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Roadway infrastructure is key to continued economic growth in California, supporting our daily needs for moving both people and goods. In the era of connected and automated vehicles (CAVs), there is a strong need for testing the new enabling technology and upgrading current infrastructure to support different types of connectivity and automation. This report summarizes the research team’s efforts to: a) inventory California’s CAV testing facilities and testbeds; b) collaborate with the City of Riverside to upgrade communication capabilities along the Riverside Innovation Corridor and enable both dedicated short range communications (DSRC) and cellular-based communications; c) develop an innovative connected eco-approach and departure (EAD) application for actuated signalized corridors; and d) conduct field operational tests to assess the costs and benefits from infrastructure upgrades. The research shows that despite relatively slower communication time compared to DSRC, cellular-based communications can provide additional benefits to vehicles equipped with eco-driving CAV applications such as EAD, due to its greater communication range. Furthermore, broadcasting Radio Technical Commission for Maritime Services correction messages is a cost-effective solution to improving positioning accuracy for equipped vehicles.

For arterial roadways, most Active Traffic and Demand Management (ATDM) strategies focus on traffic signal timing optimization at signalized intersections. A critical drawback of conventional traffic signal control strategies is that they rely on measurements from point detection, and estimate traffic states such as queue length based on very limited information. The introduction of Connected Vehicle (CV) technology can potentially address the limitations of point detection via wireless communications to assist signal phase and timing optimization. In this project report, the authors present an agent-based online adaptive signal control (ASC) strategy based on real-time traffic information available from vehicles equipped with CV technology. The authors then evaluate the proposed strategy in terms of travel delay and fuel consumption, relative to a Highway Capacity Manual (HCM) based method in which hourly traffic demand is assumed to be known accurately a priori. The Connected Vehicle Adaptive Signal Control (CV-ASC) strategy is applied to an isolated traffic intersection as well as to a corridor of traffic intersections. The baseline signalization strategy for the corridor of traffic intersections is coordinated signal control. Study results indicate that for both the isolated intersection and corridor contexts, the proposed strategy outperforms the HCM based method and is very robust to traffic demand variations.View the NCST Project Webpage

With the rapid development of the cooperative vehicle infrastructure system, eco‐approach and departure has been attracting increasing attention for mitigating the impact of signalized intersections, which helps improve the energy efficiency of equipped vehicles. However, existing studies have rarely considered the balance of electric bus energy consumption and traveling time. Meanwhile, the joint effect of bus stops and signalized intersections, and their interactions were not discussed widely with connected and automated electric buses (CAEBs). In this study, an integrated CAEB trajectory planning methodology with the balance of traveling time and energy consumption is proposed. A bi‐level programming model is developed to obtain the optimal trajectories of CAEBs. Numerical experiments are conducted to analyse and compare the model with two baseline models. The results demonstrate the optimal performance of the derived scheme in terms of bus energy consumption, with approximately 30–50% energy savings. Furthermore, model sensitivity analysis is conducted to quantify the impacts of the related system parameters. The findings also reveal that the entry time, conversion coefficient, and final speed have significant impacts on electric bus energy consumption along signalized arterials with bus stops. [ABSTRACT FROM AUTHOR]

Deploying the dynamic wireless power transfer (DWPT) equipment at intersections can improve the transportation efficiency and decrease the energy consumption. It can easily turn the disadvantages of waiting time for red phase into the advantages of charging benefits for electric vehicles (EVs). This study develops a multiobjective speed planning model with differentiated charging strategy to optimize the EV's trajectory at signalized intersections with DWPT. To mitigate the negative impact of DWPT, the proposed model is divided into charging priority driving (C-eco-driving) mode and time priority driving (T-eco-driving) mode for connected and automated electric vehicles (CAEVs) to apply different scenarios. Meanwhile, a widely applied car-following model, i.e., the Intelligent Driver Model (IDM), has been calibrated with the ground-truth dataset to meet the intersection characteristic for EVs in mixed traffic. The efficiency and robustness of two eco-driving modes were validated in single-vehicle and mixed traffic based on MATLAB simulation. For single vehicle, the electricity benefits of the C-eco-driving mode and T-eco-driving mode increase about 0.0867 kWh and 0.0532 kWh, respectively. T-eco-driving mode reduces 2.5 s of travel time. For mixed traffic, the C-eco-driving mode provides more charging benefits about 51~73% than no-control strategy. In contrast, the T-eco-driving mode provides significant time benefits of 2.6 s and slight electricity benefits of 12~30% than no-control strategy. The increment of market penetration rate and wireless charging length can significantly improve the charging benefits. When charging length has the same value, the closer the DWPT facilities to the intersection, the more obvious the charging benefits. [ABSTRACT FROM AUTHOR]

Many studies have simulated traffic behavior at signalized intersections using various Car-Following (CF) models. However, the performance of which CF Model is superior at signalized intersections has not been thoroughly analyzed and evaluated. In this study, two novel Artificial Neural Network (ANN) CF models, the Convolutional Neural Network—Long Short-term Memory (CNN-LSTM) and the Convolution-LSTM (Conv-LSTM)—are first applied to predict CF behaviors at signalized intersections. Both models can extract spatial and temporal information to address the long-term dependency problem more effectively. Based on the filtered NGSIM dataset, we conduct a comparative empirical study of three conventional CF models and five ANN CF models. The dataset is divided into two categories based on the characteristics of CF behavior at signalized intersections: continuous and discontinuous. The experiments demonstrated that ANN CF models outperformed conventional CF models when the output was the velocity in two categories of traffic flow but only failed to do so when the output was acceleration in discontinuous traffic flow. The proposed models were capable of accurately predicting acceleration, but the traffic fluctuations also existed as time passed. Additionally, it was discovered that while the ANN CF model is preferable for traffic flow simulation, the conventional CF model still cannot be ignored for discontinuous traffic flow simulation, particularly when acceleration is required. [ABSTRACT FROM AUTHOR]

The emergence of connected and automated vehicle (CAV) technology has the potential to bring a number of benefits to our existing transportation systems. Specifically, when CAVs travel along an arterial corridor with signalized intersections, they can not only be driven automatically using pre-designed control models but can also communicate with other CAVs and the roadside infrastructure. In this paper, we describe a cooperative eco-driving (CED) system targeted for signalized corridors, focusing on how the penetration rate of CAVs affects the energy efficiency of the traffic network. In particular, we propose a role transition protocol for CAVs to switch between a leader and following vehicles in a string. Longitudinal control models are developed for conventional vehicles in the network and for different CAVs based on their roles and distances to intersections. A microscopic traffic simulation evaluation has been conducted using PTV VISSIM with realistic traffic data collected for the City of Riverside, CA, USA. The effects on traffic mobility are evaluated, and the environmental benefits are analyzed by the U.S. Environmental Protection Agency’s MOtor Vehicle Emission Simulator (MOVES) model. The simulation results indicate that the energy consumption and pollutant emissions of the proposed system decrease, as the penetration rate of CAVs increases. Specifically, more than 7% reduction on energy consumption and up to 59% reduction on pollutant emission can be achieved when all vehicles in the proposed system are CAVs. [ABSTRACT FROM AUTHOR]

In the urban corridor with a mixed traffic composition of connected and automated vehicles (CAVs) alongside human-driven vehicles (HDVs), vehicle operations are intricately influenced by both individual driving behaviors and the presence of signalized intersections. Therefore, the development of a coordinated control strategy that effectively accommodates these dual factors becomes imperative to enhance the overall quality of traffic flow. This study proposes a bi-level structure crafted to decouple the joint effects of the vehicular driving behaviors and corridor signal offsets setting. The objective of this structure is to optimize both the average travel time (ATT) and fuel consumption (AFC). At the lower-level, three types of car-following models while considering driving modes are presented to illustrate the desired driving behaviors of HDVs and CAVs. Moreover, a trigonometry function method combined with a rolling horizon scheme is proposed to generate the eco-trajectory of CAVs in the mixed traffic flow. At the upper-level, a multi-objective optimization model for corridor signal offsets is formulated to minimize ATT and AFC based on the lower-level simulation outputs. Additionally, a revised Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is adopted to identify the set of Pareto-optimal solutions for corridor signal offsets under different CAV penetration rates (CAV PRs). Numerical experiments are conducted within a corridor that encompasses three signalized intersections. The performance of our proposed eco-driving strategy is validated in comparison to the intelligent driver model (IDM) and green light optimal speed advisory (GLOSA) algorithm in single-vehicle simulation. Results show that our proposed strategy yields reduced travel time and fuel consumption to both IDM and GLOSA. Subsequently, the effectiveness of our proposed coordinated control strategy is validated across various CAV PRs. Results indicated that the optimal AFC can be reduced by 4.1%–32.2% with CAV PRs varying from 0.2 to 1, and the optimal ATT can be saved by 2.3% maximum. Furthermore, sensitivity analysis is conducted to evaluate the impact of CAV PRs and V/C ratios on the optimal ATT and AFC. • A bi-level structure was designed for coordinated control. • An eco-driving model was proposed to regulate CAV's trajectory. [ABSTRACT FROM AUTHOR]

• Able to overtake slowly-moving vehicles for ecological purpose. • Able to optimize the travel duration approaching a signalized intersection. • Balancing fuel saving and vehicle's mobility. • Considering stochasticity of surrounding traffic. • Functional under partially connected and automated environment. This research presents an enhanced eco-approach controller with overtaking capability. The proposed controller overcomes the shortcomings of the conventional eco approach and is able to: i) overtake slowly-moving vehicles for the ecological purpose; ii) optimize the travel duration approaching an intersection; iii) guarantee both fuel saving and vehicle's mobility; iv) consider stochasticity of surrounding traffic; v) functional under partially connected and automated environment. It takes full advantage of connected vehicle technology by taking in real-time vehicle and infrastructure information as optimization input. The problem is formulated as an optimal control problem and is solved by GPOPS. The nonlinear bicycle model is adopted as the system dynamics to realize CAV's longitudinal and lateral coupling control, and linearized to reduce the computational burden. The stochasticity of surrounding traffic is considered as a probability distribution that is transformed into a linear chance constraint. Quantitative evaluation is conducted to compare the proposed controller against human drivers and the conventional eco approach which only has longitudinal automation. The evaluation results demonstrate that the proposed controller improves the fuel efficiency by 4.13–70.12%, and outperforms two baseline controllers by 6.06–36.73% in terms of fuel saving. The range is caused by the different arrival time of the ego CAV. In addition, the simulation experiment in VISSIM is conducted to analyze how background traffic flow influences the performance of the proposed controller. [ABSTRACT FROM AUTHOR]