PurposeThe aim of the research was to develop a model for establishing and maintaining alignment of purpose in business change initiatives.Design/methodology/approachThe research methodology combined a synthesis of the literature across the diverse fields of change leadership, project management, and organisational alignment; and a parallel analysis of two industrial case studies.FindingsFrom an analysis of the cases, and a synthesis of the literature, a Project Alignment Model was developed. To help industrial project leaders operationalise the model and hence maintain alignment in their projects, the key points from the Project Alignment Model are also presented as a checklist.Practical implicationsManaging change is increasingly relevant for all industries and companies, and the rate of change is predicted to increase. Project managers in this environment must have more than just technical delivery skills; they need to be good leaders, capable of influencing strategic direction, and skilled in managing the political dimensions of their projects. The model presented will help these leaders improve their change management capability.Originality/valueThe developed model can be useful both as a descriptive model and as a prescriptive model. Used descriptively, the model can help structure the analysis of change projects. As such it could be a useful research instrument. Academics could use the model to analyse change projects and structure their findings in a way that allows ready cross‐case comparisons. Such an approach can, by categorisation, lead to a more detailed understanding of the factors affecting project alignment and successful change. Used prescriptively, the model can guide project managers in creating and maintaining project alignment, and in doing so increase their chance of success in implementing change.

This paper shows how temporal difference learning can be used to build a signalized junction controller that will learn its own strategies though experience. Simulation tests detailed here show that the learned strategies can have high performance. This work builds upon previous work where a neural network based junction controller that can learn strategies from a human expert was developed . In the simulations presented, vehicles are assumed to be broadcasting their position over WiFi giving the junction controller rich information. The vehicle's position data are pre-processed to describe a simplified state. The state-space is classified into regions associated with junction control decisions using a neural network. This classification is the strategy and is parametrized by the weights of the neural network. The weights can be learned either through supervised learning with a human trainer or reinforcement learning by temporal difference (TD). Tests on a model of an isolated T junction show an average delay of 14.12 s and 14.36 s respectively for the human trained and TD trained networks. Tests on a model of a pair of closely spaced junctions show 17.44 s and 20.82 s respectively. Both methods of training produced strategies that were approximately equivalent in their equitable treatment of vehicles, defined here as the variance over the journey time distributions.

This work investigates the application of Gaussian processes to capturing the probability distribution of a set of aircraft trajectories from historical measurement data. To achieve this, all data are assumed to be generated from a probabilistic model that takes the shape of a Gaussian process. The approach to Gaussian process modelling used here is based on a linear expansion of trajectory data into set of basis functions that may be parametrized by a multivariate Gaussian distribution.The parameters are learned through maximum likelihood estimation. The resulting probabilistic model can be used for both modelling the dispersion of trajectories along the common flightpath and for generating new samples that are similar to the historical data. The performance of this approach is evaluated using three trajectory datasets; toy trajectories generated from a Gaussian distribution, sounding rocket trajectories that are generated by a stochastic rocket flight simulator and aircraft trajectories on a given departure path from Dallas Fort Worth airport, as measured by ground-based radar. The results indicate that the maximum deviation between the probabilistic model and test data obtained for the three data sets are respectively 4.9%, 7.6% and 13.1%.

The increase in traffic volumes in urban areas makes network delay and capacity optimisation challenging. However, the introduction of connected vehicles in intelligent transport systems presents unique opportunities for improving traffic flow and reducing delays in urban areas. This paper proposes a novel traffic signal control algorithm called Multimode Adaptive Traffic Signals (MATS) which combines position information from connected vehicles with data obtained from existing inductive loops and signal timing plans in the network to perform decentralised traffic signal control at urban intersections. The MATS algorithm is capable of adapting to scenarios with low numbers of connected vehicles, an area where existing traffic signal control strategies for connected environments are limited. Additionally, a framework for testing connected traffic signal controllers based on a large urban road network in the city of Birmingham (UK) is presented. The MATS algorithm is compared with MOVA on a single intersection, and a calibrated TRANSYT plan on the proposed testing framework. The results show that the MATS algorithm offers reductions in mean delay up to 28% over MOVA, and reductions in mean delay and mean numbers of stops of up to 96% and 33% respectively over TRANSYT, for networks with 0-100% connected vehicle presence. The MATS algorithm is also shown to be robust under non-ideal communication channel conditions, and when heavy traffic demand prevails on the road network.

An automated signalized junction control system that can learn strategies from a human expert has been developed. This system applies Machine Learning techniques based on Logistic Regression and Neural Networks to affect a classification of state space using evidence data generated when a human expert controls a simulated junction.The state space is constructed from a series of bids from agents, which monitor regions of the road network. This builds on earlier work, which has developed the High Bid auctioning agent system to control signalized junctions using localization probe data. For reference the performance of the Machine Learning signal control strategies are compared to that of High Bid and the MOVA system, which uses inductive loop detectors.Performance is evaluated using simulation experiments on two networks. One is an isolated T-junction and the other is a two junction network modelled on the High Road area of Southampton, UK. The experimental results indicate that Machine Learning junction control strategies, trained by a human expert can outperform High Bid and MOVA both in terms of minimizing average delay and maximizing equitability; where the variance of the distribution over journey times is taken as a quantitative measure of equitability. Further experimental tests indicate that the Machine Learning control strategies are robust to variation in the positioning accuracy of localization probes and to the fraction of vehicles equipped with probes.

This study investigates theoretical signal control algorithms based solely on probe vehicle data. Through development of a simulation system that can model urban signalised junction control using localisation probe data from all vehicles in the local area, improvements in junction operational efficiency that result from the improved input data are demonstrated for both isolated and coordinated junctions. Results from the isolated junction scenario show that the richness of the information contained within probe vehicle data means that control algorithms based just on positions and velocities of vehicles can produce 25% reductions in average delay compared to the current standard control algorithm MOVA. Results from the twin junction scenario confirm the importance of using high-level synchronisation to coordinate closely connected junctions, achieving reductions in average delays (compared to independent control approaches) of up to 40% through a process of weighting the probe vehicle data to reflect prior stage decisions of other parts of the junction. Critical to achieving these benefits, however, is the availability of high localisation accuracy probe data, with results indicating that the levels of accuracy necessary are representative of the typical performance of current in-vehicle global positioning system units, except when those vehicles are operating in urban canyon environments.

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Technological developments in the last decade have shifted challenges in traffic flow management from obtaining and storing data, to analysing and presenting the enormous amount of available trajectory data in a comprehensible manner. This paper introduces a novel approach to visualising air-traffic, shifting the focus from displaying traffic density, towards directly visualising the flight corridors used by air-traffic. Such an approach is suitable for visualising air-traffic in three dimensions, which is particularly helpful in the vicinity of an airport where the air-traffic often changes level. Furthermore, the approach is data-driven, allowing the comparison of multiple trajectory datasets in order to identify changes in traffic corridors related to changing air-traffic and weather conditions. Finally, by using the probabilistic nature of the approach, it is possible to quantify the air-traffic complexity in terms of the traffic structure. The results presented in this paper show the approach applied to a trajectory dataset as measured by ground-radar near Denver airport (DEN