The value of slow travel: An econometric method for valuing the user benefits of active transport infrastructure

Transport infrastructure investments are typically justified largely on the basis of their ability to increase travel speeds. However, new bicycle facilities, such as separated cycleways, may result in slower journeys. Economic appraisals of proposed bicycle facilities therefore tend to focus on the social benefits, in particular, improvements in public health resulting from increased physical activity. Yet, some welfare benefit must also accrue to the users of the new facilities, given they willingly choose to use them over faster alternatives. This thesis explores how discrete choice modelling can be used to analyse the trade-offs people make when choosing how they travel, and thereby (a) forecast changes in travel demand resulting from bicycle network improvements, and (b) quantify and monetise the resulting benefits to users. Despite the theory having been established in the 1970s, there have been few practical applications of this methodology, and it is yet to be used to value the user benefits of new bicycle facilities in a car-centric city. This thesis also assesses the short-term reliability of such assessments, by analysing changes in travel demand and preferences following an actual infrastructure intervention. It is found that bicycle network improvements offer substantial welfare benefits to users, in terms of improved accessibility, comfort, perceived safety, and transport choice – even though their journeys may end up being slower. Furthermore, these benefits amplify when links are connected into a network. By ignoring such benefits in project appraisal, bicycle facilities may be significantly undervalued, and transport investment decisions inadequately informed

Toward Sustainability: Bike-Sharing Systems Design, Simulation and Management

The goal of this Special Issue is to discuss new challenges in the simulation and management problems of both traditional and innovative bike-sharing systems, to ultimately encourage the competitiveness and attractiveness of BSSs, and contribute to the further promotion of sustainable mobility. We have selected thirteen papers for publication in this Special Issue

Modelling individual accessibility using Bayesian networks: A capabilities approach

The ability of an individual to reach and engage with basic services such as healthcare, education and activities such as employment is a fundamental aspect of their wellbeing. Within transport studies, accessibility is considered to be a valuable concept that can be used to generate insights on issues related to social exclusion due to limited access to transport options. Recently, researchers have attempted to link accessibility with popular theories of social justice such as Amartya Sen's Capabilities Approach (CA). Such studies have set the theoretical foundations on the way accessibility can be expressed through the CA, however, attempts to operationalise this approach remain fragmented and predominantly qualitative in nature. The data landscape however, has changed over the last decade providing an unprecedented quantity of transport related data at an individual level. Mobility data from dfferent sources have the potential to contribute to the understanding of individual accessibility and its relation to phenomena such as social exclusion. At the same time, the unlabelled nature of such data present a considerable challenge, as a non-trivial step of inference is required if one is to deduce the transportation modes used and activities reached. This thesis develops a novel framework for accessibility modelling using the CA as theoretical foundation. Within the scope of this thesis, this is used to assess the levels of equality experienced by individuals belonging to different population groups and its link to transport related social exclusion. In the proposed approach, activities reached and transportation modes used are considered manifestations of individual hidden capabilities. A modelling framework using dynamic Bayesian networks is developed to quantify and assess the relationships and dynamics of the different components in fluencing the capabilities sets. The developed approach can also provide inferential capabilities for activity type and transportation mode detection, making it suitable for use with unlabelled mobility data such as Automatic Fare Collection Systems (AFC), mobile phone and social media. The usefulness of the proposed framework is demonstrated through three case studies. In the first case study, mobile phone data were used to explore the interaction of individuals with different public transportation modes. It was found that assumptions about individual mobility preferences derived from travel surveys may not always hold, providing evidence for the significance of personal characteristics to the choices of transportation modes. In the second case, the proposed framework is used for activity type inference, testing the limits of accuracy that can be achieved from unlabelled social media data. A combination of the previous case studies, the third case further defines a generative model which is used to develop the proposed capabilities approach to accessibility model. Using data from London's Automatic Fare Collection Systems (AFC) system, the elements of the capabilities set are explicitly de ned and linked with an individual's personal characteristics, external variables and functionings. The results are used to explore the link between social exclusion and transport disadvantage, revealing distinct patterns that can be attributed to different accessibility levels

Community Detection in Multimodal Networks

Community detection on networks is a basic, yet powerful and ever-expanding set of methodologies that is useful in a variety of settings. This dissertation discusses a range of different community detection on networks with multiple and non-standard modalities. A major focus of analysis is on the study of networks spanning several layers, which represent relationships such as interactions over time, different facets of high-dimensional data. These networks may be represented by several different ways; namely the few-layer (i.e. longitudinal) case as well as the many-layer (time-series cases). In the first case, we develop a novel application of variational expectation maximization as an example of the top-down mode of simultaneous community detection and parameter estimation. In the second case, we use a bottom-up strategy of iterative nodal discovery for these longer time-series, abetted with the assumption of their structural properties. In addition, we explore significantly self-looping networks, whose features are inseparable from the inherent construction of spatial networks whose weights are reflective of distance information. These types of networks are used to model and demarcate geographical regions. We also describe some theoretical properties and applications of a method for finding communities in bipartite networks that are weighted by correlations between samples. We discuss different strategies for community detection in each of these different types of networks, as well as their implications for the broader contributions to the literature. In addition to the methodologies, we also highlight the types of data wherein these ``non-standard" network structures arise and how they are fitting for the applications of the proposed methodologies: particularly spatial networks and multilayer networks. We apply the top-down and bottom-up community detection algorithms to data in the domains of demography, human mobility, genomics, climate science, psychiatry, politics, and neuroimaging. The expansiveness and diversity of these data speak to the flexibility and ubiquity of our proposed methods to all forms of relational data.Doctor of Philosoph

Community Detection in Weighted Multilayer Networks with Ambient Noise

We introduce a novel class of stochastic blockmodel for multilayer weighted networks that accounts for the presence of a global ambient noise that governs between-block interactions. We induce a hierarchy of classifications in weighted multilayer networks by assuming that all but one cluster (block) are governed by unique local signals, while a single block is classified as ambient noise, which behaves identically as interactions across differing blocks. Hierarchical variational inference is employed to jointly detect and typologize block-structures as local signals or global noise. These principles are incorporated into novel community detection algorithm called Stochastic Block (with) Ambient Noise Model (SBANM) for multilayer weighted networks. We apply this method to several different domains. We focus on the Philadelphia Neurodevelopmental Cohort to discover communities of subjects that form diagnostic categories relating psychopathological symptoms to psychosis.Comment: 27 page

Sustainable Mobility and Transport

This Special Issue is dedicated to sustainable mobility and transport, with a special focus on technological advancements. Global transport systems are significant sources of air, land, and water emissions. A key motivator for this Special Issue was the diversity and complexity of mitigating transport emissions and industry adaptions towards increasingly stricter regulation. Originally, the Special Issue called for papers devoted to all forms of mobility and transports. The papers published in this Special Issue cover a wide range of topics, aiming to increase understanding of the impacts and effects of mobility and transport in working towards sustainability, where most studies place technological innovations at the heart of the matter. The goal of the Special Issue is to present research that focuses, on the one hand, on the challenges and obstacles on a system-level decision making of clean mobility, and on the other, on indirect effects caused by these changes

Spatial analysis of bicycle use and accident risks for cyclists.

Most developed countries nowadays face environmental, health and mobility problems as a consequence of widespread car use. Policies are now being reappraised in favour of more sustainable modes of transport. In particular, bicycle use holds the potential to provide a ‘green’ and healthy alternative to car commuting. There are however still important barriers that discourage people cycling… This thesis aims at identifying some of the main factors that influence cycle commuting and cycling accidents. Identifying such factors would in turn provide greater support to enable policy makers developing supportive environmental conditions for cycling. In the first part of this thesis, we examine which factors influence the spatial variation of bicycle use for commuting to work at the level of the municipalities in Belgium. Special attention is paid to bicycle-specific factors and spatial econometric methods are used to account for the presence of spatial effects in the data. The second part of this thesis examines which factors are associated with cycling accidents in Brussels. Spatial point pattern methods extended to networks are used to compare the ‘locational tendencies’ of cycling accidents officially registered by the police with those that are unregistered. An innovative case-control approach, based on a rigorous sampling design of controls and an exhaustive data collection of spatial factors, is also proposed to allow modelling the risk of cycling accident along the Brussels’ road network. This thesis not only provides sound recommendations helping planners and policy makers to encourage bicycle use, but it also offers new research directions for pinpointing locations where accidents are more likely to occur.

The potential role of walking and cycling to increase resilience of transport systems to future external shocks: creating an indicator of who could get to work by walking and cycling if there was no fuel for motorised transport

There are finite limits to resources, both extractable raw materials and planetary life support resources. Because of this, it is possible that there will be a severe and long lasting reduction in the fuel available for motorised transport which could manifest itself suddenly as a fuel shock. This thesis is concerned with the conceptual design, methodological development and application of a new spatially explicit transport policy indicator which estimates: Who could get to work tomorrow by walking and cycling if there was a fuel shock today? This thesis estimates the potential that walking and cycling have to increase resilience to fuel shocks in the period immediately after the fuel shock. A conceptual model of resilience to fuel shocks by individuals was devised. A novel hybrid static spatial microsimulation technique was developed. It was used to generate a population of individuals with the appropriate attributes to estimate for large populations the capacity to make journeys using only walking and cycling. This modelling process is generic and can be used to generate indicator results wherever suitable data exist. Using a simple scenario of a fuel shock which occurs today, current data could be used to estimate the indicator. A case study using the census data covering England, the Health Survey For England and other data sets was produced. Validation of the modelling process informs the analysis of the results. The results demonstrate the ability of the indicator to show variation between areas, in both a base case and when specific policy measures are applied. The base case indicator estimated that nationally in England only 44% (±4.85%) of individuals have capacity to commute to work by walking and cycling following a fuel shock. A local analysis of Leeds identified the spatial patterns of attributes which influence the indicator, allowing greater understanding of the geographical influences on capacity to travel by active modes. A policy package increasing bicycle availability, health and fitness and ensuring the ability of children to travel to school without needing adult escort was found to have a significant effect in 99% of English Output Areas. The indicator calculation methodology has produced significant improvements in the estimation of capacity to travel by active modes. Assuming everyone can cycle 8km (a common assumption in transport planning) overestimates capacity of the population to commute by active modes. The indicator identified a mean difference of 26% across all OAs. By considering constraints the indicator estimates of mean maximum distance travel distance by active modes differ by 73% compared to methods which ignore constraints. The indicator produced is policy relevant; The indicator can be judged as a good indicator when assessed against criteria for good indicators established by other workers. The modelling process is generic and can be applied to other scenarios. The results were presented at different extents and resolutions; making a useful and flexible spatially explicit indicator tool