage of extreme maneuvers performed on a location in road network. For demonstration, we modeled and visualized real-world high-resolution geo-referenced data. The data comes from a connected vehicle safety pilot program in Ann Arbor, Michigan. We found measured location-based volatility is significantly related to safety outcomes. Therefore, location-based driving volatility can serve as a valuable piece of information to be added to navigation maps in CAVs in order to help them navigate volatile hot-spots.
Conference Paper
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The objective of this research is to analyze by means of microscopic traffic flow simulation whether the integration of traffic emissions in the control criteria of a dynamic line control system can lead to a reduction in emissions. A traffic flow model of a German freeway, equipped with a dynamic line control system, is built and calibrated in PTV Vissim. The current control logic is implemented in Python and integrated into Vissim through the COM interface. The acceptance of the road users towards the displayed speed limits is investigated using measured traffic data from the examined section and is included in the model. Traffic emissions are determined during the simulation, and the control logic is adapted. We performed the emission assessment using the Handbook Emission Factors for Road Transport (HBEFA) for air emissions (CO2, NOx, and PM) and the German guidelines for noise protection on roads (RLS-90) for noise emissions. To evaluate the control logic’s influence on resulting traffic emissions, the existing control logic, as it operates currently, is compared to altered control logics with additional consideration of emissions. An analysis of the emissions shows that the integration of pollution-based control criteria has a positive effect. All air and noise emissions can be slightly reduced by adjusting the control logic, with no significant changes in travel times.
Article
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The sequence of instantaneous driving decisions and its variations, known as driving volatility, prior to involvement in safety critical events can be a leading indicator of safety. This study focuses on the component of "driving volatility matrix" related to specific normal and safety-critical events, named "event-based volatility." The research issue is characterizing volatility in instantaneous driving decisions in the longitudinal and lateral directions, and how it varies across drivers involved in normal driving, crash, and/or near-crash events. To explore the issue, a rigorous quasi-experimental study design is adopted to help compare driving behaviors in normal vs unsafe outcomes. Using a unique real-world naturalistic driving database from the 2nd Strategic Highway Research Program (SHRP), a test set of 9593 driving events featuring 2.2 million temporal samples of real-world driving are analyzed. This study features a plethora of kinematic sensors, video, and radar spatiotemporal data about vehicle movement and therefore offers the opportunity to initiate such exploration. By using information related to longitudinal and lateral accelerations and vehicular jerk, 24 different aggregate and segmented measures of driving volatility are proposed that captures variations in extreme instantaneous driving decisions. In doing so, careful attention is given to the issue of intentional vs. unintentional volatility. The volatility indices, as leading indicators of near-crash and crash events, are then linked with safety critical events, crash propensity, and other event specific explanatory variables. Owing to the presence of unobserved heterogeneity and omitted variable bias, fixed- and random-parameter discrete choice models are developed that relate crash propensity to unintentional driving volatility and other factors. Statistically significant evidence is found that driver volatilities in near-crash and crash events are significantly greater than volatility in normal driving events. After controlling for traffic, roadway, and unobserved factors, the results suggest that greater intentional volatility increases the likelihood of both crash and near-crash events. A one-unit increase in intentional volatility is associated with positive vehicular jerk in longitudinal direction increases the chance of crash and near-crash outcome by 15.79 and 12.52 percentage points, respectively. Importantly, intentional volatility in positive vehicular jerk in lateral direction has more negative consequences than intentional volatility in positive vehicular jerk in longitudinal direction. Compared to acceleration/deceleration, vehicular jerk can better characterize the volatility in microscopic instantaneous driving decisions prior to involvement in safety critical events. Finally, the magnitudes of correlations exhibit significant heterogeneity, and that accounting for the heterogeneous effects in the modeling framework can provide more reliable and accurate results. The study demonstrates the value of quasi-experimental study design and big data analytics for understanding extreme driving behaviors in safe vs. unsafe driving outcomes.
Article
Heavy-duty diesel truck (HDDT) is one of the major sources of air pollution and energy consumption. To reduce the estimation bias and improve the interpretability, the random parameters logit (RPL) model was employed to examine the effects of influencing factors on fuel consumption of HDDTs in the real world. The unobserved heterogeneity effects varying across the samples on fuel efficiency were extracted from the long-term daily trip-based data. In order to further illustrate the advantages of the RPL model in explaining the impacts of factors, a fixed parameters logit model with twenty parameters was constructed and compared. The Akaike information criterion and the Bayesian information criterion were used to select a more reasonable model structure. The findings show that the RPL model performs better and the unobserved heterogeneity would affect the effects of factors of rolling without engine load proportion and temperature and, consequently, map the level of fuel consumption. This reveals the variability of the fuel consumption among the samples. Driving compensation effects were also identified in this study (i.e., the drivers tend to perform the fuel-saving operations in adverse driving circumstances and vice versa). The methodology proposed in this paper can provide a new insight for researchers to identify the instability of energy-related factors under real road conditions. Future research could be implemented to assess the similar effects of alternative fuel vehicles.
Book
The optimality-based design of the energy management of hybrid electric vehicles is a challenging task due to the extensive and complex nonlinear reciprocal effects in the system, as well as the unknown vehicle use in real traffic. The optimization has to consider multiple continuous values of sensor and control variables and has to handle uncertain knowledge. In this thesis a Deep Reinforcement Learning Energy Management is presented. This energy management minimizes the energy consumption and ensures at the same time that no inadmissible actions are executed during the learning and execution process. Compared to classical methods, the approach takes into account other state variables, such as battery temperature and derating, while also controlling the battery cooling.
Thesis
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Traffic signals are used at intersections to manage the flow of vehicles by allocating right-of-way in a timely manner for different users of the intersection. Traffic signals are therefore installed at an intersection to improve overall safety and to decrease vehicular average delay. However, the variation of driving speed in response to these signals causes an increase in fuel consumption and air emission levels. One solution to this problem is Eco-Cooperative Adaptive Cruise Control (Eco-CACC), which attempts to reduce vehicle fuel consumption and emission levels by optimizing driver behavior in the vicinity of a signalized intersection. Various Eco-CACC algorithms have been proposed by researchers to address this issue. With the help of vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication, algorithms are being developed that utilize signal phasing and timing (SPaT) data together with queue information to optimize vehicle trajectories in the vicinity of signalized intersections. The research presented in this thesis constitutes the third phase of a project that entailed developing and evaluating an Eco-CACC system. Its main objective is to evaluate the benefits of the newly developed Eco-CACC algorithm that was proposed by the Center for Sustainable Mobility at the Virginia Tech Transportation Institute. This algorithm uses advanced signal information (SPaT) to compute the fuel-optimal trajectory of vehicles, and, then, send recommended speeds to drivers as an audio message or implement them directly into the subject vehicle. The objective of this study is to quantitatively quantify the fuel-efficiency of the Eco-CACC system in a real field environment. In addition, another goal of this study is to address the implementation issues and challenges with the field application of the Eco-CACC system. A dataset of 2112 trips were collected as part of this research effort using a 2014 Cadillac SRX equipped with a vehicle onboard unit for (V2V) and (V2I) communication. A total of 32 participants between the ages of 18 and 30 were randomly selected from one age group (18-30) with an equal number of males and females. The controlled experiment was conducted on the Virginia Smart Road facility during daylight hours for dry pavement conditions. The controlled iii field experiment included four different scenarios: normal driving, driving with red indication countdown information provided to drivers, driving with recommended speed information computed by the Eco-CACC system and delivered to drivers, and finally automated driving (automated Eco-CACC system). The controlled field experiment was conducted for four values of red indication offsets along an uphill and downhill approach. The collected data were compared with regard to fuel economy and travel time over a fixed distance upstream and downstream of the intersection (820 ft (250 m) upstream of the intersection to 590 ft (180 m) downstream for a total length of 1410 ft (430 m)). The results demonstrate that the Eco-CACC system is very efficient in reducing fuel consumption levels especially when driving downhill. The field data indicates that the automated scenario could produce fuel and travel time savings of 31% and 9% on average, respectively. In addition, the study demonstrates that driving with a red indication countdown and recommended speed information can produce fuel savings ranging from 4 to 21 percent with decreases in travel times ranging between 1 and 10 percent depending on the value of red indication offset and the direction. Split-split-plot design was used to analyze the data and test significant differences between the four scenarios with regards to fuel consumption and travel time. The analysis shows that the differences between normal driving and driving with either the manual or automated Eco-CACC systems are statistically significant for all the red indication offset values.
Preprint
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Electric vehicles (EVs) are considered as sustainable alternatives to conventional vehicles, as they reduce emission and fossil fuel dependency. A recent study has proposed a charging infrastructure planning tool to support intercity trips for the estimated EV market share (6 percent) in Michigan for 2030. The main goal of this study is to estimate the emission reduction associated with this electrification rate and infrastructure investment for light duty vehicles. To this end, a state-of-the-art emission estimation framework is proposed to be applied to the state-wide intercity travels. The main contributions of the proposed framework includes: 1) Incorporating a micro emission estimation model for simulated vehicle trajectories of the intercity network of Michigan, 2) Adjusting the micro emission model results considering impacts of monthly travel demand and temperature variations, and heterogeneity of vehicles based on their make, model, and age. The emission estimation framework is then compared with the traditional VMT analysis method as a benchmark. Finally, five different scenarios are explored for EV adoption to assess potential emission savings from the given electrification rate for each scenario. The results suggest an annual CO2 emission savings of 0.58-0.92 million-ton. The CO2 social cost savings may justify the investment on the network electrification. Note that only 3.7 to 8.6 percent of the total EV energy requirements must be provided via the DC fast charger network proposed by the charging infrastructure planning tool. This requires annual energy consumption of 22.15 to 51.76 BWh for the estimated EV market share in Michigan for 2030.
Research
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The objective of this research is to study factors that effect on the CO2 vehicles emissions on Egyptian roads. The models were calibrated using vehicles emission records collected during the study for the period (November 2017). Data recorded for eight vehicles, emission data were classified according to the fuel type to three categories (Diesel, Natural Gas and Petrol Vehicles), and to conduct a comparative analysis of various statistical modeling techniques generalized linear regression models were used such as "Linear Regression with Link Function of Identity, Linear Regression. with Link Function of Log, Gamma Regression with Link Function of Log and Tweedy Regression with Link Function of Log " to predict vehicle emission rates as a function of the independent variables. Vehicles emission measurements CO2 (g/s) used in this study were obtained from Egyptian Environmental Affairs Agency (EEAA) recorded for the period (November 2017), Seven independent variables were selected in this research (vehicle speed, angle between horizontal alignments, profile grade, ambient temperature, ambient pressure, ambient relative humidity and numbers of rotation per minute for vehicle engine) which affect directly on the vehicle emissions for the different vehicles categories then a comparison of these results obtained from the (SPSS) mathematical model. Finally, it was found that Linear regression model with link function of log was the best generalized regression model to represent the correlation between CO2 emission for Diesel vehicles, Natural Gas and Petrol vehicles emission.
Research
The objective of this research is to study factors that effect on the CO2 vehicles emissions on Egyptian roads. The models were calibrated using vehicles emission records collected during the study for the period (November 2017). Data recorded for eight vehicles, emission data were classified according to the fuel type to three categories (Diesel, Natural Gas and Petrol Vehicles), and to conduct a comparative analysis of various statistical modeling techniques generalized linear regression models were used such as "Linear Regression with Link Function of Identity, Linear Regression. with Link Function of Log, Gamma Regression with Link Function of Log and Tweedy Regression with Link Function of Log " to predict vehicle emission rates as a function of the independent variables. Vehicles emission measurements CO2 (g/s) used in this study were obtained from Egyptian Environmental Affairs Agency (EEAA) recorded for the period (November 2017), Seven independent variables were selected in this research (vehicle speed, angle between horizontal alignments, profile grade, ambient temperature, ambient pressure, ambient relative humidity and numbers of rotation per minute for vehicle engine) which affect directly on the vehicle emissions for the different vehicles categories then a comparison of these results obtained from the (SPSS) mathematical model. Finally, it was found that Linear regression model with link function of log was the best generalized regression model to represent the correlation between CO2 emission for Diesel vehicles, Natural Gas and Petrol vehicles emission.
Research Proposal
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The objective of this research is to study factors that effect on the NOX vehicles emissions on Egyptian roads. The models were calibrated using vehicles emission records collected during the study for the period (November 2017). Data recorded for eight vehicles, emission data were classified according to the fuel type to three categories (Diesel, Natural Gas and Petrol Vehicles), and to conduct a comparative analysis of various statistical modeling techniques generalized linear regression models were used such as "Linear Regression with Link Function of Identity, Linear Regression. with Link Function of Log, Gamma Regression with Link Function of Log and Tweedy Regression with Link Function of Log " to predict vehicle emission rates as a function of the independent variables. Vehicles emission measurements NOX (mg/s) used in this study were obtained from Egyptian Environmental Affairs Agency (EEAA) recorded for the period (November 2017), Seven independent variables were selected in this research (vehicle speed, angle between horizontal alignments, profile grade, ambient temperature, ambient pressure, ambient relative humidity and numbers of rotation per minute for vehicle engine) which affect directly on the vehicle emissions for the different vehicles categories then a comparison of these results obtained from the (SPSS) mathematical model. Finally, it was found that Linear regression model with link function of log was the best generalized regression model to represent the correlation between NOX emission for Diesel vehicles, Natural Gas and Petrol vehicles emission.
Article
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Hybrid electric vehicles (HEVs), as a promising solution to mitigate environmental pollution and reduce fuel consumption, employ a combination of fuel and electric power as power supply for boosting the vehicle's fuel economy. Comparing to conventional internal combustion engine (ICE) driven vehicles, the additional propulsion power source in electrified powertrain systems of HEVs leads to the extra control degree of freedom. Thus, a well-designed energy management strategy (EMS) is indispensable to cope with the complexity of the power distribution existing in multiple power source system. Equivalent consumption minimisation strategy (ECMS) is one of the most promising EMS techniques due to its capability of achieving the real-time local optimal control. In ECMS, a key parameter – equivalent factor (EF) is usually employed to unify the ICE fuel consumption and the electric energy consumption into a single variable representing the equivalent fuel economy, thereby achieving the instantaneous fuel economy optimisation. This paper comprehensively surveys the state-of-the-art in ECMSs for PHEVs and HEVs. Firstly, the basic operation mechanism of ECMSs is discussed. Then, ECMSs are classified based on their dependence on either online computation or offline pre-computation. Moreover, the core technique of ECMSs – EF adaptation is elaborated in terms of their principles, key characteristics, advantages, and disadvantages. In addition, the key factors for the EF adaptation as well as the corresponding factor integration methods are analysed and summarised. Finally, future research trends and the gaps for the development of ECMSs are discussed.
Article
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The increasing use of road vehicles has caused a number of transport and environmental issues throughout the world. To cope with them, traffic calming schemes are being increasingly implemented in built-up areas. An example of such schemes are Tempo-30 zones. The traffic calming measures applied as part of this scheme must be carefully planned in terms of location and design details in order to obtain the desired reduction in speed, traffic volume and exhaust emissions and, last but foremost, to increase the safety and facilitate the movement of vulnerable road users. The coexistence and combined effect of these measures and their design details must also be taken into account. The purpose of this study was to investigate whether the applied traffic calming measures had a considerable bearing on the reduction in speed to the desired level, as assumed in the traffic calming plan. Three street sections starting and ending with different intersection types were chosen to examine the synergy of the applied traffic calming measures. The numbers and speeds of vehicles were measured in three day-long continuous surveys. As it was expected, the amount of speed reduction depended on the hourly traffic volume on a one-way street and various other traffic engineering aspects. The obtained results may be used to modify the existing speed profile models and can guide traffic engineers in choosing the most effective traffic calming measures.
Article
This study examined the environmental attitude of drivers towards vehicle emission. The survey design that employed a five point Likert scale questionnaire and administered to 402 respondents (drivers) generated the data analysed. Data analysis involved descriptive and regression statistical tools. The results suggest that there was significant association between respondents’ sex, occupation, education and their environmental attitudes. A greater percentage (87. 3%) of the respondents was slightly more likely to agree that emissions from cars and trucks have serious impact on air quality. Majority (57.5%) of the respondents who were civil servants appeared to possess positive (favourable) attitudes towards the influence of emissions on the environment. Logistic regression suggest that respondents’ sex, occupation, education and vehicle’s purpose, income, age and social group significantly predicted their environmental attitudes. The study concludes that most of the sample possessed positive (favourable) environmental attitude towards vehicle emissions. This suggests that the environmental attitude of drivers towards emissions is not responsible for poor air quality. The policy implications of the findings include the need for the adoption of the polluter-pay-principle to reduce the volume of vehicles on our road, the necessity of promoting mass public transportation (such as the BRT and LAGBUS) as a way of reducing vehicle emission. Finally, sensitization program through social groups and schools is imperative.
Article
The European Union (EU) introduced the real driving emission (RDE)-LDV management system in September 2017, and it has been studying its implementation to ensure consistency with established regulations. This study investigated NOx emissions and applied different after-treatment methods in the new European driving cycle (NEDC) and world-wide harmonized light-duty vehicles test cycle (WLTC) modes on 25 test vehicles. In addition, this study was conducted on the KOR-route, which satisfies the RDE-LDV regulations. This study aims to compare and analyze the results of the evaluation methods of the 3rd and 4th RDE packages using data measured from 25 test vehicles after employing different after-treatments. The results indicated that vehicles with an LNT system satisfied the NOx emission limit (0.08 g/km) in the NEDC mode; however, it was more than 2.5 times over the NOx emission limits in the WLTC mode. In addition, vehicles with an SCR system satisfied the NOx emission limit in both modes. Compared to the evaluation methods of the 3rd and 4th RDE packages, the slope of the complete RDE trip was close to 1, and the slope of the urban part of the RDE trip was measured as 0.94. The first parameter of the function used to calculate the first parameter of the result evaluation factor (RFL1) changed to 1.3 after January 2020. The RDE-NOx emission value was higher than that measured before; this was because of the RDE result evaluation factors, which were affected by the first parameter of the function used to calculate the increase in the result evaluation factor.
Conference Paper
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Das Ziel dieser Untersuchung ist es, mittels einer mikroskopischen Verkehrsflusssimulation zu analysieren, inwiefern die Integration von Verkehrsemissionen in den Steuerungsalgorithmus einer Streckenbeeinflussungsanlage zu einer Reduktion der Emissionen führen kann. Es wurde ein Verkehrsflussmodell einer deutschen Bundesautobahn, ausgestattet mit einer Streckenbeeinflussungsanlage, in PTV Vissim aufgebaut und kalibriert. Der aktuelle Steuerungsalgorithmus wurde in Python implementiert und über die COM-Schnittstelle in Vissim integriert. Die Akzeptanz der Verkehrsteilnehmenden gegenüber den angezeigten Geschwindigkeitsbeschränkungen wurde mit Hilfe von Verkehrsmessungen am betrachteten Streckenabschnitt untersucht und im Modell hinterlegt. Die Verkehrsemissionen während der Simulation wurden ermittelt und daraufhin der Steuerungsalgorithmus angepasst. Eine Analyse der Emissionen vor und nach der Implementierung zeigt, dass die Integration von emissionsabhängigen Steuerungskriterien einen positiven Effekt hat. Alle Luft- und Lärmemissionen können durch die Anpassung des Steuerungsalgorithmus leicht gesenkt werden, ohne dass sich die Reisezeiten wesentlich ändern.
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Traffic congestion is a major environmental and social problem whose causes include urban sprawl, imbalanced home-job distributions, increased car ownership, and lack of public transportation. We focus on a relatively understudied factor: the existence of geographic barriers. We study traffic times and flows in the Boston metropolitan area, a major coastal city with substantial shape non-convexities. We show that natural barriers not only cause additional delays to the trips affected directly, but also worsen downtown congestion for everyone. Additionally, commuter flows between places separated by barriers decrease, generating additional traffic elsewhere. We also find that places next to geographic obstacles suffer from higher risks of congestion, due to their lower traffic-diffusion ability. Policymakers may consider specific solutions for congestion arising from constraining physical geographies.
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GPS loggers and cameras aboard connected vehicles can produce vast amounts of data. Analysts can mine such data to decipher patterns in vehicle trajectories and driver–vehicle interactions. Ability to process such large-scale data in real time can inform strategies to reduce crashes, improve traffic flow, enhance system operational efficiencies, and reduce environmental impacts. However, connected vehicle technologies are in the very early phases of deployment. Therefore, related datasets are extremely scarce, and the utility of such emerging datasets is largely unknown. This paper provides a comprehensive review of studies that used large-scale connected vehicle data from the United States Department of Transportation Connected Vehicle Safety Pilot Model Deployment program. It is the first and only such dataset available to the public. The data contains real-world information about the operation of connected vehicles that organizations are testing. The paper provides a summary of the available datasets and their organization, and the overall structure and other characteristics of the data captured during pilot deployments. Usage of the data is then classified into three categories: driving pattern identification, development of surrogate safety measures, and improvements in the operation of signalized intersections. Finally, some limitations experienced with the existing datasets are identified.
Article
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Realistic emission and fuel consumption rates of petrol-driven cars were determined by on-the-road experiments in 1995. A validated, in-house developed, on-board measuring system was used. Six three-way catalyst (TWC) cars and one carburetted non-catalyst car were measured. The effects of road type, driving behaviour and cold start on CO, HC and NOx emissions and fuel consumption were analysed. In real traffic situations, emissions for TWC cars were found to be at least 70% lower than for the non-catalyst car. For TWC cars, emissions decreased across the board from city to rural and motorway traffic. Without a catalyst, motorway traffic resulted in the highest NOx emissions. Compared to normal driving, aggressive driving gave emissions which were up to four times higher. Except for NOx, calm driving resulted in lower emissions still. Comparable fuel consumption rates were obtained from normal and calm driving. Those from aggressive driving were higher, by as much as 40% in city traffic. Cold starts resulted in significantly higher CO and HC emission values than hot starts. These differences were less pronounced for NOx. Emissions from TWC cars were higher than generally expected, compared to the European emission limit values (91/441/EEC) and the emission factors used in Flanders and the Netherlands (Klein,1993) for the national emission inventories. Low-emitting cars during the emission test on a chassis dynamometer, as prescribed by the 91/441/EEC directive, did not necessarily give low emissions in real traffic situations.
Article
On-road emission factors for CO, CO<sub align="right"> 2 </sub>, NO<sub align="right"> x </sub>, N<sub align="right"> 2 </sub>O, total hydrocarbons and 9 speciated hydrocarbons were measured in a traffic tunnel in Goteborg, Sweden, in winter 1994/95. Ten-minute resolution pollutant measurements were made by using both FTIR-spectroscopy and conventional analysers during a 3-week period, in parallel with measurements of windspeed, traffic flow and speed through the tunnel. In addition, 7 speciated hydrocarbons were measured as weekly averages by means of diffusive samplers during a six-month period. On-road emission factors typically increased by a factor of up to 10 during congestion compared to smooth driving conditions, clearly demonstrating the large impact of driving behaviour on emissions. Furthermore, it was found that on-road emission factors agreed with emission factors derived from chassis dynamometer measurements within on ±10-20% for CO and NO<sub align="right"> x </sub>, whereas for HC the on-road emission factor was 50% higher.
Article
The emissions of hydrocarbons, nitric oxide, and carbon monoxide from one modern vehicle were measured using on-board instrumentation during about 350 miles of driving in Los Angeles, California. Emissions during on-road driving were compared to those obtained on dynamometers using the urban dynamometer driving schedule (UDDS). Although this study only used one driver and vehicle, tested over a relatively short distance, the analysis technique may be useful for a larger evaluation of off-cycle emissions.The test vehicle had low warmed-up running emissions over the UDDS and for most of the on-road testing where the air-to-fuel ratio was maintained at the stoichiometric point. However, occasional heavily-loaded conditions during the on-road testing led to richerthan-stoiehiometric operation.During these brief enrichment events, which lasted up to 29 seconds, CO emissions were increased by a factor of 2500 and HC by a factor of 40 over closed-loop stoichiometric operation. Nitrogen oxide emissions were similar during low-load stoichiometric operation and high-load enriched operation. The relatively constant gram-per-second emission rate of CO and HC observed during enriched operation suggests that such information can be combined with determinations of the frequency of enrichment for a large number of vehicles with similar calibrations to estimate the impact of the additional emissions from enrichment on emissions inventories.
Conference Paper
An exhaust gas measurement system for on-board use has been developed, which enables the direct and continuous determination of the exhaust mass emissions in vehicles on the road. Such measurements under realistic traffic conditions are a valuable supplement to measurements taken on test benches, the latter, however, still being necessary. In the last two years numerous test runs were undertaken. The reliability of the on-board system could be demonstrated and a very informative view of the exhaust emissions behavior of a vehicle on the road was obtained from the test results.
Article
On-board emission measurements were performed on a Ford Escort 1.3 l gasoline car and on two Van Hool A300D turbo diesel buses. When compared to the limit values of the 91/441/EEC directive, the TWC car yields lower emissions in motor way traffic and values are about equal in rural traffic. However, in the city with a hot start the CO- and HC + NOx-emissions amount to 5.7 and 2.3 g/km, that is double the limit values. Respectively, 16 and 4.2 g/km are measured with a cold start which is five and four times the limit values. The Euro-1 diesel city buses, that are tested during the regular bus service, have on-the-road emission intervals for CO, HC and NOx of respectively 7–8, 0.9–1.1 and 18–23 g/km. These transient emissions, transformed to g/kWh, are about 1, 0.5 and 1 to 1.5 times the Euro-1 limit values, respectively. The car to bus comparison of hot start city emissions in g/km shows that CO and HC are about equal, whereas NOx is 30 times higher for the bus. However, in g/passenger km, CO and HC-emissions are respectively 30 and 50 times lower for the bus, while NOx-emission is equal.
Article
Sumario: The context (Reporting on Europe's environment. Environmental changes and human development. Europe: the continent) -- The assessment (Air. Inland waters. The seas. Soil. Landscapes. Nature and wildlife. The urban environment. Human health) -- Pressures (Population, production and consumption. Exploitation of natural resources. Emissions. Waste. Noise and radiation. Chemicals and genetically modified organisms. Natural and technological hazards) -- Human activities (Energy. Industry. Transport. Agriculture. Forestry. Fishing and aquaculture. Tourism and recreation. Households) -- Problems (Climate change. Stratospheric ozone depletion. Loss of biodiversity. Major accidents. Acidification. Tropospheric ozone and other photochemical oxidants. The management of freshwater. Forest degradation. Coastal zone threats and management. Waste production and management. Urban stress. Chemical risks) -- Conclusions: General findings. Highlights and responses) -- Appendix 1: An inventory of 56 environmental problems -- Appendix 2: Information strenghts and weaknesses The report on the state of the pan-European environment requested by the environment ministers for the whole of Europe at the ministerial conference held at Dobris Castle, Czechoslovakia, June 1991 / prepared by the European Environment Agency Task Force ; in cooperation with United Nations Economic Commission for Europe... [et al.]
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