Capturing the behavioural determinants behind the adoption of autonomous vehicles: conceptual frameworks and measurement models to predict public transport, sharing and ownership trends of self-driving cars.

The aim of this review paper is to provide comprehensive and up-to-date material for both researchers and practitioners interested in land-use-transport interaction (LUTI) modeling. The paper brings together some 60 years of published research on the subject. The review discusses the dominant theoretical and conceptual propositions underpinning research in the field and the existing operational LUTI modeling frameworks as well as the modeling methodologies that have been applied over the years. On the basis of these, the paper discusses the challenges, on-going progress and future research directions around the following thematic areas: 1) the challenges imposed by disaggregation-data availability, computation time, stochastic variation and output uncertainty; 2) the challenges of and progress in integrating activity-based travel demand models into LUTI models; 3) the quest for a satisfactory measure of accessibility; and 4) progress and challenges toward integrating the environment into LUTI models.

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Autonomous cars controlled by an artificial intelligence are increasingly being integrated in the transport portfolio of cities, with strong repercussions for the design and sustainability of the built environment. This paper sheds light on the urban transition to autonomous transport, in a threefold manner. First, we advance a theoretical framework to understand the diffusion of autonomous cars in cities, on the basis of three interconnected factors: social attitudes, technological innovation and urban politics. Second, we draw upon an in-depth survey conducted in Dublin (1,233 respondents), to provide empirical evidence of (a) the public interest in autonomous cars and the intention to use them once available, (b) the fears and concerns that individuals have regarding autonomous vehicles and (c) how people intend to employ this new form of transport.Third, we use the empirics generated via the survey as a stepping stone to discuss possible urban futures, focusing on the changes in urban design and sustainability that the transition to autonomous transport is likely to trigger. Interpreting the data through the lens of smart and neoliberal urbanism, we picture a complex urban geography characterized by shared and private autonomous vehicles, human drivers and artificial intelligences overlapping and competing for urban spaces.

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Retrospective understanding of the magnitude and pace of urban expansion is necessary for effective growth management in metropolitan regions. The objective of this paper is to quantify the spatialtemporal patterns of urban expansion in the Greater Kumasi Sub-Region (GKSR)-a functional region comprising eight administrative districts in Ghana, West Africa. The analysis is based on Landsat remote sensing images from 1986, 2001 and 2014 which were classified using supervised maximum likelihood algorithm in ERDAS IMAGINE. We computed three complementary growth indexes namely; Average Annual Urban Expansion Rate, Urban Expansion Intensity Index (UEII) and Urban Expansion Differentiation Index to estimate the amount and intensity of expansion over the 28-year period. Overall, urban expansion in the GKSR has been occurring at an average annual rate of 5.6 %. Consequently, the subregion's built-up land increased by 313 km 2 from 88 km 2 in 1986 to 400 km 2 in 2014. The analysis further show that about 72 % of the total built-up land increase occurred in the last 13 years alone, with UEII value of 0.605 indicating a moderate intensity of urban expansion. Moreover, the metropolitan-core of the sub-region, being the focal point of urban development and the historical origins of expansion, accounted for more than half of the total built-up land increase over the 28-year period. Over the last decade and half however, urban expansion has spilled into the neighbouring peripheral districts, with the highest intensity and fastest rate of expansion occurring in districts located north and north east of the subregional core. We recommend a comprehensive regional growth management strategy grounded in effective strategic partnerships among the respective administrative districts to curb unsustainable urban expansion.

This paper examines the determinants of utility bicycling through a cross-sectional study in Tamale, a metropolis with a long history of an embedded cycling culture in Ghana, West Africa. Using a socio-ecological framework, we model the extent to which individual-level characteristics, social environment factors and perceptions of physical environment factors at the neighbourhood and metropolitan scales influence choice of the bicycle as the main transport mode. An exploratory factor analysis distilled the indicators of the latent constructs of the socio-ecological framework into factors, which reflect physical environment challenges and opportunities perceived at the neighbourhood and metropolitan scales; influence of significant others; perceived status symbol of the bike; and perceived commuting benefits of bicycling. A binary logistic regression analysis of the determinants of utility cycling shows that while overall, bicycle ownership is an important determinant of cycling, between the genders, males are more likely to bicycle than females. Also, cyclists are more likely to be non-tertiary educated individuals. Whereas 'perceived neighbourhood-scale challenges' decrease the odds of cycling, 'perceived neighbourhood-scale opportunities', which reflect the availability of bicycle lanes, alternative roads and traffic control measures increase the likelihood of cycling among the study respondents. An interaction term between neighbourhood-scale physical environment opportunities and challenges, however, correlates negatively with cycling, suggesting that overall the metropolitan physical environment is not ideal for cycling. The study points to a huge potential for cycling in the metropolis and provides an empirical basis for interventions needed to remove barriers to bicycle commuting.

Mobility-on-demand systems consisting of shared autonomous vehicles (SAVs) are expected to improve the efficiency of urban mobility through reduced vehicle ownership and parking demand. However, several issues in their implementation remain open, such as unifying the vehicle and ride-sharing assignment with rebalancing non-occupied vehicles. Furthermore, proposed SAV systems are evaluated in isolation from other traffic; no congestion is taken into account when assigning requests or calculating routes. To address this gap, we present Shared Autonomous Mobility-on-Demand system (SAMoD), a reinforcement learning-based approach to vehicle relocation and ride-sharing request assignment. Each vehicle learns its pickup and rebalancing behaviour based on local current and observed historical demand. We evaluate SAMoD on Manhattan network using NYC taxi data in microsimulator SUMO. We investigate SAMoD performance in the presence of congestion generated by private vehicles, as well as investigate impact of different percentages of SAMoD vehicles in the system on overall traffic network performance.