Transport 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.

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:

1. 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.

2. 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.

3. 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.

Related research output

A quantitative urban model for transport appraisal

Working paper by Daniel Hörcher and Daniel J. Graham

Transport 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 that it is largely confined to partial equilibrium (PE) models, ignoring the potentially transformative impacts of an intervention 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. This feature allows us to compare the welfare estimates in SGE with those of the PE method, netting out the uncertainty stemming from the empirical estimation of time valuations. 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 produce fundamentally different results in policy evaluation compared to a well-designed PE CBA. However, our approach complements aggregate welfare estimates with a detailed pattern of local economic outcomes, and the causal estimation of model parameters becomes an integral part of the modelling exercise. We argue that spatial modelling has the potential to thus enhance trust in transport appraisal among decision-makers and the public and to make its results more relevant to typical policy concerns.