AnD: A Many-Objective Evolutionary Algorithm with Angle-based Selection and Shift-based Density Estimation

Evolutionary many-objective optimization has been gaining increasing attention from the evolutionary computation research community. Much effort has been devoted to addressing this issue by improving the scalability of multiobjective evolutionary algorithms, such as Pareto-based, decomposition-based, and indicator-based approaches. Different from current work, we propose an alternative algorithm in this paper called AnD, which consists of an angle-based selection strategy and a shift-based density estimation strategy. These two strategies are employed in the environmental selection to delete poor individuals one by one. Specifically, the former is devised to find a pair of individuals with the minimum vector angle, which means that these two individuals have the most similar search directions. The latter, which takes both diversity and convergence into account, is adopted to compare these two individuals and to delete the worse one. AnD has a simple structure, few parameters, and no complicated operators. The performance of AnD is compared with that of seven state-of-the-art many-objective evolutionary algorithms on a variety of benchmark test problems with up to 15 objectives. The results suggest that AnD can achieve highly competitive performance. In addition, we also verify that AnD can be readily extended to solve constrained many-objective optimization problems

Predicting Bus Travel Time with Hybrid Incomplete Data – A Deep Learning Approach

The application of predicting bus travel time with real-time information, including Global Positioning System (GPS) and Electronic Smart Card (ESC) data is effective to advance the level of service by reducing wait time and improving schedule adherence. However, missing information in the data stream is inevitable for various reasons, which may seriously affect prediction accuracy. To address this problem, this research proposes a Long Short-Term Memory (LSTM) model to predict bus travel time, considering incomplete data. To improve the model performance in terms of accuracy and efficiency, a Genetic Algorithm (GA) is developed and applied to optimise hyperparameters of the LSTM model. The model performance is assessed by simulation and real-world data. The results suggest that the proposed approach with hybrid data outperforms the approaches with ESC and GPS data individually. With GA, the proposed model outperforms the traditional one in terms of lower Root Mean Square Error (RMSE). The prediction accuracy with various combinations of ESC and GPS data is assessed. The results can serve as a guideline for transit agencies to deploy GPS devices in a bus fleet considering the market penetration of ESC

Open Source Foundations for Spatial Decision Support Systems

Spatial Decision Support Systems (SDSS) were a hot topic in the 1990s, when researchers tried to imbue GIS with additional decision support features. Successful practical developments such as HAZUS or CommunityViz have since been built, based on commercial desktop software and without much heed for theory other than what underlies their process models. Others, like UrbanSim, have been completely overhauled twice but without much external scrutiny. Both the practical and the theoretical foundations of decision support systems have developed considerably over the past 20 years. This article presents an overview of these developments and then looks at what corresponding tools have been developed by open source communities. In stark contrast to the abundance of OpenGeo software, there is a dearth of open source SDSS. The core of the article is a discussion of different approaches that lend themselves as platforms to develop an open source framework to build a variety of SDSS

Assessment of water resources management strategy under different evolutionary optimization techniques

Competitive optimization techniques have been developed to address the complexity of integrated water resources management (IWRM) modelling; however, model adaptation due to changing environments is still a challenge. In this paper we employ multi-variable techniques to increase confidence in model-driven decision-making scenarios. Here, water reservoir management was assessed using two evolutionary algorithm (EA) techniques, the epsilon-dominance-driven self-adaptive evolutionary algorithm (∈-DSEA) and the Borg multi-objective evolutionary algorithm (MOEA). Many objective scenarios were evaluated to manage flood risk, hydropower generation, water supply, and release sequences over three decades. Computationally, the ∈-DSEA's results are generally reliable, robust, effective and efficient when compared directly with the Borg MOEA but both provide decision support model outputs of value

Type-2 Fuzzy Single and Multi-Objective Optimisation Systems for Telecommunication Capacity Planning

Capacity planning in the telecommunications industry aims to maximise the effectiveness of implemented bandwidth equipment whilst allowing for equipment to be upgraded without a loss of service. The better implemented hardware can be configured, the better the service provided to the consumers can be. Additionally, the easier it is to rearrange that existing hardware with minimum loss of service to the consumer, the easier it is to remove older equipment and replace it with newer more effect equipment. The newer equipment can provide more bandwidth whilst consuming less power and producing less heat, lowering the overall operating costs and carbon footprint of a large scale network. Resilient routing is the idea of providing multiple independent non-intersecting routes between two locations within a graph. For telecommunications organisations this can be used to reduce the downtime faced by consumers if there is a fault within a network. It can also be used to provide assurances to customers that rely on a network connection such as: financial institutions or government agencies. This thesis looks at capacity planning within telecommunications with the aspiration of creating a set of optimisation systems that can rearrange data exchange hardware to maximise their performance with minimal cost and minimising downtime while allowing adaptations to an exchange’s configuration in order to perform upgrades. The proposed systems were developed with data from British Telecom (BT) and are either deployed or are planned to be in the near future. In many cases the data used is confidential, but when this is the case an equivalent open source data set has been used for transparency. As a result of this thesis the Heated Stack (HS) algorithm was created which has been shown to outperform the popular and successful NSGA-II algorithm by up to 92 % and NSGA-III by up to 69% at general optimisation tasks. HS also outperforms NSGA-II in 100% of the physical capacity planning experiments run and NSGA-II in 68% of the physical capacity planning experiments run. Additionally, as a result of this thesis the N-Non-Intersecting-Routing algorithm was shown to outperform Dijkstra’s algorithm by up to 38% at resilient routing. Finally, a new method of performing configuration planning through backwards induction with Monte Carlo Tree Search was proposed

Sequence modelling using deep learning approaches for spatiotemporal public transport data.

Encouraging the use of public transport is essential to combat congestion and pollution in an urban environment. To achieve this, the reliability of public transport arrival time prediction should be improved, as this is often requested by passengers. This will make the use of urban bus networks more convenient for passengers and, thus, will play a crucial role in shifting traffic to public transport. Ultimately, this will alleviate pollution and congestion and save a substantial amount of cost to society associated with the use of private cars. Here, the overarching objective was to investigate novel prediction methods and improve predictions for urban bus networks with a focus on short-horizon predictions. ETA predictions are unreliable due to the lack of good quality historical data, while ‘live’ positions in mobile apps suffer from delays in data transmission. The assessment of different of data quality regimes on the next-step prediction accuracy of Recurrent Neural Networks (RNN) showed that that without data cleaning, model predictions can give false confidence if mean errors are used, highlighting the importance of a holistic assessment of the results. It was demonstrated that noisy data is a problem and simple but effective approaches to address these issues are discussed. It became apparent that RNNs are exceptionally good at predicting stationary positions at either end of a journey. The maximum model improvement of the Sharpe ratio compared to noisy data was 4.71%. This provides insight into the value of addressing data quality issues in urban transport data to enable better predictions and improve the passenger experience. Furthermore, a comparison of different target representations was tested by encoding targets as unconstrained geographical coordinates, progress along a known trajectory, or ETA at the next two stops. The target representation was shown to affect the accuracy of the prediction by constraining the prediction space and reduced the prediction error from 244.8 to 142.3 m for the Long Short-Term Memory (LSTM) network. This error was further reduced if an ETA was predicted and if a distance is estimated from the ETA error resulted in a a reduction to 4.5 and 14.5 m for the next 2 stops on the route. Due to the observed lack of data quality, a method was to developed for synthesising data, using a reference curve approach derived from very limited real-world data without a reliable ground truth. This approach allows the controlled introduction of artefacts and noise to simulate their impact on prediction accuracy. To illustrate these impacts, a RNN next-step prediction was used to compare different scenarios in two different UK cities. Two model architectures were used as comparison: a Gated Unit and a LSTM model. Hybrid data was generated where real-world and synthetic data was mixed. When compared to the inference of a model trained purley on synthetic data, the error was reduced from 53.5 to 47.4 m for the LSTM and from 53.4 to 44.0 m for the GRU. The results show that realistic data synthesis is possible, allowing controlled testing of predictive algorithms. Urban traffic networks are interconnected systems that behave in complex ways to any disturbance. As urban buses operate in such networks and are influenced by traffic within this system, estimated arrival time (ETA) predictions can be challenging and are often inaccurate. To enable the use of network-wide data, a novel model architecture was developed. This attention-mechanism based predictor incorporated the states of other vehicles in the network by encoding their positions using gated recurrent units (GRU) of the individual bus line to encode their current state. By muting specific parts of the imputed information, their impact on prediction accuracy were estimated on a subset of the available data. The results showed that a network-based predictor outperforms models based on a single vehicle or all vehicles of a single line. However, a model limited to vehicles of the same line ahead of the target was the best performing model, suggesting that the incorporation of additional data can have a negative impact on the prediction accuracy if it does not add any useful information. This could be caused by poor data quality, but also by a lack of interaction between the included lines and the target line. The technical aspects of this architecture are challenging and resulted in a very inefficient training procedure. It can be expected that if a more efficient training regime is developed or the model is trained for a longer time, usable predictive accuracy can be achieved