Showing papers by "Yunpeng Wang published in 2019"

Missing data detection and imputation for urban ANPR system using an iterative tensor decomposition approach

Abstract: The automatic number plate recognition (ANPR) system has been widely implemented as an important part of intelligent transportation system (ITS). However, similar to other traffic monitoring devices, missing data is a common and critical problem in the ANPR system. To solve the missing data problem, numerous tensor-based methods have been proposed in previous studies. Most of them, however, assume that where and when missing data occur in the dataset are known. This would be impractical, because missing data may occur randomly. In this study, we propose a novel tensor-based algorithm, specifically, an iterative tensor decomposition (ITD) approach, that utilizes multidimensional inherent correlation of traffic data to detect and impute missing data in the ANPR system. The proposed algorithm is tested with a real-world ANPR system dataset. The experimental results show that missing data from the ANPR system can be classified into three cases, i.e., no missing, random elements missing, and extreme missing. The proposed ITD can accurately detect and correct missing data under different missing cases. Furthermore, ITD is also compared with other state-of-the-art methods and the results show that ITD outperforms the existing methods.

Allocation method for transit lines considering the user equilibrium for operators

Abstract: The purpose of this study is to address the allocation of transit lines problem in operation-sharing. An allocation method for urban transit lines is proposed to guide public authorities to pursue an optimal plan considering the User Equilibrium for operators (UE-O). The method utilizes the concepts from mathematical programming and game theory to present the UE-O and proposes a set partitioning formulation considering the benefits of both passengers and operators. A branch-and-price algorithm employing both column generation and branch-and-bound is used to tackle the problem. The proposed method is validated through a case study using data from the Development District of Dalian. Results show that the proposed line allocation method considering the UE-O can reduce the potential competitions among operators. This method and findings can provide a guidance to the problems in operation-sharing regarding allocation of transit lines.

Reliability-Oriented Optimization of Computation Offloading for Cooperative Vehicle-Infrastructure Systems

Abstract: Computation offloading is critical for mobile applications that are sensitive to computational power, while dynamic and random nature of vehicular networks makes it challenging to guarantee the reliability of vehicular computation offloading. In this letter, we propose a reliability-oriented stochastic optimization model based on the dynamic programming for computation offloading in the presence of the deadline constraint on application execution. Specifically, a theoretical lower bound of the expected reliability of computation offloading is derived, and then an optimal data transmission scheduling mechanism is proposed to maximize the lower bound with consideration of randomness in vehicle-to-infrastructure communications. Experimental results demonstrate that our mechanism can outperform the conventional scheme and benefits vehicular computation offloading in terms of reliability performance in stochastic situations.

Influence of Driving Behaviors on the Stability in Car Following

Abstract: Car following is the most common phenomenon in single-lane traffic. However, the propagation of the small perturbation of the velocity of the leading car will affect traffic flow. Driving behaviors play an important role in the determination of the qualitative dynamics of vehicles in the car-following process. Under different traffic environments, driving behaviors depend on the level of perceived risk, acceleration and deceleration habits, and reaction characteristics of the driver. The desired safety margin (DSM) model can directly describe the driving behaviors in the car-following process by using the parameters of the risk perception of drivers, sensitivity coefficient of acceleration and deceleration, and response time. In this paper, we investigate the influence of the accepted risk level, response time, and sensitivity factor on the traffic flow via the DSM model. The stability criterion of the simplified DSM model is derived via linear stability theory. Analytical results indicate that a backward propagating of perturbation would enlarge or shrink with the change of three driving behavior parameters of accepted risk level, sensitivity coefficient of acceleration or deceleration, and response time. Results show that careful driving can improve the stability of traffic flow and that system stability can be maintained by adjusting the acceleration and deceleration control parameters, increasing DSM, or decreasing response time for the adaptive cruise control or vehicular platoon control system. The results can provide reasonable values of driving behavior parameters for the stability of the primitive DSM model using the simplified DSM model. Furthermore, we analyze the influence of interval DSMs, and the acceleration and deceleration sensitivity of the primitive DSM model on the stability of traffic flow through numerical simulations. Results imply that the lower limit of the DSM influences traffic flow more significantly than the upper limit of the DSM. Moreover, the increase in deceleration sensitivity has a more important influence on the stability of traffic flow than the increase in acceleration sensitivity. The numerical simulation results are in good agreement with the analytical results and the relevant experimental results of previous studies.

Spatial Interpolation of Missing Annual Average Daily Traffic Data Using Copula-Based Model

Abstract: Accurate estimation of traffic counts [(i.e., annual average daily traffic (AADT)] is essential to transportation agencies for traffic demand forecasting, emission evaluation, pavement design, and project prioritization. Traditional AADT estimation methods rely on either temporal data imputation techniques based on historical records or kriging-based spatial interpolation approaches. However, Kriging method utilizes the correlation function as the sole descriptor of spatial dependency, posing limitations to yield accurate interpolation results for unstable AADTs under complex traffic patterns due to diverse road functions or land uses. This study proposed a copula-based model that combines spatial dependency and marginal distribution for missing AADT interpolation to weaken the limitation of Kriging method. Thus, the proposed model not only can describe the spatial dependency but also is robust to outliers. AADT data collected from the California state highway network were used to evaluate the effectiveness of spatial copula models with varying missing data rates. Four road segments with regular and recreational traffic patterns were selected to compare with existing kriging-based approaches. Results suggested that the spatial copulas yielded significantly higher accuracy rates than kriging did for irregular travel patterns with high missing data rates. Spatial copula models hold a great potential to improve the performance of large-scale transportation network-wide data imputation for planning and operational usages.

A Wireless Charging Facilities Deployment Problem Considering Optimal Traffic Delay and Energy Consumption on Signalized Arterial

Abstract: With the looming promise of wireless recharging technology, electric vehicles (EVs) are going to be able to acquire energy while still in motion. This paper focuses on the optimal deployment of wireless recharging facilities on signalized arterials for EVs. To address this issue, a bi-objective model considering both traffic operation efficiency (i.e., traffic delay saving) and charging infrastructure utilization rate (i.e., electricity gain from charging) has been formulated. A modified cell transmission model (CTM) is used as a base to simulate traffic flow on an arterial with traffic signals. The cells in the CTM also serve as a potential installation site for wireless recharging facilities. The essential goal of this model is to maximize the recharging electricity for EVs traveling on arterials while maintaining low travel delay. Due to the complexity in solving the bi-objective model, heuristic approaches, such as genetic algorithm and particle swarm optimization, are employed. The numerical experiments based on real day-to-day traffic demand are executed. A Pareto set is obtained and a sensitivity analysis regarding recharging rate, investment, and minimum recharging region length is provided.

Tunable and Transferable RBF Model for Short-Term Traffic Forecasting

Abstract: The application of short-term traffic forecasting can guide the operation of traffic networks efficiently and reduce the traffic cost for travelers. On the basis of radial basis function (RBF) neural network, this paper introduces a tunable and transferable RBF (TT-RBF) model to conduct on-line forecasting and transfer forecasting. Considering the spatiotemporal correlation of traffic flows in a road network, a spatiotemporal state matrix formed by the detrended cross-correlation analysis is used for the model input. With the on-line forecasting process, an improved on-line structure and parameter adjustment are proposed to enhance the existing model. Thus, the TT-RBF model can be adaptive to time-varying traffic states, especially to deal with the difference between non-peak and peak hours. Moreover, the proposed model can be transferred from one road segment to act on other road segments. By this way, the traffic states of numerous road segments can be forecasted conveniently without complex model training processes. The floating car data of a typical road network in Beijing are used for the performance verification of the TT-RBF model, and some frequently used forecasting models are selected for comparisons. The numerical experiments show that the TT-RBF model can get more accurate results than those in single-step forecasting, multi-step forecasting, and transfer forecasting.

An Effective Fuel-Level Data Cleaning and Repairing Method for Vehicle Monitor Platform

Abstract: With energy scarcity and environmental pollution becoming increasingly serious, the accurate estimation of fuel consumption of vehicles has been important in vehicle management and transportation planning toward a sustainable green transition. Fuel consumption is calculated by fuel-level data collected from high-precision fuel-level sensors. However, in the vehicle monitor platform, there are many types of error in the data collection and transmission processes, such as the noise, interference, and collision errors that are common in the high speed and dynamic vehicle environment. In this paper, an effective method for cleaning and repairing the fuel-level data is proposed, which adopts the threshold to acquire abnormal fuel data, the time quantum to identify abnormal data, and linear interpolation based algorithm to correct data errors. Specifically, a modified Gaussian mixture model (GMM) based on the synchronous iteration method is proposed to acquire the thresholds, which uses the particle swarm optimization algorithm and the steepest descent algorithm to optimize the parameters of GMM. The experiment results based on the fuel-level data of vehicles collected over one month prove that the modified GMM is superior to GMM-expectation maximization on fuel-level data, and the proposed method is effective for cleaning and repairing outliers of fuel-level data.

A simulation-based approach to investigate the driver route choice behavior under the connected vehicle environment

Abstract: Drivers can move with the real-time traffic information under the connected vehicle environment. Meanwhile, individual driving behavior (e.g., route choice) can influence the traffic state of the connected vehicle. This study contributes to investigate the relationship between the performance of traffic system and the driver route choice behavior in the connected vehicle environment. Especially, the heterogeneity of drivers in attention to fuel consumption and travel time is considered. A dynamic process of commuter day-to-day route choice behavior under the connected vehicle environment was proposed, the driving experience and the real-time information that affect the driver route choice are taken into considered simultaneously. Using the multi-agent simulation approach, the microcosmic and macroscopic behaviors of driver-vehicle-unit agents were described. A series of simulation experiments were conducted under the different percentages of drivers who pay enough attention to fuel consumption, and then simulation results were compared. The simulation results demonstrated that the performance of traffic state is better when enough drivers put emphasis on fuel consumption, and the optimal percentage of these drivers is about 60%. It is indicated that the travel time is more sensitive to the change of traffic state than fuel consumption, and the traffic system with enough drivers who care the fuel consumption could bring the better performance. This study has a guidance function to the application and promotion of eco-driving for government to optimize the structure of different types of drivers in the connected vehicle environment.

Fine-tuning time-of-day partitions for signal timing plan development: revisiting clustering approaches

Abstract: Time of day (TOD) control, i.e. applying different signal timings during specified time of day to accommodate temporal traffic patterns, is widely used in the operation of most signalized intersect...

Analytical Model for Optimal Departure Time Choice with Perception Errors for Planners

Abstract: An extension of a recent framework for analyzing scheduling disutility with perception errors is derived. In such framework, the traveler has (α,β,γ) scheduling preferences. Although the actual tra...

Multi-mode communication method and device for mine car unmanned transportation system

Abstract: The invention provides a multi-mode communication method and device for a mine car unmanned transportation system. The method comprises the following steps: acquiring performance parameters of each channel, wherein the performance parameters comprise bandwidth, delay, jitter and packet loss rate; calculating a performance function of each channel according to the obtained performance parameters ofeach channel; standardizing the performance function of each channel; constructing a judgment matrix for representing the relative importance degree of the performance parameters of each channel; carrying out consistency inspection on the constructed judgment matrix; calculating a weight index matrix according to the parameters of the constructed judgment matrix, and carrying out normalization processing; calculating a weighted evaluation index; and judging whether to switch the communication network according to the calculated weighted evaluation index. According to the technical scheme, theefficiency and reliability of mining area vehicle communication can be improved, signal radiation equipment and signal enhancement equipment which are too high in energy consumption are not needed, only performance judgment needs to be conducted on existing multi-mode communication equipment, and whether the communication mode is switched or not and which communication mode is switched are judged.

Trajectory Reconstruction Using Automated Vehicles Motion Detection Data: A Hybrid Approach Integrating Wiedemann Model and Cellular Automation*

Abstract: In view of the increasing development of automated vehicles (AVs) technologies, it will be likely that road traffic is made up of a mixture of human-driven vehicles (HVs) and AVs in the coming years. To support traffic operation and management, this study proposed a hybrid approach integrating Wiedemann car-following model and cellular automation (CA) to reconstruct the trajectories of fully-sampled traffic flow on freeways. First, Wiedemann car-following model is applied to classify the vehicle driving states into following and closing. Then, human-driven vehicles (HVs) are inserted between the leading and following AVs based on the vehicle’s behavior within the following AV’s detection range. Next, the trajectories of inserted HVs are reconstructed by resorting to CA with four update rules set to determine vehicles’ acceleration, deceleration, randomization and position. Last, the proposed hybrid approach is performed under different traffic densities and AVs penetration rates. Results show that the proposed method for trajectory reconstruction performs satisfactorily on freeways even at low penetration rates of AVs.

A Multi-object Detection Method Based on Connected Vehicles

Abstract: Nowadays, with internet of vehicle developing, more and more research institute begin to research intelligent transportation systems. Vehicle-road collaborative system is a prominent one of these systems. Its main function is to percept traffic situation. Using image recognition technology is one of the methods. The advantages of this method are low cost, high data-correcting rate, and small interference to traffic flow. Traditional image recognition algorithms always have problems with high-processing time and low accuracy, such as HOG and DPM. They are not suitable to monitor real-time traffic videos of numerous image frames. In this paper, the structure of convolutional neural network is improved, different from original YOLOv3 algorithms. Compared with original YOLOv3 algorithm, the algorithm in this paper can not only realize multi-object detection, but also consume time and recognize more accurate. Moreover, this paper constructs a special identification data set of common traffic participants (pedestrians, cars, buses, etc.) to monitor traffic flow, basing on INRIA and KITTI data sets. Moreover, this paper collects images of commonly-seen objects at traffic intersections to test the performance of convolutional neural network structure of this paper. At the end of this paper, the performance of the proposed algorithm is verified, based on the real-time monitoring video of traffic intersections. According to the results, for common traffic participants, the mean average precise is 13.2% higher than that of original YOLOv3 algorithm, and detection time is reduced by 7.8%.