Browsing by Author "Ahn, Kyoungho"

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    AERIS : Eco-Vehicle Speed Control at Signalized Intersections Using I2V Communication
    Rakha, Hesham A.; Kamalanathsharma, Raj Kishore; Ahn, Kyoungho (United States. Joint Program Office for Intelligent Transportation Systems, 2012-06)
    This report concentrates on a velocity advisory tool, or decision support system, for vehicles approaching an intersection using communication capabilities between the infrastructure and vehicles. The system uses available signal change information, vehicle characteristics, lead vehicle characteristics, and intersection features to compute the fuel-optimal vehicle trajectory. The proposed system involves a complex optimization logic incorporating roadway characteristics, lead vehicle information, vehicle acceleration capabilities and microscopic fuel consumption models to generate a fuel-optimal speed profile. The research also develops a MATLAB application in order to demonstrate the potential of an in-vehicle application of such a technology.
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    AERIS: Eco-driving Application Development and Testing
    Rakha, Hesham A.; Ahn, Kyoungho; Park, Sangjun (United States. Department of Transportation. Research and Innovative Technology Administration, 2012-06)
    This exploratory study investigates the potential of developing an Eco-Driving application that utilizes an eco-cruise control (ECC) system within state-of-the-art car-following models. The research focuses on integrating predictive cruise control and optimal vehicle acceleration and deceleration controllers within car-following models to minimize vehicle fuel consumption levels. This system makes use of topographic information, spacing to lead vehicle, and a desired (or target) vehicle speed and distance headway as input variables.
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    Developing a Hydrogen Fuel Cell Vehicle (HFCV) Energy Consumption Model for Transportation Applications
    Ahn, Kyoungho; Rakha, Hesham A. (MDPI, 2022-01-12)
    This paper presents a simple hydrogen fuel cell vehicle (HFCV) energy consumption model. Simple fuel/energy consumption models have been developed and employed to estimate the energy and environmental impacts of various transportation projects for internal combustion engine vehicles (ICEVs), battery electric vehicles (BEVs), and hybrid electric vehicles (HEVs). However, there are few published results on HFCV energy models that can be simply implemented in transportation applications. The proposed HFCV energy model computes instantaneous energy consumption utilizing instantaneous vehicle speed, acceleration, and roadway grade as input variables. The mode accurately estimates energy consumption, generating errors of 0.86% and 2.17% relative to laboratory data for the fuel cell estimation and the total energy estimation, respectively. Furthermore, this work validated the proposed model against independent data and found that the new model accurately estimated the energy consumption, producing an error of 1.9% and 1.0% relative to empirical data for the fuel cell and the total energy estimation, respectively. The results demonstrate that transportation engineers, policy makers, automakers, and environmental engineers can use the proposed model to evaluate the energy consumption effects of transportation projects and connected and automated vehicle (CAV) transportation applications within microscopic traffic simulation models.
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    Developing Procedures for Screening High Emitting Vehicles and Quantifying the Environmental Impacts of Grades
    Park, Sangjun (Virginia Tech, 2005-11-29)
    Since the transportation sector is highly responsible for U.S. fuel consumption and emissions, assessing the environmental impacts of transportation activities is essential for air-quality improvement programs. Also, high emitting vehicles need to be considered in the modeling of mobile-source emissions, because they contribute to a large portion of the total emissions, although they comprise a small portion of the vehicle fleet. In the context of this research, the thesis quantifies the environmental impacts of roadway grades and proposes a procedure that can enhance the screening of high emitting vehicles. First, the study quantifies the environmental impacts of roadway grades. Although roadway grades are known to affect vehicle fuel consumption and emission rates, there do not appear to be any systematic evaluations of these impacts in the literature. Consequently, this study addresses this void by offering a systematic analysis of the impact of roadway grades on vehicle fuel consumption and emission rates using the INTEGRATION microscopic traffic simulation software. The energy and emission impacts are quantified for various cruising speeds, under stop and go conditions, and for various traffic signal control scenarios. The study demonstrates that the impact of roadway grade is significant with increases in fuel consumption and emission rates in excess of 9% for a 1% increase in roadway grade. Consequently, a reduction in roadway grades in the range of 1% can offer savings that are equivalent to various forms of advanced traffic management systems. Second, the study proposes a new procedure for estimating vehicle mass emissions from remote sensing device measurements that can be used to enhance HEV screening procedures. Remote Sensing Devices (RSDs) are used as supplementary tools for screening high emitting vehicles (HEVs) in the U.S. in order to achieve the National Ambient Air Quality Standards (NAAQS). However, tailpipe emissions in grams cannot be directly measured using RSDs because they use a concentration-based technique. Therefore, converting a concentration measurement to mass emissions is needed. The research combines the carbon balance equation with fuel consumption estimates to make the conversion. In estimating vehicle fuel consumption rates, the VT-Micro model and a Vehicle Specific Power (VSP)-based model (the PERE model) are considered and compared. The results of the comparison demonstrate that the VSP-based model under-estimates fuel consumption at 79% and produces significant errors (R2 = 45%), while the VT-Micro model produces a minimum systematic error of 1% and a high degree of correlation (R2 = 87%) in estimating a sample vehicle's (1993 Honda Accord with a 2.4L engine) fuel consumption. The sample vehicle was correctly identified 100%, 97%, and 89% as a normal vehicle in terms of HC, CO, NOX emissions, respectively, using its in-laboratory measured emissions. Its estimated emissions yielded 100%, 97%, and 88% of correct detection rates in terms of HC, CO, NOX emissions, respectively. The study clearly demonstrates that the proposed procedure works well in converting concentration measurements to mass emissions and can be applicable in the screening of HEVs and normal emitting vehicles for several vehicle types such as sedans, station wagons, full-size vans, mini vans, pickup trucks, and SUVs.
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    Development of a Microscopic Emission Modeling Framework for On-Road Vehicles
    Abdelmegeed, Mohamed Ahmed Elbadawy Taha (Virginia Tech, 2017-04-27)
    The transportation sector has a significant impact on the environment both nationally and globally since it is a major vehicle fuel consumption and emissions contributor. These emissions are considered a major environmental threat. Consequently, decision makers desperately need tools that can estimate vehicle emissions accurately to quantify the impact of transportation operational projects on the environment. Microscopic fuel consumption and emission models should be capable of computing vehicle emissions reliably to assist decision makers in developing emission mitigation strategies. However, the majority of current state-of-the-art models suffer from two major shortcomings, namely; they either produce a bang-bang control system because they use a linear fuel consumption versus power model or they cannot be calibrated using publicly available data and thus require expensive laboratory or field data collection. Consequently, this dissertation attempts to fill this gap in state-of-the-art emission modeling through a framework based on the Virginia Tech Comprehensive Power-Based Fuel consumption Model (VT-CPFM), which overcomes the above mentioned drawbacks. Specifically, VT-CPFM does not result in a bang-bang control and can be calibrated using publicly available vehicle and road pavement parameters. The main emphasis of this dissertation is to develop a simple and reliable emission model that is able to compute instantaneous emission rates of carbon monoxide (CO), hydrocarbons (HC) and nitrogen oxides (NOx) for the light-duty vehicles (LDVs) and heavy-duty diesel trucks (HDDTs). The proposed extension is entitled Virginia Tech Comprehensive Power-Based Fuel consumption and Emission Model (VT-CPFEM). The study proposes two square root models where the first model structure is a cubic polynomial function that depends on fuel estimates derived solely from VT-CPFM fuel estimates, which enhances the simplicity of the model. The second modeling framework combines the cubic function of the VT-CPFM fuel estimates with a linear speed term. The additional speed term improves the accuracy of the model and can be used as a reference for the driving condition of the vehicle. Moreover, the model is tested and compared with existing models to demonstrate the robustness of the model. Furthermore, the performance of the model was further investigated by applying the model on driving cycles based on real-world driving conditions. The results demonstrate the efficacy of the model in replicating empirical observations reliably and simply with only two parameters.
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    Development of Passenger Car Equivalents for Basic Freeway Segments
    Ingle, Anthony (Virginia Tech, 2004-07-08)
    Passenger car equivalents (PCEs) are used in highway capacity analysis to convert a mixed vehicle flow into an equivalent passenger car flow. This calculation is relevant to capacity and level of service determination, lane requirements, and determining the effect of traffic on highway operations. The most recent Highway Capacity Manual 2000 reports PCEs for basic freeway segments according to percent and length of grade and proportion of heavy vehicles. Heavy vehicles are considered to be either of two categories: trucks and buses or RVs. For trucks and buses, PCEs are reported for a typical truck with a weight to power ratio between 76.1 and 90.4 kg/kW (125 and 150 lb/hp). The weight to power ratio is an indicator of vehicle performance. Recent development of vehicle dynamics models make it possible to define PCEs for trucks with a wider variety of weight to power ratios. PCEs were calculated from the relative impact of trucks on traffic density using the simulation model INTEGRATION. The scope of this research was to evaluate PCEs for basic freeway segments for trucks with a broader range of weight to power ratios. Such results should make freeway capacity analysis more accurate for mixed vehicle flow with a non-typical truck population. In addition, the effect of high proportion of trucks, pavement type and condition, truck aerodynamic treatment, number of freeway lanes, truck speed limit, and level of congestion was considered. The calculation of PCEs for multiple truck weight to power ratio populations was not found to be different from single truck weight to power ratio populations. The PCE values were tabulated in a compatible format to that used in the Highway Capacity Manual 2000.
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    Do Roundabouts Work? An Evaluation for Uniform Approach Demands
    Jackson, Meredith A. (Virginia Tech, 2011-08-01)
    With the increased prevalence of roundabouts in the United States, there is a need to evaluate the performance of roundabouts relative to other intersection control strategies. Few studies have compared roundabouts with other intersection control strategies in a systematic fashion. Consequently, this Thesis compares four types of intersection control strategies considering a single lane approach with a 58 km/hr speed limit and equal demand on all approaches. The study demonstrates that vehicle delay is minimized with the use of a roundabout intersection control for all demand levels below 500 veh/hr/approach. Above this point if the left turn percentage exceeds 70% traffic signal control is more efficient. The roundabout alternative also produces the fewest vehicle stops for low demand levels, low left turn demand and high right turn demand, however a TWSC alternative produces the least number of vehicle stops when the through and total demand is high. This study illustrates that fuel consumption and carbon dioxide, carbon monoxide, hydrocarbon and nitrogen oxide emissions can be improved with roundabout control over other intersection control strategies. The research presented here demonstrates that for low traffic demand levels roundabouts should be part of design alternatives considered for isolated intersection control.
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    Eco-Driving in the Vicinity of Roadway Intersections - Algorithmic Development, Modeling and Testing
    Kamalanathsharma, Raj Kishore (Virginia Tech, 2014-05-06)
    Vehicle stops and speed variations account for a large percentage of vehicle fuel losses especially at signalized intersections. Recently, researchers have attempted to develop tools that reduce these losses by capitalizing on traffic signal information received via vehicle connectivity with traffic signal controllers. Existing state-of-the-art approaches, however, only consider surrogate measures (e.g. number of vehicle stops, time spent accelerating and decelerating, and/or acceleration or deceleration levels) in the objective function and fail to explicitly optimize vehicle fuel consumption levels. Furthermore, the majority of these models do not capture vehicle acceleration and deceleration limitations in addition to vehicle-to-vehicle interactions as constraints within the mathematical program. The connectivity between vehicles and infrastructure, as achieved through Connected Vehicles technology, can provide a vehicle with information that was not possible before. For example, information on traffic signal changes, traffic slow-downs and even headway and speed of lead vehicles can be shared. The research proposed in this dissertation uses this information and advanced computational models to develop fuel-efficient vehicle trajectories, which can either be used as guidance for drivers or can be attached to an electronic throttle controlled cruise control system. This fuel-efficient cruise control system is known as an Eco-Cooperative Adaptive Cruise Control (ECACC) system. In addition to the ECACC presented here, the research also expands on some of the key eco-driving concepts such as fuel-optimizing acceleration models, which could be used in conjunction with conventional vehicles and even autonomous vehicles, or assistive systems that are being implemented in vehicles. The dissertation first presents the results from an on-line survey soliciting driver input on public perceptions of in-vehicle assistive devices. The results of the survey indicate that user-acceptance to systems that enhance safety and efficiency is ranked high. Driver–willingness to use advanced in-vehicle technology and cellphone applications is also found to be subjective on what benefits it has to offer and safety and efficiency are found to be in the top list. The dissertation then presents the algorithmic development of an Eco-Cooperative Adaptive Cruise Control system. The modeling of the system constitutes a modified state-of-the-art path-finding algorithm within a dynamic programming framework to find near-optimal and near-real-time solutions to a complex non-linear programming problem that involves minimizing vehicle fuel consumption in the vicinity of signalized intersections. The results demonstrated savings of up to 30 percent in fuel consumption within the traffic signalized intersection vicinity. The proposed system was tested in an agent-based environment developed in MATLAB using the RPA car-following model as well as the Society of Automobile Engineers (SAE) J2735 message set standards for vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication. The results showed how multi-vehicle interaction enhances usability of the system. Simulation of a calibrated real intersection showed average fuel savings of nearly 30 percent for peak volumes. The fuel reduction was high for low volumes and decreased as the traffic volumes increased. The final testing of the algorithm was done in an enhanced Traffic Experimental Simulation tool (eTEXAS) that incorporates the conventional TEXAS model with a new web-service interface as well as connected vehicle message set dictionary. This testing was able to demonstrate model corrections required to negate the effect of system latencies as well as a demonstration of using SAE message set parsing in a connected vehicle application. Finally, the dissertation develops an integrated framework for the control of autonomous vehicle movements through intersections using a multi-objective optimization algorithm. The algorithm integrated within an existing framework that minimizes vehicle delay while ensuring vehicles do not collide. A lower-level of control is introduced that minimizes vehicle fuel consumption subject to the arrival times assigned by the upper-level controller. Results show that the eco-speed control algorithm was able to reduce the overall fuel-consumption of autonomous vehicles passing through an intersection by 15 percent while maintaining the 80 percent saving in delay when compared to a traditional signalized intersection control.
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    Environmental Impact of Freight Signal Priority with Connected Trucks
    Park, Sangjun; Ahn, Kyoungho; Rakha, Hesham A. (MDPI, 2019-12-01)
    Traffic signal priority is an operational technique employed for the smooth progression of a specific type of vehicle at signalized intersections. Transit signal priority is the most common type of traffic signal priority, and it has been researched extensively. Conversely, the impacts of freight signal priority (FSP) has not been widely investigated. Hence, this study aims to evaluate the energy and environmental impacts of FSP under connected vehicle environment by utilizing a simulation testbed developed for the multi-modal intelligent transportation signal system. The simulation platform consists of VISSIM microscopic traffic simulation software, a signal request messages distributor program, an RSE module, and an Econolite ASC/3 traffic controller emulator. The MOVES model was employed to estimate the vehicle fuel consumption and emissions. The simulation study revealed that the implementation of FSP significantly reduced the fuel consumption and emissions of connected trucks and general passenger cars; the network-wide fuel consumption was reduced by 11.8%, and the CO2, HC, CO, and NOX emissions by 11.8%, 28.3%, 24.8%, and 25.9%, respectively. However, the fuel consumption and emissions of the side-street vehicles increased substantially due to the reduced green signal times on the side streets, especially in the high truck composition scenario.
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    Freeway Travel Time Estimation Based on Spot Speed Measurements
    Zhang, Wang (Virginia Tech, 2006-06-16)
    As one of the kernel components of ITS technology, Travel Time Estimation (TTE) has been a high-interest topic in highway operation and management for years. Out of numerous vehicle detection technologies being applied in this project, intrusive loop detector, as the representative of spot measurement devices, is the most common. The ultimate goal of this dissertation is to seek a TTE approach based primarily on spot speed measurement and capable of successfully performing in a certain accuracy range under various traffic conditions. The provision of real-time traffic information could offer significant benefits for commuters looking to make optimum travel decisions. The proposed research effort attempts to characterize typical variability in traffic conditions using traffic volume data obtained from loop detectors on I-66 Virginia during a 3-month period. The detectors logged time-mean speed, volume, and occupancy measurements for each station and lane combination. Using these data, the study examines the spatiotemporal link and path flow variability of weekdays and weekends. The generation of path flows is made through the use of a synthetic maximum likelihood approach. Statistical Analysis of Variance (ANOVA) tests are performed on the data. The results demonstrate that in terms of link flows and total traffic demand, Mondays and Fridays are similar to core weekdays (Tuesdays, Wednesdays, and Thursdays). In terms of path flows, Fridays appear to be different from core weekdays. A common procedure for estimating roadway travel times is to use either queuing theory or shockwave analysis procedures. However, a number of studies have claimed that deterministic queuing theory and shock-wave analysis are fundamentally different, producing different delay estimates for solving bottleneck problems. Chapter 5 demonstrates the consistency in the delay estimates that are derived from both queuing theory and shock-wave analysis and highlights the common errors that are made in the literature with regards to shock-wave analysis delay estimation. Furthermore, Chapter 5 demonstrates that the area between the demand and capacity curves can represent the total delay or the total vehicle-hours of travel if the two curves are spatially offset and queuing theory has its advantages on this because of its simplicity. As the established relationship between time-mean and space-mean speed is suitable for estimating time-mean speeds from space-mean speeds in most cases, it is also desired to estimate the space-mean speeds from time-mean speeds. Consequently, Chapter 6 develops a new formulation that utilizes the variance of the time-mean speed as opposed to the variance of the space-mean speed for the estimation of space-mean speeds. This demonstrates that the space-mean speeds are estimated within a margin of error of 0 to 1 percent. Furthermore, it develops a relationship between the space- and time-mean speed variance and between the space-mean speed and the spatial travel-time variance. In addition, the paper demonstrates that both the Hall and Persaud and the Dailey formulations for estimating traffic stream speed from single loop detectors are valid. However, the differences in the derivations are attributed to the fact that the Hall and Persaud formulation computes the space-mean speed (harmonic mean) while the Dailey formulation computes the time-mean speed (arithmetic mean). Chapter 7 focuses on freeway Travel Time Estimation (TTE) algorithms that are based on spot speed measurements. Several TTE approaches are introduced including a traffic dynamics TTE algorithm that is documented in literature. This traffic dynamics algorithm is analyzed, highlighting some of its drawbacks, followed by some proposed corrections to the traffic dynamics formulation. The proposed approach estimates traffic stream density from occupancy measurements, as opposed to flow measurements, at the onset of congestion. Next, the study validates the proposed model using field data from I-880 and simulated data. Comparison of five different TTE algorithms is conducted. The comparison demonstrates that the proposed approach is superior to the TTE traffic dynamics approach. Particularly, a multi-link simulation network is built to test spot-speed-measurement TTE performance on multi links, as well as the data smoothing technique's effect on TTE accuracy. Findings further prove advantages of utilizing space-mean speed in TTE rather than time-mean speed. In summary, a feasible TTE procedure that is adaptive to various traffic conditions has been established. Since each approach would under-/over-estimate travel time depending on the concrete traffic condition, different models will be selected to ensure TTE's accuracy window. This approach has broad applications because it is based on popular loop detectors.
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    High Automobile Emissions: Modeling Impacts and Developing Solutions
    Park, Sangjun (Virginia Tech, 2008-09-03)
    In the last few years, scientific consensus is that emission of greenhouse gases (GHGs) into the atmosphere is contributing to changes in the earth's climate. While uncertainty remains over the pace and dimensions of the change, a consensus on the need for action has grown among the public and elected officials. In part, this shift has been accelerated by concern over energy security and rising fuel prices. The new political landscape has led many cities, states, and regions to institute policies aimed at reducing GHG emissions. These policies and emerging initiatives have significant implications for the transportation planning process. The transportation sector accounts for approximately 27% of GHG production in the U.S. (as of 2003) and while the U.S. accounts for only roughly 5% of the world's population, it is estimated that it produces over 20% of the world's GHG emissions. Note that this does not include "lifecycle" emissions that result from the processes undertaken to extract, manufacture, and transport fuel. Carbon dioxide represents approximately 96% of the transportation sector's radiative forcing effects. Unlike conventional air pollutants, carbon dioxide emissions are directly tied to the amount of fuel consumed and its carbon intensity. Therefore, emissions reductions can be achieved by increasing the use of low-carbon fuels, improving fuel economy, or reducing total vehicle miles of travel - often called the three legged stool. (A fourth leg is congestion reduction, at certain optimal speeds). These same factors are related to our use of imported oil, so actions taken to reduce GHG emissions may actually produce benefits in both policy areas. The climatic risks of additional emissions associated with capacity projects must be balanced against the mobility, safety, and economic needs of a community or region. Consequently, this dissertation attempts to quantify the impacts of high-emitting vehicles on the environment and to propose solutions to enhance the currently-used high-emitting vehicle detection procedures. In addition, fuel consumption and emission models for high-speed vehicles are developed in order to provide more reliable estimates of vehicle emissions and study the impact of vehicle speeds on vehicle emissions. The dissertation extends the state-of-the-art analysis of high emitting vehicles (HEVs) by quantifying the network-wide environmental impact of HEVs. The literature reports that 7% to 12% of HEVs account for somewhere between 41% to 63% of the total CO emissions, and 10% are responsible for 47% to 65% of HC emissions, and 10% are responsible for 32% of NOx emissions. These studies, however, are based on spot measurements and do not necessarily reflect network-wide impacts. Consequently, the research presented in this dissertation extends the state-of-knowledge by quantifying HEV contributions on a network level. The study uses microscopic vehicle emission models (CMEM and VT-Micro model) along with pre-defined drive cycles (under the assumption that the composite HEV and VT-LDV3 represent HEVs and NEVs, respectively) in addition to the simulation of two transportation networks (freeway and arterial) to quantify the contributions of HEVs. The study demonstrates that HEVs are responsible for 67% to 87% of HC emissions, 51% to 78% of CO emissions, and 32% to 62% of the NOX emissions for HEV percentages ranging from 5% to 20%. Additionally, the traffic simulation results demonstrate that 10% of the HEVs are responsible for 50% to 66% of the I-81 HC and 59% to 78% of the Columbia Pike HC emissions, 35% to 67% of the I-81 CO and 38% to 69% of the Columbia Pike CO emissions, and 35% to 44% of the I-81 NOX and 35% to 60% of the Columbia Pike NOX emissions depending on the percentage of the normal-emitting LDTs to the total NEVs. HEV emission contributions to total HC and CO emissions appear to be consistent with what is reported in the literature. However, the contribution of NOX emissions is greater than what is reported in the literature. The study demonstrates that the contribution of HEVs to the total vehicle emissions is dependent on the type of roadway facility (arterials vs. highways), the background normal vehicle composition, and the composition of HEVs. Consequently, these results are network and roadway specific. Finally, considering that emission control technologies in new vehicles are advancing, the contribution of HEVs will increase given that the background emission contribution will decrease. Given that HEVs are responsible for a large portion of on-road vehicle emissions, the dissertation proposes solutions to the HEV screening procedures. First, a new approach is proposed for estimating vehicle mass emissions from concentration remote sensing emission measurements using the carbon balance equation in conjunction with either the VT-Micro or PERE fuel consumption rates for the enhancement of current state-of-the-art HEV screening procedures using RSD technology. The study demonstrates that the proposed approach produces reliable mass emission estimates for different vehicle types including sedans, station wagons, full size vans, mini vans, pickup trucks, and SUVs. Second, a procedure is proposed for constructing on-road RS emission standards sensitive to vehicle speed and acceleration levels. The proposed procedure is broadly divided into three sub-processes. In the first process, HE cut points in grams per second are developed as a function of a vehicle's speed and acceleration levels using the VT-Micro and CMEM emission models. Subsequently, the HE cut points in grams per second are converted to concentration emissions cut points in parts per million using the carbon balance equation. Finally, the scale factors are computed using either ASM ETW- and model-year-based standards or engine-displacement-based standards. Given the RS emissions standards, the study demonstrated that the use of on-road RS cut points sensitive to speed and acceleration levels is required in order to enhance the effectiveness of RS. Finally, the dissertation conducted a study to develop fuel consumption and emissions models for high-speed vehicles to overcome the shortcomings of state-of-practice models. The research effort gathered field data and developed models for the estimation of fuel consumption, CO₂, CO, NO, NO2, NOx, HC, and PM emissions at high speeds. A total of nine vehicles including three semi-trucks, three pick-up trucks, and three passenger cars were tested on a nine-mile test track in Pecos, Texas. The fuel consumption and emission rates were measured using two portable emission measurement systems. Models were developed using these data producing minimum errors for fuel consumption, CO₂, NO2, HC, and PM emissions. Alternatively, the NO and NOx emission models produced the highest errors with a least degree of correlation. Given the models, the study demonstrated that the newly constructed models overcome the shortcomings of the state-of-practice models and can be utilized to evaluate the environmental impacts of high speed driving.
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    Impact of Intersection Control on Battery Electric Vehicle Energy Consumption
    Ahn, Kyoungho; Park, Sangjun; Rakha, Hesham A. (MDPI, 2020-06-19)
    Battery electric vehicle (BEV) sales have significantly increased in recent years. They have different energy consumption patterns compared to the fuel consumption patterns of internal combustion engine vehicles (ICEVs). This study quantified the impact of intersection control approaches—roundabout, traffic signal, and two-way stop controls—on BEVs’ energy consumption. The paper systematically investigates BEVs’ energy consumption patterns compared to the fuel consumption of ICEVs. The results indicate that BEVs’ energy consumption patterns are significantly different than ICEVs’ patterns. For example, for BEVs approaching a high-speed intersection, the roundabout was found to be the most energy-efficient intersection control, while the two-way stop sign was the least efficient. In contrast, for ICEVs, the two-way stop sign was the most fuel-efficient control, while the roundabout was the least efficient. Findings also indicate that the energy saving of traffic signal coordination was less significant for BEVs compared to the fuel consumption of ICEVs since more regenerative energy is produced when partial or poorly coordinated signal plans are implemented. The study confirms that BEV regenerative energy is a major factor in energy efficiency, and that BEVs recover different amounts of energy in different urban driving environments. The study suggests that new transportation facilities and control strategies should be designed to enhance BEVs’ energy efficiency, particularly in zero emission zones.
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    Impacts of Vehicle-to-Everything Enabled Applications: Literature Review of Existing Studies
    Du, Jianhe; Ahn, Kyoungho; Farag, Mohamed; Rakha, Hesham A. (Universal Wiser Publisher, 2023-03-10)
    As communication technology is developing at a rapid pace, connected vehicles (CVs) can potentially enhance vehicle safety while reducing vehicle energy consumption and emissions via data sharing. Many researchers have attempted to quantify the impacts of such CV applications and vehicle-to-everything (V2X) communication, or the instant and accurate communication among vehicles, devices, pedestrians, infrastructure, network, cloud, and grid. Cellular V2X (C-V2X) has gained interest as an efficient method for this data sharing. In releases 14 and 15, C-V2X uses 4G LTE technology, and in release 16, it uses the latest 5G new radio (NR) technology. Among its benefits, C-V2X can function even with no network infrastructure coverage; in addition, C-V2X surpasses older technologies in terms of communication range, latency, and data rates. Highly efficient information interchange in a CV environment can provide timely data to enhance the transportation system's capacity, and it can support applications that improve vehicle safety and minimize negative impacts on the environment. Achieving the full benefits of CVs requires rigorous investigation into the effectiveness, strengths, and weaknesses of different CV applications. It also calls for deeper understanding of the communication protocols, results with different CV market penetration rates (MPRs), CV- and human-driven vehicle interactions, integration of multiple applications, and errors and latencies associated with data communication. This paper includes a review of existing literature on the safety, mobility, and environmental impacts of CV applications; gaps in current CV research; and recommended directions for future research. The results of this paper will help shape future research for CV applications to realize their full potential.
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    Mesoscopic Fuel Consumption and Emission Modeling
    Yue, Huanyu (Virginia Tech, 2008-03-25)
    The transportation sector is a major contributor to U.S. fuel consumption and emissions. Consequently, assessing the environmental impacts of transportation activities is essential for air-quality improvement programs. Current state-of-the-art models estimate vehicle emissions based on typical urban driving cycles. Most of these models offer simplified mathematical expressions to compute fuel consumption and emission rates based on average link speeds while ignoring transient changes in a vehicle's speed and acceleration level as it travels on a highway network. Alternatively, microscopic models capture these transient effects; however, the application of microscopic models may be costly and time consuming. Also, these tools may require a level of input data resolution that is not available. Consequently, this dissertation attempts to fill the void in energy and emission modeling by a framework for modeling vehicle fuel consumption and emissions mesoscopically. This framework is utilized to develop the VT-Meso model using a number of data sources. The model estimates average light-duty vehicle fuel consumption and emission rates on a link-by-link basis using up to three independent variables, namely: average travel speed, average number of stops per unit distance, and average stop duration. The mesoscopic model utilizes a microscopic vehicle fuel consumption and emission model that was developed at Virginia Tech to compute mode-specific fuel consumption and emission rates. This model, known as VT-Micro, predicts the instantaneous fuel consumption and emission rates of HC, CO and NOx of individual vehicles based on their instantaneous speed and acceleration levels. The mesoscopic model utilizes these link-by-link input parameters to construct a synthetic drive cycle and compute average link fuel consumption and emission rates. After constructing the drive cycle, the model estimates the proportion of time that a vehicle typically spends cruising, decelerating, idling and accelerating while traveling on a link. A series of fuel consumption and emission models are then used to estimate the amount of fuel consumed and emissions of HC, CO, CO2, and NOX emissions for each mode of operation. Subsequently, the total fuel consumed and pollutants emitted by a vehicle while traveling along a segment are estimated by summing across the different modes of operation and dividing by the distance traveled to obtain distance-based average vehicle fuel consumption and emission rates. The models are developed for normal and high emitting vehicles. The study quantifies the typical driver deceleration behavior for incorporation within the model. Since this model constructs a drive cycle which includes a deceleration mode, an accurate characterization of typical vehicle deceleration behavior is critical to the accurate modeling of vehicle emissions. The study demonstrates that while the deceleration rate typically increases as the vehicle approaches its desired final speed, the use of a constant deceleration rate over the entire deceleration maneuver is adequate for environmental modeling purposes. Finally, the study validates the model on a freeway and urban arterial network. The results demonstrate that the model provides accurate estimates of vehicle fuel consumption and emission rates and is adequate for the evaluation of transportation operational projects.
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    Microscopic Fuel Consumption and Emission Modeling
    Ahn, Kyoungho (Virginia Tech, 1998-11-10)
    Mathematical models to predict vehicle fuel consumption and emission metrics are presented in this thesis. Vehicle fuel consumption and emissions are complex functions to be approximated in practice due to numerous variables affecting their outcome. Vehicle energy and emissions are particularly sensitive to changes in vehicle state variables such as speed and acceleration, ambient conditions such as temperature, and driver control inputs such as acceleration pedal position and gear shift speeds, among others. Recent empirical studies have produced large amounts of data concerning vehicle fuel consumption and emissions rates and offer a wealth of information to transportation planners. Unfortunately, unless simple relationships are found between fuel consumption and vehicle emission metrics, their application in microscopic traffic and macroscopic planning models becomes prohibitive computationally. This thesis describes the development of microscopic energy and emission models using nonlinear multiple regression and neural network techniques to approximate vehicle fuel consumption and emissions field data. The energy and emission models described in this thesis utilized data collected by the Oak Ridge National Laboratory. The data include microscopic fuel consumption and emission measurements (CO, HC, and NOx) for eight light duty vehicles as a function of vehicle speed and acceleration. The thesis describes modeling processes and the tradeoffs between model accuracy and computational efficiency. Model verification results are included for two vehicle driving cycles. The models presented estimate vehicle fuel consumption within 2.5% of their actual measured values. Vehicle emissions errors fall in the range of 3-33% with correlation coefficients ranging between 0.94 and 0.99. Future transportation planning studies could also make use of the modeling approaches presented in the thesis. The models developed in this study have been incorporated into a microscopic traffic simulation tool called INTEGRATION to further demonstrate their application and relevance to traffic engineering studies. Two sample Intelligent Transportation Systems (ITS) application results are included. In the case studies, it was found that vehicle fuel consumption and emissions are more sensitive to the level of vehicle acceleration than to the vehicle speed. Also, the study shows signalization techniques can reduce fuel consumption and emissions significantly, while incident management techniques do not affect the energy and emissions rates notably.
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    Modeling Light Duty Vehicle Emissions Based on Instantaneous Speed and Acceleration Levels
    Ahn, Kyoungho (Virginia Tech, 2002-05-29)
    This dissertation develops a framework for modeling vehicle emissions microscopically. In addition, the framework is utilized to develop the VT-Micro model using a number of data sources. Key input variables to the VT-Micro model include instantaneous vehicle speed and acceleration levels. Estimating accurate mobile source emissions is becoming more and more critical as a result of increasing environmental problems in large metropolitan urban areas. Current emission inventory models, such as MOBILE and EMPAC, are designed for developing large scale inventories, but are unable to estimate emissions from specific corridors and intersections. Alternatively, microscopic emission models are capable of assessing the impact of transportation scenarios and performing project-level analyses. The VT-Micro model was developed using data collected at the Oak Ridge National Laboratory (ORNL) that included fuel consumption and emission rate measurements (CO, HC, and NOx) for five light-duty vehicles (LDVs) and three light-duty trucks (LDTs) as a function of the vehicle's instantaneous speed and acceleration levels. The hybrid regression models predict hot stabilized vehicle fuel consumption and emission rates for LDVs and LDTs. The model is found to be highly accurate compared to the ORNL data with coefficients of determination ranging from 0.92 to 0.99. The study compares fuel consumption and emission results from MOBILE5a, VT-Micro, and CMEM models. The dissertation presents that the proposed VT-Micro model appears to be good enough in terms of absolute light-duty hot stabilized normal vehicle tailpipe emissions. Specifically, the emission estimates were found to be within the 95 percent confidence limits of field data and within the same level of magnitude as the MOBILE5a model estimates. Furthermore, the proposed VT-Micro model was found to reflect differences in drive cycles in a fashion that was consistent with field observations. Specifically, the model accurately captures the increase in emissions for aggressive acceleration drive cycles in comparison with other drive cycles. The dissertation also presents a framework for developing microscopic emission models. The framework develops emission models by aggregating data using vehicle and operational variables. Specifically, statistical techniques for aggregating vehicles into homogenous categories are utilized as part of the framework. In addition, the framework accounts for temporal lags between vehicle operational variables and vehicle emissions. Finally, the framework is utilized to develop the VT-Micro model version 2.0 utilizing second-by-second chassis dynamometer emission data for a total of 60 light duty vehicles and trucks. Also, the dissertation introduces a procedure for estimating second-by-second high emitter emissions. This research initially investigates high emitter emission cut-points to verify clear definitions of high emitter vehicles (HEVs) and derives multiplicative factors for newly developed EPA driving cycles. Same model structure with the VT-Micro model is utilized to estimate instantaneous emissions for a total of 36 light duty vehicles and trucks. Finally, the dissertation develops a microscopic framework for estimating instantaneous vehicle start emissions for LDVs and LDTs. The framework assumes a linear decay in instantaneous start emissions over a 200-second time horizon. The initial vehicle start emission rate is computed based on MOBILE6's soak time function assuming a 200-second decay time interval. The validity of the model was demonstrated using independent trips that involved cold start and hot start impacts with vehicle emissions estimated to within 10 percent of the field data. The ultimate expansion of this model is its implementation within a microscopic traffic simulation environment in order to evaluate the environmental impacts of alternative ITS and non-ITS strategies. Also, the model can be applied to estimate vehicle emissions using instantaneous GPS speed measurements. Currently, the VT-Micro model has been implemented in the INTEGRATION software for the environmental assessment of operational-level transportation projects.
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    Modeling Traffic Dispersion
    Farzaneh, Mohamadreza (Virginia Tech, 2005-11-17)
    The dissertation studies traffic dispersion modeling in four parts. In the first part, the dissertation focuses on the Robertson platoon dispersion model which is the most widely used platoon dispersion model. The dissertation demonstrates the importance of the Yu and Van Aerde calibration procedure for the commonly accepted Robertson platoon dispersion model, which is implemented in the TRANSYT software. It demonstrates that the formulation results in an estimated downstream cyclic profile with a margin of error that increases as the size of the time step increases. In an attempt to address this shortcoming, the thesis proposes the use of three enhanced geometric distribution formulations that explicitly account for the time-step size within the modeling process. The proposed models are validated against field and simulated data. The second part focuses on implementation of the Robertson model inside the popular TRANSYT software. The dissertation first shows the importance of calibrating the recurrence platoon dispersion model. It is then demonstrated that the value of the travel time factor β is critical in estimating appropriate signal-timing plans. Alternatively, the dissertation demonstrates that the value of the platoon dispersion factor α does not significantly affect the estimated downstream cyclic flow profile; therefore, a unique value of α provides the necessary precision. Unfortunately, the TRANSYT software only allows the user to calibrate the platoon dispersion factor but does not allow the user to calibrate the travel time factor. In an attempt to address this shortcoming, the document proposes a formulation using the basic properties of the recurrence relationship to enable the user to control the travel time factor indirectly by altering the link average travel time. In the third part of the dissertation, a more general study of platoon dispersion models is presented. The main objective of this part is to evaluate the effect of the underlying travel time distribution on the accuracy and efficiency of platoon dispersion models, through qualitative and quantitative analyses. Since the data used in this study are generated by the INTEGRATION microsimulator, the document first describes the ability of INTEGRATION in generating realistic traffic dispersion effects. The dissertation then uses the microsimulator generated data to evaluate the prediction precision and performance of seven different platoon dispersion models, as well as the effect of different traffic control characteristics on the important efficiency measures used in traffic engineering. The results demonstrate that in terms of prediction accuracy the resulting flow profiles from all the models are very close, and only the geometric distribution of travel times gives higher fit error than others. It also indicates that for all the models the prediction accuracy declines as the travel distance increases, with the flow profiles approaching normality. In terms of efficiency, the travel time distribution has minimum effect on the offset selection and resulting delay. The study also demonstrates that the efficiency is affected more by the distance of travel than the travel time distribution. Finally, in the fourth part of the dissertation, platoon dispersion is studied from a microscopic standpoint. From this perspective traffic dispersion is modeled as differences in desired speed selection, or speed variability. The dissertation first investigates the corresponding steady-state behavior of the car-following models used in popular commercially available traffic microsimulation software and classifies them based on their steady-state characteristics in the uncongested regime. It is illustrated that with one exception, INTEGRATION which uses the Van Aerde car-following model, all the software assume that the desired speed in the uncongested regime is insensitive to traffic conditions. The document then addresses the effect of speed variability on the steady-state characteristics of the car-following models. It is shown that speed variability has significant influence on the speed-at-capacity and alters the behavior of the model in the uncongested regime. A method is proposed to effectively consider the influence of speed variability in the calibration process in order to control the steady-state behavior of the model. Finally, the effectiveness and validity of the proposed method is demonstrated through an example application.
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    Predictive Eco-Cruise Control (ECC) System: Model Development, Modeling and Potential Benefits
    Rakha, Hesham A.; Ahn, Kyoungho; Park, Sangjun (United States. Department of Transportation. Research and Innovative Technology Administration, 2013-02-19)
    The research develops a reference model of a predictive eco-cruise control (ECC) system that intelligently modulates vehicle speed within a pre-set speed range to minimize vehicle fuel consumption levels using roadway topographic information. The study includes five basic tasks: (a) develop a vehicle powertrain model that can be easily implemented within eco-driving tools, (b) develop a simple fuel consumption model that computes instantaneous vehicle fuel consumption levels based on power exerted, (c) evaluate manual driving and conventional cruise control (CC) driving using field-collected data, (d) develop a predictive ECC system that uses the developed vehicle powertrain and fuel consumption models, and (e) evaluate the potential benefits of the proposed predictive ECC system on a pre-trip and fleet-aggregate basis. This study develops a predictive ECC system that can save fuel and reduce CO2 emissions using road topography information. The performance of the system is tested by simulating a vehicle trip on a section of Interstate 81 in the state of Virginia. The results demonstrate fuel savings of up to 15 percent with execution times within real time. The study found that the implementation of the predictive ECC system could help achieving better fuel economy and air quality.
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    Simple Diesel Train Fuel Consumption Model for Real-Time Train Applications
    Ahn, Kyoungho; Aredah, Ahmed; Rakha, Hesham A.; Wei, Tongchuan; Frey, H. Christopher (MDPI, 2023-04-20)
    This paper introduces a simple diesel train energy consumption model that calculates the instantaneous energy consumption using vehicle operational input variables, including the instantaneous speed, acceleration, and roadway grade, which can be easily obtained from global positioning system (GPS) loggers. The model was tested against real-world data and produced an error of −1.33% for all data and errors ranging from −12.4% to +8.0% for energy consumption of four train datasets amounting to a total of 5854 km trips. The study also validated the proposed model with separate data that were collected between Valencia and Cuenca, Spain, which had a total length of 198 km and found that the model was accurate, yielding a relative error of −1.55% for the total energy consumption. These results show that the proposed model can be used by train operators, transportation planners, policy makers, and environmental engineers to evaluate the energy consumption effects of train operational projects and train simulation within intermodal transportation planning tools.
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    Transit Signal Priority Project Phase II Field and Simulation Evaluation Results
    Rakha, Hesham A.; Ahn, Kyoungho (Virginia Center for Transportation Innovation and Research, 2006-03-01)
    Transit Signal Priority (TSP) is recognized as an emerging technology that is capable of enhancing traditional transit services. Basic green-extension TSP was implemented on U.S. Route 1 in the Northern Virginia Area (or Washington, DC metropolitan area). This study quantifies the impact of TSP technology on transit-vehicle performance using field-collected Global Positioning System (GPS) data and evaluates the system-wide benefits of TSP operations using computer simulations to expand on the field evaluation study. The field study demonstrated that overall travel-time improvements in the order of 3% to 6% were observed for TSP-operated buses. However, the results also demonstrated that green-extension TSP can increase transit-vehicle travel times by approximately 2.5% during congested morning peak periods. In addition, the study demonstrated that TSP strategies reduce transit-vehicle intersection delay by as much as 23%. The field study demonstrated that the benefits associated with TSP were highly dependent on the roadway level of congestion and were maximized under moderate to low levels of congestion. However, the simulation results indicated that TSP did not result in statistically significant changes in auto or system-wide travel times (differences less than 1%). Furthermore, a paired t-test concluded that basic green-extension TSP did not increase side-street queue lengths. An increase in the traffic demand along Route 1 resulted in increased system-wide detriments; however, these detriments were minimal (less than 1.37%). The study demonstrated that an increase in side-street demand did not result in any statistically significant system-wide detriments. Increasing the frequency of transit vehicles resulted in additional benefits to transit vehicles (savings in transit vehicle travel times by up to 3.42%), but no system-wide benefits were observed. Finally, TSP operations at near-side bus stops (within the detection zone) resulted in increased delays in the range of 2.85%, while TSP operations at mid-block and far-side bus stops resulted in network-wide savings in delay in the range of 1.62%. Consequently, we recommend not implementing TSP in the vicinity of near-side stops that are located within the TSP detection zone. The simulation results indicated that a TSP system generally benefits transit vehicles, but does not guarantee system-wide benefits. In this study, a maximum transit vehicle travel-time savings of 3% to 6% was observed with the provision of green-extension TSP from both the field and simulation evaluation studies. However, the green-extension TSP operation did not benefit nor damage the non-transit vehicles in most cases. Also, it should be noted that the results of the study may be specific to Route 1 corridor because of the unique characteristics of the study corridor, the specific traffic demand, and TSP logic implemented. Finally, the study recommends the calibration of current TSP settings to improve the effectiveness of TSP operation. Also, different transit priority strategies or a combination of other TSP strategies should be investigated to increase the benefits of TSP operations. A conditional TSP system that only provides priority to transit vehicles behind schedule and an intelligent transit monitoring system are also recommended to improve the TSP system on the Route 1 corridor.