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Alexander Skabardonis - One of the best experts on this subject based on the ideXlab platform.

  • Connected Vehicle penetration rate for estimation of arterial measures of effectiveness
    Transportation Research Part C-emerging Technologies, 2015
    Co-Authors: Juan Argotecabanero, Eleni Christofa, Alexander Skabardonis
    Abstract:

    Abstract The Connected Vehicle (CV) technology is a mobile platform that enables a new dimension of data exchange among Vehicles and between Vehicles and infrastructure. This data source could improve the estimation of Measures of Effectiveness (MOEs) for traffic operations in real-time, allowing to perfectly monitor traffic states after being fully adopted. However, as with any novel technology, the CV adoption will be a gradual process. This research focuses on determining minimum CV technology penetration rates that would guarantee accurate MOE estimates on signalized arterials. First, we present estimation methods for various MOEs such as average speed, number of stops, acceleration noise, and delay, followed by an initial assessment of the penetration rates required to accurately estimate them in undersaturated and oversaturated conditions. Next, we propose a methodology to determine the minimum CV market penetration rates to guarantee accurate MOE estimates as a function of traffic conditions, signal settings, sampling duration, and the MOE variability. A correction factor is also provided to account for small Vehicle populations where sampling is done without replacement. The methodology is tested in a simulated segment of the San Pablo Avenue arterial in Berkeley, CA. The outcomes show that the minimum penetration rate required can be estimated within 1% for most MOEs under a wide range of traffic conditions. The proposed methodology can be used to determine if MOE estimates obtained with a portion of CV equipped Vehicles can yield accurate enough results. The methodology could also be used to develop and assess control strategies towards improved arterial traffic operations.

  • estimating queue length under Connected Vehicle technology using probe Vehicle loop detector and fused data
    Transportation Research Record, 2013
    Co-Authors: Kun Zhou, Steven E Shladover, Alexander Skabardonis
    Abstract:

    With the emergence of Connected Vehicle technology, the use of probe trajectory data to estimate queue length has recently received considerable attention. Unlike data collected by loop detectors, probe trajectory data can provide a lower bound on the queue length even if the market penetration rate is low. An event-based method is developed: it uses both probe trajectory and signal timing data to estimate queue length, and the estimation accuracy under different market penetration rates is examined. A data fusion method is developed: it combines probe trajectory data and loop detector data, and the situation in which the data fusion method is expected to work well is investigated. Case studies are conducted with microscopic simulation data, and some observations are made.

Xiaosi Zeng - One of the best experts on this subject based on the ideXlab platform.

  • queue length estimation using Connected Vehicle technology for adaptive signal control
    IEEE Transactions on Intelligent Transportation Systems, 2015
    Co-Authors: Kamonthep Tiaprasert, Yunlong Zhang, Xiubin Bruce Wang, Xiaosi Zeng
    Abstract:

    This paper presents a mathematical model for real-time queue estimation using Connected Vehicle (CV) technology from wireless sensor networks. The objective is to estimate the queue length for queue-based adaptive signal control. The proposed model can be applied without signal timing, traffic volume, or queue characteristics as basic inputs. The model is also developed so that it can work with both fixed-time signals and actuated signals. Furthermore, a discrete wavelet transform (DWT) is applied to the queue estimation algorithm in this paper for the first time. The purpose of the DWT is to enhance the proposed queue estimation to be more accurate and consistent regardless of the randomness in the penetration ratio. Experimental results are provided to validate the proposed model in both pretimed control and actuated control with a microscopic simulator, i.e., VISSIM. The results indicate that the proposed algorithm is able to estimate the queue length from VISSIM in the test case with pretimed signal control reasonably well. The results in actuated control cases, which have not been studied previously, showed that the proposed algorithm remains as accurate as the pretimed control cases. The accuracy of the proposed queue estimation algorithm is obtained without relying on basic inputs that other models typically require but are often impractical to obtain. Therefore, it is expected that the proposed queue estimation model is applicable for adaptive signal control using CV technology in practice.

  • potential Connected Vehicle applications to enhance mobility safety and environmental security
    2012
    Co-Authors: Kevin N Balke, Praprut Songchitruksa, Xiaosi Zeng
    Abstract:

    "Researchers first conducted a comprehensive review of the state-of-the-art Connected-Vehicle research and technologies. Once researchers had a thorough understanding of the technology, they focused on selecting and developing the near-term practical applications that use Connected Vehicle technology. The research team then sought expert opinions from the Texas Transportation Institute working group during two brainstorming sessions, which produced two lists of potential applications and prioritized the applications based on deployment feasibility. In particular, a total of five applications were selected for development of the full concept of operations, including two in safety, two in mobility, and one in environmental security. These applications address various problems, including wrong-way driving and unprotected-grade-crossing crashes (safety): work-zone merge efficiency and safety, and freeway speed harmonization (mobility): and slippery pavement-related crashes (environmental security)."

  • Route-Based Transit Signal Priority Using Connected Vehicle Technology to Promote Bus Schedule Adherence
    IEEE Transactions on Intelligent Transportation Systems, 2026
    Co-Authors: Xiaosi Zeng, Yunlong Zhang, Jia Jiao
    Abstract:

    In this paper, we explore the use of enriched bus data enabled by the Connected Vehicle (CV) technologies and propose signal timing optimization models that aim to improve bus service reliabilities via Transit Signal Priorities (TSP). Specifically, a route-based TSP model (R-TSP) and its local version (L-TSP) are formulated. Timing and progression deviations are introduced as a simple and novel way to approximate the TSP impacts on passenger Vehicle delays. An online adaptive TSP system is developed to leverage the continuous availability of CV data to monitor bus priority needs, trigger new formulations of the TSP models with newly updated bus running data, and implement new signal timings in real-time. Simulation studies are conducted to evaluate a variety of R-TSP and L-TSP model variants to understand their respective effectiveness to improve bus schedule adherence and their impacts to other traffic. Simulation study on a hypothetical corridor show that an R-TSP model variant with both timing and progression deviation defined can improve a 100% late bus fleet to 98.4% of on-time arrival with only 5.5% increase in passenger car delays. In comparison, none of the L-TSP models could produce comparable benefits for buses and caused too much delay on passenger Vehicles. These simulation studies conclude that (1) Connected Vehicle technologies provide critical data to allow route-based TSP model to be formulated simply and solved continuously, and (2) granting bus priority at route level is much more beneficial than only providing bus priority on a signal by signal basis.

Naixue Xiong - One of the best experts on this subject based on the ideXlab platform.

  • Connected Vehicle as a mobile sensor for real time queue length at signalized intersections
    Sensors, 2019
    Co-Authors: Kai Gao, Farong Han, Pingping Dong, Naixue Xiong
    Abstract:

    With the development of intelligent transportation system (ITS) and Vehicle to X (V2X), the Connected Vehicle is capable of sensing a great deal of useful traffic information, such as queue length at intersections. Aiming to solve the problem of existing models’ complexity and information redundancy, this paper proposes a queue length sensing model based on V2X technology, which consists of two sub-models based on shockwave sensing and back propagation (BP) neural network sensing. First, the model obtains state information of the Connected Vehicles and analyzes the formation process of the queue, and then it calculates the velocity of the shockwave to predict the queue length of the subsequent unConnected Vehicles. Then, the neural network is trained with historical Connected Vehicle data, and a sub-model based on the BP neural network is established to predict the real-time queue length. Finally, the final queue length at the intersection is determined by combining the sub-models by variable weight. Simulation results show that the sensing accuracy of the combined model is proportional to the penetration rate of Connected Vehicles, and sensing of queue length can be achieved even in low penetration rate environments. In mixed traffic environments of Connected Vehicles and unConnected Vehicles, the queuing length sensing model proposed in this paper has higher performance than the probability distribution (PD) model when the penetration rate is low, and it has an almost equivalent performance with higher penetration rate while the penetration rate is not needed. The proposed sensing model is more applicable for mixed traffic scenarios with much looser conditions.

Gabor Orosz - One of the best experts on this subject based on the ideXlab platform.

  • optimal control of Connected Vehicle systems with communication delay and driver reaction time
    IEEE Transactions on Intelligent Transportation Systems, 2017
    Co-Authors: Gabor Orosz
    Abstract:

    In this paper, linear quadratic regulation is used to obtain an optimal design of Connected cruise control (CCC). We consider Vehicle strings where a CCC Vehicle receives position and velocity signals through wireless Vehicle-to-Vehicle communication from multiple Vehicles ahead. Communication delay, driver reaction time, and heterogeneity of Vehicles are considered. The optimal feedback law is obtained by minimizing a cost function defined by headway and velocity errors and the acceleration of the CCC Vehicle on an infinite horizon. We show that, by decomposing the optimization problem, the feedback gains can be obtained recursively as signals from Vehicles farther ahead become available, and that the gains decay exponentially with the number of cars between the source of the signal and the CCC Vehicle. Such properties allow graceful degradation of CCC performance under imperfect communication. The effects of the cost function on the head-to-tail string stability are also investigated and the robustness against variations in human parameters is tested. The analytical results are verified by numerical simulations at the nonlinear level. The results allow us to significantly reduce the complexity of CCC design.

  • analysis of Connected Vehicle networks using network based perturbation techniques
    Nonlinear Dynamics, 2017
    Co-Authors: Sergei S Avedisov, Gabor Orosz
    Abstract:

    In this paper we propose a novel technique to decompose networked systems and use this technique to investigate the dynamics of Connected Vehicle networks with wireless Vehicle-to-Vehicle (V2V) communication. We apply modal perturbation analysis to approximate the modes of the perturbed network about the modes of the corresponding cyclically symmetric network. By exploiting the cyclic symmetry, we approximate the dynamics of a given mode by solving a small number of linear algebraic equations. We apply this approach to decompose Connected Vehicle networks into traveling waves which allows us to assess the impacts of long-range V2V communication on the stability of traffic flow.

  • optimal control of Connected Vehicle systems
    Conference on Decision and Control, 2014
    Co-Authors: Gabor Orosz
    Abstract:

    In this paper, linear quadratic tracking (LQT) is used to optimize the control gains for Connected cruise control (CCC). We consider a Vehicle string where the CCC Vehicle at the tail receives position and velocity signals through wireless Vehicle-to-Vehicle (V2V) communication from other Vehicles ahead (that are not equipped with CCC). An optimal feedback law is obtained by minimizing a cost function defined by headway and velocity errors and the acceleration of the CCC Vehicle on an infinite horizon. We show that the feedback gains can be obtained recursively as signals from Vehicles farther ahead become available, and that the gains decay exponentially with the number of cars between the source of the signal and the CCC Vehicle. The effects of the cost function on the head-to-tail string stability are investigated and the robustness against variations in human parameters is tested. The analytical results are verified by numerical simulations.

  • dynamics of Connected Vehicle systems with delayed acceleration feedback
    Transportation Research Part C-emerging Technologies, 2014
    Co-Authors: Gabor Orosz
    Abstract:

    In this paper, acceleration-based Connected cruise control (CCC) is proposed to increase roadway traffic mobility. CCC is designed to be able to use acceleration signals received from multiple Vehicles ahead through wireless Vehicle-to-Vehicle (V2V) communication. We consider various connectivity structures in heterogeneous platoons comprised of human-driven and CCC Vehicles. We show that inserting a few CCC Vehicles with appropriately designed gains and delays into the flow, one can stabilize otherwise string unstable Vehicle platoons. Exploiting the flexibility of ad-hoc connectivity, CCC can be applied in a large variety of traffic scenarios. Moreover, using acceleration feedback in a selective manner, CCC provides robust performance and remains scalable for large systems of Connected Vehicles. Our conclusions are verified by simulations at the nonlinear level.

  • designing network motifs in Connected Vehicle systems delay effects and stability
    Volume 3: Nonlinear Estimation and Control; Optimization and Optimal Control; Piezoelectric Actuation and Nanoscale Control; Robotics and Manipulators, 2013
    Co-Authors: Linjun Zhang, Gabor Orosz
    Abstract:

    Arising technologies in Vehicle-to-Vehicle (V2V) communication allow Vehicles to obtain information about the motion of distant Vehicles. Such information can be presented to the driver or incorporated in advanced autonomous cruise control (ACC) systems. In this paper, we investigate the effects of multi-Vehicle communication on the dynamics of Connected Vehicle platoons and propose a motif-based approach that allows systematical analysis and design of such systems. We investigate the dynamics of simple motifs in the presence of communication delays, and show that long-distance communication can stabilize the uniform flow when the flow cannot be stabilized by nearest neighbor interactions. The results can be used for designing driver assist systems and communication-based cruise control systems.Copyright © 2013 by ASME

Henry X Liu - One of the best experts on this subject based on the ideXlab platform.

  • on the estimation of Connected Vehicle penetration rate based on single source Connected Vehicle data
    Transportation Research Part B-methodological, 2019
    Co-Authors: Wai Wong, Shengyin Shen, Yan Zhao, Henry X Liu
    Abstract:

    Abstract With more Connected Vehicles (CVs) in the networks, the big data era leads to the availability of abundant data from CVs. CV penetration rate is the fundamental building block of tremendous applications, such as traffic data estimation, CV-based adaptive signal control and origin-destination estimation. While CV penetration rate is a random variable unknown in nature, the current estimation method of penetration rate mainly relies on two sources of data —detector and CV data. Penetration rate across the link is computed as CV flow divided by all traffic flow over a certain period of time. However, the current method is constrained by availability and quality of detector data. This paper proposes a simple, analytical, non-parametric, and most importantly, unbiased single-source data penetration rate (SSDPR) estimation method for estimating penetration rate solely based on CV data. It subtly and simultaneously fuses two estimation mechanisms—(1) the measurement of the probability of the first Vehicle in a queue being a CV and (2) the direct estimation of the penetration rate of a sample queue—to constitute a single estimator to handle all the possible sample queue patterns. Applicability of the proposed method is not confined to a specific arrival pattern. It solely utilizes the number of the observed CVs and the number of Vehicles before the last observed CV in a sample queue. Combining with bridging the queue algorithm, the proposed SSDPR estimation method is extended to overflow or oversaturated conditions. Simulation results show that the proposed method is able to accurately estimate penetration rate as low as 0.1% for all the situations considered. To illustrate the applicability of the proposed method, a case study of fundamental diagram estimation of a link without being installed with any detector is presented.

  • hardware in the loop testbed for evaluating Connected Vehicle applications
    Transportation Research Part C-emerging Technologies, 2017
    Co-Authors: Mohd Azrin Mohd Zulkefli, Henry X Liu, Jianfeng Zheng, Pratik Mukherjee, Zongxuan Sun, Peter Huang
    Abstract:

    Abstract Connected Vehicle environment provides the groundwork of future road transportation. Researches in this area are gaining a lot of attention to improve not only traffic mobility and safety, but also Vehicles’ fuel consumption and emissions. Energy optimization methods that combine traffic information are proposed, but actual testing in the field proves to be rather challenging largely due to safety and technical issues. In light of this, a Hardware-in-the-Loop-System (HiLS) testbed to evaluate the performance of Connected Vehicle applications is proposed. A laboratory powertrain research platform, which consists of a real engine, an engine-loading device (hydrostatic dynamometer) and a virtual powertrain model to represent a Vehicle, is Connected remotely to a microscopic traffic simulator (VISSIM). Vehicle dynamics and road conditions of a target Vehicle in the VISSIM simulation are transmitted to the powertrain research platform through the internet, where the power demand can then be calculated. The engine then operates through an engine optimization procedure to minimize fuel consumption, while the dynamometer tracks the desired engine load based on the target Vehicle information. Test results show fast data transfer at every 200 ms and good tracking of the optimized engine operating points and the desired Vehicle speed. Actual fuel and emissions measurements, which otherwise could not be calculated precisely by fuel and emission maps in simulations, are achieved by the testbed. In addition, VISSIM simulation can be implemented remotely while Connected to the powertrain research platform through the internet, allowing easy access to the laboratory setup.