Transportation Investment Decisions Research Articles

How daily activities and built environment affect health? A latent segmentation-based random parameter logit modeling approach

Transport studies have recently started focusing on the link between activity participation and general health. This study explores the relationship between general health and travel activities, physical activity, and built environment attributes. Individuals’ self-rated health is considered a general health indicator. Using data from the Impacts of Bicycle Infrastructure in Mid-sized Cities (IBIMS) survey of Kelowna, British Columbia, Canada, this study develops a latent segmentation-based random parameter logit (LSRPL) model. The motivation for adopting the LSRPL model is to capture multi-dimensional heterogeneity by allocating individuals into discrete latent segments (inter-segment heterogeneity) and then allowing a continuous distribution of the parameters within the segments (intra-segment heterogeneity). The model is estimated for two segments where segment one includes younger suburban dwellers; segment two includes older urban dwellers. The model results confirm the effect of travel, physical activity, and built environment on general health revealing multi-dimensional heterogeneity. In the case of inter-segment heterogeneity, results confirm that a higher share of travel duration using a car reduces the likelihood of excellent health in segment one. In contrast, a positive relationship is found in segment two. Whereas, for intra-segment heterogeneity results reveal that, a higher commute duration is found to be negatively associated with good health in segment two. However, this variable shows higher heterogeneity in mean for land use mix index, which indicates that urban areas coupled with higher land use mix increase the propensity towards good health. These findings will assist in developing transportation policies and investment decisions to build healthier cities.

Exploring joint activity-tour participation, time allocation, and mode choice decisions

ABSTRACT This paper presents the findings of an activity-based tour-level model that jointly investigates individuals’ decisions of activity-tour participation, time allocation, and mode choice. A multiple discrete continuous extreme value (MDCEV) model is developed that explores the participation and time allocation decisions for mandatory, maintenance, and discretionary activity-tour types. Mixed logit (MXL) models are developed to evaluate the mode choice decisions of individuals for different activity-based tours. Logsum values, which represent the modal accessibility, are introduced within the MDCEV modeling framework as feedback mechanisms from mixed logit models that exclusively examine the influence of individuals’ mode choice on their activity-tour engagement decisions as well as ensure the integrity of the decision modeling system. Findings of this study would assist the policymakers to develop effective strategies for better transportation investment decisions.

Travel Probability Fields – An approach to understand travel behavior: Case study of slum dwellers in Kolkata, India

Transport is an important contributor to the well-being of individuals and communities. Lack of suitable and affordable transportation can be a significant barrier to participation of different groups of population in a developing society. The current approach for planning for informal population in developing countries such as India is sadly not based on informed policy decisions backed up by scientific investigation of travel behavior of such communities. As a result, the transport investment decisions are more political in nature, which do not adequately address the needs of the informal community in their day-to-day mobility requirements. In this context, Travel Probability Fields offer a potential analytical tool to understand the travel behavior of population under study and evolve need-based transport investment decisions particularly when the resources are scarce in the context of developing countries. The present paper is an attempt to assess the mobility needs of slum dwellers in the city of Kolkata in east India to identify their travel behavior using Travel Probability Fields (TPF) in order to arrive at scientific based transport policy intervention for urban poor. The paper based on mobility patterns of slum dwellers located in different locations in the city of Kolkata develops Travel Probability Fields for various trip purposes and travel modes in order to assess the impact of location of slums and accessibility to public transport on travel patterns.

The Social Cost of Automobility, Cycling and Walking in the European Union

Cost-benefit-analyses (CBA) are widely used to assess transport projects. Comparing various CBA frameworks, this paper concludes that the range of parameters considered in EU transport CBA is limited. A comprehensive list of criteria is presented, and unit costs identified. These are used to calculate the external and private cost of automobility, cycling and walking in the European Union. Results suggest that each kilometer driven by car incurs an external cost of €0.11, while cycling and walking represent benefits of €0.18 and €0.37 per kilometer. Extrapolated to the total number of passenger kilometers driven, cycled or walked in the European Union, the cost of automobility is about €500 billion per year. Due to positive health effects, cycling is an external benefit worth €24 billion per year and walking €66 billion per year. CBA frameworks in the EU should be widened to better include the full range of externalities, and, where feasible, be used comparatively to better understand the consequences of different transport investment decisions.

The use of focus groups to foster stakeholder engagement in intercity passenger rail planning

Recent changes in the United States (U.S.) passenger rail policies have affected a number of National Railroad Passenger Corporation (Amtrak) corridors. One of the major changes is that, since the Passenger Rail Investment and Improvement Act (PRIIA) of 2008 was passed, all short-distance Amtrak corridors should become state-supported. Stakeholder and public involvement is strongly advocated by the U.S. Department of Transportation as a fundamental component of the transportation planning process and an important step to keep the public informed and engaged in decision making. Thus, states should attempt to engage the public in any funding decisions that might affect passenger rail service changes (such as line discontinuance). In addition, states should seek the input of the stakeholder and public to evaluate the alternative transportation investment options available in case such service changes occur. Nevertheless, both literature and practice of stakeholder engagement on policy making and planning of strategic operational changes and/or other broader decision making is scarce.This article presents a case study of stakeholder engagement in transportation decision making pertaining to decisions of investments in passenger rail and state and local fund allocation, using a focus group approach. This engagement aimed to (1) consult stakeholders in a group setting in an attempt to produce shared knowledge and (2) explore how the transportation investment decision-making process can incorporate public preferences and community values in a participatory manner via the assessment of potential investment directions. The goals of the study are to explore whether stakeholder engagement at a smaller-size projects and/or broader policy and planning decision making can be beneficial, and whether focus groups are suitable to facilitate such engagement.The findings verify that stakeholder involvement can provide states and other decision-makers with valuable insights on several topics supporting future policy and funding decisions. For example, in the case explored, the engagement of the stakeholders revealed that public investments towards the continuation and potentially improvement of the intercity passenger rail services are in line with the communities’ goals and are viewed as more beneficial than alternative transportation investments that can enable mobility in the area. In addition, the findings suggest that the benefits of engaging stakeholders in transportation decision making go beyond the act of gathering stakeholders’ input; focus groups or similar dynamic forums can facilitate the production of innovative ideas and shared knowledge while fostering collegiality among stakeholders. Finally, the findings of the case study also verify that focus groups can be a suitable approach to engage and consult a small group of stakeholders for higher-level transportation investment decision making. Overall, the case study results indicate that stakeholder engagement can benefit smaller-size projects and/or broader policy and planning decision making, such as decisions involving the financing of an intercity passenger rail line and investment directions towards operational changes of the line and/or alternative transportation modes and infrastructure.

Forecasting current and next trip purpose with social media data and Google Places

Trip purpose is crucial to travel behavior modeling and travel demand estimation for transportation planning and investment decisions. However, the spatial-temporal complexity of human activities makes the prediction of trip purpose a challenging problem. This research, an extension of work by Ermagun et al. (2017) and Meng et al. (2017), addresses the problem of predicting both current and next trip purposes with both Google Places and social media data. First, this paper implements a new approach to match points of interest (POIs) from the Google Places API with historical Twitter data. Therefore, the popularity of each POI can be obtained. Additionally, a Bayesian neural network (BNN) is employed to model the trip dependence on each individual’s daily trip chain and infer the trip purpose. Compared with traditional models, it is found that Google Places and Twitter information can greatly improve the overall accuracy of prediction for certain activities, including “EatOut”, “Personal”, “Recreation” and “Shopping”, but not for “Education” and “Transportation”. In addition, trip duration is found to be an important factor in inferring activity/trip purposes. Further, to address the computational challenge in the BNN, an elastic net is implemented for feature selection before the classification task. Our research can lead to three types of possible applications: activity-based travel demand modeling, survey labeling assistance, and online recommendations.

The CLIOSjre Process: A Flexible Multicriteria, Multistakeholder Decision Framework for Transportation Planning under Uncertainty

Transportation investments determine the long-term success or failure of a transportation provider. It is therefore vital for decision makers to have an in-depth understanding of the alternatives available before they choose to invest. However, often, the process of evaluating alternatives is lengthy, costly, and contentious, particularly for transportation infrastructure investment decisions that are large, complex, and interconnected with other economic development and sustainability goals. Furthermore, transportation investments involve many decision makers, each with different priorities and expertise. Therefore, there is a need for transparent, accurate, flexible, and practicable decision-making aids that can handle the complex challenges facing the decision-making bodies of transportation providers and planning organizations. This paper introduces a new decision aid—the CLIOSjre Process—that combines insights from multicriteria decision analysis, multistakeholder negotiation theory, and uncertainty analysis. The CLIOSjre Process helps decision makers compare multiple alternatives across multiple objectives and seek an informed collective transportation investment decision. Unlike other multicriteria decision aids, the CLIOSjre Process accounts for differences of opinion among decision makers and is designed to facilitate constructive negotiation among them. Finally, the CLIOSjre Process formally accounts for sources of uncertainty inherent in these decisions. In this way, the CLIOSjre Process provides a unique and flexible framework for investment analysis that can adapt to changes in transportation alternatives, decision-maker priorities, and emerging real-world conditions. The usefulness of this new decision aid is illustrated for the East Japan Railway Company’s consideration of a transportation investment opportunity in high-speed rail development on the Northeast Corridor of the United States.

Entropy-STEP Multiobjective Trade-Off Analysis Method for Optimal Transportation Investment Decisions

AbstractThe transportation decision-making process is complicated in that it often involves conflicting interests and expectations of different stakeholders. To ensure achieving sound decisions, va...

Uncertainty Propagation from the Cell Transmission Traffic Flow Model to Emission Predictions: A Data-Driven Approach

Road traffic exhaust emission predictions are used to inform transport policy and investment decisions aimed at reducing emissions and achieving sustainable mobility. Emission predictions are also used as inputs when modeling air quality and human exposure to traffic-related air pollutants. To be effective, such policies and/or integration must be based on robust models that not only provide point-based predictions but also inform these with an interval of confidence that properly accounts for the propagation of uncertainties through the complex chain of models involved. This paper develops a data-driven methodological framework that enables calculating the uncertainty in average speed–based emission predictions induced by uncertainty in its traffic data inputs, which are most often predictions (or outputs) of traffic flow models. An ensemble-based optimisation approach is used to estimate both calibration and validation errors arising from uncertainty in the structure and parameterisation of the cell transmission model, a discretised first-order macroscopic traffic flow model that is often integrated with average speed–based emission models. A Monte Carlo sampling approach is proposed to propagate the uncertainty in traffic flow inputs to emission predictions. To ensure transferability of findings, this methodology has been tested using multiple real data sets on three motorway road networks, one of which operates under variable speed limits. The online appendix is available at https://doi.org/10.1287/trsc.2017.0787 .

Optimal Corridor Selection for a Road Space Management Strategy: Methodology and Tool

Nationwide, there is a growing realization that there are valuable benefits to using the existing roadway facilities to their full potential rather than expanding capacity in a traditional way. Currently, state DOTs are looking for cost-effective transportation solutions to mitigate the growing congestion and increasing funding gaps. Innovative road space management strategies like narrowing of multiple lanes (three or more) and shoulder width to add a lane enhance the utilization while eliminating the costs associated with constructing new lanes. Although this strategy (among many) generally leads to better mobility, identifying optimal corridors is a challenge and may affect the benefits. Further, there is a likelihood that added capacity may provide localized benefits, at the expense of system level performance measures (travel time and crashes) because of the relocation of traffic operational bottlenecks. This paper develops a novel transportation programming and investment decision method to identify optimal corridors for adding capacity in the network by leveraging lane widths. The methodology explicitly takes into consideration the system level benefits and safety. The programming compares two conflicting objectives of system travel time and safety benefits to find an optimal solution.

Transportation Investment Decision Making for Medium to Large Transportation Networks

One of the challenges for transportation decision makers is to identify capacity expansion in the network under the constraint of budget such that various objectives of the decision maker [such as total system travel time is minimized, social welfare (consumer surplus) is maximized, or total system emission is minimized], while accounting for the route choice behavior of users. Such type of investment decision making in the context of network design problems can be solved using optimization techniques. This type of optimization problem is particularly difficult to solve since two hierarchical decision making entities are involved (decision makers and road users). These two players have different objectives. The road users select routes such that their individual travel costs are minimized while the decision makers’ optimally seek to select capacity expansion projects in the network in such a way that planning objectives are achieved. The objective of this paper is to propose numerical methods and application algorithms such that optimal investment decisions are made in moderate and large transportation networks. The problem is formulated as a bi-level network design problem in which upper level determines the optimal capacity expansions of links and the lower level consist of the traditional Wardrop’s user equilibrium. The upper level provides a trial capacity expansion vector with additional network capacities. The lower level considers new link capacities for user equilibrium. The output from the lower level is a vector of link flows which is transferred to the upper level. This process is iterated using kth best algorithm till convergence. The upper level problem is solved using PSWARM algorithm and the solution for the lower level is obtained using an efficient static traffic assignment algorithm. Adequacy of the model is examined by first conducting numerical experiments using small networks from the literature and then using moderate to large scale networks. Results of numerical experiments indicates that proposed methodology from this study can be efficiently used for real-world applications in practice for transportation infrastructure capacity expansion decision making.

Estimation of Value of Travel Time for Work Trips

Value of travel time (VOT) is one of the key inputs to travel demand models and is important for management and appraisal of transport investment decisions. The value of travel time can be defined as the price people are willing to pay to acquire an additional unit of time. Value of travel time has most often been determined by estimating mode choice models and evaluating marginal rate of substitution between the cost and travel time of the alternative modes. This study is aimed to find out the value of travel time associated with work trips of travellers within Calicut city. It refers to the concept of willingness to pay for reduction of travel time. Data were collected by means of combined revealed - stated preference survey on representative sample of population in Calicut city by work place interview. Stated preference experiment was designed to capture the responses for estimating VOT values for work trips. Responses in the form of “choice” among the presented choice alternatives were utilized to develop utility models and the estimated coefficients from the developed models were used to estimate VOT measures. The results indicate that the VOT associated with work trips is higher in high income groups. As income increases VOT also increases. Similarly, trip length also has a positive influence on VOT. As trip length increases, VOT increases. Within the same trip length itself, VOT varies among different income groups.

Trade-Off Analysis Approach for Multiobjective Transportation Investment Decision Making

AbstractTransportation planning is multidimensional, complex, and dynamic in nature. The decision-making process often involves multiple stakeholders with conflicting preferences. Effective decision outcomes can only be reached by explicitly addressing such conflicts. Over the last several decades, optimization techniques have been used for project-selection decisions to achieve maximized overall returns on investments. The existing methods for project selection capable of conducting trade-off analyses mainly focus on assessing trade-offs between project construction time, duration, and cost, as well as swapping between transportation agency costs and user costs. However, they have largely not addressed impacts on the overall economic returns by changing a few important decision factors such as differentiating relative importance of various transportation performance goals and measures, and different types of highway facilities, and further relaxing the budget constraints by management programs dealing wi...

Spatial interpolation of traffic counts based on origin–destination centrality

This paper presents a new method to estimate Annual Average Daily Traffic. Often traffic volumes are estimated based on roadway characteristics, such as number of lanes, speed limit, and adjacent land use. However, for many communities, especially small communities, these attributes are uniform across roadway types and therefore unable to adequately explain observed variation in traffic volumes. The new method uses novel explanatory variables that are intrinsically derived through a modified form of centrality, a network analysis metric that quantifies the topological importance of a link in a network. The new approach requires minimal data collection and is easily executed using a geographic information system. The case study showed high quality results (out-of-sample validation R2=0.95). The new approach can be used for various activities related to transportation planning and investment decision making.

Pre-Conference Satellite Meeting

Walking and cycling as transport modes offer positive health, environmental and economic outcomes however these interdisciplinary co-benefits are typically underestimated in conventional transport planning appraisals. This seminar will provide an overview of the co-benefits and hidden health costs of conventional transport planning decisions and describe current international practices regarding the quantification of the health benefits of active travel. The seminar will conclude with a panel discussion to explore the role of political leadership, inter and intra governmental partnerships and cross-sectoral collaboration to ensure the inclusion of health as a required component of transportation planning and investment decisions.

Evaluation Methodology of Public Transport Operators’ Management & Service in China

Till now, China is still short of science-based indicator system and methodology to evaluate the management and service of public transport operators. Because of this, public transport authorities and operators have no principles to follow when executing the public transport priority strategy. As a result, this paper builds up a set of evaluation indicator system in compliance with the national conditions of China, which can be used to evaluate the management of the public transport operators, to locate the problems and shortcomings of the urban public transport operators in their operations, and to help them improve their service quality. Furthermore, the evaluation indicator system can also provide basis for the government to make public transport investment decisions and allocate subsidies, balance the public transport capacity and volume, reform the make-up of the urban public transport vehicles, strengthen the supervising capacity and adopt the incentive/penalty policy for the public transport operators.

Multimodal infrastructure Investment Decision Making: An Institutional and Funding Perspective

This paper presents a case analysis of multimodalism in transportation investment decision making as it relates to other policy decisions, including infrastructure ownership structures, geographic scales, and revenue sources. This allows a more complete understanding of multimodalism’s benefits, drawbacks, and opportunities. Private infrastructure developers are more likely to evaluate and select investments in a multimodal fashion than either the public sector or public-private partnerships. Decentralized, municipal decision making tends to reduce barriers to multimodalism, although with reduced capacity for large-scale project implementation. Experience suggests that the source of transportation revenues is a less important influence on multimodalism than the organizations collecting them.

New Methodology for Transportation Investment Decisions with Consideration of Project Interdependencies

A new methodology for transportation project evaluation and selection explicitly addresses issues of networkwide impacts of a project and interdependencies of multiple projects. The methodology consists of two key elements: a multicommodity minimum-cost network model to estimate networkwide benefits of a single project or multiple interdependent projects and a hypergraph Knapsack model to identify the best subcollection of projects to yield maximized overall benefits. The proposed methodology is applied in a computational study with data on roadway network design, geometrics, travel demand, and traffic operations as well as the scope and costs of six major projects proposed for investment in the Loop area of Chicago, Illinois. The study reveals that the overall benefits with consideration of project networkwide impacts and their interdependencies are significantly lower by 38% to 64% as compared with those benefits established without consideration of project interdependencies, and the rate of increase in overall benefits follows a diminishing trend with higher budget levels; these findings suggest that an optimal level of investment could be established. The proposed methodology may be refined by developing a decision rule to identify a reasonably small number of subcollections of projects guaranteed to contain the optimal solution so that it could practically support large-scale transportation investment decisions.

Estimating Mobile-Source Greenhouse Gas Emissions in Los Angeles County, California: Web-Based Tool for Calculation of Benefits of Transportation Improvement Projects for Reduced Emissions

California's Sustainable Communities and Climate Protection Act of 2008 (Senate Bill 375, enacted in 2008) requires jurisdictions to identify changes to policies and systems to reduce mobile source greenhouse gas (GHG) emissions. As a result, local public officials and planners need flexible tools beyond traditional travel demand models to estimate the impacts of transportation policy and investment decisions on carbon emissions. Although several tools and calculators offer high-level estimates of reductions in GHG emissions in multiple sectors, the available tools and calculations generally lack local contexts and sensitivities, which are the primary drivers of the potential to reduce GHG emissions at the project level. Given this analytic need, the Los Angeles County Metropolitan Transportation Authority (LACMTA), California, recognized the connection between the data estimation needs of the legislation and the existing calculation capabilities of the web-based platform for its congestion mitigation fee program. The agency partnered with Cambridge Systematics to develop a tool to estimate the project-level impacts on the carbon emissions of a diverse range of local projects included in the congestion mitigation fee program. The tools for sketch planning developed for LACMTA offer easy data entry, a transparent framework, a web-accessible interface, and immediate reporting of results while remaining sensitive to local conditions. The tool's accessible design was central to its successful use in the development of the sustainable communities strategy of the Gateway Cities Council of Governments. This paper presents the policy context that guided tool development; highlights of the development process and methodology; a summary of the testing, delivery, and application of the sustainable communities strategy of the Gateway Cities Council of Governments; and a view of the tool's limitations and opportunity for enhancements.

Do Cost–Benefit Analyses Influence Transport Investment Decisions? Experiences from the Swedish Transport Investment Plan 2010–21

Cost–benefit analysis (CBA) for transport investments is particularly useful for situations where a large number of investments have to be ranked against each other. This study draws on experiences from the development of the Swedish National Transport Investment Plan 2010–21. We study how CBA results were used in the process of shaping the investment plan and what influence they had on investment decisions. In particular, we compare the planners' rankings versus the politicians' rankings. We find that planners' rankings of investments are influenced by benefit–cost ratios (BCRs), in particular for low and moderate BCRs, while the politicians' rankings are not. By interviewing planners about how CBA was used in the process, we clarify what role CBA actually played in the planning process. We find that not only did the CBAs play a role in investment selection, they also forced investment design to be more cost-efficient. Furthermore, we explore planners' implicit valuations, as revealed by their investment selection, finding that freight benefits were implicitly valued higher and traffic safety lower than the officially recommended CBA weights. Finally, we identify the most important areas for improvement of CBA state-of-practice methodology.