From footway design to the street environment: removing the barriers to walking

7-9 October 200

Can AMELIA be used for consultation?

Transport planners are increasingly concerned with improving the accessibility of services and facilities for disadvantaged groups such as those without access to a car and those with disabilities. AMELIA is a software tool that will enable planners to test that their transport and other policies do increase social inclusion. The tool was design to be easy to use, thus it may also serve as a consultation tool with people who are socially excluded. Research was undertaken to explore whether this was the case and also whether the assumptions embedded in AMELIA reflect the views and behaviour of disadvantaged groups. To do this, a series of consultations were set up with three different groups of people who are vulnerable to social exclusion: a group containing older people and people with disabilities; a group of children aged 12-15 and a group of young adults aged 16-19. This paper describes the results of this work and discusses the extent to which tools such as AMELIA can truly represent the views and behaviour of vulnerable groups and the role they can play in consultation processes

Mapping accessibility changes to test transport policies and improve social inclusion

Accessibility measures and maps are useful in helping to identify social groups and locations with poor levels of access to services and facilities. These measures however fail to directly account for differences in physical capabilities and mobility levels of different social groups of people. Also, many of the access issues for the excluded groups of people such as the elderly are micro level such as the obstructions in pavements, while accessibility measures tend to be at macro level and do not include the whole journey. To help do this a GIS-based tool, AMELIA, has been developed. This paper discusses the specific elements of accessibility incorporated in AMELIA such as the modelling of walk and public transport accessibility, the micro level data required, the capabilities of the different social groups considered and how these affect the accessibility measures. Public transport accessibility maps produced for the elderly people are compared to those of the younger people using St Albans in Hertfordshire, UK, as a case study area

The impact of changes in access to local facilities on the wellbeing of elderly and disabled people

An important element of wellbeing is the ability to reach the facilities and services needed for a healthy and fulfilling life. Many of the needs of everyday life for elderly and disabled people are met through the provision of local services such as post offices and public libraries. Elderly and disabled people with no car or on low incomes may need such services in order to maintain their health, income and quality of life. In Britain there is a policy of rationalising such services, sometimes in order to save money, and sometimes as part of a modernisation programme. For example, the British Government initiated the Post Office Network Change Programme in order reshape the network in order to reduce the cost of providing such services. Since October 2007, approximately 2500 branches have been closed. Public libraries are being closed to save public expenditure. However, in developing these re-organisation programmes the access and equity issues are rarely considered systematically. One way to address these issues this is to use a computer-based tool, such as AMELIA (A Methodology for Enhancing Life by Improving Accessibility), which was developed in the Centre for Transport Studies at University College London as part of the research programme of the AUNT-SUE consortium (Accessibility and User Needs in Transport in a Sustainable Urban Environment). AMELIA has been designed to test the extent to which transport and other policies influence social inclusion. AMELIA is a user-friendly, policy-oriented interface to a Geographic Information System (GIS). It requires data on the population in the group being considered (elderly people, disabled people and so on), the destinations that they wish to reach (shops, post offices, health facilities and so on) and how they can travel there. AMELIA can then be used to see how many more (or fewer) of this group can reach the opportunities as a result of the policy actions. In the paper AMELIA is applied to examine the implications of reorganisation programmes for post offices and public libraries in Hertfordshire, a relatively wealthy area to the north of London with high car ownership and a mixture of urban and rural areas, and St Albans a city within Hertfordshire. The results are presented in terms of the changes in the access to post offices and public libraries by elderly and disabled people to see the effects of the policies underlying the programmes of change on their wellbeing. The discussion revolves around the importance of access to local facilities for elderly and disabled people and how changes in the pattern of services can have a disproportionate impact on elderly and disabled people

WODIS: Water Obstacle Detection Network based on Image Segmentation for Autonomous Surface Vehicles in Maritime Environments

A reliable obstacle detection system is crucial for Autonomous Surface Vehicles (ASVs) to realise fully autonomous navigation with no need of human intervention. However, the current detection methods have particular drawbacks such as poor detection for small objects, low estimation accuracy caused by water surface reflection and a high rate of false-positive on water-sky interference. Therefore, we propose a new encoderdecoder structured deep semantic segmentation network, which is Water Obstacle Detection network based on Image Segmentation (WODIS), to solve above mentioned problems. The first design feature of WODIS utilises the use of an encoder network to extract high-level data based on different sampling rates. In order to improve obstacle detection at sea-sky-line areas, an Attention Refine Module (ARM) activated by both global average pooling and max pooling to capture high-level information has been designed and integrated into WODIS. In addition, a Feature Fusion Module (FFM) is introduced to help concatenate the multi-dimensional high-level features in the decoder network. The WODIS is tested and cross validated using four different types of maritime datasets with the results demonstrating that mIoU of WODIS can achieve superior segmentation effects for sea level obstacles to values as high as 91.3

Combining machine learning with computational hydrodynamics for prediction of tidal surge inundation at estuarine ports

Accurate forecasts of extreme storm surge water levels are vital for operators of major ports. Existing regional tide-surge models perform well at the open coast but their low spatial resolution makes their forecasts less reliable for ports located in estuaries. In December 2013, a tidal surge in the North Sea with an estimated return period of 760 years partially flooded the Port of Immingham in the Humber estuary, on the UK east coast. Damage to critical infrastructure caused several weeks of disruption to vital supply chains and highlighted a need for additional forecasting tools to supplement national surge warnings. In this paper, we show that Artificial Neural Networks (ANNs) can generate better short-term forecasts of extreme water levels at estuarine ports. Using Immingham as a test case, an ANN is configured to simulate the tidal surge residual using an input vector that includes observations of surge at distant tide gauges in NW Scotland, wind and atmospheric pressure, and the predicted astronomical tide at Immingham. The forecast surge time-series, combined with the astronomical tide, provides a boundary condition for a local high-resolution 2D hydrodynamic model that predicts flood extent and damage potential across the port. Although the forecasting horizon of the ANN is limited, 6 to 24 hour forecasts at Immingham achieve an accuracy comparable to or better than the UK national tide-surge model and at far less computational cost. Use of a local rather than a larger regional hydrodynamic model means that potential inundation can be simulated very rapidly at high spatial resolution. Validation against the 2013 surge shows that the hybrid ANN-hydrodynamic model generates realistic flood extents that can inform port resilience planning

A ship movement classification based on Automatic Identification System (AIS) data using Convolutional Neural Network

With a wide use of AIS data in maritime transportation, there is an increasing demand to develop algorithms to efficiently classify a ship’s AIS data into different movements (static, normal navigation and manoeuvring). To achieve this, several studies have been proposed to use labelled features but with the drawback of not being able to effectively extract the details of ship movement information. In addition, a ship movement is in a free space, which is different to a road vehicle’s movement in road grids, making it inconvenient to directly migrate the methods for GPS data classification into AIS data. To deal with these problems, a Convolutional Neural Network-Ship Movement Modes Classification (CNN-SMMC) algorithm is proposed in this paper. The underlying concept of this method is to train a neural network to learn from the labelled AIS data, and the unlabelled AIS data can be effectively classified by using this trained network. More specifically, a Ship Movement Image Generation and Labelling (SMIGL) algorithm is first designed to convert a ship’s AIS trajectories into different movement images to make a full use of the CNN’s classification ability. Then, a CNN-SMMC architecture is built with a series of functional layers (convolutional layer, max-pooling layer, dense layer etc.) for ship movement classification with seven experiments been designed to find the optimal parameters for the CNN-SMMC. Considering the imbalanced features of AIS data, three metrics (average accuracy, score and Area Under Curve (AUC)) are selected to evaluate the performance of the CNN-SMMC. Finally, several benchmark classification algorithms (K-Nearest Neighbours (KNN), Support Vector Machine (SVM) and Decision Tree (DT)) are selected to compare with CNN-SMMC. The results demonstrate that the proposed CNN-SMMC has a better performance in the classification of AIS data