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Open Access Publications from the University of California

Scholarly Works (9 results)

  • Thesis
  • Peer Reviewed
UC Irvine Electronic Theses and Dissertations (2016)

The purpose of this study is to develop a traffic estimation framework which combines different data sources to better reconstruct the traffic states on the freeways. The framework combines both traffic parameter and state estimation in the same work flow, which resolves the inconsistency issue of most existing traffic state estimation methods.

To examine the quality of the traffic sensor data, the study starts with proposing the network sensor health problem (NSHP). The optimal set of sensors is selected from all sensors such that the violation of flow conservation is minimized. The health index for individual detector is then calculated based on the solutions. We also developed a tailored greedy search algorithm to find the solutions effectively. The proposed method is tested using the loop detector data from PeMS on a stretch of the SR-91 freeway. We compared the results with PeMS health status and found considerable level of consistency.

Two different traffic state estimation methods are proposed based on the data availability and traffic states. The LoopReid method is derived from the Newell's simplified kinematic wave model by assuming the whole road segment is fully congested. We formulate a least square optimization problem to find the initial states and traffic parameters based on the first-in-first-out principle and the congested part of the Newell's model. While developing the LoopCT method, we derived a counterpart of the Newell's kinematic wave model in the Lagrangian coordinates under Eulerian boundary conditions. This model also leads to a new method to estimate vehicle trajectories within a road segment. We formulate a least square optimization problem in initial states and traffic parameters which works for mixed traffic states. The two estimation methods turned out to be highly related and the LoopCT method degenerates to the LoopReid method when the traffic is fully congested. The two methods are validated using two datasets from the NGSIM project. Both methods achieved considerable level of accuracy at reconstructing the traffic states and parameters.

    Cover page: Freeway Traffic Parameter and State Estimation with Eulerian and Lagrangian DataCreative Commons 'BY-NC-SA' version 4.0 license
    • Article
    • Peer Reviewed
    Working Paper Series (2020)
    Monitoring Commerical Vehicle Activities is very important for developing and  maintaining efficient freight transport systems. In the existing Literature this is broadly done through vehicle classification and reidentification problems using various sensing technologies. Lidar is an emerging traffic sensing technology which could potentially serve as a multi functional sensor for transport systems. In out current work we mainly focused on developing and qualitatively assessing a Lidar based Reconstruction framework for Truck surveillance purpose. We proposed a two stage Truck body reconstruction framework and found the results of reconstructed Truck bodies are quite promising for several truck-trailer configurations. For certain types of Truck-Trialer configurations such as containers due to the sparsity of scanned points in lateral direction, the wheel portion of reconstructed body still has noticeable deformations. We would like to address the same in our future work.
      Cover page: Lidar Based Reconstruction framework for Truck Surveillance
      • Article
      • Peer Reviewed
      UC Irvine Previously Published Works (2017)
      The traffic state estimation process estimates various traffic states from available data in a road network and provides valuable information for travelers and decision makers to improve both travel experience and system performance. In many existing methods, model parameters and initial states have to be given in order to estimate traffic states, which limits the accuracy of the results as well as their transferability to different locations and times. In this paper, we propose a new framework to simultaneously estimate model parameters and traffic states for a congested road segment based on Newell's simplified kinematic wave model (Newell, 1993). Given both Eulerian traffic count data and Lagrangian vehicle reidentification data, we formulate a single optimization problem in terms of the initial number of vehicles and model parameters. Then we decouple the optimization problem such that the initial number of vehicles can be analytically solved with a closed-form formula, and the model parameters, including the jam density and the shock wave speed in congested traffic, can be computed with the Gauss-Newton method. Based on Newell's model, we can calculate individual vehicles’ trajectories as well as the average densities, speeds, and flow-rates inside the road segment. We also theoretically show that the optimization problem can have multiple solutions under absolutely steady traffic conditions. We apply the proposed method to the NGSIM datasets, verifying the validity of the method and showing that this method yields better results in the estimation of average densities than existing methods.
        Cover page: Simultaneous estimation of states and parameters in Newell’s simplified kinematic wave model with Eulerian and Lagrangian traffic data
        • Article
        • Peer Reviewed
        UC Irvine Previously Published Works (2017)
        A computational method, based on ℓ1-minimization, is proposed for the problem of link flow correction, when the available traffic flow data on many links in a road network are inconsistent with respect to the flow conservation law. Without extra information, the problem is generally ill-posed when a large portion of the link sensors are unhealthy. It is possible, however, to correct the corrupted link flows accurately with the proposed method under a recoverability condition if there are only a few bad sensors which are located at certain links. We analytically identify the links that are robust to miscounts and relate them to the geometric structure of the traffic network by introducing the recoverability concept and an algorithm for computing it. The recoverability condition for corrupted links is simply the associated recoverability being greater than 1. In a more realistic setting, besides the unhealthy link sensors, small measurement noises may be present at the other sensors. Under the same recoverability condition, our method guarantees to give an estimated traffic flow fairly close to the ground-truth data and leads to a bound for the correction error. Both synthetic and real-world examples are provided to demonstrate the effectiveness of the proposed method.
          Cover page: ℓ1-minimization method for link flow correction
          • Article
          • Peer Reviewed
          UC Irvine Previously Published Works (2021)
          Understanding the spatiotemporal distribution of commercial vehicles is essential for facilitating strategic pavement design, freight planning, and policy making. Hence, transportation agencies have been increasingly interested in collecting truck body configuration data due to its strong association with industries and freight commodities, to better understand their distinct operational characteristics and impacts on infrastructure and the environment. The rapid advancement of Light Detection and Ranging (LiDAR) technology has facilitated the development of non-intrusive detection solutions that are able to accurately classify truck body types in detail. This paper proposes a new truck classification method using a LiDAR sensor oriented to provide a wide field-of-view of roadways. In order to enrich the sparse point cloud obtained from the sensor, point clouds originating from the same truck across consecutive frames were grouped and combined using a two-stage vehicle reconstruction framework to generate a dense three-dimensional (3D) point cloud representation of each truck. Subsequently, PointNet – a deep representation learning algorithm – was adopted to train the classification model from reconstructed point clouds. The model utilizes low-level features extracted from the 3D point clouds and detects key features associated with each truck class. Finally, model ensemble techniques were explored to reduce the generalization error by averaging the results of seven PointNet models and further enhancing the overall model performance. The optimal number of models in the ensemble was determined through a comprehensive sensitivity analysis with the consideration of the average correct classification rate (CCR), the variability of the prediction results, and the computation efficiency. The developed model is capable of distinguishing passenger vehicles and 29 different truck body configurations with an average CCR of 83 percent. The average correct classification rate of the developed method on the test dataset was 90 percent for trucks pulling a large trailer(s).
            Cover page: Truck body type classification using a deep representationlearning ensemble on 3D point sets
            • Article
            Working Paper Series (2021)
              Cover page: Vehicle Point Cloud Reconstruction Framework for FHWA axle-based Classification using Roadside LiDAR Sensor
              • Article
              • Peer Reviewed
              UC Irvine Previously Published Works (2018)
              This paper presents the design and results for field tests regarding the environmental benefits in stop-and-go traffic of an algorithmic green driving strategy based on inter-vehicle communication (IVC), which was proposed in Yang and Jin (2014). The green driving strategy dynamically calculates advisory speed limits for vehicles equipped with IVC devices so as to smooth their speed profiles and reduce their emissions and fuel consumption. For the field tests, we develop a smartphone-based IVC system, in which vehicles’ speeds and locations are collected by GPS and accelerometer sensors embedded in smartphones, and communications among vehicles are enabled by specially designed smartphone applications, a central server, and 4G cellular networks. Six field tests are carried out on an uninterrupted ring road under slow or fast stop-and-go traffic conditions. We compare the performances of three alternatives: no green driving, heuristic green driving, and the IVC-based algorithmic green driving. Results show that heuristic green driving has better smoothing and environmental effects than no green driving, but the IVC-based algorithmic green driving outperforms both. In the future, we are interested in field tests under more realistic traffic conditions.
                Cover page: Field tests of a dynamic green driving strategy based on inter-vehicle communication
                • Article
                • Peer Reviewed
                UC Berkeley Previously Published Works (2023)
                In kagome metal CsV_{3}Sb_{5}, multiple intertwined orders are accompanied by both electronic and structural instabilities. These exotic orders have attracted much recent attention, but their origins remain elusive. The newly discovered CsTi_{3}Bi_{5} is a Ti-based kagome metal to parallel CsV_{3}Sb_{5}. Here, we report angle-resolved photoemission experiments and first-principles calculations on pristine and Cs-doped CsTi_{3}Bi_{5} samples. Our results reveal that the van Hove singularity (vHS) in CsTi_{3}Bi_{5} can be tuned in a large energy range without structural instability, different from that in CsV_{3}Sb_{5}. As such, CsTi_{3}Bi_{5} provides a complementary platform to disentangle and investigate the electronic instability with a tunable vHS in kagome metals.
                  Cover page: Tunable Van Hove Singularity without Structural Instability in Kagome Metal CsTi3Bi5Creative Commons 'BY' version 4.0 license
                  • Article
                  • Peer Reviewed
                  UC Berkeley Previously Published Works (2018)
                    Cover page: The Tsinghua–Lancet Commission on Healthy Cities in China: unlocking the power of cities for a healthy China
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