Evaluating the public investment mix in US freight transportation infrastructure

This research empirically evaluates the public sector investment in the US freight transportation infrastructure. In particular, the infrastructures to support the two most comparable modes of freight transportation – highway and intermodal rail – are examined as alternatives for public fund allocation. Indicators for public sector transportation infrastructure investment mix are established based on financial analysis of both private and social costs and benefits, as well as the propensity of freight shippers to utilize such infrastructures. The research results in recommendations for the aggregate allocation of public funds in the US based on these indicators. We find that approximately a quarter of truck freight could be handled at a 25% lower cost if rail infrastructure to support it existed. Because an additional 80% reduction in social costs could be achieved through this modal conversion, the public sector is a critical participant in creating a more efficient transportation infrastructure.     

Simulating deep CO2 emission reduction in transport in a general equilibrium framework: The GEM-E3T model

Transport sector restructuring to achieve deep GHG emission cuts has attracted much attention because transportation is important for the economy and inflexible in greenhouse gas emission reduction. The aim of this paper is to simulate transition towards low carbon transportation in the European Union until 2050 and to assess the ensuing macroeconomic and sectorial impacts. Transport restructuring is dynamically simulated using a new transport-oriented version of the computable general equilibrium model GEM-E3 which is linked with the PRIMES-TREMOVE energy and transport sectors model. The analysis draws from comparing a reference scenario projection for the EU member-states up to 2050 to alternative transport policy scenarios and sensitivities which involve deep cutting of CO₂ emissions. The simulations show that transport restructuring affects the economy through multiple channels, including investment in infrastructure, the purchasing and manufacturing of new technology vehicles, the production of alternative fuels, such as biofuels and electricity. The analysis identifies positive impacts of industrial activity and other sectors stemming from these activities. However, the implied costs of freight and passenger transportation are of crucial importance for the net impact on GDP and income. Should the transport sector transformation imply high unit costs of transport services, crowding out effects in the economy can offset the benefits. This implies that the technology and productivity progress assumptions can be decisive for the sign of GDP impacts. A robust conclusion is that the transport sector decarbonisation, is likely to have only small negative impacts on the EU GDP compared to business as usual.     

Application of particle swarm optimization and robust net present value for BOT-type contracts

Major infrastructure construction projects contracted to private companies by governments are important for maximizing profitability. This paper extends an existing build–operate–transfer (BOT) concession model (BOTCcM) for identifying the reasonable concession period which would be profitable both to the government and to the private sector. There are some major limitations with BOTCcM – for example, the total investment cost is pre-given and the impact of uncertainty of parameters affecting the concession period were not considered. In this research, the total investment cost is assumed as variable which should be optimally determined and the uncertainty of net cash flows is considered. Further, the proposed model is implemented to calculate the robust concession period and required capital for the construction period, using the obtained values and particle swarm optimization method.     

Adaptation planning for climate-resilient urban infrastructures

This paper analyzes the interdependency across two critical infrastructures of transportation and motor fueling supply chains, and investigates how vulnerability to climatic extremes in a fueling infrastructure hampers the resilience of a transportation system. The proposed model features both a bi-stage mathematical program and an extension to an ‘α-reliable mean-excess’ regret model. The former aspect allows decision makers to optimize the pre-disaster asset prepositioning against the maximum post-disaster system resilience. The latter aspect of the proposed model devalues the impact of ‘low-probability, high-cost’ sub-scenarios upon model results. The model reveals the reliance of post-disaster urban mobility on the interdependent critical infrastructure of motor fueling supply chains. The results also suggest how investment in the fueling infrastructure’s vulnerable elements protects urban mobility while the transportation network is stressed or under attack.     

Transport infrastructure costs in low-carbon pathways

The rate and manner in which transport infrastructure (e.g. roads, railway tracks, airports) is deployed, will play an important role in determining energy demand, greenhouse gas emissions and the economic impact of the transport sector. This paper describes an exercise where the costs of infrastructure deployment for the transport sector have been incorporated into the IMACLIM-R Global E3 IAM. In addition to adding these costs, the modelling of the criteria for the deployment of infrastructure for roads has also been improved. It is found that this model recalibration results in a more accurate baseline as compared to historically observed data (2001–2013) for investments in energy demand, road infrastructure, and passenger kilometers travelled. Regarding macroeconomic effects, it is found that the imposition of a carbon emission trajectory to 2100 cause GDP to decrease relative to the newly calibrated baseline – this is a standard IAM result. However, when the deployment of infrastructure for roads and air travel is further constrained, the GDP loss is less than with a fixed carbon emission trajectory only. This is because early restriction of infrastructure for roads and air travel allows an expansion of public transport infrastructure which is adequate to meet low-carbon transport service demand whereas when less public transport infrastructure is available, more costly mitigation investments must be made in other parts of the economy. This suggests that restricting infrastructure deployment as a complementary policy to carbon pricing, lowers the cost of mitigation.     

Incorporating uncertainty and risk in transportation investment decision-making

This paper presents a framework for addressing uncertainty and risk for large-scale transportation investments involving public–private participation. Demand, fare/toll and demand responsive costs are considered in the uncertainty analysis. Uncertainty analysis provides information on economic feasibility of the project. A set of relaxation policies is proposed to form various Ownership, Tenure and Governance (OTG) strategies reflecting the nature and level of participation by the public and private entity. A Monte Carlo Simulation-based Value at Risk is used to quantify risk. Finally, a methodology is proposed to integrate uncertainty and risk. The framework is tested on the proposed multibillion dollar Detroit River International Crossing connecting the cities of Detroit in the USA with Windsor in Canada. The analysis provides insights to probable outcomes for this transportation infrastructure investment under different OTG scenarios.     

an analysis of transportation energy conservation projects in developing countries

Industrialized countries have extensive experience with various transportation energy conservation measures and have been making steady progress in improving the efficiency of their transport sector. Less Developed Countries (LDCs) have also been making progress, but their experience in many aspects of transportation energy conservation is very limited. Development organizations have funded many transportation projects in developing countries, but the vast majority of these projects were designed to improve the transportation infrastructure. Very few transportation energy conservation projects have been implemented and decision-makers face a scarcity of information on effective strategies. This paper gives an overview of transportation projects in LDCs in order to identify those transportation energy conservation measures that offer the greatest potential for LDCs. Two case studies, from Tunisia and Costa Rica, are given to illustrate the issues involved in implementing transportation energy conservation measures in LDCs. Conclusions are drawn to suggest actions for developing countries and for development organizations.Acronyms DECAT Driver Energy Conservation Awareness Training - DSE Direccion Sectorial de Energia (Costa Rica) - GDP Gross domestic product - km Kilometer - LDCs Less Developed Countries - TOE Metric tonne of oil equivalent - USAID US Agency for International Development     

Private tollroads in urban areas

Governments of all persuasions are increasingly seeking the participation of the private sector in the supply of transport facilities and services. Private sector participation in the financing, construction, operation and maintenance of infrastructure is considered a serious option in a number of countries in the search for ways of providing much needed investment which would otherwise be deferred. This paper considers some economic and financial problems in the private sector provision of major road infrastructure within urban areas. The main issues are attaching prices (i.e. tolls) to the provision of the service, the value of government rights which are being given up either permanently or temporarily, and the identification required by the promoters of the cost of capital which is essential information in establishing the risk. Broader environmental and equity issues are not addressed. If the approach to establishing a private presence in a previously public supply context is handled properly from the outset, the benefits can be significant. Contrarily however, the prospects could be quite undesirable if badly managed, despite the presence of an extended public purse.requests for offprints     

Remuneration models and revenue risk mitigation in road public–private partnership projects – a case study from Serbia

As a consequence of renewed interest in attracting private financing for infrastructure investments, public–private partnership (PPP) arrangements are mostly seen as a suitable mechanism for ensuring sound and quicker delivery of transport infrastructure projects. However, a general concern is that expectations of mobilizing private-sector funds have been overestimated in a number of cases. The purpose of this paper is to contribute to the risk analysis of transport PPP projects with substantial exogenous demand risk which could serve as a rationale for choosing the appropriate PPP model. The objective of this paper is to construct an analytical cash flow-based project model to facilitate the choice of the remuneration mechanism suitable for both private investors and public sector. The model provides an indication whether the project should be implemented as a ‘users pay’, a hybrid or an ‘annuity’ PPP model. The proposed methodology is illustrated using a case study from Serbia.     

Public–private partnerships: a review of economic considerations with particular reference to transportation projects

The paper sets in context some of the more recent work that has been conducted on public–private partnerships (PPPs) in the provision and operation of infrastructure. PPPs essentially involve a government or its agent signing an agreement with a private company or consortium to supply it with services with the private sector actor involved in major elements of designing, building, temporarily ‘owning’, and running the physical assets; basically they are long-term development and service contracts between government and a private partner. The paper outlines the development of economic thinking regarding the rationale behind PPPs, the extent to which unbundling is optimal and the forms that it may take, the nature of the contracts that are enacted and their renegotiation, the awarding of contracts, and matters of possible corruption. By way of focus, it also provides some indication of what empirical studies in the transportation have thrown up regarding the outcomes of PPPs.     

Urban transportation and equity: A case study of Beijing and Karachi

Development of mega cities of Pakistan and China has greatly been affected by the growth in urbanization and motorization. The uncontrolled rise in urbanization, motorization, exclusionary planning and disproportionate investment in transportation infrastructure has created a socio-economic imbalance, thereby challenging the issue of equity. This paper focuses on a comparative social equity assessment of urban development, characteristics of supply and demand of transportation and infrastructure systems and the impact of existing strategies over equity in the development of urban and transportation system of Beijing and Karachi. The paper concludes by suggesting some strategies for the development of sustainable and equitable urban transportation systems.     

An aggregate capacity estimation model for the evaluation of railroad passing constructions

The Netherlands Railways operates a double tracked intensively used network of railroads. To expand the transportation capacity, each year a number of infrastructure expansions are considered. The evaluation of these expansions is traditionally done by establishing a set of detailed timetables that serve the forecasted transportation demand and that can be executed with the proposed infrastructure expansion. However, the development of a detailed timetable is a very time consuming process, and therefore leaves little opportunity for comparing many alternatives. In this paper, we present and test an aggregate model that can be used to single out the most promising investment alternatives in the railroad infrastructure, specifically passing constructions. The aggregate model provides the user with insight into the ranking of the various alternatives and additionally gives a relative insight into the theoretical capacity of the proposed infrastructure change.     

Transport infrastructure and long-run economic growth in OECD countries

This paper provides an empirical evaluation of the growth impact of public infrastructure in a panel of 18 OECD countries during 1870–2009. This study goes beyond the traditional analysis of growth accounting models by exploring the indirect effect of stock of core infrastructure on output growth through its impact on productivity. Constructing a long-run historical dataset on infrastructural capital formation spanning from 1870, estimated results show that growth in both labour productivity and total factor productivity are positively, but not substantially, influenced by growth in the stock of infrastructure. Furthermore, applying the system GMM technique (Generalised Method of Moments) revels that although rate of returns to investment in infrastructure exceed the private rate in OECD countries, it is not as high as positive externalities associated with investment in equipment and structure investment.     

Estimating traffic demand risk – A multiscale analysis

This paper proposes a novel method for estimating the traffic demand risk associated with transportation. Using mathematical properties of wavelets, we develop a statistical measure of traffic demand sensitivity with respect to GDP. This measure can be adapted in a flexible way to capture risk levels relevant for different investment horizons. We demonstrate the timescale decomposition of risk with Swedish traffic demand data for 1950–2005. In general, rail transport shows a stronger co-movement with GDP than road transport. Moreover, we examine the volatility exhibited by traffic demand. Our findings suggest that rail investments are more risky than road investments. Since the findings can be used for optimal investment timing and for choice between public investment alternatives, they are deemed important for public policy in general.     

Learning from Los Angeles: transport,urban form,and air quality

Los Angeles is well known around the world as an automobile-oriented low density community, yet recent transportation policies have emphasized greater capital investment in rail transportation than in highways, and recent policies have attempted to discourage automobile usage through transportation demand management. While these policies have accomplished small shifts toward public transport and somewhat lower dependence upon singly occupied automobilies for work commuting, the financial costs of these policy changes has been very large in relation to their benefits. Proper pricing of transportation alternatives, more creative use of new and emerging transportation technologies, and the provision of many more opportunities for simpler private sector transport services, would all appear to be more promising as cost-effective approaches to coping with congestion in Los Angeles than the current regional transportation policies.     

Planning for economic and environmental resilience

Climate protection will require major reductions in GHG emissions from all sectors of the economy, including the transportation sector. Slowing growth in vehicle miles traveled (VMT) will be necessary for reducing transportation GHG emissions, even with major breakthroughs in vehicle technologies and low-carbon fuels (Winkelman et al., 2009). The Center for Clean Air Policy (CCAP) supports market-based policy approaches that minimize costs and maximize benefits. Our research indicates that significant GHG reductions can be achieved through smart growth and travel efficiency measures that increase accessibility, improve travel choices and make optimum use of existing infrastructure. Moreover, we find such measures can deliver compelling economic benefits, including avoided infrastructure costs, leveraged private investment, increased local tax revenues and consumer vehicle ownership and operating cost savings (Winkelman et al., 2009).As a society, what we build – where and how – has a tremendous impact on our carbon footprint, from building design to transportation infrastructure and land-use patterns. The empirical and modeling evidence is clear – people drive less in locations with efficient land use patterns, high quality travel choices and reinforcing policies and incentives (Ewing et al., 2008). It is also clear that there is growing and unmet market demand for walkable communities, reinforced by demographic shifts and higher fuel prices (Leinberger, 2006, Nelson, 2007). Transportation policy in the United States must rise to meet this demand for more travel choices and more livable communities.The academic, ideological and political debates about the level of GHG reductions and penetration rates that can or should be achieved via smart growth and pricing on the one hand, or measures such as ‘eco-driving’ and signal optimization on the other, have served their purpose: we know which policies are ‘directionally correct’ – policies that reduce GHG emissions even though we may not know the scope of those reductions. Now is the time to implement directionally correct policies, assess what works best where, and refine policy based on the results. It is a framework that CCAP calls “Do. Measure. Learn.”The Federal government is poised to spend $500 billion on transportation (Committee on Transportation and Infrastructure, 2009). CCAP encourages Congress to “Ask the Climate Question” – will our transportation investments help reduce GHG emissions or exacerbate the problem? Will they help increase our resilience to climate change impacts or increase our vulnerability? And, while we’re at it, will our investment foster energy security, livable communities and a vibrant economy? Federal transportation and climate policies should empower communities to implement locally-determined travel efficiency solutions by providing appropriate funding, tools and technical support.     

Electric vehicle parking in European and American context: Economic,energy and environmental analysis

The transportation sector faces increasing challenges related to energy consumption and local and global emissions profiles. Thus, alternative vehicle technologies and energy pathways are being considered in order to overturn this trend and electric mobility is considered one adequate possibility towards a more sustainable transportation sector.In this sense, this research work consisted on the development of a methodology to assess the economic feasibility of deploying EV charging stations (Park-EV) by quantifying the tradeoff between economic and energy/environmental impacts for EV parking spaces deployment. This methodology was applied to 4 different cities (Lisbon, Madrid, Minneapolis and Manhattan), by evaluating the influence of parking premium, infrastructure cost and occupancy rates on the investment Net Present Value (NPV). The main findings are that the maximization of the premium and the minimization of the equipment cost lead to higher NPV results. The NPV break-even for the cities considered is more “easily” reached for higher parking prices, namely in the case of Manhattan with the higher parking price profile. In terms of evaluating occupancy rates of the EV parking spaces, shifting from a low usage (LU) to a high usage (HU) scenario represented a reduction in the premium to obtain a NPV = 0 of approximately 14% for a 2500 € equipment cost, and, in the case of a zero equipment cost (e.g. financed by the city), a NPV = 0 was obtained with approximately a 2% reduction in the parking premium. Moreover, due to the use of electric mobility instead of the average conventional technologies, Well-to-Wheel (WTW) gains for Lisbon, Madrid, Minneapolis and Manhattan were estimated in 58%, 53%, 52% and 75% for energy consumption and 66%, 75%, 62% and 86% for CO₂ emissions, respectively.This research confirms that the success of deploying an EV charging stations infrastructure will be highly dependent on the price the user will have to pay, on the cost of the infrastructure deployed and on the adhesion of the EV users to this kind of infrastructure. These variables are not independent and, consequently, the coordination of public policies and private interest must be promoted in order to reach an optimal solution that does not result in prohibitive costs for the users.     

Valuation of strategic options in public–private partnerships

This paper investigates the feasibility of and develops an economic valuation model for strategic options in Comprehensive Development Agreements (CDAs). A CDA is a form of public–private partnership whereby the right to price and collect revenues from toll roads is leased to a private entity for a long but finite period of time. In exchange, this provides local and state governments with a quick influx of cash and/or additional infrastructure. Uncertainty associated with such long-term leases is of substantial public concern. This paper examines five different strategic options, namely a buyout option, a conditional buyout option, a revenue-sharing option, and two types of minimum revenue guarantee options. The buyout option in particular could give the public sector additional control over the future use of leased facilities and address potential concerns regarding long-run uncertainty and possible unforeseen windfalls for the private sector. The paper’s contributions include the analysis, feasibility assessment and valuation of several strategic options, sensitivity analysis of the solutions, an economic consumer demand-based revenue model for purposes of cash flow simulation, and analysis of option price sensitivity to “moneyness”. The main conclusion is that strategic options can provide useful risk reduction, but generally have significant value relative to the lease itself. By scaling down payoffs, options could be realistically included in CDAs and other PPPs. For some parameter values, option values to the developer and public authority are offsetting, allowing for costless risk reduction.     

A multi-criteria decision support methodology for implementing truck operation strategies

This study proposes a multi-criteria decision support methodology to enable the prioritization of potential alternative transportation system operations strategies and then demonstrates the effectiveness of the methodology using a case study involving truck operations. The primary feature of this methodology is its ability to help policymakers consider economic, public, and private sector standpoints simultaneously. The economic criterion is cost to the public sector where four criteria related to truck impacts on the transportation system are incorporated. These are traffic congestion, safety hazards, air pollution, and pavement damage. In addition, reliability and productivity are regarded as metrics representing the private sector viewpoint since they can significantly affect profitability. The methodology combines qualitative and quantitative aspects of these standpoints. In order to demonstrate the applicability of this methodology, a corridor with some of the highest truck traffic in the US is selected as a case study and three forms of left lane restrictions for trucks are considered. For qualitative analysis, survey data were collected from two groups classified as public agency and transportation industry professionals who are experts in trucking. In addition, a micro traffic simulation model was used to produce various performance measurements that can describe quantitative impacts. As a result, the methodology provides a rational argument for prioritizing potential alternative truck strategies.     

Exploring the impact of walk–bike infrastructure,safety perception,and built-environment on active transportation mode choice: a random parameter model using New York City commuter data

This study estimates a random parameter (mixed) logit model for active transportation (walk and bicycle) choices for work trips in the New York City (using 2010–2011 Regional Household Travel Survey Data). We explored the effects of traffic safety, walk–bike network facilities, and land use attributes on walk and bicycle mode choice decision in the New York City for home-to-work commute. Applying the flexible econometric structure of random parameter models, we capture the heterogeneity in the decision making process and simulate scenarios considering improvement in walk–bike infrastructure such as sidewalk width and length of bike lane. Our results indicate that increasing sidewalk width, total length of bike lane, and proportion of protected bike lane will increase the likelihood of more people taking active transportation mode This suggests that the local authorities and planning agencies to invest more on building and maintaining the infrastructure for pedestrians. Further, improvement in traffic safety by reducing traffic crashes involving pedestrians and bicyclists, will increase the likelihood of taking active transportation modes. Our results also show positive correlation between number of non-motorized trips by the other family members and the likelihood to choose active transportation mode. The model would be an essential tool to estimate the impact of improving traffic safety and walk–bike infrastructure which will assist in investment decision making.