Does green transportation promote accessibility for equity in medium-size U.S. cites?

The link between accessibility and social equity attracts much attention for promoting sustainability. However, there is no comprehensive approach to elevate the role of accessibility in evaluating transportation system over social equity by considering the variety of urban opportunities and population groups from green transportation perspective. Our goal is to develop such a framework to evaluate transportation equity by focusing on accessibility via transit and cycling. Applying the framework to Fresno, California, and Cincinnati, Ohio with different development patterns, we delineate service areas at block-group level with five time-thresholds. The service area is used to count the number of urban opportunities: jobs, dining, churches, libraries, parks, multi-use paths, schools. We then use statistical comparison and geographical mapping to identify accessibility gap to these opportunities between advantaged and disadvantaged groups defined by income, property value, education, vehicle ownership, race, and age. The results indicate the extent of differences in accessibility is sensitive to threshold specification of grouping population. The findings suggest that the efficiency of transit service needs to be improved to reach the same level of cycling, while they do help with the accessibility for economically disadvantaged neighborhoods. Considering school enrollment, the accessibility to opportunities in Fresno performs differently while students in Cincinnati benefit from good accessibility to most resources. The results of accessibility to multi-use paths highlight the need of providing more efficient green transportation facilities for less wealthy neighborhoods. Variation in accessibility between groups underscores the importance of developing policies to meet the needs of diverse social groups.     

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.     

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.     

Integrated modeling of urban hierarchy and transportation network planning

A major problem addressed during the preparation of spatial development plans relates to the accessibility to facilities where services of general interest such as education, health care, public safety, and justice are offered to the population. In this context, planners typically aim at redefining the level of hierarchy to assign to the urban centers of the region under study (with a class of facilities associated with each level of hierarchy) and redesigning the region’s transportation network. Traditionally, these two subjects – urban hierarchy and transportation network planning – have been addressed separately in the scientific literature. This paper presents an optimization model that simultaneously determines which urban centers and which network links should be promoted to a new level of hierarchy so as to maximize accessibility to all classes of facilities. The possible usefulness of the model for solving real-world problems of integrated urban hierarchy and transportation network planning is illustrated through an application to the Centro Region of Portugal.     

Trip generation and distribution: the inconsistency problem and a possible remedy

There are many shortcomings commonly associated with the conventional urban transportation modeling process. This paper focuses on one of the more important problems — the inconsistency between trip generation and distribution components — and suggests a possible way of alleviating it. The suggested approach involves sorting out the independent effects on tripmaking of origin, destination and travel cost characteristics, and introducing accessibility measures explicitly into the modeling process. The resulting modeling framework can be used to obtain consistent estimates of trip generation and distribution quantities which are responsive to changes in the transportation and spatial systems.     

Economic resilience to transportation failure: a computable general equilibrium analysis

This study develops and applies a multimodal computable general equilibrium (CGE) framework to investigate the role of resilience in the economic consequences of transportation system failures. Vulnerability and economic resilience of different modes of transportation infrastructure, including air, road, rail, water and local transit, are assessed using a CGE model that incorporates various resilience tactics including modal substitution, trip conservation, excess capacity, relocation/rerouting, and service recapture. The linkages between accessibility, vulnerability, and resilience are analyzed. The model is applied to the transportation system failures in the aftermath of Hurricane Katrina to illustrate its capabilities. The analytical framework, however, has broader applications and can provide insights for resource allocations to enhance emergent responses to unexpected events and to improve resilient design of transportation infrastructure systems.     

Contract Design,Financing Arrangements and Public Ownership—An Assessment of the US Airport Governance Model

Abstract

US airports negotiate legally binding contracts with airlines and finance large investment projects with revenue bonds. Applying insights from transaction cost economics, we argue that the observed variation in contractual and financing arrangements at US airports corresponds to the parties' needs for safeguarding and coordination. The case evidence presented reveals that public owners set the framework for private investments and contracting. We suggest that airline contracts and capital market control result in comparative efficient investments and act as a check on the cost inefficiency typically linked to public ownership.     

Optimal deployment of public charging stations for plug-in hybrid electric vehicles

This paper develops an equilibrium modeling framework that captures the interactions among availability of public charging opportunities, prices of electricity, and destination and route choices of plug-in hybrid electric vehicles (PHEVs) at regional transportation and power transmission networks coupled by PHEVs. The modeling framework is then applied to determine an optimal allocation of a given number of public charging stations among metropolitan areas in the region to maximize social welfare associated with the coupled networks. The allocation model is formulated as a mathematical program with complementarity constraints, and is solved by an active-set algorithm. Numerical examples are presented to demonstrate the models and offer insights on the equilibrium of the coupled transportation and power networks, and optimally allocating resource for public charging infrastructure.     

A dynamic modeling approach to investigate impacts to protected and low-income populations in highway planning

Environmental justice (EJ) assessment has traditionally focused on identifying distributive effects to protected populations. Federal and State highway improvement programs have been established to stimulate economic development for these populations. While this issue has long been recognized as part of EJ initiatives, no quantitative comparisons of highway construction impacts on protected populations have been reported in the literature. This paper presents a dynamic modeling approach to investigate impacts to protected and low-Income populations in highway planning using an integrated Geographic Information System (GIS) and Genetic Algorithms (GAs) optimization framework. Using census and county level parcel data, the model integrates various socioeconomic factors into a GIS while generating highway alignments using GAs. Examples using county level census data from North Carolina are demonstrated to test the sensitivity of generated highway alignments with constrained distances from protected populations. The results indicate that it is important to consider local social and economic effects, in addition to regional planning objectives when measuring the effectiveness of feasibility studies associated with highway construction. Within the proposed modeling framework attention is directed on various EJ initiatives, such as environmental health and safety laws in minority and low-income areas. The model would help planners, designers, and policy-makers understand the intricate interrelationships among local communities, while facilitating more scientific and economically equitable planning for highway construction projects.     

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.     

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.     

Efficiency of European Funds in the Accession Countries: The Case of Transport Infrastructure Investments in Latvia

ABSTRACT

A transport initiative, like any kind of public action, has an impact on the monetary cost, time cost, efficiency and comfort of the transportation of goods and people, in particular transport infrastructure investments. All such initiatives are subject to cost benefit analyses at the national and EU level to know whether the present value of total net benefits including environmental impacts exceeds their cost. However, several important policy issues remain unresolved in standard evaluation procedures. One issue is whether the so-called direct measurement of user benefit, which consists of quantifying changes in surplus of the users of the transport system, captures all welfare generated in the economy. Another issue is how the gains (or possibly losses) of a transport initiative are distributed among regions. The aim of this article is to perform a systematic and quantitative analysis of the socio-economic and spatial impacts of alternative transport investments by carrying out scenario simulations in order to improve the understanding of the impact of transportation policies on the short- and long-term spatial development in Latvia. The general result from the scenario simulations is that rail projects seem to be more effective in terms of promoting regional economic activity than road projects.     

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.     

Heterogeneous sensor location model for path reconstruction

A new traffic sensor location problem is developed and solved by strategically placing both passive and active sensors in a transportation network for path reconstruction. Passive sensors simply count vehicles, while active sensors can recognize vehicle plates but are more expensive. We developed a two-stage heterogeneous sensor location model to determine the most cost-effective strategies for sensor deployment. The first stage of the model adopts the path reconstruction model defined by Castillo et al. (2008b) to determine the optimal locations of active sensors in the network. In the second stage, an algebraic framework is developed to strategically replace active sensors so that the total installation cost can be reduced while maintaining path flow observation quality. Within the algebraic framework, a scalar product operator is introduced to calculate path flows. An extension matrix is generated and used to determine if a replacement scheme is able to reconstruct all path flows. A graph model is then constructed to determine feasible replacement schemes. The problem of finding the optimal replacement scheme is addressed by utilizing the theory of maximum clique to obtain the upper bound of the number of replaced sensors and then revising this upper bound to generate the optimal replacement scheme. A polynomial-time algorithm is proposed to solve the maximum clique problem, and the optimal replacement scheme can be obtained accordingly. Three numerical experiments show that our proposed two-stage method can reduce the total costs of transportation surveillance systems without affecting the system monitor quality. The locations of the active sensors play a more critical role than the locations of the passive sensors in the number of reconstructed paths.     

Customized bus service design for jointly optimizing passenger-to-vehicle assignment and vehicle routing

Emerging transportation network services, such as customized buses, hold the promise of expanding overall traveler accessibility in congested metropolitan areas. A number of internet-based customized bus services have been planned and deployed for major origin-destination (OD) pairs to/from inner cities with limited physical road infrastructure. In this research, we aim to develop a joint optimization model for addressing a number of practical challenges for providing flexible public transportation services. First, how to maintain minimum loading rate requirements and increase the number of customers per bus for the bus operators to reach long-term profitability. Second, how to optimize detailed bus routing and timetabling plans to satisfy a wide range of specific user constraints, such as passengers’ pickup and delivery locations with preferred time windows, through flexible decision for matching passengers to bus routes. From a space-time network modeling perspective, this paper develops a multi-commodity network flow-based optimization model to formulate a customized bus service network design problem so as to optimize the utilization of the vehicle capacity while satisfying individual demand requests defined through space-time windows. We further develop a solution algorithm based on the Lagrangian decomposition for the primal problem and a space-time prism based method to reduce the solution search space. Case studies using both the illustrative and real-world large-scale transportation networks are conducted to demonstrate the effectiveness of the proposed algorithm and its sensitivity under different practical operating conditions.     

Perceived accessibility,mobility, and connectivity of public transportation systems

Although public transportation is considered effective at reducing the external cost of driving private vehicles, many urbanites do not use public transportation. This study develops measures employing accessibility, mobility, and seamless connectivity for an entire public transportation service chain as indicators for evaluating public transport services, prioritizes underperforming scenarios from the perspective of urban travelers, and derives various market segmentation strategies that consider different socio-demographic characteristics. A conceptual model is set up herein to assess these latent constructs that describe unobservable and immeasurable characteristics. As a Likert ordinal scale can generate misleading statistical inferences, the Rasch model is used to eliminate bias generated by an ordinal scale when measuring these three latent constructs separately. The Rasch model compares person parameters with item parameters, which are then subjected to logarithmic transformation along a logit scale so as to recognize specific difficulties of service scenarios that cannot be easily eliminated by certain urban travelers. The multidimensional Rasch model also measures the perceptions of urban travelers in terms of the interactions between accessibility, mobility, and seamless connectivity of this public transportation system. While comparing urban travelers of two large cities in Taiwan, Taipei and Kaohsiung, the empirical results demonstrate that perceived accessibility, mobility, and seamless connectivity differ based on travelers’ age, frequency of weekly sports activities, and environmental awareness. This paper also advances appropriate improvement strategies and provides policy suggestions for urban planners, public transportation service operation agencies, and policy makers when they seek to create user-friendly public transportation services. The proposed approach can be generalized in other cities by considering their local context uniqueness and further evaluating their public transport services.     

A toll-based bi-level programming approach to managing hazardous materials shipments over an intermodal transportation network

This research proposes a bi-level bi-objective model to regulate the usage of rail intermodal terminals for hazardous materials (hazmat) shipments, where government imposes tolls to deter carriers from using certain terminals. The complexity of the resulting mathematical program motivates the development of a hybrid speed-constrained multi-objective particle swarm optimization algorithm, which is then integrated with CPLEX, to solve the model. Through a real problem instance based on the intermodal service chain of Norfolk Southern in US, the toll-setting model is examined and further compared with a regular network design approach, in which certain terminals are closed to hazmat containers. The computational results show that the toll-setting policy is more practical and efficient, and the two models can be combined as a two-stage strategy in long-term hazmat transportation regulations. Additional managerial insights are derived for different stakeholders.     

Mapping spatial accessibility of public transportation network in an urban area – A case study of Shanghai Hongqiao Transportation Hub

Accessibility is a key metric when assessing the efficiency of an urban public transportation network. This research develops a methodology for evaluating spatial accessibility of a system with multiple transportation modes in an urban area, Shanghai Hongqiao Transportation Hub (SHTH) and its surrounding area. The method measured total traveling time as an accessibility indicator, which summed the time people spent on walking, waiting, transfer and transportation in a journey from the SHTH to destinations. Spatial accessibility was classified into five levels from very high to very low. The evaluation was conducted at a cell-by-cell basis under an overall scenario and three specific scenarios. Evaluation results were presented in a series of maps. The overall accessibility scenario shows a concentric-ring trend decreasing from the SHTH to the fringe of the study area. Areas along the metro lines generally have a much higher accessibility than those along the bus lines with some exceptions where bus routes play a more important role. Metro stations identified as either time-saving or time-consuming points are mainly distributed around the urban center. Some suggestions are proposed for improving accessibility of public transportation network in the study area. The results from this study provide a scientific basis and useful information for supporting decision-making on urban planning, and assisting dwellers to travel in a time-efficient manner. The methodology offers a simple, flexible way to the spatial evaluation, metro station identification and network modification of accessibility of public transportation systems with multiple transportation modes.     

Improving the accessibility of urban transportation networks for people with disabilities

What is the most effective way to enhance the accessibility of our oldest and largest public transportation systems for people with reduced mobility? The intersection of limits to government support with the growing mobility needs of the elderly and of people with disabilities calls for the development of tools that enable us to better prioritise investment in those areas that would deliver the greatest benefits to travellers. In principle and, to a lesser extent, in practice, many trains and buses are already accessible to nearly all users, leaving the stations and interchanges as the single largest and most expensive challenge facing operators trying to improve overall access to the network.Focussing on travel time and interchange differences, we present a method that uses network science and spatio-temporal analysis to rank stations in a way that minimises the divergence between accessible and non-accessible routes. Taking London as case study, we show that 50% of the most frequently followed journeys become 50% longer when wheelchair accessibility becomes a constraint. Prioritising accessibility upgrades using our network approach yields a total travel time that is more than 8 times better than a solution based on random choice, and 30% more effective than a solution that seeks solely to minimise the number of interchanges facing those with mobility constraints. These results highlight the potential for the analysis of ‘smart card’ data to enable network operators to obtain maximum value from their infrastructure investments in support of expanded access to all users.