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Plastic film mulching technology can effectively enhance crop yield and quality, and the use of mulch has been increasing in recent years; however, the problem of mulch residue is worsening due to the large amount of recycling work and slow natural degradation. In this study, a potato seedling killing and residual film recycling machine is designed to provide good working conditions for potato harvesters before harvesting in response to the problems of difficult separation of film tangles, the low net rate of recycling due to the mixing of residual film with soil, and the high soil content in residual film recycling operations in northwest China. The machine is based on the potato monoculture and double row planting mode in Gansu area. This paper puts forward the overall design scheme and carries out the theoretical analysis and parameter determination of the key components, such as the seedling killing device, the film surface cleaning device, the film unloading device, and so on. Using EDEM software to carry out the virtual simulation test and Design-Expert13 to analyze the test results, we determined the optimal working scheme for the machine, with a forward speed of 0.8 m/s, a film gap of 125 mm, and a spiral stirrer speed of 600 r/min. Based on a field test for verification, the test results show that the machine’s residual film recovery rate was 83.3%, the impurity rate was 3.8%, and the rate of injury to the potatoes was 1.4%. The machine meets the requirements of national and industry standards, and it can simultaneously realize straw crushing, film surface cleaning, residual film recycling, and hydraulic film unloading operations, with better operating results and while reaching the expected results. It can also provide a reference for the design and testing of a seeding and residual film recycling machine.

To address the issues of the poor filling and clearing efficiency of spoon–chain potato seed dischargers, an optimization design was implemented in this study. Based on the motion characteristics of potatoes during the filling and transport processes, an inclination angle was set for the seed spoon cavity, and a seed-clearing brush was installed at the top of the seed discharger. A DEM-MBD coupled simulation model of the seed discharger was constructed. The working speed of the driving sprocket, the inclination angle of the seed spoon cavity, and the seed holding height were used as experimental factors, while the single-seed qualification rate, missed seed rate, and over-seeding rate were used as evaluation indices to conduct a quadratic regression orthogonal rotation combination experiment. This determined the optimal technical parameter combination for the best working performance. Based on the results of the DEM-MBD coupled simulation experiments, a response surface optimization test was conducted. The results showed that the optimal working performance was achieved when the working speed of the driving sprocket was 43 rpm, the inclination angle of the seed spoon cavity was 15°, and the seed holding height was 0.2 m. Under these conditions, the single-seed qualification rate was 95.28%, the missed seed rate was 0.92%, and the over-seeding rate was 3.80%. Further soil bin tests confirmed that, under the optimal working parameters, the relative deviations of all test indices from the response surface optimization test results were less than 2%. The research results provide new insights for the optimization design of spoon–chain potato seed-metering devices.

Aiming at the problems of poor passability of a tracked chassis due to small plots, complicated road conditions and steep slopes during mechanized potato harvesting in hilly and mountainous areas. To design a potato combine harvester and take the tracked chassis of the harvester as the research object to study its driving performance under typical road conditions in mountainous areas. Firstly, mechanical analysis and theoretical calculation are carried out on the tracked chassis to obtain the relevant parameters of key components, and secondly, its driving performance is analyzed to obtain the driving limit values of passing performance under different working conditions; RecurDyn software was used to establish the dynamics simulation and analysis model of the whole machine, and the driving limit values of the harvester were determined under five different road conditions through simulation. The results show the following: the driving limit gradient angle is 20° in cross-gradient conditions, 25° in longitudinal up-gradient conditions, 25° in longitudinal down-gradient conditions, the limiting height of the chassis that can be overpassed in obstacle-crossing conditions is 450 mm, and the limiting width of the chassis that can be spanned in trench-crossing conditions is 1200 mm. The simulation results were verified through field tests, and the results of the field tests showed that the harvester met the requirements of stable travelling on longitudinal slopes of 24°, climbed over a 450 mm straight wall and crossed over a 1200 mm trench, which were similar to the simulation results, indicating that the simulation results were accurate and feasible, and met the design requirements for the travelling passage of the crawler potato harvester. This study provides an in-depth understanding of the tracked chassis of the potato combine harvester in hilly mountainous areas, with a view to providing reference for the design of tracked harvesters for other crops in hilly mountainous areas.

Aiming at the problems of low soil cover and serious wear and tear of transmission parts in the soil lifting device of potato planters, this paper carries out innovative optimization design of its key parts based on TRIZ theory. The discrete element model of the soil model and the multi-body dynamic model of the soil lifting device are established, the upper scraping and lower scraping soil lifting devices are simulated respectively through the method of DEM-MBD coupling, and the working mechanism of the soil lifting device is further explored. The simulation results show that the lower scraping type lifting device has a large conveying capacity and a stable flow rate at the soil outlet. The results of the soil tank performance comparison test show that: the improved soil lifting device has a simple structure, large conveying capacity, stable flow rate, and fast flow speed and is not easily congested. It meets the standard requirements of dryland potato seedling strip mulching planting technology on mulching parameters and is of great significance for improving dryland food production on the Loess Plateau.

Due to environmental concerns, the energy-saving train regulation is necessary for urban metro transportation, which can improve the service quality and energy efficiency of metro lines. In contrast to most of the existing research of train regulation based on centralized control, this paper studies the energy-saving train regulation problem by utilizing distributed model predictive control (DMPC), which is motivated by the breakthrough of vehicle-based train control (VBTC) technology and the pressing real-time control demand. Firstly, we establish a distributed control framework for train regulation process assuming each train is self-organized and capable to communicate with its preceding train. Then we propose a DMPC algorithm for solving the energy-saving train regulation problem, where each train determines its control input by minimizing a constrained local cost function mainly composed of schedule deviation, headway deviation, and energy consumption. Finally, simulations on train regulation for the Beijing Yizhuang metro line are carried out to demonstrate the effectiveness of the proposed DMPC algorithm, and the results reveal that the proposed algorithm exhibits significantly improved real-time performance without deteriorating the service quality or energy efficiency compared with the centralized MPC method.

With the rapid development of urban rail transit systems and the consequent sharp increase of energy consumption, the energy-saving train operation problem has been attracting much attention. Extensive studies have been devoted to optimal control of a single metro train in an inter-station run to minimize the energy consumption. However, most of the existing work focuses on offline optimization of the energy-saving driving strategy, which still needs to be tracked in real train operation. In order to attain better performance in the presence of disturbances, this paper studies the online optimization problem of the energy-saving driving strategy for a single metro train, by employing the model predictive control (MPC) approach. Firstly, a switched-mode dynamical system model is introduced to describe the dynamics of a metro train. Based on this model, an MPC-based online optimization problem is formulated for obtaining the optimal mode switching times with minimal energy consumption for a single train in an inter-station run. Then we propose an algorithm to solve the constrained optimization problem at each time step by utilizing the exterior point penalty function method. The proposed online optimal train control algorithm which determines the mode switching times can not only improve the computational efficiency but also enhances the robustness to disturbances in real scenarios. Finally, the effectiveness and advantages of this online optimal train control algorithm are illustrated through case studies of a single train in an inter-station run.

Short-term traffic state prediction has become an integral component of an advanced traveler information system (ATIS) in intelligent transportation systems (ITS). Accurate modeling and short-term traffic prediction are quite challenging due to its intricate characteristics, stochastic, and dynamic traffic processes. Existing works in this area follow different modeling approaches that are focused to fit speed, density, or the volume data. However, the accuracy of such modeling approaches has been frequently questioned, thereby traffic state prediction over the short-term from such methods inflicts an overfitting issue. We address this issue to accurately model short-term future traffic state prediction using state-of-the-art models via hyperparameter optimization. To do so, we focused on different machine learning classifiers such as local deep support vector machine (LD-SVM), decision jungles, multi-layers perceptron (MLP), and CN2 rule induction. Moreover, traffic states are evaluated using traffic attributes such as level of service (LOS) horizons and simple if−then rules at different time intervals. Our findings show that hyperparameter optimization via random sweep yielded superior results. The overall prediction performances obtained an average improvement by over 95%, such that the decision jungle and LD-SVM achieved an accuracy of 0.982 and 0.975, respectively. The experimental results show the robustness and superior performances of decision jungles (DJ) over other methods.

In order to estimate traffic densities in a large-scale urban freeway network in an accurate and timely fashion when traffic sensors do not cover the freeway network completely and thus only local measurement data can be utilized, this paper proposes a decentralized state observer approach based on a macroscopic traffic flow model. Firstly, by using the well-known cell transmission model (CTM), the urban freeway network is modeled in the way of distributed systems. Secondly, based on the model, a decentralized observer is designed. With the help of the Lyapunov function and S-procedure theory, the observer gains are computed by using linear matrix inequality (LMI) technique. So, the traffic densities of the whole road network can be estimated by the designed observer. Finally, this method is applied to the outer ring of the Beijing’s second ring road and experimental results demonstrate the effectiveness and applicability of the proposed approach.

In this paper, in order to describe complex network systems, we firstly propose a general modeling framework by combining a dynamic graph with hybrid automata and thus name it Dynamic Graph Hybrid Automata (DGHA). Then we apply this framework to model traffic flow over an urban freeway network by embedding the Cell Transmission Model (CTM) into the DGHA. With a modeling procedure, we adopt a dual digraph of road network structure to describe the road topology, use linear hybrid automata to describe multi-modes of dynamic densities in road segments and transform the nonlinear expressions of the transmitted traffic flow between two road segments into piecewise linear functions in terms of multi-mode switchings. This modeling procedure is modularized and rule-based, and thus is easily-extensible with the help of a combination algorithm for the dynamics of traffic flow. It can describe the dynamics of traffic flow over an urban freeway network with arbitrary topology structures and sizes. Next we analyze mode types and number in the model of the whole freeway network, and deduce a Piecewise Affine Linear System (PWALS) model. Furthermore, based on the PWALS model, a multi-mode switched state observer is designed to estimate the traffic densities of the freeway network, where a set of observer gain matrices are computed by using the Lyapunov function approach. As an example, we utilize the PWALS model and the corresponding switched state observer to traffic flow over Beijing third ring road. In order to clearly interpret the principle of the proposed method and avoid computational complexity, we adopt a simplified version of Beijing third ring road. Practical application for a large-scale road network will be implemented by decentralized modeling approach and distributed observer designing in the future research.

The problems of robust stability analysis and synthesis for a class of uncertain switched time-delay systems with polytopic type uncertainties are addressed. Based on the constructive use of an appropriate switched Lyapunov function, sufficient linear matrix inequalities (LMIs) conditions are investigated to make such systems a uniform quadratic stability with an L2-gain smaller than a given constant level. System synthesis is to design switched feedback schemes, whether based on state, output measurements, or by using dynamic output feedback, to guarantee that the corresponding closed-loop system satisfies the LMIs conditions. Two numerical examples are provided that demonstrate the efficiency of this approach.

Accurate traffic estimation on urban networks is a prerequisite for efficient traffic detection, congestion warning, and transportation schedule. The current estimation methods can be roughly divided into model-driven and the data-driven methods. The estimation accuracy of the model-driven methods cannot satisfy certain applications. Meanwhile, the data-driven methods have the disadvantages of poor generalization ability and weak interpretability. To overcome these challenges, this paper proposes a framework named the physics-informed spatiotemporal graph convolution neural network (PSTGCN) based on physics-informed deep learning theories. The PSTGCN uses a spatiotemporal graph convolution neural network combined with traffic flow models to estimate the traffic state. The proposed model not only considers the temporal and spatial dependence of traffic flow but also abides by the internal law of traffic flow. Furthermore, the estimation objects of the proposedmodel are multiple variables that comprehensively represent the traffic state. Experiments on real-world traffic data reveal the error of the PSTGCN is reduced by 38.39% compared to the baselines. Also, the PSTGCN can achieve a similar prediction effect as the baselines by using half of the global spatial information. These results demonstrate that the PSTGCN outperforms the state-of-the-art models in urban traffic estimation and is robust under variable road conditions and data scales. [ABSTRACT FROM AUTHOR]

The objective of this article is to investigate the effects of temperature on rutting and fatigue cracking resistance of asphalt binders based on the time-temperature superposition principle (TTSP). In this study, the TTSP approach was employed to interpret the results of multiple stress creep recovery (MSCR) and linear amplitude sweep (LAS) tests for both neat and styrene-butadiene-styrene (SBS)-modified asphalt binders. The higher stress level of 3.2 kPa in the standard MSCR test procedure was extended to 10 and 15 kPa to verify the stress sensitivity of the nonrecoverable compliance (Jnr). A loading duration of 5 minutes in the standard LAS test was extended to 10 and 15 minutes to develop the fatigue failure criterion at different conditions. The results demonstrate that the time-strain curves of the neat binder during the MSCR tests under different temperatures can be unified into a single strain master curve using the TTSP shift factors, based on which the predictive Jnr showed good agreement with the measured values. This TTSP-based analysis approach does not work well for the MSCR results of the SBS-modified binder at high temperatures. However, the fatigue failure criterion determined from the LAS tests under different temperatures was verified to be successfully constructed into a single failure criterion according to the TTSP approach for both the neat and SBS-modified binders. Therefore, only an additional caution is raised for the TTS-based analysis on rutting resistance of the modified binder in a high temperature range. [ABSTRACT FROM AUTHOR]

Non-recurrent traffic incidents (accidents, stalled vehicles and spilled loads) often bring about traffic congestion and even secondary accidents. Detecting and positioning them quickly and accurately has important significance for early warning, timely incident-disposal and speedy congestion-evacuation. This study proposes a video-based detecting and positioning method by analysing distribution characteristics of traffic states in a road segment. Each lane in the monitored segment is divided into a cluster of cells. Traffic parameters in each cell, including flow rate, average travel speed and average space occupancy, are obtained by detecting and tracking traffic objects (vehicles and spilled loads). On the basis of the parameters, traffic states in the cells are judged via a fuzzy-identification method. For each congested cell, a feature vector is constructed by taking its state together with states of its upstream and downstream neighbouring cells in the same lane. Then, a support vector machine classifier is trained to detect incident point. If a cell is judged to be corresponding to an incident point at least for two successive time periods, an incident is detected and its position is calculated based on the identity number of the cell. Experiments prove the efficiency and practicability of the proposed method. [ABSTRACT FROM AUTHOR]