Impacts of Transport Infrastructure on Productivity and Economic Growth: Recent Advances and Research Challenges

ABSTRACT

The paper provides an update of the survey focusing on estimating the contribution of transport infrastructure to productivity and economic growth. The central questions addressed are possible reasons behind the conflicting results reported in the literature on the elasticity of economic output with respect to transport infrastructure investment. After providing a systematic review of recent empirical studies on the effects of transport infrastructure on productivity and economic growth, the paper notes that controversial results can be attributed to ten causes (grouped into three categories for distinguishing): (1) related to different contexts: research period, geographical scales, and country's capability in enabling economic development; (2) related to different phenomena that are being measured: different economic sectors, different types of transport infrastructure, and different quality levels of transport infrastructure; and (3) related to distinct ways of measuring a similar phenomenon: measures used to describe the dependent variable and explanatory variable, functional specification, and estimation method of the econometric model. Strong network externalities of transport infrastructure may result in nonlinearity of the relationship between transport infrastructure and economic growth. Moreover, the absence of spatial concerns in infrastructure's impacts is another important source of inconclusive results. Finally, building on recent literature, the paper has discussed policy implications and identified several research avenues for further research.     

Has the transport-led economic growth effect reached a peak in China? A panel threshold regression approach

Using China’s province-level panel data from 1987 to 2010, this study explores the optimal level of transport infrastructure accumulation maximizing the growth rate. We investigate under what circumstances can additional transportation infrastructure capacity positively affect economic growth, based on panel threshold regression models. Our empirical findings suggest that there is a non-monotonic relationship between the stock of transport infrastructure and the long-run growth rate. The magnitude of transport-led economic growth effect significantly depends on the level of the existing transport network. The empirical results identify two endogenous cut-off points of efficiency of transport-led economic growth effect. When the highway network density is lower than 0.17 km/km², an insignificant positive relationship between highway infrastructure accumulation and economic growth was found. When the highway density is estimated between 0.17 and 0.38 km/km² or higher than 0.38 km/km², expanding the highway network has a significant positive effect on economic growth, but the magnitude of the impact is weaker in the latter, with the estimated coefficients equal to 0.23 and 0.09 respectively. Although China still enjoys a positive economic growth effect led by building more large-scale highway infrastructure, the magnitude of the effects of most provinces in China has already passed the saturation point and continuously expanding the highway network is not very productive.     

Tales on the dark side of the transport infrastructure provision: a systematic literature review of the determinants of cost overruns

ABSTRACT

Cost overruns are an endemic feature of the provision of transport infrastructure worldwide. In recent decades, a considerable amount of studies has been devoted to assessing the magnitude and determinants of cost overruns in the transportation sector. However, the empirical findings are scattered between different strands of literature, ranging from the fields of construction engineering and management to that of applied economics. To shed light on the determinants of cost overruns in the execution of transport infrastructure projects, we conduct a systematic review of the empirical literature on the topic. Of the 945 articles retrieved, 26 articles published between 2000 and 2016 meet our inclusion criteria. For them, we describe the different empirical approaches, we provide a classification of the determinants employed in the analyses and summarise their impact on cost overruns. Finally, we suggest some directions for further research in the field.     

How Do Governments Support the Development of Public Private Partnerships? Measuring and Comparing PPP Governmental Support in 20 European Countries

Abstract

Taking an institutional perspective, in this article we develop an index of the governmental support for public private partnership (PPP) — a ‘PPP Governmental Support Index’ (GSI) — which aims to measure the extent to which national governments provide an institutional framework that is either conducive or preventive for the introduction and diffusion of PPPs within transport infrastructure and other sectors. First, based on a substantive review of the literature, we define the elements of the PPP GSI, including the policy and political commitment regarding PPPs, the legal and regulatory framework, and the presence/absence of dedicated PPP-supporting arrangements. Second, we calculate the PPP GSI for 20 European countries, cluster them and compare similarities and differences in national governmental support of infrastructure PPPs. Third, we explore the potential link between national institutional index scores and infrastructure PPP activity in the 20 countries. Lastly, we discuss the potential and usefulness of the presented PPP GSI, as well as methodological limitations, and elaborate on how this index might be utilised to strengthen future comparative research on PPP in transport and other sectors.     

Resilience in railway transport systems: a literature review and research agenda

ABSTRACT

Critical infrastructure networks, such as transport and power networks, are essential for the functioning of a society and economy. The rising transport demand increases the congestion in railway networks and thus they become more interdependent and more complex to operate. Also, an increasing number of disruptions due to system failures as well as climate changes can be expected in the future. As a consequence, many trains are cancelled and excessively delayed, and thus, many passengers are not reaching their destinations which compromises customers need for mobility. Currently, there is a rising need to quantify impacts of disruptions and the evolution of system performance. This review paper aims to set-up a field-specific definition of resilience in railway transport and gives a comprehensive, up-to-date review of railway resilience papers. The focus is on quantitative approaches. The review analyses peer-reviewed papers in Web of Science and Scopus from January 2008 to August 2019. The results show a steady increase of the number of published papers in recent years. The review classifies resilience metrics and approaches. It has been recognised that system-based metrics tend to better capture effects on transport services and transport demand. Also, mathematical optimization shows a great potential to assess and improve resilience of railway systems. Alternatively, data-driven approaches could be potentially used for detailed ex-post analysis of past disruptions. Finally, several rising future scientific topics are identified, spanning from learning from historical data, to considering interdependent critical systems and community resilience. Practitioners can also benefit from the review to understand a common terminology, recognise possible applications for assessing and designing resilient railway transport systems.     

Exposing the role of exposure: Public transport network risk analysis

Network risk assessment takes into consideration the probability that adverse events occur and the impacts of such disruptions on network functionality. In the context of transport networks, most studies have focused on vulnerability, the reduction in performance indicators given that a disruption occurs. This study presents and applies a method to explicitly account for exposure in identifying and evaluating link criticality in public transport networks. The proposed method is compared with conventional measures that lack exposure information. A criticality assessment is performed by accounting for the probability of a certain event occurring and the corresponding welfare loss. The methodology was applied for a multi-modal public transport network in the Netherlands where data concerning disruptions was available. The results expose the role of exposure in determining link criticality and overall network vulnerability. The findings demonstrate that disregarding exposure risks prioritizing links with high passenger volumes over links with a higher failure probability that are significantly more critical to network performance. The inclusion of exposure allows performing a risk analysis and has consequences on assessing mitigation measures and investment priorities.     

Critical success conditions of collaborative methods: a comparative evaluation of transport planning projects

This paper explores critical success conditions of collaborative planning projects in the area of urban transport, evaluating the impact of new collaborative methods, instruments and processes on project performance. Hypothesis building is based on a comparative, empirical research design, rather than on deductive theory construction. Potential critical success conditions are derived from literature. Based on five urban transport planning projects in Gothenburg (Sweden), London (United Kingdom), Milwaukee (United States), Tokyo (Japan) and Mexico City (Mexico), a rough set analysis of the five cases reveals validated success conditions, which can be used for formulating hypotheses for further research or for policy and process improvement. The results suggest that a dedicated management of the multi-actor network, a high diversity of actors, as well as an extensive use of knowledge integration methods in combination with a high network density are critical success conditions of these planning processes. Surprisingly, the extensive use of unilateral methods also showed to be an important success condition. The traditional role of the planner will have to be complemented with the expertise of network and methodology management. The authors conclude that rough set analysis can be a valuable addition to narrative, single-case analysis of collaborative urban transport planning processes.     

Valuing Freight Transport Time using Transport Demand Modelling: A Bibliographical Review

The value of time for freight transport is of major importance in infrastructure-related cost–benefit analysis and yet its study has been largely neglected when compared with its passenger counterpart. In fact, one of the attributes that could decide the profitability of a project is how much can be saved if freight vehicles use new infrastructure. Despite being the primary benefit of most investments in transport infrastructure, researchers have not yet reached agreement over either the size or the nature of the values of time that should be used when evaluating projects. This article provides a review of the estimation of freight value of time through transport demand modelling and extant empirical evidence on this topic. Similarly, the bibliographic review of studies undertaken has allowed us to pinpoint the most critical issues when modelling freight transport demand and the position of various research teams regarding these aspects. Such issues include identifying the decision-maker, heterogeneity in the transport flows and transport attributes considered by decision-makers.     

Train design and routing optimization for evaluating criticality of freight railroad infrastructures

Freight transportation by railroads is an integral part of the U.S. economy. Identifying critical rail infrastructures can help stakeholders prioritize protection initiatives or add necessary redundancy to maximize rail network resiliency. The criticality of an infrastructure element, link or yard, is based on the increased cost (delay) incurred when that element is disrupted. An event of disruption can cause heavy congestion so that the capacity at links and yards should be considered when freight is re-routed. This paper proposes an optimization model for making-up and routing of trains in a disruptive situation to minimize the system-wide total cost, including classification time at yards and travel time along links. Train design optimization seeks to determine the optimal number of trains, their routes, and associated blocks, subject to various capacity and operational constraints at rail links and yards. An iterative heuristic algorithm is proposed to attack the computational burden for real-world networks. The solution algorithm considers the impact of volume on travel time in a congested or near-congested network. The proposed heuristics provide quality solutions with high speed, demonstrated by numerical experiments for small instances. A case study is conducted for the network of a major U.S. Class-I railroad based on publicly available data. The paper provides maps showing the criticality of infrastructure in the study area from the viewpoint of strategic planning.     

Predicting disruptions and their passenger delay impacts for public transport stops

Disruptions in public transport can have major implications for passengers and service providers. Our study objective is to develop a generic approach to predict how often different disruption types occur at different stations of a public transport network, and to predict the impact related to these disruptions as measured in terms of passenger delays. We propose a supervised learning approach to perform these predictions, as this allows for predictions for individual stations for each time period, without the requirement of having sufficient empirical disruption observations available for each location and time period. This approach also enables a fast prediction of disruption impacts for a large number of disruption instances, hence addressing the computational challenges that rise when typical public transport assignment or simulation models would be used for real-world public transport networks. To improve transferability of our study results, we cluster stations based on their contribution to network vulnerability using unsupervised learning. This supports public transport agencies to apply the appropriate type of measure aimed to reduce disruptions or to mitigate disruption impacts for each station type. Applied to the Washington metro network, we predict a yearly passenger delay of 5.9 million hours for the total metro network. Based on the clustering, five different types of station are distinguished. Stations with high train frequencies and high passenger volumes located at central trunk sections of the network show to be most critical, along with start/terminal and transfer stations. Intermediate stations located at branches of a line are least critical.

     

Empirics of multi-modal traffic networks – Using the 3D macroscopic fundamental diagram

Traffic is multi-modal in most cities. However, the impacts of different transport modes on traffic performance and on each other are unclear – especially at the network level. The recent extension of the macroscopic fundamental diagram (MFD) into the 3D-MFD offers a novel framework to address this gap at the urban scale. The 3D-MFD relates the network accumulation of cars and public transport vehicles to the network travel production, for either vehicles or passengers. No empirical 3D-MFD has been reported so far.In this paper, we present the first empirical estimate of a 3D-MFD at the urban scale. To this end, we use data from loop detectors and automatic vehicle location devices (AVL) of the public transport vehicles in the city of Zurich, Switzerland. We compare two different areas within the city, that differ in their topology and share of dedicated lanes for public transport. We propose a statistical model of the 3D-MFD, which estimates the effects of the vehicle accumulation on car and public transport speeds under multi-modal traffic conditions. The results quantify the effects of both, vehicles and passengers, and confirm that a greater share of dedicated lanes reduces the marginal effects of public transport vehicles on car speeds. Lastly, we derive a new application of the 3D-MFD by identifying the share of public transport users that maximizes the journey speeds in an urban network accounting for all motorized transport modes.     

On the use of Lagrangian observations from public transport and probe vehicles to estimate car space-mean speeds in bi-modal urban networks

The Macroscopic Fundamental Diagram (MFD) has been recognized as a powerful framework to develop network-wide control strategies. Recently, the concept has been extended to the three-dimensional MFD, used to investigate traffic dynamics of multi-modal urban cities, where different transport modes compete for, and share the limited road infrastructure. In most cases, the macroscopic traffic variables are estimated using either loop detector data (LDD) or floating car data (FCD). Taking into account that none of these data sources might be available, in this study we propose novel estimation methods for the space-mean speed of cars based on: (i) the automatic vehicle location (AVL) data of public transport where no FCD is available; and (ii) the fused FCD and AVL data sources where both are available, but FCD is not complete. Both methods account for the network configuration layout and the configuration of the public transport system. The first method allows one to derive either uni-modal or bi-modal macroscopic fundamental relationships, even in the extreme cases where no LDD nor FCD exist. The second method does not require a priori knowledge about FCD penetration rates and can significantly improve the estimation accuracy of the macroscopic fundamental relationships. Using empirical data from the city of Zurich, we demonstrate the applicability and validate the accuracy of the proposed methods in real-life traffic scenarios, providing a cross-comparison with the existing estimation methods. Such empirical comparison is, to the best of our knowledge, the first of its kind. The findings show that the proposed AVL-based estimation method can provide a good approximation of the average speed of cars at the network level. On the other hand, by fusing the FCD and AVL data, especially in case of sparse FCD, it is possible to obtain a more representative outcome regarding the performance of multi-modal traffic.     

Assessment of Cross‐Border Spillover Effects of National Transport Infrastructure Plans: An Accessibility Approach

Abstract

Traditional transport infrastructure assessment methodologies rarely include the full range of strategic benefits for the transportation system. One of these benefits is the contribution to cross‐border integration, critical for the European integration process. However, this is a key issue in strategic planning and decision‐making processes, as its inclusion may increase the probability of large‐scale transport infrastructure projects being funded. This paper presents a methodology for the measurement of the contribution of transport infrastructure plans to European integration. The methodology is based on the measurement of the improvement in network efficiency in cross‐border regions of neighbouring countries, via accessibility calculations in a Geographical Information System support. The methodology was tested by applying it to the ambitious road and rail network extensions included in the Spanish Strategic Transport and Infrastructure Plan (PEIT) 2005–2020. The results show significant and important network efficiency improvements of the PEIT outside the Spanish border. For the road mode, while the Spanish average accessibility improvement accounts for 2.6%, average improvements in cross‐border regions of France and Portugal are of 1.8%. And for the rail mode, the corresponding Spanish value is 34.5%, whereas in neighbouring regions it accounts for 20.2%. These results stress the significant importance of this strategic benefit and the consequent need for its inclusion in strategic planning processes. Finally, the paper identifies the potential of the methodology when applied at different administrative levels, such as the local or state levels.     

Locating fixed roadside units in a bus transport network for maximum communications probability

A key issue in solving the difficult bus-bunching problem is being able to have reliable information about the location of the buses in the network. Most advanced public transport systems have buses with GPS devices, but the problem remains of how to send reliable information from the buses to the control unit, particularly when the density of buses is low, but there are high communications reliability requirements. As a solution, we study locating roadside units (RSUs) along the route. The buses, together with the RSUs, form a linear vehicular ad-hoc network (VANET). The RSUs are deployed so to maximize the probability of a vehicle communicating with an RSU in at most two hops. Previous studies on RSU location never took into account two hops, a conceptually different type of network. Rather, they consider that a vehicle is able to communicate only directly to an RSU (one hop), which is a well-known Maximum Covering Problem, in which one of the parties is always immobile, similar to a mobile phone network. Oppositely, our method solves the problem in which two of the intervening parties are mobile and communicate with each other, not possible to solve as a Maximum Covering Problem. We estimate the probability of a vehicle accessing successfully an RSU either directly or through the relay of another vehicle. This probability is later embedded in an integer programming formulation that optimizes the RSU locations for maximum communications likelihood.Numerical examples show that the connection probability is strongly dependent on the coverage ratio of the transmitters and receivers and relatively independent on the vehicle density on the network, when densities are low. Results also show that it is possible to find some cost-efficient solutions which result in a smaller number of RSUs located while assuring a connection probability of 0.9 or higher.     

An integrated measure of accessibility and reliability of mass transit systems

The growth of a city or a metropolis requires well-functioning transit systems to accommodate the ensuing increase in travel demand. As a result, mass transit networks have to develop and expand from simple to complex topological systems over time to meet this demand. Such an evolution in the networks’ structure entails not only a change in network accessibility, but also a change in the level of network reliability on the part of stations and the entire system as well. Network accessibility and reliability are popular measures that have been widely applied to evaluate the resilience and vulnerability of a spatially networked system. However, the use of a single measure, either accessibility or reliability, provides different results, which demand an integrated measure to evaluate the network’s performance comprehensively. In this paper, we propose a set of integrated measures, named ACCREL (Integrated Accessibility and Reliability indicators) that considers both metrics in combination to evaluate a network’s performance and vulnerability. We apply the new measures for hypothetical mass transit system topologies, and a case study of the metro transit system in Seoul follows, highlighting the dynamics of network performance with four evolutionary stages. The main contribution of this study lies in the results from the experiments, which can be used to inform how transport network planning can be prepared to enhance the network functionality, thereby achieving a well-balanced, accessible, and reliable system. Insights on network vulnerability are also drawn for public transportation planners and spatial decision makers.     

The Myth of Travel Time Saving

Abstract

The idea that the main benefit of improvements to transport infrastructure is the saving of travel time has been central to transport economic analysis. There is, however, little empirical evidence to support this proposition. Indeed, in the long run average travel time is conserved, implying that travellers take the benefit of improvements in the form of additional access to more distant destinations made possible by higher speeds. Such a perspective, based on considerations of the value of access, has implications for economic appraisal, modelling and policy.     

The role of travel demand and network centrality on the connectivity and resilience of an urban street system

In the transportation literature, two major and parallel approaches exist to identify the critical elements of a transportation system. On the one hand, conventional transportation engineering emphasizes travel demand, often in terms of traffic volume (i.e., demand side). On the other hand, newer techniques from Network Science emphasize network topology (i.e., supply side). To better understand the relationship between the two approaches, we first investigate whether they correlate by comparing traffic volume and node centrality. Second, we assess the impact of the two approaches on the connectivity and resilience of a transportation network; connectivity is measured by the relative size of the giant component, and resilience is measured by the network’s adaptive capacity (the amount of extra flow it can handle). The urban road system of Isfahan (Iran) is used as a practical case study. Overall, we find that traffic volume indeed correlates with node centrality. In addition, we find that the weighted degree of a node, i.e., the sum of the capacities of its incident links (for small disruptions) and node betweenness (for large disruptions), best captures node criticality. Nodes with high weighted degree and betweenness should therefore be given higher priority to enhance connectivity and resilience in urban street systems. Regarding link criticality, roads with higher capacities showed a more important role as opposed to betweenness, flow, and congestion.

     

Long-term impacts of transport infrastructure networks on land-use change: an international review of empirical studies

Improvements in geographical information systems, the wider availability of high-resolution digital data and more sophisticated econometric techniques have all contributed to increasing academic interest and activity in long-term impacts of transport infrastructure networks (TINs) on land use (LU). This paper provides a systematic review of recent empirical evidence from the USA, Europe and East Asia, classified regarding the type of transport infrastructure (road or rail), LU indicator (land cover, population or employment density, development type) and outcome (significance, relationship’s direction) as well as influential exogenous factors. Proximity to the rail network is generally associated with population growth (particularly soon after the development of railway infrastructure), conversion to residential uses and the development of higher residential densities. Meanwhile, proximity to the road network is frequently associated with increases in employment densities as well as the conversion of land to a variety of urban uses including commercial and industrial development. Compared with road infrastructure, the impact of rail infrastructure is often less significant for land cover or population and employment density change. The extent of TINs’ impact on LU over time can be explained by the saturation in TIN-related accessibility and LU development.     

Patchwork in an interconnected world: the challenges of transport networks in Sub-Saharan Africa*

Sub-Saharan Africa has recently undergone, or still do in many countries, a period of transport infrastructure expansion. Current policies are centred on the development of international links, which require large capital-intensive projects and are sometimes economically dubious. This paper reviews the past policies and transport functions since colonial times by placing them in their economic and political context. We find that present strategies have similarities to the ones prevailing in previous periods, where expansion phases dominated by transport-led economic growth theories were followed by a stagnation of Africa’s infrastructure development. In view of the challenges in translating findings from empirical research into right policies, we identify the potential of more balanced and sustainable strategic investments, notably by reinforcing the existing secondary transport networks converging into urban centres.     

Short-term planning and policy interventions to promote cycling in urban centers: Findings from a commute mode choice analysis in Barcelona,Spain

BackgroundCycling for transportation has become an increasingly important component of strategies to address public health, climate change, and air quality concerns in urban centers. Within this context, planners and policy makers would benefit from an improved understanding of available interventions and their relative effectiveness for cycling promotion. We examined predictors of bicycle commuting that are relevant to planning and policy intervention, particularly those amenable to short- and medium-term action.MethodsWe estimated a travel mode choice model using data from a survey of 765 commuters who live and work within the municipality of Barcelona. We considered how the decision to commute by bicycle was associated with cycling infrastructure, bike share availability, travel demand incentives, and other environmental attributes (e.g., public transport availability). Self-reported and objective (GIS-based) measures were compared. Point elasticities and marginal effects were calculated to assess the relative explanatory power of the independent variables considered.ResultsWhile both self-reported and objective measures of access to cycling infrastructure were associated with bicycle commuting, self-reported measures had stronger associations. Bicycle commuting had positive associations with access to bike share stations but inverse associations with access to public transport stops. Point elasticities suggested that bicycle commuting has a mild negative correlation with public transport availability (−0.136), bike share availability is more important at the work location (0.077) than at home (0.034), and bicycle lane presence has a relatively small association with bicycle commuting (0.039). Marginal effects suggested that provision of an employer-based incentive not to commute by private vehicle would be associated with an 11.3% decrease in the probability of commuting by bicycle, likely reflecting the typical emphasis of such incentives on public transport.ConclusionsThe results provide evidence of modal competition between cycling and public transport, through the presence of public transport stops and the provision of public transport-oriented travel demand incentives. Education and awareness campaigns that influence perceptions of cycling infrastructure availability, travel demand incentives that encourage cycling, and policies that integrate public transport and cycling may be promising and cost-effective strategies to promote cycling in the short to medium term.