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Transport networks in dynamic urban form
A quantitative spatial economic approach
Ongoing ProjectTransport networks in dynamic urban form
A quantitative spatial economic approach
Cities and their transport networks work in close symbiosis: our travel needs are determined by the sturcture of cities, while urban form heavily depends on the cost of moving around in space. This co-dependence is driven by complex sets of mechanisms in human and firm behaviour. Despite repeated research efforts over the past decades, we are still unable to quantify the future impact of transport improvements on the spatial distribution of real estate prices, firm productivity, employment, and wages within and between cities.
This ongoing research project combines methods from economics, transport science, urban planning, and statistics into a single framework to provide a quantifiable and mappable picture of the impact of transport developments on urban form. We establish the spatial models of London, Budapest and the largest cities of Hungary. We model households' residential and workplace location choices, consumer and travel decisions, firms' location choices and local productivity, and the market forces behind real estate prices and wages simultaneously.
Our models are applied to address some of the most pressing challenges of contemporary transport policy. (1) We explore new ways to make public transport more efficient amid rising energy prices and the post-pandemic trand of remote work. (2) We benchmark potential regulatory strategies to replace the current fuel duty revenues with road pricing when electric vehicle become dominant. (3) We create a new guidance for the cross-sectoral cost-benefit analysis of the most transformative infrastructure development projects.
Project Team
Project Team
Daniel Hörcher
Senior Research Associate at CIAS, Corvinus University of Budapest;
Research Associate at BME Budapest;
Honorary Research Fellow at Imperial College London
Daniel Ruiz Palomo
PhD student at Universitat Autònoma de Barcelona;
Research Assistant at CIAS, Corvinus University of Budapest;
Research Assistant at International Growth Centre, London
Réka Kertész-Doffkay
Research Assistant at CIAS, Corvinus University of Budapest;
Junior Researcher at KTI Institute for Transport Sciences
Hans Koster
Full Professor, Vrije Universiteit Amsterdam;
Collaborator at CIAS, Corvinus University of Budapest
(joining soon)
Detailed Project Description
Detailed Project Description
Our research aims to develop novel general equilibrium models and combine the toolbox of quantitative spatial economics (QSE) with the state-of-the-art of transport science. Our models have the potential to predict the impact of large-scale transport interventions on urban form, firm productivity, wages and housing prices, and perform a welfare economic assessment of new policies.
Our transport models retain the core properties of QSE when it comes to parameter estimation. First, we design the equilibrium conditions of the theoretical model such that they can be applied as regression equations as well, to identify the causal impact of the core structural parameters. Second, we ensure that the model remains invertible, that is, that a deterministic relationship exists between the patterns of mobility, wages and floorspace prices we observe in data, and the location-specific fundamental variables of the model. (The fundamentals capture geographical properties such as local residential amenities and local resources with an impact on productivity. Fundamentals are difficult to quantify with traditional empirical tools.) The QSE literature has achieved remarkable success in recent years, with publications in the leading journals of economics (e.g. AER, QJE, Econometrica). However, transport networks and services are modelled in a stylised way in these papers, mainly due to the lack of cross-fertilisation between the economic and engineering disciplines.
We apply the theoretical and empirical contributions of this research to evaluate explicit transport planning problems in real cities and regions. We calibrate the model to replicate London, and Budapest and Hungarian cities with a population above 100,000. In case of the Budapest case study, the budget enables us to procure granular smartphone location datasets. This allows us to encapsulate leisure and business trips into the model, on top of the commuting mobility.
The policies we select for investigation reflect on the most pressing challenges in the transport sector:
We derive optimal supply decisions for public transport, considering the post-pandemic emergence of remote work and the impact of rising energy and labour costs.
In preparation for the advent of electric vehicles, we model alternative road pricing strategies which will be able to replace traditional fuel duties and government revenues that reach €2.5 billion per year in Hungary.
We transform our spatial economic approach into a more general project appraisal methodology. Our aim is to replace the currently used partial equilibrium method of cost-benefit analysis. Thus, the project will quantify and elucidate the cross-sectoral benefits of billions of euros of spending on large-scale infrastructure projects.
This is a 2-year reseach project funded by the Excellence_24 call of the National Research, Development and Innovation Office of Hungary. This grant provides a unique opportunity to realise a research journey bridging the spatial economics, transport engineering and urban planning disciplines.
What is a Quantitative Spatial Model?
What is a Quantitative Spatial Model?
In fact, it would be more appropriate to refer to QSMs as Quantitative Spatial General Equilibrium Models. This terminology also allows for a more precise definition of what constitutes a QSM:
A spatial model: We model a set of distinct geographical locations (that is, the nodes of a graph) connected by the transport network, replicating a real geography; for example, a city, a region or an entire country. Locations have unique characteristics quantified by exogenous parameters and endogenous variables in the model.
A general equilibrium model: We combine the transport market with other sectors of the spatial economy. In particular, we model local labour, land, floorspace and consumer markets, allowing each location of the model to host varying levels of economic activity. Wages, floorspace prices and consumer prices are also differentiated by location. In equilibrium, the local wage, floorspace price and consumer price values equate supply and demand in each sector.
A spatial general equilibrium (SGE) model: Individuals choose where they live and work within the model. Workers typically prefer to live where floorspace is cheap and the residential amenities are attractive, and they prefer to work where wages are high and additional amenities are available. The challenge is that such locations are often far from each other, and transport is costly. Transport improvements allow individuals to perform these activities at more separated locations.
A quantitative SGE model: Quantify the the model using advanced mathematical and statistical methods, including model inversion and causal parameter estimation. This reduces the need for ad-hoc parameter selection during the process of model calibration. QSMs are especially powerful in quantifying local geographical characteristics, including local amenities, productivity enablers, zoning restrictions, at a high level of spatial granularity (up to 10k+ distinct locations in one model).
Are QSMs entirely new?
Are QSMs entirely new?
No, they are not. The QSM literature builds upon decades of research in land-use transport interaction (LUTI) and spatial computable general equilibrium (SCGE) models. In fact, QSMs can be regarded as a subset of SCGE models, which themselves form a subset of LUTI models.
The key distinction between a traditional SCGE model and a QSM lies in the latter’s use of more refined techniques for model estimation. The main difference between the SCGE (including QSM) and LUTI classes of models is that the former are grounded entirely in microeconomic theory: in SCGE and QSM models, local floorspace prices, wages and other prices emerge from market mechanisms, whereas LUTI models are typically based on more flexible rules that are often supported only by intuitive reasoning rather than strict theoretical foundations.
A shared feature of QSMs, SCGEs and LUTI models is that individuals’ location choices are derived from a random utility discrete choice framework, originating from Daniel McFadden’s well-known theory. However, these choices are specified in different ways. Most LUTI and SCGE models employ standard additive utility functions with a Gumbel-distributed random component. By contrast, QSMs use a multiplicative utility function with a Fréchet-distributed idiosyncratic shock. This is not a fundamental difference: applying a logarithmic transformation converts the Fréchet model into a standard logit form. Nonetheless, the multiplicative specification enables a range of beneficial properties that we exploit during model estimation.
Are QSMs superior to SCGE and LUTI models?
Are QSMs superior to SCGE and LUTI models?
No, their suitability depends on the specific context in which the models are intended to be applied. LUTI models are particularly effective for predicting future spatial outcomes, including changes in land use. SCGE models are often the most complex in terms of their theoretical structure and the degree to which they realistically replicate market mechanisms. QSMs, by contrast, offer advantages in terms of model estimation and spatial resolution.
Are QSMs universally suitable for transport policy appraisal?
Are QSMs universally suitable for transport policy appraisal?
We believe that QSMs are particularly well suited to assess large-scale transformational policies with substantial spillovers in property and labour markets. More generally, spatial general equilibrium models are valuable tools when the expected scale of household and firm relocation following a policy intervention is significant. Many transport policy measures do not fall into this category. In such cases, we recommend the use of mainstream partial equilibrium appraisal techniques.
Does this project aim to replace the mainstream methodology of transport CBA?
Does this project aim to replace the mainstream methodology of transport CBA?
No, we believe it is more likely that QSMs will play a complementary role alongside the partial equilibrium CBA methodology.
Traditional transport appraisal is surrounded by a number of misconceptions, often rooted in a limited understanding of the underlying economic theory. The CBA methodology can also be misused, whether inadvertently or, in some cases, deliberately due to anticipation bias. Nevertheless, we consider the theoretical foundations of the method to be robust, and we believe that a well-informed analyst with an impartial perspective can derive reasonably accurate welfare measures, which provide a solid basis for any policy appraisal.
A spatial equilibrium model such as a QSM can deepen our understanding of the spatial implications of transformative projects and help identify additional externalities linked to market failures in the production, construction and labour markets. The outputs of such models may strengthen the economic case for a transport project by providing more intuitive performance metrics, such as changes in wages and housing prices. QSMs also offer richer insight into the spatial distribution of the expected impacts. In general, supplementing a traditional CBA with a QSM can help to improve trust in the economic appraisal exercise.
Related research output
Related research output
Working paper by Daniel Hörcher and Daniel J. Graham
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5067099Transport cost-benefit analysis (CBA) has evolved since its inception to become one of the most influential and ubiquitous applications of microeconomic theory, shaping billions of dollars of investment in the infrastructure sector. A key limitation of transport appraisal practice is its reliance on partial equilibrium (PE) models, which limits the ability to quantify potentially transformative impacts outside the transport market. In this paper, we build upon the principles of quantitative spatial economics to design an empirically relevant appraisal method in spatial general equilibrium (SGE). Our model yields travel time valuations that are micro-founded through an explicit leisure-labour trade-off, making them unique to each residence-workplace pair. We decompose the aggregate welfare change in SGE into direct user benefits and wider economic impacts, and we quantify the statistical uncertainty surrounding each welfare component. These features allow us to compare the welfare estimates in SGE with those of the mainstream PE method. In the numerical implementation of the model, we replicate Greater London with 983 distinct spatial units and the introduction of the Elizabeth Line, a major urban rail project. We find that transport appraisal in SGE is unlikely to yield fundamentally different policy evaluation results compared with a correctly quantified PE CBA. However, the SGE approach (i) is well suited to assessing transport and land-use policies simultaneously, (ii) complements aggregate welfare estimates with a detailed spatial pattern of local economic outcomes, and (iii) integrates the causal estimation of key parameters into the modelling exercise.
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