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Marcela A. Munizaga

After many years of data scarcity in transportation-related sciences, we have now entered the era of big data. Large amounts of data are available from GPS devices, mobile phone traces, payment transactions, social media, and other sources. The opportunities that this new availability presents are enormous. High-quality data is available at very low or negligible cost. These data can be used to develop new tools, to explore and understand travel behavior and to formulate new policies. However, the challenges are also big: the access to the data is not guaranteed, confidentiality has to be considered, the capacity of processing and enriching these databases has to be developed, and only then will they become really useful for decision-making and for the definition of public policies. This chapter presents an overview of the current state of play, and discusses the future perspectives, focusing on the challenges of building new predictive models.

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Edited by John Stanley and David A. Hensher

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Edited by John Stanley and David A. Hensher

Everyone has an opinion on transport: it significantly affects daily lives. This book highlights key transport opportunities and challenges, and identifies research requirements to inform policy discussion and support better societal outcomes. It does this by scanning across modes, continents, technologies and socio-economic settings, looking for common threads, points of difference and opportunities to make a difference. The book should appeal to prospective post-graduate students, professionals in transport and related fields, and those interested in better places and good discussions.
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Transit Oriented Development and Sustainable Cities

Economics, Community and Methods

Edited by Richard D. Knowles and Fiona Ferbrache

This book provides new dimensions and a contemporary focus on sustainable transport, urban regeneration and development in eight countries spanning four continents at different stages of development. It examines the role of transit oriented development (TOD) in improving urban sustainability and providing different transport choices, exploring how these can be implemented in modern cities.
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Lasse Gerrits and Stefan Verweij

We explain and demonstrate how the selected cases have to be prepared for the actual comparison. This involves a serious effort with regard to the interpretation of the case materials. In QCA, this process of interpreting data is guided by calibration, where raw (qualitative) case data are transformed into quantitative values. Calibration is important because it systematizes interpretation and makes it transparent. There are three principle types of calibration in QCA: crisp-set QCA, fuzzy-set QCA, and multi-value QCA. We explain and demonstrate the different types of calibration using real examples. We also discuss good practices that will help the researcher in making sound decisions when calibrating. The calibration results in a calibrated data matrix, which forms the input for the formal comparison in QCA. Having completed this chapter, the researcher will be able to start the comparison.

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Lasse Gerrits and Stefan Verweij

We explain why it is important to research specific cases and how exactly cases are to be understood and studied using QCA. Cases allow the researcher to account for the heterogeneity, uniqueness, and contextuality of projects. Whereas the term ‘case’ is often used indiscriminately, in QCA it is a clearly defined and important building block. In QCA, cases are conceptualized as configurations of conditions. This configurational nature highlights the complexity of the case. Cases can be researched in two principal ways: case-driven and theory-driven. The case-driven route is decidedly grounded in empirical material, with the boundaries and aspects of cases being constructed during the empirical research process. In the more theory-driven route, the boundaries and aspects of cases are defined by prior theories. Both routes constitute dialogues between data and theory. The chapter explains the concrete research steps involved in both routes.

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Lasse Gerrits and Stefan Verweij

We explain and demonstrate how the researcher can identify recurring patterns across cases on the basis of the calibrated data matrix, in a systematic and transparent way. The comparative process in QCA consists of three main steps. First, the calibrated data matrix needs to be transformed into a truth table. In the truth table, the cases are sorted across the logically possible configurations of conditions. Second, the truth table has to be minimized. This is done through the pairwise comparison of truth table rows that are considered to agree on the outcome and differ in their score in but one of the conditions. The result of the minimization is a solution formula. Third, the solution formula needs to be interpreted. Two common issues in the truth table minimization are limited diversity and logical contradictions. We present various strategies for dealing with these issues.

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Lasse Gerrits and Stefan Verweij

In this concluding chapter, some of the main issues concerning the evaluation of complex infrastructure projects with QCA are revisited. First, QCA’s capacity to truly capture and study the complexity of the development of infrastructure projects is discussed. QCA’s take on complex causality is relatively static because it does not explicitly integrate the time dimension. Various strategies to integrate time in QCA are discussed, including Temporal QCA (TQCA) and Time-Series QCA (TS/QCA). The different strategies have their strengths and weaknesses and they relate to different research steps (i.e., the case, the calibration, and the comparison) involved in QCA. Second, the deployment of QCA in real-world evaluations and various issues evaluators may run into are discussed. These issues include learning and political accountability, the presentation and visualization of results, and the transfer of lessons learned.

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The Evaluation of Complex Infrastructure Projects

A Guide to Qualitative Comparative Analysis

Lasse Gerrits and Stefan Verweij

Infrastructure projects are notoriously hard to manage so it is important that society learns from the successes and mistakes made over time. However, most evaluation methods run into a conundrum: either they cover a large number of projects but have little to say about their details, or they focus on detailed single-case studies with little in terms of applicability elsewhere. This book presents Qualitative Comparative Analysis (QCA) as an alternative evaluation method that solves the conundrum to enhance learning.
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Lasse Gerrits and Stefan Verweij

We argue that infrastructure projects are complex and that evaluations of such projects need to do justice to that complexity. The three principal aspects discussed here are heterogeneity, uniqueness, and context. Evaluations that are serious about incorporating the complexity of projects need to address these aspects. Often, evaluations rely on single case studies. Such studies are useful because they allow researchers to focus on the heterogeneous, unique, and contextual nature of projects. However, their relevance for explaining other (future) projects is limited. Larger-n studies allow for the comparison of cases, but they come with the important downside that their relevance for explaining single projects is limited because they cannot incorporate heterogeneity, uniqueness, and context sufficiently. The method Qualitative Comparative Analysis (QCA) presents a promising solution to this conundrum. This book offers a guide to using QCA when evaluating infrastructure projects.