Baigen Cai

Multiobjective Optimization for Train Speed Trajectory in CTCS High-Speed Railway With Hybrid Evolutionary Algorithm

A speed trajectory profile indicating the authorized train speed at each position can be used to guide the driver or the automatic train operation (ATO) system to operate the train more efficiently, which is the most important part of the Chinese Train Control System (CTCS) and will decide the safety and efficiency of train operation. The efforts produced by the train to follow the speed trajectory will directly affect the evaluation of train operation. This paper studies the optimization approach for the speed trajectory of high-speed train in a single section. First, we take the energy consumption as the measure of satisfaction of the railway company, and the trip time is being regarded as the passenger satisfaction criterion; then, we present optimal speed trajectory searching strategies under different track characteristics by dividing the section into some subsections according to different speed limitations. After that, we develop a multiobjective optimization model for the speed trajectory, which is subject to the constraints such as safety requirement, track profiles, passenger comfort, and the dynamic performance. For obtaining the Pareto frontier of train speed trajectory, which has equal satisfaction degree on all the objects, a hybrid evolutionary algorithm is designed and applied to solve the model based on the differential evolution and simulating annealing algorithms. By showing some numerical results of simulations, the efficiency of the proposed model and solution methodology is illustrated.

A CKF based GNSS/INS train integrated positioning method

Train positioning with high precision and integrity is crucial to train control system. For conventional positioning methods in multi-sensor integration frame could not meet the performance requirements, a GNSS/INS integrated positioning system architecture is built, in which the cubature Kalman filter is analyzed and employed to solve the problem of nonlinearity and computation efficiency, and the integrity design is realized by the principal component analysis based fault detection and diagnosis. Validation with practical measurement and results of simulation demonstrate that the CKF approach earns a high accuracy and efficiency ability than traditional UKF and PF solutions, and the integrity performance of the train positioning system could be improved by the PCA based FDD strategies.

Modeling and Algorithms of GPS Data Reduction for the Qinghai–Tibet Railway

Satellites are currently being used to track the positions of trains. Positioning systems using satellites can help reduce the cost of installing and maintaining trackside equipment. This paper develops a nonlinear combinatorial data reduction model for a large amount of railway Global Positioning System (GPS) data to decrease the memory space and, thus, speed up train positioning. Three algorithms are proposed by employing the concept of looking ahead, using the dichotomy idea, or adopting the breadth-first strategy after changing the problem into a shortest path problem to obtain an optimal solution. Two techniques are developed to substantially cut down the computing time for the optimal algorithm. The surveyed GPS data of the Qinghai-Tibet railway (QTR) are used to compare the performance of the algorithms. Results show that the algorithms can extract a few data points from the large amount of GPS data points, thus enabling a simpler representation of the train tracks. Furthermore, these proposed algorithms show a tradeoff between the solution quality and computation time of the algorithms.

Study of the Track–Train Continuous Information Transmission Process in a High-Speed Railway

In the experiments and practical applications in a high-speed railway, it is observed that the carrier frequency of the sampled signal in a track circuit reader (TCR) is changed with train speed and goes beyond the upper permissive range prescribed for a jointless track circuit (JTC) in some cases. This can directly affect the availability of train target speed in train control systems and thus has an effect on the generation of the distance-to-go profile. It not only reduces the safety and efficiency of train traveling but also limits the improvement of train speed. To find the primary cause of the deviation in carrier frequency of the sampled signal in TCR (CFSST), this paper models the track-to-train continuous information transmission process using the transmission line theory based on the structures and principles of JTC and TCR. Then, the relation between the deviation in CFSST and the train speed is derived. Experimental results in high-speed railway have verified the correctness of the analysis, and the study can provides a strong theoretical basis for improving the safety level of railway traffic. Moreover, it can be a good reference for other countries where the similar track circuits are applied.

A GPS/compass based train integrated positioning method for high-speed railways

Accurate and real-time positioning is an important part of train control, which is crucial for safety assurance of high speed railway in China. For GPS based train positioning scheme, the signal unavailability and signal interference could not be completely solved with current configuration mode. In this paper, from the safety requirements of train positioning in high-speed railways, based on the consideration of signal availability and fault-tolerant performance, an integrated train positioning system is formed by integration of GPS and Compass. The structure and function of the integrated system are given, and the nonlinear filter solution for information fusion is presented, which is compared to traditional Kalman filter based strategies. An HTL-based integrity monitoring method is presented to enhance fault tolerant ability. Results from experiments and simulations demonstrate the integrated positioning method could meet the precision requirement with effective integrity characteristics, and keep its potential for efficiency and safety in various environments.

A lane level positioning-based cooperative vehicle conflict resolution algorithm for unsignalized intersection collisions

We consider the problem of vehicle operation safety at unsignalized intersections. The scheme of cooperative vehicle infrastructure system provides a promising solution to safety-related traffic issues. In this paper, we propose a novel cooperative vehicle conflict resolution algorithm for unsignalized intersection collisions. An enhanced road map is utilized to achieve lane level position precision. Because of the integration of map data, few sensors are required, which promotes a simple and cost-efficient solution for perceiving vehicle situations. Furthermore, we introduce an evaluation method for describing the emergency degree of ongoing collisions. Particle swarm optimization is employed to calculate the target acceleration and control the vehicle to prevent unexpected collisions. The results from experiments and simulations indicate that the proposed algorithm achieves better perception compared to similar previous strategies and illustrates the effectiveness of maintaining a vehicle in safe motion using emergent braking control. The simulation results also indicate the algorithms tremendous power for driver assistance in practical unsignalized intersection environments.

Short communication: An integrated error-detecting method based on expert knowledge for GPS data points measured in Qinghai-Tibet Railway

As there are huge amounts of Global Positioning System (GPS) data points measured in the Qinghai-Tibet Railway (QTR) with a length of 1142km, it was inevitable that some measuring errors existed due to various situations in measurement. It is very important to develop a method to automatically detect the possible errors in all data points so as to modify them or measure them again to improve the reliability of GPS data. Four error patterns, including redundant measurement, sparse measurement, back-and-forth measurement, and big angle change, were obtained based on expert knowledge. Based on the four error patterns, four algorithms were developed to detect the corresponding possible errors in data points. To delete the repetitive errors by different algorithms and effectively display the possible errors, an integrated error-detecting method was developed by reasonably assembling the four algorithms. After four performance indices were given to evaluate the performance of the error-detecting method, six GPS track data sets between seven railway stations in the QTR were used to validate the method. Thirty-eight segments of some sequential points that are possibly wrong were found by the method and fourteen of them were confirmed by measurement experts. The detecting rate of the method was 100% and the duration time of the detecting process was less than half an hour compared with the 94h manual workload. The validation results show that the method is effective not only in decreasing workload, but also in ensuring correctness by integrating the domain expert knowledge to make the final decision.

Research on deeply integrated GPS/INS for autonomous train positioning

With the speed-up of trains, the trains positioning environment becomes increasingly complex which requires the trains positioning system have enough anti-interference capability to ensure the precision and reliability of trains positioning. GNSS/INS deeply integrated positioning system could improve the anti-interference ability of the positioning system, achieving real-time positioning with much higher precision and reliability. In this paper, a deeply integrated GNSS/INS strategy for train locating based on vector tracking loop was proposed, and the mathematical model for GNSS/INS integrated system was established.

Research of train control system special database and position matching algorithm

The research on low-cost train control system is one of the most important part in train traffic field, using GPS data could realized real-time, safe and reliable train positioning. The train control system special database was studied systematically, the data model and mathematical topology model of data format was established, and the whole seven steps of special database creation were designed. Then according to feature of railway, using adaptive position matching algorithm in rail track positioning, the track was divided into beeline and circle curve, the algorithm base on movable distance frame could realized accurate, real-time and quick positioning. Finally, experiment was done in Sanjiadian marshalling station; the experiment result proves that the special database is valuable and effective for train positioning.

Cooperative Localization of Connected Vehicles: Integrating GNSS With DSRC Using a Robust Cubature Kalman Filter

Cooperative localization of the connected vehicles is significant for many advanced intelligent transportation system (ITS) applications. Vehicle-to-vehicle communication using dedicated short-range communication (DSRC) has great potential to enhance global navigation satellite systems (GNSSs) for the capability of cooperative localization. In the integration of DSRC and GNSS, the tolerance against the unknown and time-varying observation conditions is a key factor to fulfill the requirements of several specific ITS applications. Under a GNSS/DSRC integrated architecture for cooperative localization, a novel robust cubature Kalman filter (CKF) is proposed in this paper to improve the performance of the data fusion under uncertain sensor observation environments. In the proposed solution, the structure of the standard CKF is enhanced using the Huber M-estimation technique, in which the original measurement update in the CKF is modified considering the probable anomalies in state estimation. Furthermore, based on the investigation of the adjustment effect from the constraint factor, an adaptive strategy for this parameter is introduced to optimize the performance comprehensively. The proposed method is validated using a specific simulation system. Results of experiment and simulations demonstrate the capability of improving the robustness and adaptive performance over the original filters under the unknown operation conditions.