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.     

Factors affecting public transportation usage rate: Geographically weighted regression

As the number of private vehicles grows worldwide, so does air pollution and traffic congestion, which typically constrain economic development. To achieve transportation sustainability and continued economic development, the dependency on private vehicles must be decreased by increasing public transportation usage. However, without knowing the key factors that affect public transportation usage, developing strategies that effectively improve public transportation usage is impossible. Therefore, this study respectively applies global and local regression models to identify the key factors of usage rates for 348 regions (township or districts) in Taiwan. The global regression model, the Tobit regression model (TRM), is used to estimate one set of parameters that are associated with explanatory variables and explain regional differences in usage rates, while the local regression model, geographically weighted regression (GWR), estimates parameters differently depending on spatial correlations among neighbouring regions. By referencing related studies, 32 potential explanatory variables in four categories, social-economic, land use, public transportation, and private transportation, are chosen. Model performance is compared in terms of mean absolute percentage error (MAPE) and spatial autocorrelation coefficient (Moran’ I). Estimation results show that the GWR model has better prediction accuracy and better accommodation of spatial autocorrelation. Seven variables are significantly tested, and most have parameters that differ across regions in Taiwan. Based on these findings, strategies are proposed that improve public transportation usage.     

Decomposition analysis of energy-related carbon emissions from the transportation sector in Beijing

In the process of rapid development and urbanization in Beijing, identifying the potential factors of carbon emissions in the transportation sector is an important prerequisite to controlling carbon emissions. Based on the expanded Kaya identity, we built a multivariate generalized Fisher index (GFI) decomposition model to measure the influence of the energy structure, energy intensity, output value of per unit traffic turnover, transportation intensity, economic growth and population size on carbon emissions from 1995 to 2012 in the transportation sector of Beijing. Compared to most methods used in previous studies, the GFI model possesses the advantage of eliminating decomposition residuals, which enables it to display better decomposition characteristics (Ang et al., 2004). The results show: (i) The primary positive drivers of carbon emissions in the transportation sector include the economic growth, energy intensity and population size. The cumulative contribution of economic growth to transportation carbon emissions reaches 334.5%. (ii) The negative drivers are the transportation intensity and energy structure, while the transportation intensity is the main factor that restrains transportation carbon emissions. The energy structure displays a certain inhibition effect, but its inhibition is not obvious. (iii) The contribution rate of the output value of per unit traffic turnover on transportation carbon emissions appears as a flat “M”. To suppress the growth of carbon emissions in transportation further, the government of Beijing should take the measures of promoting the development of new energy vehicles, limiting private vehicles’ increase and promoting public transportation, evacuating non-core functions of Beijing and continuingly controlling population size.     

Modelling and simulation of transportation system effectiveness to reduce traffic congestion: a system dynamics framework

ABSTRACT

This paper is designed to evaluate and improve the effectiveness of transportation systems and reduce traffic congestion through the use of simulation models and scenario development. A system dynamics framework is used to test and evaluate the alternatives of future strategies for the city of Surabaya, Indonesia. Some factors affecting the effectiveness of transport systems include operational effectiveness and service effectiveness, as well as uncertainty. To improve the effectiveness of transportation systems, several strategies can be implemented, such as subsidizing public transportation, increasing the cost of private vehicle parking fees, raising taxes on private vehicles, and reducing delays in public transportation through scenario development. Scenario results show that, by pursuing these strategies, effectiveness could be improved by 80% as the impact of the increase in operational and service effectiveness, helping to mitigate traffic congestion. Congestion could be reduced to 70% (on average) due to the decrease in daily traffic.     

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.     

The role of accessibility in basic transportation choice behavior

Accessibility measures reflect the level of service provided by transportation systems to various locations. Basic transportation choice behavior is defined to include those decisions of how many automobiles to own and how many trips to which destinations to make by automobile and by public transit. Here, these decisions are assumed to be made jointly by urban households and are conditional upon residential location decisions. It is the purpose of this paper to explore the role of accessibility as a causal factor in such basic transportation choice behavior.An economic utility theory model of choice behavior is postulated in which the benefits from making trips to specific destinations are reflected by measures of destination attraction. Through determination of utility-maximizing trip frequencies, indirect utility functions are developed which include accessibility concepts. Behavioral implications of these concepts are proposed and contrasts are drawn to accessibility measures used in conventional segregated models of trip distribution, modal choice, and automobile ownership.Sensitivity analyses of alternative empirical definitions of accessibility in the choice model are conducted using data from the Detroit Regional Transportation and Land Use Study — covering counties in southeastern Michigan. These analyses employ a multinomial logit estimation technique and focus on definitions of trip attraction. Results of these analyses indicate that more complicated attraction measures can be replaced by measures involving the proportion of either urban area population or urban area employment within a traffic analysis zone. Also, evidence is found that decision-makers in the case study area consider trips of up to 60 or even 90 minutes duration when evaluating accessibilities offered by alternative public and private transportation systems.     

Public financing of private freight rail infrastructure to reduce highway congestion: A case study of public policy and decision making in the United States

As goods movement continues to increase it is expected to outpace infrastructure capacity in the United States. Moving a larger share of goods by rail rather than truck is a potentially cost effective part of a solution. Freight rail not only offers a substitute for truck trips but is a cleaner, more energy efficient, and safer alternative. Recently a number of private freight rail projects have received public funding. The public funds are aimed at increasing freight rail capacity with the goal of diverting some goods currently moved by truck to rail. While the benefits of moving goods by rail are relatively clear, it is unclear if public decision makers can effectively identify strategic rail investments that will achieve their policy goals. This study critically examines the analytical methods, models, and data that are commonly used to support decisions to provide public funds for private freight rail projects. This is accomplished through a case study of California’s Trade Corridors Improvement Fund program which provided $680 million for 11 freight rail projects. The study’s contributions include identifying critical analytical flaws and challenges affecting the benefit estimates that public funding decisions rely on. Improvements to current evaluation methods are also identified as are regulatory reforms and policy interventions that may offer more effective and reliable outcomes.     

Optimization Of Headway,Vehicle Size and Route Choice for Minimum Cost Feeder Service

ABSTRACT

Many people use public transportation systems to reach their destination, while others use personal vehicles. Poor transportation systems do not attract ridership. Therefore, the usage of passenger cars increases, and traffic and environmental conditions deteriorate. Efficient public transportation has been recognized as one of the potential ways of mitigating air pollution, reducing energy consumption, improving mobility and alleviating traffic congestion. The objective of this study is to optimize a bus feeder service that provides the shuttle service between a recreation center (e.g. Sandy Hook, NJ) and a major public transportation facility, subject to site-specific constraints such as vehicle schedules, bus availability, service capacity and budget. The decision variables include bus headway, vehicle size and route choice. The solution methodology integrating both analytical and numerical techniques is developed, which optimizes the decision variables. Finally, the proposed solution methodology is applied to a case study. Numerical results, including optimal solutions and sensitivity analyses, are presented while the level of coordination between the feeder service and a major transportation service is discussed.     

Greenhouse gas reduction potential of advanced traffic control

Reducing greenhouse gas (GHG) emissions from transportation in the context of the climate change issue and the associated Kyoto Agreement of 1997 is a challenge. Since urban transportation is a major contributor to greenhouse gases, measures are required to reduce these emissions. Given that during peak periods, road vehicles propelled by petroleum fuel‐based internal combustion engines produce a high level of GHG emissions due to stop and go operations, measures to improve traffic flow can play an effective mitigation role. This paper describes a simulation‐based methodology and a case study for the quantification of GHG emission reduction owing to advanced traffic control systems.     

Priority for public transit in Germany

Priority for public transit includes a large variety of measures, including improvements to infrastructure and vehicles. For vehicles, the low floor concept is of particular importance. The central points of priority measures, however, are improvements of traffic control by traffic signals. Here, an improved sensitivity regarding public transit vehicles is the key to a remarkable reduction of factors causing delay. Different techniques for a traffic actuated signal control and different strategies regarding the degree of priority are applied. Thus, especially the reliability of public transit operations is increased. The priority efforts must be embedded in an integrated plan covering the whole urban or metropolitan transportation system.     

Analysis of the efficiency of local government services delivery. An application to urban public transport

In recent years in the European Union (EU), we have witnessed an externalization process of the provision of local government services, in order to separate the political responsibility and the direct delivery of the service. The reasons that justify this process are focused on the belief that the private sector is more efficient in carrying out economic activities, the pressure to reduce the public deficit and the public debt, the search for management systems that bypass public administration procedures, and the increase of control on local governments in auditing and accountability issues.The objective of this paper is to compare the efficiency of public and private sectors in the provision of urban transportation services. This paper shows the results of an empirical study commissioned by the Regional Audit Office of Catalonia (Spain), in order to evaluate the efficiency with which urban transportation services are delivered in the most important cities of this region. This efficiency study has been carried out using the Data Envelopment Analysis (DEA) model, multiple linear regression and logit and cluster analysis. The results allow us to conclude that, in the cities studied, exogenous factors are not relevant and the private management of urban transport service is not more efficient than public management.     

Multi-scale robustness model for highway networks under flood events

Transportation system infrastructure often experiences severe flood-related disruptions such as overtopping, erosion, and scour. The ensuing damages can result in enormous direct and indirect economic losses to the traffic network and consequently the individuals through conditions like inaccessibility to commuters and reduction in traffic safety. Many studies have claimed that a robust transportation system could significantly prevent such consequences from natural hazards such as floods, highlighting the importance of robustness measures that could be used by decision-makers to properly manage flooded transportation system. Most available measures related to network robustness assessment are qualitative, and while some recent studies have focused on such evaluation using quantitative assessment approaches related to environmental or social-economic operations, they lack the holistic view towards robustness under flood events. This study develops a composite multi-scale transportation-system robustness model considering flood hazards by synthesizing geographical damage recognition, topological functionality analysis, network operation evaluation, and traffic-user loss estimation. This integrated model has been applied in a real-world highway network, mainly revealing that a given intensive flood occurrence at different locations may result in a variety of after-flood disruptions in the transportation network. To assist the asset owners with developing more reasonable prevention and recovery plans, the developed multi-scale robustness index presents both visible multi-denominational flood consequences and an overall post-event transportation-system robustness indicator.     

An impact analysis of traffic image information system on driver travel choice

A driver is one of the main components in a transportation system that influences the effectiveness of any active demand management (ADM) strategies. As such, the understanding on driver behavior and their travel choice is crucial to ensure the successful implementation of ADM strategies in alleviating traffic congestion, especially in city centres. This study aims to investigate the impact of traffic information dissemination via traffic images on driver travel choice and decision. A relationship of driver travel choice with respect to their perceived congestion level is developed by an integrated framework of genetic algorithm–fuzzy logic, being a new attempt in driver behavior modeling. Results show that drivers consider changing their travel choice when the perceived congestion level is medium, in which changing departure time and diverting to alternative roads are two popular choices. If traffic congestion escalates further, drivers are likely to cancel their trip. Shifting to public transport system is the least likely choice for drivers in an auto-dependent city. These findings are important and useful to engineers as they are required to fully understand driver (user) sensitivity to traffic conditions so that relevant active travel demand management strategies could be implemented successfully. In addition, engineers could use the relationships established in this study to predict drivers’ response under various traffic conditions when carrying out modeling and impact studies.     

Integrated analysis of GHGs and public health damage mitigation for developing urban road transportation strategies

This study attempts to present an urban road transportation strategy focusing on the mitigation of both GHGs emission and public health damage, taking Xiamen City as a case study. We developed a Public Health and GHGs Emission model to estimate the impacts of direct energy-consumption-related GHGs emissions and public health damage in Xiamen’s road transportation strategies from 2008 to 2025, considering the environmental benefits and economic costs. Two scenarios were designed to describe future transportation strategies for Xiamen City, and mitigation potentials for both GHGs emission and public health costs were estimated from 2008 to 2025 under a series of options. The results show that enacting controls on private vehicles would be most effective to GHGs mitigation, while enacting controls on government and rental vehicles would contribute the most to NO₂ and PM2.5 reductions. Compared with the Business as Usual scenario, the Integrated scenario would achieve about a 68% energy consumption reduction and save 0.23 billion yuan (95% CI: 0.16, 0.32) in health costs in 2025. It is clear that integrated and advisable strategies need to mitigate the adverse impacts of urban road vehicles on GHGs emissions and public health and economic costs, particularly in regions of rapid urbanization.     

Categorizing bicycling environments using GPS-based public bicycle speed data

A promising alternative transportation mode to address growing transportation and environmental issues is bicycle transportation, which is human-powered and emission-free. To increase the use of bicycles, it is fundamental to provide bicycle-friendly environments. The scientific assessment of a bicyclist’s perception of roadway environment, safety and comfort is of great interest. This study developed a methodology for categorizing bicycling environments defined by the bicyclist’s perceived level of safety and comfort. Second-by-second bicycle speed data were collected using global positioning systems (GPS) on public bicycles. A set of features representing the level of bicycling environments was extracted from the GPS-based bicycle speed and acceleration data. These data were used as inputs for the proposed categorization algorithm. A support vector machine (SVM), which is a well-known heuristic classifier, was adopted in this study. A promising rate of 81.6% for correct classification demonstrated the technical feasibility of the proposed algorithm. In addition, a framework for bicycle traffic monitoring based on data and outcomes derived from this study was discussed, which is a novel feature for traffic surveillance and monitoring.     

Short-term impact analysis of fuel price policy change on travel demand in Malaysian cities

Abstract

Malaysia is one of the few countries in the world that provides a fuel subsidy to consumers. Due to the recent economic crisis, the Malaysian Government decided to revise its fuel subsidization policy from a fixed price subsidy to a floating price subsidy dependent on global oil demand. Recognizing that the change in fuel subsidization policy can have an impact on travel behavior, this article investigates the short-term impact of the policy change on private and public transportation in the Klang Valley region of Malaysia. Spectral analyses are performed to investigate if the policy change has an impact on private vehicle travel demand, measured in terms of road traffic, and short-term travel demand elasticity with respect to fuel price is estimated. To measure the impact on the public transportation system, the demand cross-elasticity values of rail transit and buses are also estimated. It was found that traffic flow reduces with an increase in fuel price, although elasticity and cross-elasticity values obtained are low. The article finds that there is a potential mode shift from private vehicles to rail transit with increasing fuel price. It is demonstrated that reducing fuel price subsidy can be an effective travel demand management strategy to alleviate congestion.     

Optimal transport strategies for European cities

A new procedure for generating optimal transport strategies has been applied in nine European cities. A public sector objective function which reflects concerns over efficiency, environmental impact, finance and sustainability is specified and a set of policy measures with acceptable ranges on each, identified. Optimal strategies based on combinations of these policy measures which generate the optimal value of the objective function, are identified, and compared between cities. Resulting policy recommendations are presented. The results demonstrate the importance of an integrated approach to transport strategy formulation. They emphasise the role of changes in public transport service levels and of fares, and of charges for car use. By contrast, new infrastructure projects are less frequently justified. In the majority of cities the revenues from car use charges are sufficient to finance other elements in the strategy. However, private sector involvement either in initial financing or in operation may be desirable. Revised objective functions to reflect private sector involvement are specified, and optimal strategies with private sector operation of public transport are also identified. The requirement to meet private sector rates of return for public transport operation typically results in lower frequencies and higher fares; charges for car use then need to be raised to satisfy public policy objectives, but system performance is reduced. This revised version was published online in June 2006 with corrections to the Cover Date.     

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.     

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.