A Blockchain-based Model Sharing and Calculation Method for Urban Rail Intelligent Driving Systems

In the domain of intelligent driving vehicles, radar and camera sensors play a crucial role, and their fusion has been widely acknowledged to enhance sensing range and accuracy. This article delves into the challenge of asynchronous information fusion for object detection between radar and camera, taking into consideration different sampling frequencies of radar and camera. Initially, different state observation and prediction models for radar and camera are formulated, leading to the proposal of a comprehensive target tracking model. Subsequently, the matching and fusion process of detection results is undertaken, yielding an optimal estimation as the output of the sensor information fusion system. Furthermore, the article introduces the intelligent vehicle sensor fusion task and describes the stepwise approach to sensor fusion in traffic scenarios, including coordinate transform, sensor data preprocessing, and target track. To validate the proposed sensor asynchronous fusion algorithm, a simulation experiment and a road test are conducted focusing on interaction between ego and environment vehicles. The simulation results underscore the effectiveness of the algorithm, with an average lateral error reduction of 32% and an average longitudinal error reduction of 63%. Road test shows that effective fusion can expand the detection range of sensor system and improve the detection performance.

In reality, the risk in urban rail transit system usually happens. In this paper, we construct the risk chain network in urban rail transit system, where the different factors are considered in detail. Moreover, in order to describe the characteristic of the risk chain network, the propagation risk is proposed. Based on these, the controllability of risk chain network is calculated in four kinds of systems in urban rail transit system. In addition, we analyze the key node, which has a serious impact on the controllability. Our paper may be useful to control the risk in urban rail transit system and improve the safety of urban rail transit.

With the accelerated urbanization in China, passenger demand has dramatically increased in large cities, and traffic congestion has become serious in recent years. Developing public urban rail transit systems is an indispensable approach to overcome these problems. However, the high energy consumption of daily operations is an emerging issue due to increased rail transit networks and passenger demands. Thus, reducing the energy consumption and operational cost by using advanced optimization methodologies is an urgent task for operation managers. This work systematically introduces energy-saving approaches for urban rail transit systems in three aspects, namely, train speed profile optimization, utilization of regenerative energy, and integrated optimization of train timetable and speed profile. Future research directions in this field are also proposed to meet increasing passenger demands and network-based urban rail transit systems.

The reduction of train energy consumption is becoming more important due to increasing worldwide environmental concerns. This paper presents a driving strategy optimization study and field test results on an urban rail transit system. A genetic algorithm based optimization method has been developed specifically for this purpose. In order to identify and evaluate the practicability and performance of the optimization results, a field test has been carried out on Guangzhou Metro Line No. 7. A driver training study has been developed to help drivers to implement the energy saving features of the optimization. The field test results show that by applying the optimal driver strategy the train traction energy consumption can be significantly reduced within the given journey time constant, proving the developed optimization method is practicable and effective.

With the increasing energy consumption in urban rail transit systems, energy-efficient train operation has been paid significant attention. Considering the application of regenerative braking technology, many studies focus on offline energy-saving train trajectory optimization, in which an accelerating regime is inserted into the train trajectory when there is regenerative braking energy (RBE) that can be utilized. However, in practical operation, train states are dynamic and scheduled trajectory might be useless. This paper proposes a real-time cooperative train control method based on Train-to-Train (T2T) communication technology. According to the train states transmitted through T2T communication, whether there is a train in braking regime is judged. Besides, an accelerating regime is inserted by changing train running modes when there is another train in the braking regime. A cooperative control algorithm is developed to achieve the energy-efficient cooperative control strategy. Besides, simulations based on a real-life metro line demonstrate that the proposed cooperative control method can reduce substation energy consumption and improve the utilization of RBE.

Establishing simulation models is a widely used and effective approach for analysing passenger flow distribution in urban rail transit systems. Recently, multi-agent and discrete event-based simulation models have shown exceptional performance in studying passenger flow information within urban rail transit systems. While simulations of passengers and trains often yield satisfactory results, few models capture the overall operational status of urban rail transit systems. The complex interactions among stations, trains and passengers make it challenging to integrate these elements into a unified system framework. In this paper, we introduce a triple simulation framework that integrates stations, trains and passengers as foundational elements to comprehensively simulate the entire urban rail transit system and observe overall passenger flow distribution. Experimental results demonstrate that our system surpasses existing advanced simulation models, achieving an accuracy rate of 88.44% with a tolerance for a 30% deviation. To further illustrate the effectiveness of our framework in analysing passenger flows, we conducted experiments using the Nanjing Metro AFC dataset, analysing passenger flow distributions at stations and on trains.

In order to improve the energy efficiency of urban rail transit system, it is necessary to figure out the key factor(s) that restrain(s) train rapidity and energy conservation and understand the inner complex relationships among them. We first define energy-saving and rapid train control/operation, then simulate and analyze the microscopic train behavior between two successive stations. The result reflects that due to the constraint of the short inter-station distance, the train has no space and time to take advantage of its excellent performance in speed and has to stop frequently at every station, which leads to the increase of energy consumption and the prolongation of running time. Based on the above energy consumption analysis of the microscopic train behavior, a hierarchical Pareto 0-1 integer programming with considering passenger flow distribution and public transport characteristics, is presented to organically integrate microscopic train control with macroscopic train organization. The case study shows that the presented method can greatly promote the energy-efficient and rapid train operation of urban rail transit system. This study is also helpful to the planning and construction of future cities and urban rail transit systems.

Wireless Local Area Network (WLAN) is currently used in the train-ground communication for the urban rail transit systems. Due to the high speed and frequency band limitations, WLAN is unable to meet the demand of future city and intercity rail transit. Time Division-Long Term Evolution (TD-LTE) technology allows to construct a flexible service environment compared with current WLAN based system. In this paper, a TD-LTE based train-ground communication system for the urban rail transit system is given. It integrates all the applications in urban rail transit system. An outdoor testing environment is set up to test the performance of the integrated TD-LTE based train-ground communication system. The packet loss rate, response time, throughput of the TD-LTE system are analyzed. Extensive testing results show that the Quality of Service (QoS) of the integrated TD-LTE based train-ground communication system satisfies the requirements of urban rail transit system.

An Approach to Calculate Overall Efficiency of Rolling Stock for an Urban Rail Transit System

In recent years, applications of machine learning have grown rapidly in various areas. With the accelerating rate of data generation, and recent developments in big data analytics, machine learning has become a de facto standard in many applications that benefited the society in many areas. However, with the increasing number and types of machine learning applications, it has become a target for an increasing number of malicious actors. Security challenges became more complex and diverse in machine-learning-based systems.In this paper, we present a concise survey and discussion of the mechanisms employed by attackers to exploit vulnerabilities in machine learning algorithms or injecting malicious data. The paper focuses on most recent attacks reported in literature and discusses the methods proposed to counter these attacks and reduce their impact.

An Evaluation of the Right to Privacy in the Digital Age

BackgroundAs the uptake of health information technologies increased, most healthcare organizations have become producers of big data. A growing number of hospitals are investing in the development of big data analytics (BDA) capabilities. If the promises associated with these capabilities are high, how hospitals create value from it remains unclear. The present study undertakes a scoping review of existing research on BDA use in hospitals to describe the path from BDA capabilities (BDAC) to value and its associated challenges.MethodsThis scoping review was conducted following Arksey and O’Malley’s 5 stages framework. A systematic search strategy was adopted to identify relevant articles in Scopus and Web of Science. Data charting and extraction were performed following an analytical framework that builds on the resource-based view of the firm to describe the path from BDA capabilities to value in hospitals.ResultsOf 1,478 articles identified, 94 were included. Most of them are experimental research (n=69) published in medical (n=66) or computer science journals (n=28). The main value targets associated with the use of BDA are improving the quality of decision-making (n=56) and driving innovation (n=52) which apply mainly to care (n=67) and administrative (n=48) activities. To reach these targets, hospitals need to adequately combine BDA capabilities and value creation mechanisms (VCM) to enable knowledge generation and drive its assimilation. Benefits are endpoints of the value creation process. They are expected in all articles but realized in a few instances only (n=19).ConclusionsThis review confirms the value creation potential of BDA solutions in hospitals. It also shows the organizational challenges that prevent hospitals from generating actual benefits from BDAC-building efforts. The configuring of strategies, technologies and organizational capabilities underlying the development of value-creating BDA solutions should become a priority area for research, with focus on the mechanisms that can drive the alignment of BDA and organizational strategies, and the development of organizational capabilities to support knowledge generation and assimilation.

PurposeThe purpose of this paper is to scrutinize and classify the literature linking Big Data analytics and management phenomena.Design/methodology/approachAn objective bibliometric analysis is conducted, supported by subjective assessments based on the studies focused on the intertwining of Big Data analytics and management fields. Specifically, deeper descriptive statistics and document co-citation analysis are provided.FindingsFrom the document co-citation analysis and its evaluation, four clusters depicting literature linking Big Data analytics and management phenomena are revealed: theoretical development of Big Data analytics; management transition to Big Data analytics; Big Data analytics and firm resources, capabilities and performance; and Big Data analytics for supply chain management.Originality/valueTo the best of the authors’ knowledge, this is one of the first attempts to comprehend the research streams which, over time, have paved the way to the intersection between Big Data analytics and management fields.

The Big 4 accounting firm are looking for implement the big data analytics in their audit engagements. With the growing investment in from $34 billion in 2013 increasing to $232 billion through 2016 (Gartner 2012). Specially, The Big 4 firms, sees Big Data as an increasingly important part of their assurance practice. The objective of this paper are to give first insight and literature regarding the development of big data analytics and its impact into audit and assurance engagement.

In an urban rail transit system, issues regarding headway regulation have aroused wide attention. The assurance of headway regularity can decrease train delay times and average passenger waiting times. An integrated control method is proposed to optimize train headway by adjusting the train arrival time at stations. The adjustment of train arrival time is achieved by using an analytical method, and then the speed profile for each train is calculated by a suboptimal method, which has been applied in a practical system. Through simulation, the CPU time for calculating optimal train arrival time and speed profile is analyzed, respectively. The analysis demonstrates that the proposed method satisfies the real-time requirements for solving the headway regulation problem. By adopting the proposed method, the average passenger waiting time and the energy consumption can be decreased. In particular, the proposed method has better performance when the dispatch headway is large.