New highways and land use change: Results from a quasi-experimental research design

Understanding links from new highway construction or capacity expansion to regional growth patterns is crucial for transportation planners and policy makers. In this paper, we incorporate a lagged adjustment regional growth model into a quasi-experimental research design to examine the association between new highway investments and land use change in three California counties. Our study areas provide a mix of urban, small town, and exurban highway projects in order to explore the different effects across project types and geographic contexts. The central finding of this research is that while improvements in surface transportation infrastructure can have large impacts on growth patterns, the nature of the effect depends on the context of the highway investment.     

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.     

The Bid-rent Land Use Model of the simple,efficient, elegant,and effective model of land use and transportation

Integrated land use/transportation forecasting models add significant policy and infrastructure alternatives analysis capabilities to the urban planning process. The financial, time, and staff requirements to develop these models has put them beyond the reach of most small to medium sized urban areas. This paper presents the land use allocation submodel of the Simple, Efficient, Elegant, and Effective model of land use and transportation (SE³M), an integrated land use and transportation forecasting model founded upon Economic Base Theory and Bid-rent Theory. The Bid-rent Land Use Model (BLUM) is an agent based, spatial competition model utilizing unique utility curves for willingness to pay and incomes for budget constrained abilities to pay for each agent. The model structure, estimation, calibration, implementation, and validation are presented. With a single year of land use data available, the validation approach used the Kappa Index of Agreement to spatially check model outputs against base year control data while controlling for agreement by chance. The U.S. territory of Guam is used as the case study/proof of concept implementation for this model framework. Once calibrated, BLUM could solve the spatial competition problem on Guam in less than two minutes of processing time with over 90% accuracy.     

Multi-objective bilevel construction material transportation scheduling in large-scale construction projects under a fuzzy random environment

Abstract

This paper investigates a transportation scheduling problem in large-scale construction projects under a fuzzy random environment. The problem is formulated as a fuzzy, random multi-objective bilevel optimization model where the construction company decides the transportation quantities from every source to every destination according to the criterion of minimizing total transportation cost and transportation time on the upper level, while the transportation agencies choose their transportation routes such that the total travel cost is minimized on the lower level. Specifically, we model both travel time and travel cost as triangular fuzzy random variables. Then the multi-objective bilevel adaptive particle swarm optimization algorithm is proposed to solve the model. Finally, a case study of transportation scheduling for the Shuibuya Hydropower Project in China is used as a real world example to demonstrate the practicality and efficiency of the optimization model and algorithm.     

Dealing with congestion from a regional perspective The case of Massachusetts

Traffic congestion has received considerable public and media attention over the past several years. However, many of the transportation and land use actions offered to deal with the congestion phenomenon focus only on a specific site or at most a subregion of the metropolitan area. This paper argues that congestion in many cases is an areawide phenomenon requiring consideration from a regional and programmatic viewpoint. A ten-point congestion-relief program developed for eastern Massachusetts is described. Actions in this program included those aimed to mitigate current congestion and avoid future congestion through land use management. Four policy areas are emphasized - providing transportation system improvements, managing transportation demand, managing land use, and managing the institutional and funding framework. The paper concludes that because of the political nature of the congestion problem, the congestion-relief program's importance lies more in the message it sends to the public that programmatic action is being taken. The paper also concludes that a regional approach is necessary thus requiring close examination of existing institutions, that demand management is an important component of the strategy, that the private sector has an important role to play, and that the long-term effectiveness of the program relates to the success of attempts to institutionalize efforts into zoning and permit procedures.     

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.     

A multiregional optimization model for allocating trasnportation investments

This paper presents a multiregional optimization model which explicitly considers the direct and indirect relationships between regional growth and investments in transportation infrastructure. Consumption, demand and investments for each sector and region are derived endogenously. Trade flows are simulated by a gravity function and transportation network investment decisions are represented by 0–1 integer variables. Despite its complex structure the model can be estimated by applying in two stages the Benders Partitioning Algorithm. The model is applied to Greece to obtain a comprehensive investment plan for the transportation system.     

A system dynamics approach to land use / transportation system performance modeling Part II: Application

This paper presents a system dynamics approach to simultaneous land use/transportation system performance modeling. A model is designed based on the causality functions and feedback loop structure between a large number of physical, socioeconomic, and policy variables. The model system consists of 7 sub‐models: population, migration of population, household, job growth‐employment‐land availability, housing development, travel demand, and traffic congestion level. The model is formulated in DYNAMO simulation language, and tested on a data set from Montgomery County, MD. In Part I: Methodology, the overall approach and the structure of the model system is discussed and the causal‐loop diagrams and major equations are presented. In Part II: Application, the model is calibrated and tested with data from Montgomery County, MD. Least square method and overall system behavior are used to estimate the model parameters. The model is fitted with the 1970–80 data and validated with the 1980–1990 data. Robustness and sensitivities with respect to input parameters such as birth rate or regional economy growth are analyzed. The model performance as a policy analysis tool is also examined by predicting the year by year impacts of highway capacity expansion on land use and transportation system performance. While this is a first attempt in using dynamic system simulation modeling in simultaneous treatment of land use and transportation system interactions, and model development and application are limited to some extent due to data availability, the results clearly indicate that the proposed method is a promising approach in dealing with complex urban land use/transportation modeling     

Linking land use and transportation in a rapidly urbanizing context: A study in Delhi,India

Cities in developing countries like India are facing some of the same concerns that North American cities are: congestion and urban growth. However, there is a sense of urgency in cities like Delhi, India in that this growth is far more rapid as both urbanization and motorization are ongoing processes that have not yet peaked. In this paper, we examine land use change and its relationship with transportation infrastructure and other planning related variables in a spatial context. We estimate land use change models at two different scales from separate data. Cellular automation and Markov models were used to understand change at the regional scale and discrete choice models to predict change at the local level. The results suggest that land use in the Delhi metropolitan area is rapidly intensifying while losing variety. These changes are affected by industrial, commercial and infrastructure location and planners and policy-makers need to better understand the implications of location decisions. We also examine these results in the context of a policy framework for data-based planning that links land use and transportation models for Delhi.     

A system dynamics approach to land use/transportation system performance modeling Part I: Methodology

This paper presents a system dynamics approach to simultaneous land use/transportation system performance modeling. A model is designed based on the causality functions and feedback loop structure between a large number of physical, socioeconomic, and policy variables. The model consists of 7 sub‐models: population, migration of population, household, job growth‐employment‐land availability, housing development, travel demand, and traffic congestion level. The model is formulated in DYNAMO simulation language, and tested on a data set from Montgomery County, MD. In Part I: Methodology, the overall approach and the structure of the model system is discussed and the causal‐loop diagrams and major equations are presented. In Part II: Application, the model is calibrated and tested with data from Montgomery County, MD. Least square method and overall system behavior are used to estimate the model parameters. The model is fitted with the 1970–80 data and validated with the 1980–1990 data. Robustness and sensitivities with respect to input parameters such as birth rate or regional economy growth are analyzed. The model performance as a policy analysis tool is examined by predicting the year by year impacts of highway capacity expansion on land use and transportation system performance. While this is a first attempt in using dynamic system simulation modeling in simultaneous treatment of land use and transportation system interactions, and model development and application are limited due to data availability, the results indicate that the proposed method is a promising approach in dealing with complex urban land use/transportation modeling.     

Incorporating land use in metropolitan transportation planning

In current practice, very few Metropolitan Planning Agencies attempt to capture the effects of transportation system changes on land use, and the consequent feedback effects on transportation system performance, despite substantial evidence that these effects may be significant. In this paper, we present a case study on the application of UrbanSim, a detailed land use simulation model system, and its integration with a regional travel demand model in the Greater Wasatch Front area of Utah. Like several other metropolitan areas, this region has recently been confronted with legal challenges to proposed highway projects, drawing substantial scrutiny to the land use-transportation connection. We describe the UrbanSim model specification, results from model estimation, and sensitivity analyses conducted with the combined land use and travel model system. The results of the sensitivity analysis suggest that accounting for the land use effects of a regional transportation plan may produce significant shifts in key transportation evaluation measures such as vehicle miles traveled, vehicle hours traveled, and hours of congestion delay.     

A multi-objective optimization approach to balancing economic efficiency and equity in accessibility to multi-use paths

Improving public health through active transpiration investments has increasingly become a new research focus in transportation planning. This study is to propose a multi-objective optimization modeling framework, through an optimal allocation of active transportation investments, to maximize the total accessibility while minimizing the total differences in accessibility over a city. Accessibility to multi-use paths is calculated for Fresno, California that measures the total length of multi-use paths a resident could reach with a 30-min cycling ride. Then, a geographically weighted regression (GWR) model is used to capture the local relationships between accessibility outcome and previous transportation investments. The marginal-effect analysis for the GWR results indicates economically efficient, inefficient, and indifferent locations for further investments. This study is one of the few to incorporate such a GWR model into a multi-objective optimization modeling framework to improve accessibility to multi-use paths and address inequality issues in transportation. Solving the multi-objective optimization model provides decision-makers a new insight into the making of an economically efficient and socially equal active transportation plan to improve public health.

     

A methodology to estimate the revenue anticipated from State-level alternative financing approaches in the US

A major issue that State Departments of Transportation (DOTs) in the US face relates to financing future transportation investments. Questions of interest to State DOT officials relate to the suitability and revenue potential of alternative financing approaches. This paper presents a methodology to estimate the potential levels of revenue associated with the use of selected combinations of financing approaches and to assess the adequacy of these revenues vis-a-vis various levels of investments being considered by decision-makers. The methodology is designed to accommodate a wide array of inputs such as major policy objectives and initial assumptions that may vary significantly from State to State in order to provide a greater flexibility of implementation. The application of this methodology is demonstrated with an illustrative example for Massachusetts. This paper should be of interest to State DOTs seeking an acceptable combination of financing approaches to support future transportation investments.     

Timing rules for major transportation investments

The timing decision for major transportation investments – when to build – typically is made without an objective approach for considering the economic value of implementation at different times. This paper uses a model of benefit-cost analysis and derives rules for timing major transportation investments. Three sets of conditions are considered, depending on whether annual benefits of an investment are uncertain and whether the objective is to maximize net present value or simply to achieve positive net present value. The timing rules under each set of conditions are stated in three forms: benefit-cost ratio, annual benefits, and implementation time. The paper compares these timing rules analytically, discusses potential applications, and illustrates them with a numerical example. Consequences of incorrectly using the timing rules are also examined with the example. This revised version was published online in June 2006 with corrections to the Cover Date.     

An optimization model of energy and transportation systems: Assessing the high-speed rail impacts in the United States

This paper presents a long-term investment planning model that co-optimizes infrastructure investments and operations across transportation and electric infrastructure systems for meeting the energy and transportation needs in the United States. The developed passenger transportation model is integrated within the modeling framework of a National Long-term Energy and Transportation Planning (NETPLAN) software, and the model is applied to investigate the impact of high-speed rail (HSR) investments on interstate passenger transportation portfolio, fuel and electricity consumption, and 40-year cost and carbon dioxide (CO₂) emissions. The results show that there are feasible scenarios under which significant HSR penetration can be achieved, leading to reasonable decrease in national long-term CO₂ emissions and costs. At higher HSR penetration of approximately 30% relative to no HSR in the portfolio promises a 40-year cost savings of up to $0.63 T, gasoline and jet fuel consumption reduction of up to 34% for interstate passenger trips, CO₂ emissions reduction by about 0.8 billion short tons, and increased resilience against petroleum price shocks. Additionally, sensitivity studies with respect to light-duty vehicle mode share reveal that in order to realize such long-term cost and emission benefits, a change in the passenger mode choice is essential to ensure higher ridership for HSR.     

Retrospect and prospect for subarea travel demand modeling: an empirical study

Abstract

At present, customized subarea models have been widely used in local transportation planning throughout the USA. A subarea model's biggest strengths lie in its more detailed and accurate modeling outputs which better meet local planning requirements. In addition, a subarea model can substantially reduce database size and model running time. In spite of these advantages, subarea models remain quite weak in modeling transit projects, smart growth measures, air quality conformity, and other areas. In addition to evaluating subarea models, this paper uses the Irvine Transportation Analysis Model (ITAM) as an empirical case of subarea model to illustrate the remedial procedures in maintaining its consistency with the regional model of the Orange County Transportation Analysis Model (OCTAM). Looking into the future, subarea models face both opportunities and challenges. More GIS applications, travel surveys, micro-simulation software utilization, and modeling improvements are expected to be incorporated into the subarea modeling process.     

A review of input–output models on multisectoral modelling of transportation–economic linkages

Understanding the role of transportation in urban and regional economy is a persistent analytical topic within the transportation research community. Multi-sectoral input–output (IO) modelling, as a standard economic analysis tool, has great advantages in reflecting industrial interdependencies in an economy. The simplicity of IO and the well-known concept of multiplier effect also make it broadly used in both academia and practice. This paper provides an introduction of IO models and reviews the past IO studies from 2000 onward on modelling transportation–economic linkages. The following types of models are included: single-region, multi-region and random utility-based multi-region IO models, with central methodological features described. An evaluation of modelling issues brought to light by reviewing the literature is then presented. For future research, more critical attention should be directed towards IO’s modelling assumptions, spatial linkages and the static representation of the economy. In addition, there are needs for more attention in the following areas: sectoral aggregation, specification of household sector and the integration with transportation forecasting models. The paper concludes with brief recommendations on future IO applications.     

Regulatory reform and freight mode choice

Reform of trucking Vehicle Weights and Dimensions (VWD) regulations in Canada is now underway. The effect these reforms will have on the freight transportation industry are only recently the subject of research. This paper is part of this new research effort, aimed at understanding how regulatory reform in the trucking sector will affect the existing competitive relationship between trucks and the railways. The paper presents the results of study and research into modelling the relationship between mode choice and the service characteristics that are affected by VWD reform.Using several periods of data, a series of econometric models are developed which help to elucidate the relative relationships between the mode service characteristics for both of the principal interprovincial freight modes. A technique is developed and presented to model railway user costs in order to overcome the complex and often unrepresentable pricing activities of Canadian railways.The strength of the developed econometric models is presented, including their significance and statistical reliability. This is further reinforced by the similarities exhibited by all the models in the series. The selected model is applied to predict market service responses required of the railway industry in order to compete with the trucking sector now adapting to the new regulations.The impact of the newly implemented vehicle weight and dimension reform on the rail transportation industry is analyzed and railway industry improvements aimed at maintaining its market share are presented. The results predicted by the model show that railway user cost reductions should be moderate, and likely less than the level of inflation over the period of time when trucking converts its fleet to take advantage of the new regulations.Abbreviations AASHTO American Association of State Highway and Transportation Officials - CCMTA Canadian Conference of Motor Transport Administrators - EPI End products, inedible commodity classification - GVW Gross Vehicle Weight - NA 1988 VWD National Agreement - RTAC Roads and Transportation Association of Canada - VWD Vehicle Weight(s) and Dimension(s) - WA 1988 VWD Western Agreement     

The implications of travel profiles for transportation investment: The Bronx Center Project

Investment in transportation infrastructure is generally regarded as an effective means for inducing economic growth and employment in a region. However, the ability of such investments to achieve these objectives, to a large extent, depends on the degree to which travel results from these investments support or conflict with present travel patterns and needs in this region. Using this view as a basis, this paper analyzes travel conditions and choices in the Bronx New York, where large scale transportation and other development projects (commonly called the Bronx Center Project) are presently taking place. Using a large data base, composed of census tract information on socio-economic and travel behavior, the paper first examines the travel profile of the Bronx population, by estimating travel choice elasticities. On the basis of these elasticities it then assesses the impact of the Bronx Center Project on travel patterns and trends.     

Discrete choice with spatial correlation: A spatial autoregressive binary probit model with endogenous weight matrix (SARBP-EWM)

Discrete choice modeling is widely applied in transportation studies. However, the need to consider correlation between observations creates a challenge. In spatial econometrics, a spatial lag term with a pre-defined weight matrix is often used to capture such a correlation. In most previous studies, the weight matrix is assumed to be exogenous. However, this assumption is invalid in many cases, leading to biased and inconsistent parameter estimates. Although some attempts have been made to address the endogenous weight matrix issue, none has focused on discrete choice modeling. This paper fills an existing gap by developing a Spatial Autoregressive Binary Probit Model with Endogenous Weight Matrix (SARBP-EWM). The SARBP-EWM model explicitly considers the endogeneity by using two equations whose error terms are correlated. Markov Chain Monte Carlo (MCMC) method is used to estimate the model. Model validation with simulated data shows that the model parameters can converge to their true values and the endogenous weight matrix can be reliably recovered. The model is then applied to a simplified firm relocation choice problem, assuming that similar size firms influence one another. The model quantifies the peer effect, and takes into consideration other independent variables including industry type and population density. The estimation results suggest that peer influence among firms indeed affect their relocation choices. The application results offer important insights into business location choice and can inform future policy making. The sample size for applying the model is currently limited to hundreds of observations. This paper contributes to the existing literature on discrete choice modeling and spatial econometrics. It provides a new tool to discover spatial correlations that are hidden in a wide range of transportation issues, such as land development, location choice, and various travel behavior. Those hidden spatial correlations are otherwise difficult to identify and estimation results may be biased. Establishing a new model that explicitly considers endogenous weight matrix and applying the model to a real life transportation issue represent a significant contribution to the body of literature.