A Design Framework for Big Data Analysis in Railway Transport

It is well-known that cloud computing has many potential advantages and a multitude of enterprise applications are currently migrating to public or hybrid cloud environments. This paper presents the workflow and implementation of a big data analysis framework with both descriptive and predictive analytics capability. This paper outlines the framework’s ability to support not only a high level integrated KPI framework for enterprise reporting but also enabling a structured deployment of machine learning techniques to predict key drivers of railway performance. The two use cases highlighted are prediction of anti-social behavior incidence and train punctuality. Based on our evaluation, these two models are able to assist railway staff to estimate anti-social behavior cases and train punctuality at NSW TrainLink (NSWTL), Australia. 1.Introduction With the increase of both data storage capability and the prevalence of distributed computing, big data is expected to change the railway transport domain completely. These technologies, like numerous systems before them are another step towards more informed decision making at both an operational and strategic level. Currently, the most popular areas for advanced techniques in rail network analytics are safety, operations, and maintenance (Ghofrani, He, Goverde, & Liu, 2018). It is vital for a railway operating in the 21st century to be able to not only adapt but capitalise on new technologies to ensure corporate decision making is timely, effective and cost-efficient. The features of big data can be described using the ‘5Vs’ (Laney, 2001) of volume, velocity, variety, veracity, and value correspondingly. In this paper, we address the above challenges by developing a framework for data analytics that allows multiple systems to cooperate and synchronize with each other to analyze and predict the key elements in efficient railway transport services. 4. NSW TrainLink Integrated Key Performance Indicator System BABEL has been deployed as an enabling technology for advanced analysis and predictive modelling at NSW TrainLink (NSWTL). NSWTL is a multi-modal passenger transport service provider, providing rail and coach services across New South Wales and connecting to other states inside Australia. The main vision of the Integrated Key Performance Indicator application is to identify and analyse dominant levers of business objectives to both managers and operational staff. ATRF 2018 Proceedings 2 Our KPI regime serves three main purposes. Firstly, it served customers as we mature the ability to enhance the elements of our service that affect them most. Second, it delivers enhanced evidence to our main stakeholders that the levers of our performance have not only been identified but are being actively managed. Third, it delivers NSWTL the enhanced capability of moving from an empirical recording of the performance to deriving general rules of operational performance. This translates to learning and proving the why behind the what. Figures 1 and Figure are provided as illustrative examples only. Due to privacy and security issues the real data cannot be provided. To reiterate, the below data is fabricated and the plots are provided as an example of the platform outputs. Figure 1. Customer Injury Analysis Figure 2. Technical Incidents Analysis The overall benefit of the KPI framework was to focus enterprise efforts on the levers of business performance as all employees are provided an overview of the major drivers of NSWTL performance. The senior leadership was better able to view the business in a strategic ATRF 2018 Proceedings 3 context and rectification efforts were able to be entered into, tracked and resolved more efficiently. A derivative benefit was the illumination of data quality holes in the business and the foundation for robust discussions around prioritization of fixes. 5. Predictive modelling with BABEL In BABEL, one of the key functions is predictive modelling. We present two predictive modelling applications that are currently running on BABEL in this section. 5.1. Anti-Social Behaviour (ASB) prediction Combating anti-social behavior (ASB) that allows for proper public security resource allocation is pivotal to ensure public safety on the New South Wales railway network. We are, however, recognize that the Police Transport Command has limited resources to ensure the safety of our customers and staff at all times. Rail customers who travel short or long distances by train rate safety as their highest priority (Moore, 2011). In order to enhance public safety, transport operators must find a way to allocate limited resources effectively. The introduction of predictive modelling for ASB at NSWTL is a progressive step forward to anticipate ‘hotspots’ on the network and help provide a safe environment for staff, customers and the community on both stations and trains. Machine learning is a methodology to discover latent information or patterns in datasets (Nirkhi, Dharaskar, & Thakre, 2012). It is an interdisciplinary study that involves real world situations and mining algorithms in a pure mathematical description that gives insightful analytics into various situations.Within the supervised learning area of machine learning, decision trees methods are frequently utilized. Tree-based methods for regression and classification involve stratifying and segmenting the predictor space into a number of smaller regions (James, Witten, Hastie & Tibshirani 2013). This algorithm is based on information theory that involves the splitting of nodes to allow for optimal exploration of all available features (Rokach & Maimon, 2005). The split criteria are based on information gain as trees are expected to be simple. Given defined entropy for classes as in Equation 1,