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The social functions of urbanized areas are highly dependent on and supported by the convenient access to public transportation systems, particularly for the less privileged populations who have restrained auto ownership. To accurately evaluate the public transit accessibility, it is critical to capture the spatiotemporal variation of transit services. This can be achieved by measuring the shortest paths or minimum travel time between origin-destination (OD) pairs at each time-of-day (e.g. every minute). In recent years, General Transit Feed Specification (GTFS) data has been gaining popularity for between-station travel time estimation due to its interoperability in spatiotemporal analytics. Many software packages, such as ArcGIS, have developed toolbox to enable the travel time estimation with GTFS. They perform reasonably well in calculating travel time between OD pairs for a specific time-of-day (e.g. 8:00 AM), yet can become computational inefficient and unpractical with the increase of data dimensions (e.g. all times-of-day and large network). In this paper, we introduce a new algorithm that is computationally elegant and mathematically efficient to address this issue. An open-source toolbox written in C++ is developed to implement the algorithm. We implemented the algorithm on City of St. George's transit network to showcase the accessibility analysis enabled by the toolbox. The experimental evidence shows significant reduction on computational time. The proposed algorithm and toolbox presented is easily transferable to other transit networks to allow transit agencies and researchers perform high resolution transit performance analysis.

In the practical production, the transportation of jobs is existed between different machines. These transportation operations directly affect the production cycle and the production efficiency. In this study, an improved memetic algorithm is proposed to solve the flexible job shop scheduling problem with transportation times, and the optimization objective is minimizing the makespan. In the improved memetic algorithm, an effective simulated annealing algorithm is adopted in the local search process, which combines the elite library and mutation operation. All the feasible solutions are divided into general solutions and local optimal solutions according to the elite library. The general solutions are executed by the simulated annealing algorithm to improve the quality, and the local optimal solutions are executed by the mutation operation to increase the diversity of the solution set. Comparison experiments with the improved genetic algorithm show that the improved memetic algorithm has better search performance and stability. [ABSTRACT FROM AUTHOR]

This paper is devoted to model-free attitude control of rigid spacecraft in the presence of control torque saturation and external disturbances. Specifically, a model-free deep reinforcement learning (DRL) controller is proposed, which can learn continuously according to the feedback of the environment and realize the high-precision attitude control of spacecraft without repeatedly adjusting the controller parameters. Considering the continuity of state space and action space, the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm based on actor-critic architecture is adopted. Compared with the Deep Deterministic Policy Gradient (DDPG) algorithm, TD3 has better performance. TD3 obtains the optimal policy by interacting with the environment without using any prior knowledge, so the learning process is time-consuming. Aiming at this problem, the PID-Guide TD3 algorithm is proposed, which can speed up the training speed and improve the convergence precision of the TD3 algorithm. Aiming at the problem that reinforcement learning (RL) is difficult to deploy in the actual environment, the pretraining/fine-tuning method is proposed for deployment, which can not only save training time and computing resources but also achieve good results quickly. The experimental results show that DRL controller can realize high-precision attitude stabilization and attitude tracking control, with fast response speed and small overshoot. The proposed PID-Guide TD3 algorithm has faster training speed and higher stability than the TD3 algorithm. [ABSTRACT FROM AUTHOR]

In the practical production, the transportation of jobs is existed between different machines. These transportation operations directly affect the production cycle and the production efficiency. In this study, an improved memetic algorithm is proposed to solve the flexible job shop scheduling problem with transportation times, and the optimization objective is minimizing the makespan. In the improved memetic algorithm, an effective simulated annealing algorithm is adopted in the local search process, which combines the elite library and mutation operation. All the feasible solutions are divided into general solutions and local optimal solutions according to the elite library. The general solutions are executed by the simulated annealing algorithm to improve the quality, and the local optimal solutions are executed by the mutation operation to increase the diversity of the solution set. Comparison experiments with the improved genetic algorithm show that the improved memetic algorithm has better search performance and stability. [ABSTRACT FROM AUTHOR]

It is of practical importance to extract and predict taxi hotspots in urban traffic networks. However, the extraction of taxi hotspot is generally influenced by multiple sources of dependence, which has not been well recognized in the existing literature. This study aims to investigate how the integration of clustering models and deep learning approaches can learn and extract the network-wide taxi hotspots in both temporal and spatial dimensions. A density based spatiotemporal clustering algorithm with noise (DBSTCAN) was established to extract the historical taxi hotspots, which changed with time. A conditional generative adversarial network with long short-term memory structure (LSTM-CGAN) model was proposed for taxi hotspot prediction, which is capable of capturing the spatial and temporal variations of taxi hotspots simultaneously. Specifically, the DBSTCAN was applied to process the large-scaled geo-coded taxi pickup data into time-varying historical hotspot information. The proposed LSTM-CGAN model was then trained by the network-wide hotspot data. As illustrated in the numerical tests, it was found that the proposed LSTM-CGAN model provided comparable results with different model layouts and model with 4 LSTM layers in both generator and discriminator performed best. Moreover, four typical prediction approaches were selected to compare with the proposed LSTM-CGAN model. The comparative analyses showed that the proposed LSTM-CGAN model reduced the false positive rate from 5.20% to 0.56% and the false negative rate from 63.50% to 39.93%, and improved the section consistency from 41.78% to 73.43% and the area under the curve from 0.670 to 0.969. The comparison results indicated that the proposed LSTM-CGAN model outperformed all these benchmark methods and demonstrated great potential to enable many shared mobility applications. [ABSTRACT FROM AUTHOR]

• Dynamic flexible job shop scheduling problem with machine breakdown is studied. • A two-stage algorithm based on convolutional neural network is proposed. • The improved imperialist competition algorithm is proposed to generate schedules. • A predictive model is proposed to predict the robustness of scheduling. In the actual manufacturing process, the environment of the job shop is complex. There will be many kinds of uncertainties such as random job arrivals, machine breakdowns, order cancellations and other dynamic events. In this paper, an effective two-stage algorithm based on convolutional neural network is proposed to solve the flexible job shop scheduling problem (FJSP) with machine breakdown. A bi-objective dynamic flexible job shop scheduling problem (DFJSP) model with the objective of maximum completion time and robustness is established. In the two-stage algorithm, the first stage is to train the prediction model by convolutional neural network (CNN). The second stage is to predict the robustness of scheduling through the model trained in the first stage. First, an improved imperialist competition algorithm (ICA) is proposed to generate training data. Then, a predictive model constructed by CNN was proposed, and an alternative metric called RMn was developed to evaluate robustness. RMn evaluates that the float time has an effect on the robustness through the information of machine breakdown, workload and float time of the operation. The experimental results show that the proposed two-stage algorithm is effective for solving DFJSP, and RMn can evaluate the robustness of scheduling more quickly, efficiently and accurately. [ABSTRACT FROM AUTHOR]

Space modular self-reconfigurable satellite (SMSRS) is a new type of satellite. The research on self-collision avoidance of SMSRS is important for its on-orbit safety but is not completely solved. This paper offers a new method for joint path planning for self-collision avoidance of SMSRS. Firstly, we establish the collision detection model for SMSRS based on forward kinematics and the spherical nonholonomic envelope to detect the collision occurring in SMSRS. Then, to achieve offline path planning in joint space, we proposed the self-collision avoidance strategy, which splices multiple C-spaces based on the pre-defined joint path into a binary map, and then transforms the binary map into a map with the dangerous potential field, and planning algorithms based on a map with the dangerous potential field is proposed to find the optimal collision-free path. The new method is applied to two cases and both find collision-free joint paths for SMSRS successfully, which demonstrates the feasibility of the method. In addition, this study bridges the gap in the study of self-collision avoidance of super-redundant self-reconfigurable satellites. [ABSTRACT FROM AUTHOR]

This article describes a coordinated ramp metering algorithm for systematically mitigating freeway congestion. A preemptive hierarchical control scheme with a three-priority-layer structure is employed in this algorithm. Ramp metering is formulated as a multiobjective optimization problem to enhance system performance. These optimization objectives include promptly tackling freeway congestion, sufficiently utilizing on-ramp storage capacities, and preventing on-ramp vehicles from overflowing to local streets, balancing on-ramp vehicle equity, and maximizing traffic throughputs for the entire system. Instead of relying heavily on accurate estimates of freeway traffic flow evolvement, this new approach models ramp meter control as a linear program and uses real-time traffic sensor measurements for minimizing the indeterminate impacts from the mainstream flow capacities. VISSIM-based simulation experiments are performed to examine its practicality and effectiveness using geometric and traffic demand data from one real-world freeway segment. The simulation test results show that the proposed ramp metering approach performed well in optimizing overall freeway system operations under various traffic conditions. The system-wide optimal control performance can be achieved to quickly mitigate freeway congestion, prevent traffic from overflowing to local streets, and maximize overall traffic throughputs. The proposed ramp metering approach can dynamically assemble relevant ramp meters to work together and effectively coordinate the individual meter rates to leverage their response strengths for minimizing time to clear the congestion. This study demonstrates that utilization of existing freeway infrastructure can be optimized through the proposed algorithm. [ABSTRACT FROM AUTHOR]

Travel speed ties directly to travel time, so it is an important measure for quantifying arterial performance. However, accurately estimating link travel speed for urban arterials is difficult because of traffic fluctuations and stop-and-go conditions caused by signal control. This research proposes a two-step empirical approach to effectively estimate the link-journey speeds using only advance loop detector outputs. The first step is to estimate the spot speed on the basis of advance loop measurements using Athol's algorithm. The robust regression technique can be used to calibrate the speed estimation parameter (or -factor) in Athol's algorithm. The second step is to use the proposed simplified speed estimation model to estimate the link speed using only the calculated loop-spot speed without any knowledge of signal timing plans. Traffic operations in the central business district of the City of Bellevue, Washington, are simulated in the VISSIM traffic simulation model. The test results show that only 50 cycles of data are needed to calibrate the -factor in loop-speed estimation and the same datasets can be used to calibrate the proposed link-speed model. Using this model, the average mean absolute error over the study links is reduced from 4.24 to 1.51 mph. With proper calibration, this average error can be further reduced to 0.91 mph. The results are encouraging and satisfactory. The results also show that the accuracy of speed estimation may be further increased when more data are applied for calibration. [ABSTRACT FROM AUTHOR]