On-line responsive traffic signal optimization strategies most commonly use data received from loop detectors to feed information into an underlying traffic model. The limited data available from conventional detection systems has dictated the way that current 'state-of-the-art' traffic signal control systems have been developed. Such systems tend to consider traffic as having homogenous properties to avoid the requirement for more detailed knowledge of individual vehicle properties. However, a consequence of this simplification is to limit an optimizer in achieving its objectives. The first element of this study investigates whether additional data regarding vehicle type can be reliably extracted from conventional detection to improve optimizer performance using existing infrastructure. A single detector classification algorithm is developed and it is shown that, using a modification of an existing state-of-the-art optimization method, a modest improvement in performance can be achieved. The emergence of connected vehicle technology and, in particular, Vehicle-to-Infrastructure (V2I) communications promises more comprehensive data. V2I-based optimization methods proposed in literature require a minimum penetration rate of V2I equipped vehicles before performance matches existing systems. To address this problem, the second part of the study focuses on the development of a hybrid detection model that is capable of simultaneously using information from conventional and V2I detection. It is demonstrated that the hybrid detection model can begin to realise benefits as soon as V2I data becomes available. V2I-based vehicle classification is then applied to the developed hybrid model and significant benefits are demonstrated for HGVs. The final section of the thesis introduces the use of a more sophisticated internal traffic model and a new optimization method is developed to implement it. The car-following model based optimization method addresses the lack of modelled interaction between vehicles and is shown to be capable of reducing vehicle stops over and above the developed (vertical queue based) hybrid model.

The thesis aims to explore attitudes and the perceived image of cycling in Mexico City and the extent to which this may be a deterrent for engaging in cycle commuting. It also explores the existence of attitudinal transport segments. Transport segments were investigated using a questionnaire developed by the Segment Project EU. Data about attitudes were collected in the same survey from a theory-based questionnaire developed from the Theory of Planned Behaviour (TPB), extended with constructs from the Social Comparison Theory (SCT) and the Material Possessions Model (MPM). The survey was distributed on-street and online, with a simple random sample of 401 road users. The sample characteristics were: no cyclists or infrequent cyclists, living and working in Mexico City, and aged 18-60. The results indicate that the same attitudinal transport segments identified in Europe also exist in Mexico City. They also show that cycling attributes, attitudes to cycling, social comparison, and social image and prestige were the most important factors influencing intention to cycle, explaining 42% of the variance in intention to cycle. Notably, when the evaluation of cycling was negative, the intention to commute by cycling was weaker; when the attributes of cycling and the social image linked to cycling were positive, the intention to cycle commute was strengthened. The effect of social comparison is not clearly established. Although the results from this study are specific to Mexico City, they indicate areas of interest to transportation planners in other regions, especially in those cities where intention to cycle is linked to its perceived image and there is political ambition to promote a positive image of cycling and stimulate modal shift. Moreover, this study contributes to the current literature developing applications of the TPB. Future research may include further investigating social comparison as a latent construct underlying intention to cycle.

Improving the performance and efficiency of transport systems requires sound decision-making supported by data and models. However, conducting travel surveys to facilitate travel behaviour model estimation is an expensive venture. Hence, such surveys are typically infrequent in nature, and cover limited sample sizes. Furthermore, the quality of such data is often affected by reporting errors and changes in the respondents' behaviour due to awareness of being observed. On the other hand, large and diverse quantities of time-stamped location data are nowadays passively generated as a by-product of technological growth. These passive data sources include Global Positioning System (GPS) traces, mobile phone network records, smart card data and social media data, to name but a few. Among these, mobile phone network records (i.e. call detail records (CDRs) and Global Systems for Mobile Communication (GSM) data) offer the biggest promise due to the increasing mobile phone penetration rates in both the developed and the developing worlds. Previous studies using mobile phone data have primarily focused on extracting travel patterns and trends rather than establishing mathematical relationships between the observed behaviour and the causal factors to predict the travel behaviour in alternative policy scenarios. This research aims to extend the application of mobile phone data to travel behaviour modelling and policy analysis by augmenting the data with information derived from other sources. This comes along with significant challenges stemming from the anonymous and noisy nature of the data. Consequently, novel data fusion and modelling frameworks have been developed and tested for different modelling scenarios to demonstrate the potential of this emerging low-cost data source. In the context of trip generation, a hybrid modelling framework has been developed to account for the anonymous nature of CDR data. This involves fusing the CDR and demographic data of a sub-sample of the users to estimate a demographic prediction sub-model based on phone usage variables extracted from the data. The demographic group membership probabilities from this model are then used as class weights in a latent class model for trip generation based on trip rates extracted from the GSM data of the same users. Once estimated, the hybrid model can be applied to probabilistically infer the socio-demographics, and subsequently, the trip generation of a large proportion of the population where only large-scale anonymous CDR data is available as an input. The estimation and validation results using data from Switzerland show that the hybrid model competes well against a typical trip generation model estimated using data with known socio-demographics of the users. The hybrid framework can be applied to other travel behaviour modelling contexts using CDR data (in mode or route choice for instance). The potential of CDR data to capture rational route choice behaviour for long-distance inter-regional O-D pairs (joined by highly overlapping routes) is demonstrated through data fusion with information on the attributes of the alternatives extracted from multiple external sources. The effect of location discontinuities in CDR data (due to its event-driven nature), and how this impacts the ability to observe the users' trajectories in a highly overlapping network is discussed prompting the development of a route identification algorithm that distinguishes between unique and broad sub-group route choices. The broad choice framework, which was developed in the context of vehicle type choice is then adapted to leverage this limitation where unique route choices cannot be observed for some users, and only the broad sub-groups of the possible overlapping routes are identifiable. The estimation and validation results using data from Senegal show that CDR data can capture rational route choice behaviour, as well as reasonable value of travel time estimates. Still relying on data fusion, a novel method based on the mixed logit framework is developed to enable the analysis of departure time choice behaviour using passively collected data (GSM and GPS data) where the challenge is to deal with the lack of information on the desired times of travel. The proposed method relies on data fusion with travel time information extracted from Google Maps in the context of Switzerland. It is unique in the sense that it allows the modeller to understand the sensitivity attached to schedule delay, thus enabling its valuation, despite the passive nature of the data. The model results are in line with the expected travel behaviour, and the schedule delay valuation estimates are reasonable for the study area. Finally, a joint trip generation modelling framework fusing CDR, household travel survey, and census data is developed. The framework adjusts the scaling factors of a traditional trip generation model (based on household travel survey data only) to optimise model performance at both the disaggregate and aggregate levels. The framework is calibrated using data from Bangladesh and the adjusted models are found to have better spatial and temporal transferability. Thus, besides demonstrating the potential of mobile phone data, the thesis makes significant methodological and applied contributions. The use of different datasets provides rich insights that can inform policy measures related to the adoption of big data for transport studies. The research findings are particularly timely for transport agencies and practitioners working in contexts with severe data limitations (especially in developing countries), as well as academics generally interested in exploring the potential of emerging big data sources, both in transport and beyond.

Limited research has been applied to testing intervention effects on travel behaviour of attendees at major sport events. As travel to sport events accounts for a large percentage of carbon emissions there is a need to alter travel behaviour. The underlying premise is that it may be possible to influence intentions and promote change using marketing interventions mapped to the Transtheoretical Model of Change (TTM). A quasi experimental design was adopted using a case study approach in the execution of the experiment. 4 studies were employed in this research. Study 1 articulated how the TTM was incorporated into the design of the social marketing interventions. Participants (N = 14) helped to identify the most influential marketing interventions. Using an adapted questionnaire based on the Theory of Planned Behaviour (TPB), it was concluded that sports fans were not intending to change and their use of the car was supported by peers. In determining the relationship between TPB scores and level of influence of each marketing intervention, the indication was that Subjective Norm (SN) had a mediating impact. In study 2, participants (n =192) were categorised into Stages of Change (SoC) using an adapted TTM questionnaire. The remaining TTM constructs were also assessed. In study 3, a post-intervention questionnaire was distributed to a Control (N=22) and Experimental group (N = 20). The collective results revealed that the interventions did not work. Participants did not recognise travel by car as a problem behaviour despite an awareness of the environmental and health implications. Contextual determinants dominated decisions. It was challenging to determine the theorised relationship between SoC and other TTM constructs as the majority of participants were categorised as Precontemplators or Contemplators. Findings indicated difficulty in aligning Process of Change (PoC) items with the SoC characteristics in this context. Yet the relationships between SoC and decisional balance and self-efficacy suggested alignment to the prescribed theory. In study 4 interviewees evaluated the interventions and gave their reaction. In spite of a sense of engagement, there was no change in travel behaviour. The car was seen as the solution to a problem – getting to the match on time. The findings formed the basis of recommendations which furthered the application of the TTM and its applicability within a specific leisure context.

In the face of rising emissions from transport, urbanization, global warming, security concerns, digitalization, ageing population, peak oil uncertainty and a long-standing concern for accessibility, transport practitioners and academics understand that transport planning and policies must be seen to be taking account of, and addressing all these challenges in tandem. Encouragingly, a number of interdisciplinary and integrated tools have been developed to help address each or combinations of these issues. These approaches have, for example, stressed the need for policymakers to overcome organizational boundaries, accommodate some degree of uncertainty, and to engage with all relevant stakeholders in the decision-making environment in a process of systematic thinking that is holistic rather than linear. However, a review of these tools in the area of strategic transport planning shows that they are mostly used to imagine environmentally sustainable transport futures. There is very little in the literature in terms of their use to think about socially-just transport futures. This is against the backdrop of some understanding that the goal of environmentally sustainable transport futures is intricately linked to, and contingent on socially sustainable goals such as equity, fairness and justice. To address this deficiency, this research develops a methodology for imagining socially-just transport futures by drawing upon three main futures approaches (scenario planning, backcasting and visioning) on one hand, and the principles of social justice on the other. The method was developed using desk-based research involving a review of the state-of-the-art in futures methods and conceptualisations of social justice principles. To critically evaluate its usefulness, the method was implemented in a workshop in Ghana with policymakers and other relevant stakeholders in the field of transport. The findings show that the method developed in this thesis is a useful planning tool for imagining socially-just transport futures. This it does by helping transport planners and policymakers to more openly deal with uncertainty in long-term futures, opens up debates on the various pathways to achieve socially-just transport futures, and highlights the commitments and investments needed to actualise a vision of social justice in transport. The results also show that while transport planners and practitioners in Ghana are not oblivious to the concept of social justice, the term is generally narrowly-construed.

Great Britain has a railway industry which appears to be a rare case study within an international context. The market has been privatised and restructured, with multiple companies operating freight and passenger services on a network managed by a single infrastructure manager - Network Rail. The reforms introduced by Great Britain between 1994 and 1997 are some of the fastest and widest undertaken across the world. Britain has adopted an incentivisation mechanism to ensure the infrastructure manager and operators perform in terms of punctuality and reliability. The incentive mechanism is referred to as Schedule 8. The industry offers a valuable case study for other railway industries internationally and, particularly, within the European Union (EU). The EU has issued Directives to their member states to require their railways to reform in a similar manner to the British railway industry. The Directives also require member states to adopt incentive mechanisms to ensure the infrastructure manager performs. As more countries are likely to open-up their railway markets to competition, the British case study offers evidence on the effects of performance on social welfare in a market open to such competition. In this thesis report, evidence is generated to demonstrate the value of punctuality and reliability within the British market structure. The effect of performance on different industry stakeholders is considered and evaluated to derive an understanding of the marginal social welfare effects resulting from changes in performance. It is believed that this research is the first attempt in the literature to reconcile the effects of performance on the demand and supply-sides of the railway industry; one of the earliest to estimate and discuss Extended Generalised Journey Time (EGJT) elasticities; and is a rare study in investigating the effects of a performance incentive mechanism.

This thesis synthesises two previously disparate fields of study by investigating the attitudes and aspirations of dyslexic people regarding travel information provision. One of the first in-depth qualitative studies of its kind is presented, with the findings having significance for academia, professionals and government. There are four main parts to the thesis. Part 1 considers the literature review. Part 2 presents the empirical research strategy adopted (a series of focus groups and a travel ethnography study). Following this, Part 3 presents the empirical research findings. Finally, Part 4 concludes the thesis, introducing discussion of the key findings, the contribution of the research to the fields of study, and avenues for further research in this area. It is clear that the attitude towards disabled people is changing. However, dyslexic people are experiencing the benefits of the social model of disability at a considerably slower rate than other disabled people. This is clearly the case across the transport industry, particularly within travel information provision. At present the distinct needs of dyslexic people are being lost within a 'design-for-all strategy'. Essentially, dyslexic people are facing limited travel horizons and mobility-related exclusion as a result. This thesis effectively supports the drive towards greater recognition and awareness of (and support for) dyslexia within the transport industry. Although the research provides much to consider, it constitutes an important opportunity to positively change this aspect of life for dyslexic people. Dyslexic-friendly travel information can ameliorate the access barriers to transport, which in turn could broaden the travel horizons of these individuals. For Government, the subsequent effects of better information access upon public transport patronage and use of the road network (and the perceptions of the transport industry as a whole) could be extremely positive. It is also certain that the research has the potential to further embed the social model of disability within dyslexia. By changing the fundamental attitudes of the transport industry towards dyslexia would provide a positive step forward in achieving greater social inclusion for dyslexic people. This in turn could provide greater access to opportunities previously unavailable, create better social opportunities, and more positive life experiences.

Studies have shown that neighbourhood design is one of the key factors affecting the household transport mode choice. Traditionally quantitative methods have been applied to evaluate the interaction between spatial planning and household travel behaviour. This study is based on multi-method approach (a combination of qualitative and quantitative methods) to identify the key motivational and attitudinal factors and to understand the relationship between residential location choices and transport mode choice. The research adopted an innovative approach to the evaluation of this relationship compared to the more traditional direct or indirect valuation techniques. Another unique aspect of this research is the application of the Q-method. It is a research tool which can measure and show what a respondent really thinks. By conducting focus group discussions, subjective and objective survey analysis in Greater Belfast Metropolitan Area, one of the UK's most car dependent metropolitan areas, this research incorporated the different methods to evaluate the relationship between transportation and land-use planning along with the varying methods to elicit respondent's attitude and preference towards neighbourhood choice decision and travel mode choice. This study found a significant association between neighbourhood design characteristics and transport mode choice. The results show significant evidence of residential self- selection (relationship between residential location choice and travel behaviour) but the explicit causality of direction cannot be established. It implies that the conclusive statement as to whether the quality of neighbourhood environment influences travel behaviour or that travel preferences affect residential choice cannot be made based on the multi-method approach used in this research. Overall, neighbourhood safety and proximity to friends and relatives were found to be the most important residential area factors. Generally, residents give more preference to housing unit features rather than neighbourhood design factors in the residential location decision. The study confirms that people select their neighbourhood according to the set of attitudes. For example people with a pro-public transport attitude select the neighbourhood with greater PT access, in contrast, people with a car dominant attitude prefer neighbourhoods with open space and tranquillity. Quantitative analysis reveals that trip frequencies are mostly associated with household socio-demographic factors whereas trip length is associated with neighbourhood design characteristics.

This thesis proposes that the theory underlying cost-benefit analysis (CBA) be strengthened in three respects - its treatment of social choice: quality of life and differentiated pricing. The summation across individuals in the Kaldor-Hicks test is argued to be a key weakness of CBA. Transport policymaking is reinterpreted as the resolution of co-ordination problems, and a set of three social choice criteria are developed based on the principles underpinning a mixed economy and democratic political system. These criteria include financial sustainability and consent, as well as a mean wellbeing-maximisation criterion which is close to the conventional NPV -maximisation criterion in CBA. A transport-related quality of life function is constructed, including a wide range of influences - e.g. inequality aversion and the built environment. The possibility of serious harm is recognised, and defined provisionally but quantitatively on the quality of life scale. The function is applied in an illustrative assessment of urban transport policy options. Demand-based price differentiation, sometimes regarded negatively as price discrimination, is defined and circumstances under which it is legal (a social choice) are explored. An approach is set out to assess price differentiation as a policy tool, within the theoretical framework. Application of the revised theory is found to be feasible and to change the ranking of some policy options compared with CBA. Road pricing is found to raise a consent issue, although the analysis suggests how the policy might be returned to overcome this. A metro investment, although expensive, is found to satisfy the criteria with careful use of mitigation and compensation. Differentiated pricing is found to be compatible with the criteria in an urban example, however in a non-urban example the level of prices and ‘unequal treatment' versus other localities are found to raise consent issues, which could have changed the recommendations, if included in the ex ante appraisal.

No description available.