Yuche Chen

The assessment of energy impacts associated with autonomous vehicles must extend beyond individual vehicle analysis to encompass mixed fleets with varying degrees of automation. This study presents an integrated simulation framework designed to evaluate fuel efficiency improvements resulting from both full and partial vehicle automation across diverse road types and vehicle categories. By coupling traffic microsimulation with detailed powertrain modeling, the framework captures the intricate interdependencies between automation levels and energy consumption. A comprehensive analysis reveals the complex interactions among powertrain architectures, automation levels, and driving environments in both urban and highway contexts. Results indicate that the increased penetration of Connected and Autonomous Vehicles (CAVs) is generally associated with improved energy efficiency across a range of vehicle technologies. These findings offer critical insights into the broader implications of CAV adoption on energy consumption, emphasizing the nuanced dynamics between vehicle heterogeneity and traffic conditions.

The adoption of electric vehicles at ports is a promising approach to achieve sustainability goals. However, realizing the full potential of this strategy depends on effective coordination between infrastructure planning and operational scheduling. In this paper, we propose a joint optimization framework that can co-optimize these two components to minimize the overall system cost. To capture the dynamic nature of scheduling decisions, we model the problem using dynamic programming techniques. Our model accounts for the spatial and temporal heterogeneities of charging and driving costs for different truck trips. To evaluate the effectiveness of our proposed framework, we conducted an empirical study at the Port of Los Angeles and Port of Long Beach. Specifically, we aimed to fulfill 5% of the daily 20-foot equivalent unit containers using electric drayage trucks. Our model identified the optimal number of electric trucks, charging stations, and truck schedules required to meet the container throughput requirement. We also analyzed the cost per container as a function of daily throughput level for various scenarios. Our findings provide insights on how to determine charger supply based on daily throughputs at ports, and how to choose the appropriate ratios of electric trucks and battery sizes in the truck fleet under different throughput and electric price cases.

Reliable and accurate estimation of electric bus energy consumption is critical for electric bus operation and planning. But energy prediction fo |

State departments of transportation recognize the need to incorporate pavement structural condition in their pavement performance models and/or decision processes used to select candidate projects for preservation, rehabilitation, or reconstruction at the network level. However, pavement structural condition data are costly to obtain. To this end, this paper develops and evaluates the effectiveness of two machine learning methods, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), for predicting a flexible pavement’s structural condition. The aim is to be able to predict whether a pavement section’s structural condition is poor or not based on Annual Average Daily Traffic (AADT), truck percentage, and speed limit. The structural condition of a pavement is considered poor if the Surface Curvature Index (SCI₁₂) is above 3.3. The models are developed using 950 miles of Traffic Speed Deflectometer (TSD) data collected along 8 primary routes in South Carolina. The performance of the machine learning models was compared with that of a logistic regression model. When the trained models are applied to the test data, the prediction results indicated that the XGBoost and RF models outperform the logistic regression model by 12% and 8%, respectively. XGBoost outperformed RF by 4%. With XGBoost found to be the best among the three models evaluated, its performance was examined using other poor structural condition threshold values; its prediction accuracy is found to be robust across the different scenarios. AADT and truck percentages are found to be significant factors whereas speed limit has no effect on a pavement’s structural condition.

Vehicle automation requires new onboard sensors, communication equipment, and/or data processing units, and may encourage modifications to existing onboard components (such as the steering wheel). These changes impact the vehicle’s mass, auxiliary load, coefficient of drag, and frontal area, which then change vehicle performance. This paper uses the powertrain simulation model FASTSim to quantify the impact of autonomy-related design changes on a vehicle’s fuel consumption. Levels 0, 2, and 5 autonomous vehicles are modeled for two battery-electric vehicles (2017 Chevrolet Bolt and 2017 Nissan Leaf) and a gasoline powered vehicle (2017 Toyota Corolla). Additionally, a level 5 vehicle is divided into pessimistic and optimistic scenarios which assume different electronic equipment integration format. The results show that 4–8% reductions in energy economy can be achieved in a L5 optimistic scenario and an 10–15% increase in energy economy will be the result in a L5 pessimistic scenario. When looking at impacts on different power demand sources, inertial power is the major power demand in urban driving conditions and aerodynamic power demand is the major demand in highway driving conditions.

Vehicle emission analysis currently faces a trade-off between easy-to-use, low-accuracy macroscopic models, and computationally intensive, high-accuracy microscopic models. In this study, we develop a surrogate model that leverages microscopic traffic and emission simulations to predict link-level emission rates. The input variables are obtained by aggregating 1 Hz simulated vehicle trajectories into hourly traffic condition factors (e.g., link average/variation of speed, truck fleet percentage, road grade, etc.). The emission ground truth data are generated using the Motor Vehicle Emission Simulator (MOVES) opmode-based analysis module. We explore different parameter and machine learning model structures to establish the statistical relationship of the input variables and the link-level emission rates. We demonstrate the ability of our model to accurately estimate vehicle-related emissions by using the Columbia, South Carolina road network as an example. This model can serve as a high-level planning tool to assess the impacts of emissions from transportation projects. Implications: Vehicle emission analysis is facing trade-offs between easy-to-use macroscopic emission models with low accuracy and computationally intensive microscopic models with high accuracy. Existing studies attempted to cope with the trade-off by pre-selecting representative emission rates but are still subject to the risk of not considering differentiated traffic patterns by using single emission rate. To fill in the knowledge gap in the literature, we develop a surrogate approach that fully integrates driving trajectories of heterogenous traffic patterns into a link-level emissions estimation model considering road characteristics. The model can achieve high accuracy and utilize publicly available traffic data in vehicle emission prediction. We apply the proposed model in a middle size city road network and demonstrate its capability to capture and quantify the impacts of traffic patterns on link-level vehicle-related emissions. Additionally, the proposed model can serve as a sketch planning tool for researchers and transportation air quality practitioners to quickly assess bounds of emissions benefits due to traffic operational and transportation strategies.

Free-floating car sharing (FFCS) service draws attention and has the potential to improve transportation system efficiency and achieve sustainability. This study proposes a self-efficacy-based value adoption model (SVAM) to investigate consumer adoption intention of FFCS by decomposing the perceived value and the self-efficacy. Specifically, the model examines the functional, economical, emotional, and comparative value of FFCS. Additionally, six-dimensional factors are employed to explore consumers’ self-efficacy in the context of FFCS. A population-based survey was implemented in Chinese major cities to validate the proposed decomposed SVAM. The results indicated that 66% of respondents intended to adopt FFCS, which may be explained by a combination of perceived value and self-efficacy factors. Moreover, the model explained 79% of the variance in perceived value and 77% of the variance in self-efficacy. Finally, practical implications are provided to better understand value propositions and the formation of users’ self-cognition.

Electric vehicles (EVs) are critical to the transition to a low-carbon transportation system. The successful adoption of EVs heavily depends on energy consumption models that can accurately and reliably estimate electricity consumption. This paper reviews the state-of-the-art of EV energy consumption models, aiming to provide guidance for future development of EV applications. We summarize influential variables of EV energy consumption into four categories: vehicle component, vehicle dynamics, traffic and environment related factors. We classify and discuss EV energy consumption models in terms of modeling scale (microscopic vs. macroscopic) and methodology (data-driven vs. rule-based). Our review shows trends of increasing macroscopic models that can be used to estimate trip-level EV energy consumption and increasing data-driven models that utilized machine learning technologies to estimate EV energy consumption based on large volume real-world data. We identify research gaps for EV energy consumption models, including the development of energy estimation models for modes other than personal vehicles (e.g., electric buses, electric trucks, and electric non-road vehicles); the development of energy estimation models that are suitable for applications related to vehicle-to-grid integration; and the development of multi-scale energy estimation models as a holistic modeling approach.

Light commercial vehicles account for about 10-15% of road traffic in Europe. There have only been few investigations in their on-road emissions performance. Here, on-road remote sensing vehicle emission measurements from eighteen locations across four European countries are combined for a comprehensive analysis of NOx and smoke emission rates from diesel LCVs across the past two decades. This allows differentiating the performance by emission standard, model year, curb weight, engine load, manufacturer, vehicle age and temperature, as well as by measurement device. We find in general consistency between devices and countries, justifying the pool of data of different origins. On-road NOx emission rates have been much higher than type approval limit values for all manufacturers, but some perform systematically better than others. Emission rates went down only with the introduction of Euro 6a,b emission standards since the year 2015. Smoke emission rates are considered as a proxy for particulate emissions. Their emissions decrease substantially from the year 2010 onwards for all countries measured and size classes. This is consistent with the substantial tightening of the PM emission limit value that typically forced the introduction of a diesel particulate filter. The average NOx emission rate increases with engine load and decreasing ambient temperatures, particularly for Euro 4 and 5 emission classes. This explains to a large extent the differences in absolute level between the measurement sites, together with differences in fleet composition. These dependencies have already been observed earlier with diesel passenger cars; they are considered part of an abnormal emission control strategy. Some limited increase of the NOx emission rate is observed for Euro 3 vehicles older than ten years. The strong increase for the youngest Euro 6 LCVs might rather reflect technology advances with successively younger models than genuine deterioration. However, the durability of emission controls for Euro 6 vehicles should better be monitored closely. Smoke emission rates continuously increase with vehicle age suggesting a deterioration of the after-treatment system with use.