VT-Micro Model Research Articles

Ecologically Oriented Freeway Control Methods Integrated Speed Limits and Ramp Toll Booths Layout

Traffic exhaust pollution, especially in congested areas of freeways, is one of the main causes of air pollution. With the increase in the number of vehicles, traffic and environmental issues have become more prominent. In addition, traffic congestion leads to frequent starting and stopping of vehicles, further exacerbating environmental pollution. This article focuses on the problem of frequent starting and stopping of vehicles, using variable speed limit control to smooth traffic flow, reduce vehicle speed, and alleviate exhaust emissions caused by traffic congestion. At the same time, considering the traffic and environmental benefits of bottleneck areas on freeways, the VT-Micro model is used to calculate exhaust emissions, and a coordinated control method for the mainline and ramp of freeways is proposed. The simulation experiment results show that the total driving time of the mainline and ramp collaborative control method considering environmental benefits has been reduced by 24.69%, CO emissions have been reduced by 4.79%, HC emissions have been reduced by 7.65%, NOx emissions have been reduced by 2.48%, and fuel consumption has been reduced by 4.98%.

A Dual-Stage Attention-Based Vehicle Speed Prediction Model Considering Driver Heterogeneity with Fuel Consumption and Emissions Analysis

With the development of intelligent transportation systems (ITSs), personalized driving systems are receiving more and more attention, and the development of advanced systems cannot be separated from the practical exploration of drivers’ heterogeneous driving behaviors. An important foundation for subsequent driver-targeted research is how to mine the key influencing factors that characterize drivers through real driving data and how to appropriately classify drivers as a whole. This study took heterogeneous drivers as the object, based on a dual-stage attention-based vehicle speed prediction model, and carried out research on the speed prediction of traffic flow and the impact of fuel consumption and emissions in the car-following state considering the heterogeneity of drivers. Specifically, first, Spearman’s correlation analysis and K-means clustering were used to classify different types of drivers. Then, speed predictions for different types of drivers were separated via the dual-stage attention-based encoder–decoder (DAED) model and the prediction results between models and drivers were compared. Finally, the heterogeneous drivers’ fuel consumption and emissions were further analyzed via the VT-micro model. The results show that the proposed speed prediction model can effectively discriminate the influences of heterogeneous drivers on the prediction model, and the aggressive type presents the best effect. In addition, from the experiments on traffic fuel consumption and emissions, it can be concluded that the timid driver is the friendliest to the environment. By researching individual drivers’ driving characteristics, this study may help sustainable development in traffic management.

Evaluating the safe and eco-driving performances of car-following behaviors in a vehicle platoon under foggy conditions

The study aimed to evaluate safe and eco-driving performances of vehicle car-following behaviors in a vehicle platoon under foggy conditions. Eight drivers participated in a multi-user driving simulation experiment, and their driving performances in a vehicle platoon were collected. A Fault Tree model was established to evaluate the collision risk of the vehicle platoon, and the VT-Micro model was adopted to calculate vehicles’ fuel consumption, CO2 emission, and NOx emission. The results showed that heavy fog exponentially increased the collision risk of a vehicle platoon, which cannot be compensated by adaptive behavior, and it also increased fuel consumption and NOx emission during the car-following process. At the speed limit of 60 km/h, the safe and eco-driving performances of the vehicle platoon under foggy conditions have been effectively improved. This study also found that the vehicles at the rearward positions in a vehicle platoon tended to keep short car-following gaps and large speed adjustments, which was the main cause of higher collision risks. The study provides a better understanding of the safe and eco-driving features of the vehicle platoon under foggy conditions. The established fault tree provided theoretical references for the real-time monitoring of collision risk in a vehicle platoon.

Transient fuel consumption prediction for heavy-duty trucks using on-road measurements

To confront the lack of robust and accessible models predicting diesel truck fuel consumption, this study develops an Engine-based Correction Model (ECM) using a sample containing vehicle operation and road data from heavy-duty diesel trucks (HDTs) along an urban arterial in Sichuan Province, China. This model is compared with two popular methods, the VT-Micro model and the CMEM model, in terms of goodness-of-fit and out-of-sample prediction performance. The results show that the proposed ECM models explain on average 89.7% of the variation in fuel consumption across the four modes and produce the smallest error, measured by MAPE and RMSE, for predicting each mode. Specifically, the MAPE values of the new ECM models are 7.95% to 25.59% lower than those of the VT-Micro models and 5.74% to 13.27% lower than the CMEM models. The improvement of RMSE values ranges from 0.1780 to 2.0940 cc/s and from 0.0171 to 0.7259 cc/s, compared to the VT-Micro model and CMEM model, respectively. In sum, the new ECM outperforms the other two existing models in both goodness-of-fit and predictive power across all modes. The enhanced performance of ECM can be attributed to the correction component and a model-building process powered by the simulated annealing (SA) algorithm that reliably and quickly weed out ineffective model forms. The results can inform driver training to foster energy-efficient driving behavior and assess the impact on fuel consumption of roadway design policies.

High-accuracy transient fuel consumption model based on distance correlation analysis

With the gradual aggravation of energy shortage, automobile energy saving has gained widespread attention from scholars. However, due to the lack of a high-accuracy practical fuel consumption model, it is difficult to estimate transient fuel consumption and evaluate the actual effect of real-time fuel consumption control strategies. Therefore, it is necessary to establish a more accurate and practical model according to the transient motion characteristics of the vehicles. To ensure the accuracy of the model, an integrated structure of the steady-state base module and the transient correction module is determined as the overall structure of the model. Based on the steady-state fuel consumption data, the steady-state base module is established. Then, based on the easily obtained vehicle and engine state parameters, principal component analysis and cluster analysis are used to reasonably classify different driving conditions of the vehicles. Following that, the distance correlation analysis is applied to find the combination of state parameters with the strongest correlation with the estimation error of the steady-state module, and a transient correction module is established according to the optimal state parameter combination obtained. After that, the optimal transient correction module structure is determined based on the Bayesian criterion. Finally, the model is tested, and the results show that the mean absolute percentage error (MAPE) of the fuel consumption estimation of the new model is about 15%, while that of the classical VT-Micro model and the VT-CPFM model are about 28% and 20%, respectively. It can be seen that the new model has a higher accuracy. On the other hand, compared with structured physical fuel consumption models such as VT-CPEM model, the new model has a simpler structure, a shorter computation time, and a higher computational speed. In addition, the new model has high practicability due to its clear structure and easy access to parameters.

A Comparison of Carbon Dioxide Emissions between Battery Electric Buses and Conventional Diesel Buses

To prove the important role of battery electric buses (BEBs) in reducing carbon dioxide (CO2) emissions, we propose a framework to compare CO2 emissions between BEBs and conventional diesel buses (CDBs) based on low sampling frequency BEBs data at the city scale in Shenzhen. We applied the VT-Micro model to improve the estimation of CDBs’ CO2 emissions. A modal-activity-based method was implemented to reconstruct the second-by-second trajectories from the dataset as the input of the VT-Micro model. We updated the data of the Guangdong power generation mix to improve the estimation of BEBs’ CO2 emissions. The experiments showed that BEBs could reduce CO2 emissions by 18.0–23.9% in comparison with CDBs when the frequency of air-conditioning usage was low. In addition, BEBs tended to achieve more CO2 emission reduction benefits when the transit buses traveled at a low speed. Improving the traffic efficiency of road networks and promoting inter-provincial electricity trading are important to promote the adoption of BEBs.

High-Accuracy Transient Fuel Consumption Model Based on Distance Correlation Analysis

Due to lack of a high-accuracy practical fuel consumption model, it is difficult to evaluate and control fuel consumption accurately. It is necessary to establish a more accurate and practical model according to transient motion characteristics of vehicles. To ensure accuracy of model, structural form of the steady-state basic + transient correction module is determined as overall structure of model. Then principal component analysis and cluster analysis are used to classify different driving conditions of vehicles reasonably. Then, based on distance correlation analysis, parameter combination which has strongest correlation with estimation error of steady-state is determined, and transient correction is established. After that, optimal transient correction structures are determined based on Bayesian criterion. Finally, model is tested and results show that mean absolute percentage error of fuel consumption estimation of new model is about 15%, which is lower than classical models. What’s more, new model is simple in structure and calculating speed is similar to VT-Micro model. Therefore, model not only has high accuracy, but also has high calculation speed.

Capturing driving behavior Heterogeneity based on trajectory data

Driving behavior is heterogeneous for various drivers due to the different influencing factors as reaction time, gender, driving years and so on. Some existing works tried to reproduce some of the complex characteristics of real traffic flow by taking into account the heterogeneous driving behavior, and the drivers are generally divided into two classes (including aggressive drivers and careful drivers) or three classes (including aggressive drivers, normal drivers and careful drivers). Nevertheless, the classification approaches have not been verified, and the rationality of the classifications has not been confirmed as well. In this study, the trajectory data of drivers is extracted from the NGSIM datasets. By combining the K-Means method and Silhouette measure index, the drivers are classified into four clusters (named as clusters A, B, C and D, respectively) in accordance with the acceleration and time headway. The two-dimensional approach is applied to analyze the characteristics of different clusters. Here, one dimension consists of “Cautious” and “Aggressive” behaviors in terms of velocity and acceleration, and the other dimension consists of “Sensitive” and “Insensitive” behaviors in terms of reaction time. Finally, the fuel consumption and emissions for different clusters are calculated by using the VT-Micro model. A surprising result indicates that overly “cautious” and “sensitive” behaviors may result in more fuel consumption and emissions. Therefore, it is necessary to find the balance between the driving characteristics.

Analysis of the influence of geometric design consistency on vehicle CO2 emissions

Highway vehicles driving on rural roads account for more than 50% of all CO2 emissions produced by the transportation sector in Europe. Although the policy measures to mitigate Greenhouse Gas emissions are increasing, these do not include policies aimed at reducing emissions by means of highway geometric design, which significantly influences drivers’ speeds and accelerations and, consequently, plays a major role on fuel consumption and emissions.Therefore, the main objective of this research is to study the influence of the geometric design consistency on vehicle CO2 emissions. To do this, continuous speed data were collected on 47 homogeneous road segments by means of Global Positioning System devices. Vehicle CO2 emissions were estimated by applying the VT-micro model, whereas geometric design consistency was assessed considering different global consistency models.As a conclusion, vehicle CO2 emissions decreases as the consistency level of a homogeneous road segment increases. Specifically, a good consistency road segment has been found to present an emission rate 20–30% lower than a poor-consistent one. Therefore, the design of consistent roads allows, in addition to maximize road safety, to help to achieve more environmentally sustainable highways, reducing CO2 emission production.

An Ecological Adaptive Cruise Control for Mixed Traffic and Its Stabilization Effect

With the rapid development of technologies, the ecological control strategies of connected and autonomous vehicle (CAV) technologies are gaining more and more attention. In this paper, a rule-based ecological cruise control, called the ecological smart driver model (EcoSDM), is proposed to improve the fuel efficiency of an individual vehicle and the traffic flow. By adjusting the spacing between the leading and the following vehicles, EcoSDM provides smoother deceleration and acceleration than the enhanced intelligent driver model (Enhanced-IDM) and the smart driver model (SDM). The linear stability of EcoSDM is analyzed both theoretically and numerically. The numerical results validate the results of theoretical analysis. Moreover, the simulations results show that EcoSDM outperforms the Enhanced-IDM and SDM in terms of stabilization effect on homogeneous traffic flow. In addition, the calibrated VT-Micro model is used to estimate the fuel consumption of CAVs and manually driven vehicles. The result shows that CAVs have better fuel economy than the human-driven vehicles, which is consistent with existing studies. The EcoSDM outperforms Enhanced-IDM and SDM in terms of fuel consumption. For the EcoSDM-equipped CAVs, the fuel saving benefit is greatest when a CAV is at the front of the platoon.

Environmental effects of road geometric and operational features

Abstract The aim of this study is to analyze the influence of horizontal geometric design and vehicle operation on fuel consumption and gas emissions produced by passenger cars. Continuous speed data were collected along 15 two-lane rural road sections. Different horizontal alignment indexes and operational variables were obtained. Fuel consumption and gas emissions were estimated by applying the VT-Micro model. The results showed that Curvature Change Rate and average speed have a significant impact on average fuel consumption and gas emissions. Finally, different regression models were calibrated based on these variables to estimate fuel consumption [l/100 km] and emissions [gr/km]. These results could be the basis to incorporate environmental sustainability principles into road design guidelines, since currently they do not take into account the environmental impact related to highway geometric design.

Analysis of the trip costs of a traffic corridor with two entrances and one exit under car-following model

In this paper, we first define each commuter’s first, second and third trip costs, and then apply the full velocity difference model and the VT-Micro model to explore each commuter’s three trip costs and the system’s corresponding total trip costs in a traffic corridor with two entrances and one exit. The numerical results show that one entrance has prominent effects on the commuter’s three trip costs and the system’s corresponding total trip cost and that the impacts are directly related to the commuter’s departure interval at this entrance. The results can provide some suggestions for reducing the commuters’ trip costs in a traffic corridor with two entrances and one exit.

Traffic state and emission estimation for urban expressways based on heterogeneous data

Urban expressways, as the backbone of a city's transportation network, are critical for reducing traffic congestion and improving transportation efficiency of the whole network. The estimation of traffic states and emissions for urban expressways supports traveler information provision and system-wide traffic management. This paper aims to modify the extended generalized Treiber-Helbing filter (EGTF) to fuse GPS data (probe vehicles) and traditional traffic data (loop detectors), so as to enhance more accurate estimations of traffic states and emissions on urban expressways. The speed field is first reconstructed based on heterogeneous data, and then travel time and emissions are estimated using a virtual trajectory method and the VT-Micro model, respectively. The algorithm is applied to a real-world case study for an urban expressway in Beijing, China. After the parameter tuning, the proposed algorithm is compared with existing algorithms from the literature. Numerical results show that data fusion using the proposed algorithm could make better use of heterogeneous data and increase the accuracy of travel time and emissions estimations.

Development of a transient fuel consumption model

Most existing fuel consumption models are based on steady-state fuel mapping. However, these models cannot provide satisfactory predictions for vehicles operating under transient conditions. Consequently, a new transient model that can precisely predict fuel consumption under steady-state and transient conditions was developed on the basis of the steady-state model. This new model is characterised by two sub-modules: the steady-state module whose inputs are engine speed and torque, and the transient correction module whose inputs are vehicle speed and acceleration. Vehicle Specific Power (VSP) was introduced as a filter to help develop different correction factors for positive and negative VSP. The Argonne National Laboratory’s measurements of different driving cycles including the steady-state cycle, the UDDS cycle, the Highway cycle and the US06 cycle were used to develop the new model. According to the results of this study, the prediction accuracy of fuel consumption models can be improved significantly by introducing transient corrections. The Mean Absolute Percentage Error (MAPE) between the predicted and measured fuel consumption decreased from approximately 58% for the steady-state model alone to approximately 23.5% when the transient correction module was introduced. Moreover, this new model performs better than the VT-Micro model. The MAPE values of the new transient models were approximately 2.7–4.6% lower than that of the VT-Micro model.

An improved car-following model with multiple preceding cars’ velocity fluctuation feedback

In order to explore and evaluate the effects of velocity variation trend of multiple preceding cars used in the Cooperative Adaptive Cruise Control (CACC) strategy on the dynamic characteristic, fuel economy and emission of the corresponding traffic flow, we conduct a study as follows: firstly, with the real-time car-following (CF) data, the close relationship between multiple preceding cars’ velocity fluctuation feedback and the host car’s behaviors is explored, the evaluation results clearly show that multiple preceding cars’ velocity fluctuation with different time window-width are highly correlated to the host car’s acceleration/deceleration. Then, a microscopic traffic flow model is proposed to evaluate the effects of multiple preceding cars’ velocity fluctuation feedback in the CACC strategy on the traffic flow evolution process. Finally, numerical simulations on fuel economy and exhaust emission of the traffic flow are also implemented by utilizing VT-micro model. Simulation results prove that considering multiple preceding cars’ velocity fluctuation feedback in the control strategy of the CACC system can improve roadway traffic mobility, fuel economy and exhaust emission performance.

Fuel consumption and gas emissions of an automatic transmission vehicle following simple eco‐driving instructions on urban roads

Following eco-driving instructions can reduce fuel consumption between 5 to 20% on urban roads with manual cars. The majority of Australian cars have an automatic transmission gear-box. It is therefore of interest to verify whether current eco-driving instructions are e cient for such vehicles. In this pilot study, participants (N=13) drove an instrumented vehicle (Toyota Camry 2007) with an automatic transmission. Fuel consumption of the participants was compared before and after they received simple eco-driving instructions. Participants drove the same vehicle on the same urban route under similar tra c conditions. We found that participants drove at similar speeds during their baseline and eco-friendly drives, and reduced the level of their accelerations and decelerations during eco-driving. Fuel consumption decreased for the complete drive by 7%, but not on the motorway and inclined sections of the study. Gas emissions were estimated with the VT-micro model, and emissions of the studied pollutants (CO2, CO, NOX and HC) were reduced, but no di erence was observed for CO2 on the motorway and inclined sections. The di erence for the complete lap is 3% for CO2. We have found evidence showing that simple eco-driving instructions are e cient in the case of automatic transmission in an urban environment, but towards the lowest values of the spectrum of fuel consumption reduction from the di erent eco-driving studies.

Integrated macroscopic traffic flow, emission, and fuel consumption model for control purposes

Traffic control approaches based on on-line optimization require fast and accurate integrated models for traffic flow, emission, and fuel consumption. In this context, one may want to integrate macroscopic traffic flow models with microscopic emission and fuel consumption models, which can result in shorter simulation times with fairly accurate estimates of the emissions and fuel consumption. In general, however, macroscopic traffic flow models and microscopic emission and fuel consumption models cannot be integrated with each other. We provide a general framework to integrate these two kinds of models. We illustrate the approach by considering the macroscopic traffic flow model METANET1METANET Messmer and Papageorgiou (1990) stands for “Modèle d’Ecoulement du Trafic Autoroutier: NETwork”.1 and the microscopic emission and fuel consumption model VT-micro,2VT-micro Ahn et al. (1999) stands for “Virginia Tech Microscopic”.2 resulting in the so called the “VT-macro” model. Moreover, we characterize analytically the error introduced by the VT-macro model relative to the original VT-micro model. We further present an empirical analysis of the error and the computation time based on calibrated models of the Dutch A12 freeway.

Guiding Model and Empirical Analysis of Vehicle Behavior at Signalized Intersections Based on Carbon Emissions

A model based on carbon emissions is established to guide vehicle behavior at signalized intersections. This model takes working conditions and emission characteristics of vehicles into account, uses VT-Micro model exploited by Oak Ridge National Laboratory to calculate emissions, and provides vehicles with the lowest-emission working plan to drive through intersections. This model is able to reducing carbon emissions at the source and it agrees with the development of low-carbon transportation.

Sustainability assessments of cooperative vehicle intersection control at an urban corridor

Connected Vehicle (CV) technology, formerly known as IntelliDrive, has emerged and is expected to provide unprecedented improvements in mobility. A recent study developed a cooperative vehicle intersection control (CVIC) algorithm for an urban intersection that does not require a stop-and-go style traffic signal and demonstrated significant mobility improvements over an actuated traffic signal control. This paper expanded the algorithm and implemented it to a corridor consisting of multiple intersections. In addition, this paper investigated sustainability aspects of the CVIC system for an urban traffic control system by applying surrogate safety assessment model (SSAM) and VT-Micro model to measure safety and environmental impacts, respectively. A simulation-based case study was performed on a hypothetical arterial consisting of four intersections with eight traffic congestion cases covering low to high volume conditions. When compared to the coordinated actuated control, the CVIC system dramatically reduced the total delay times for the volume cases considered (i.e., from 82% to 100% delay time savings observed). The CVIC system also reduced the number of rear-end crash events by 30–87% for the volume cases considered, indicating that safer driving conditions would be achieved with the CVIC system. Finally, the CVIC system contributed to improving the air quality (i.e., 12–36% CO2 emission reduction) and saving fuel consumptions (11–37% of gas saving).

Evaluating the impacts of urban corridor traffic signal optimization on vehicle emissions and fuel consumption

This study investigates the impacts of traffic signal timing optimization on vehicular fuel consumption and emissions at an urban corridor. The traffic signal optimization approach proposed integrates a TRANSIMS microscopic traffic simulator, the VT-Micro model (a microscopic emission and fuel consumption estimation model), and a genetic algorithm (GA)-based optimizer. An urban corridor consisting of four signalized intersections in Charlottesville, VA, USA, is used for a case study. The result of the case study is then compared with the best traffic signal timing plan generated by Synchro using the TRANSIMS microscopic traffic simulator. The proposed approach achieves much better performance than that of the best Synchro solution in terms of air quality, energy and mobility measures: 20% less network-wide fuel consumption, 8–20% less vehicle emissions, and nearly 27% less vehicle-hours-traveled (VHT).