Implementation plan of health and safety processes

An auditor was asked to review an organisation’s Health and Safety procedures to assess compliance with the new legislation imposed by The Health and Safety Act 2015. Then the organisation approached an internal source to conduct a strategic plan in order to target issues of health and safety risk. An implementation plan will be designed to achieve the auditors recommendations and improve the organisation’s Health and Safety practices. Research and audit of the current policies and procedures used at the organisation must be conducted in order to gain a better understanding of the current issues and from there develop action plans and a strategy on how to reach those action plans. Current documentation of policies and an interview with management will be analysed to detail the potential action plans.Once the research has been conducted, results will be used to determine conclusions

Modeling Taxi Drivers' Behaviour for the Next Destination Prediction

In this paper, we study how to model taxi drivers' behaviour and geographical information for an interesting and challenging task: the next destination prediction in a taxi journey. Predicting the next location is a well studied problem in human mobility, which finds several applications in real-world scenarios, from optimizing the efficiency of electronic dispatching systems to predicting and reducing the traffic jam. This task is normally modeled as a multiclass classification problem, where the goal is to select, among a set of already known locations, the next taxi destination. We present a Recurrent Neural Network (RNN) approach that models the taxi drivers' behaviour and encodes the semantics of visited locations by using geographical information from Location-Based Social Networks (LBSNs). In particular, RNNs are trained to predict the exact coordinates of the next destination, overcoming the problem of producing, in output, a limited set of locations, seen during the training phase. The proposed approach was tested on the ECML/PKDD Discovery Challenge 2015 dataset - based on the city of Porto -, obtaining better results with respect to the competition winner, whilst using less information, and on Manhattan and San Francisco datasets.Comment: preprint version of a paper submitted to IEEE Transactions on Intelligent Transportation System