Jennifer Duthie

Incorporating Environmental Justice Measures into Equilibrium-Based Network Design

A new variation of the user equilibrium-discrete network design problem (UE-DNDP) is proposed for achieving environmental justice (EJ) or equity among population groups. This research is motivated by the federal requirement that transportation plans using federal money include an evaluation of EJ and that the planning agency mitigate, where feasible, any disproportionate impacts on protected populations (i.e., minority and low-income groups). Eight potential objective functions focused on maximizing equity of congestion and travel time are developed and discussed with regard to their applicability for the upper level of this bilevel problem. On the basis of assumed knowledge of the origin-destination travel matrices by population group, numerical analysis is conducted to assess the performance of each proposed formulation. The lower-level UE problem is solved by using the Frank-Wolfe method, and because of the hard combinatorial nature of EJ-UE-DNDP, a selectorecombinative genetic algorithm is implemented to search the solution space for feasible network improvement strategies efficiently. The results of numerical analysis suggest that Pareto-optimal approaches can be successfully applied and that the most effective formulations minimize the difference between the change in congestion or travel time across population groups due to the selected improvement projects.

Environmental Justice Analysis: Challenges for Metropolitan Transportation Planning

This research focuses on three major challenges of incorporating environmental justice into metropolitan transportation planning. The data needed are compared with the data currently available on the spatial distributions of race and income, the spatial distributions of trip ends, trip tables, network performance, and cost estimates of improvements. Several conflicting definitions of equity are offered, as are applications for each within the context of environmental justice. The importance of choosing a correct unit of analysis is discussed, with particular emphasis on how the geographic unit of analysis is a poor proxy for the group unit, which is theoretically required, as the analysiss purpose is to compare performance measures across groups. The primary goal of this paper is to explore challenging topics such as these raising questions and concerns. The answers to the questions raised will differ depending on each implementing agencys objectives and resources.

A network equilibrium analysis on destination, route and parking choices with mixed gasoline and electric vehicular flows

In many countries across the world, fossil fuels, especially petroleum, are the largest energy source for powering the socio-economic system, and the transportation sector dominates the consumption of petroleum in these societies. As the petroleum price continuously climbs and the threat of global climate changes becomes more evident, the world is now facing critical challenges in reducing petroleum consumption and exploiting alternative energy sources. A massive adoption of plug-in electric vehicles (PEVs), especially battery electric vehicles (BEVs), offers a very promising approach to changing the current energy consumption structure and diminishing greenhouse gas emissions and other pollutants. Understanding how individual electric vehicle drivers behave subject to the technological restrictions and infrastructure availability and estimating the resulting aggregate supply–demand effects on urban transportation systems is not only critical to transportation infrastructure development, but also has determinant implications in environmental and energy policy enactment. This paper presents an equilibrium-based analytical tool for quantifying travel choice patterns in urban transportation networks with both gasoline and electric vehicular flows. Specifically, a network equilibrium problem with combined destination, route and parking choices subject to the driving range limit and alternative travel cost composition associated with BEVs are formulated, solved, and numerically analyzed under different network settings and scenarios. The defined problem introduces a new dimension of modeling network equilibrium problems with side constraints. The practical significance of the developed tool lies in its solution tractability and extension capability and its ease of being embedded into the existing urban travel demand forecasting framework.

Influence of Demand Uncertainty and Correlations on Traffic Predictions and Decisions

: Decisions to improve a regional transportation network are often based on predictions of future link flows that assume future travel demand is a deterministic matrix. Despite broad awareness of the uncertainties inherent in forecasts, rarely are uncertainties considered explicitly within the methodological framework due at least in part to a lack of knowledge as to how uncertainties affect the optimality of decisions. This article seeks to address this issue by presenting a new method for evaluating future travel demand uncertainty and finding an efficient technique for generating multiple realizations of demand. The proposed method employs Hypersphere Decomposition, Cholesky Decomposition, and user equilibrium traffic assignment. Numerical results suggest that neglecting correlations between the future demands of travel zone pairs can lead to improvement decisions that are less robust and could frequently rank improvements improperly. Of the six sampling techniques employed, Antithetic sampling generated travel demand realizations with the least relative bias and error.

Effects of On-Street Bicycle Facility Configuration on Bicyclist and Motorist Behavior

Growing awareness of environmental and public health problems associated with motorized transportation has led to a recent effort to promote nonmotorized modes of travel. Previous studies have shown that facility design plays a large role in encouraging bicycling. With the aim of defining the roadway configurations that lead to safe motorist and bicyclist behavior, this research examines the impact of design elements, including the type and width of the bicycle facility, the presence of adjacent motor vehicle traffic, parking turnover rate, land use, and the type of motorist-bicyclist interaction. Observational studies conducted at 48 sites in three large Texas cities characterize bicyclist and motorist behavior through lateral position measurements and instances of motorist encroachment on an adjacent lane. These observations were used to build two multivariate regression models and allowed for direct site-to-site comparisons. Notable results include the observation that bicycle lanes create a safer and more predictable riding environment relative to wide outside lanes, and that the provision of a buffer between parked cars and bicycle lanes is the only reliable method for ensuring that bicyclists do not put themselves at risk of being hit by opening car doors.

Highway Improvement Project Rankings due to Uncertain Model Inputs: Application of Traditional Transportation and Land Use Models

While much research has been devoted to analyzing the variation in transportation and land use model outputs due to uncertainty, little has been done to quantitatively answer the more important question of how decision making will change based on recognition of this uncertainty. This paper aims to begin to fill this gap by evaluating how roadway investment decisions will differ depending on whether or not uncertainty is recognized. Population and employment control totals, as well as trip generation and trip distribution parameters, are found via antithetic sampling, and a full feedback integrated gravity-based land use and four-step travel model is used. It is found that the ranking of improvement projects may indeed be different if uncertainty is considered relative to treating all parameters and data as deterministic. The experimental analysis conducted in this paper found this percent difference to be between 4 and 25% depending on the performance metric used: total system travel time, vehicle miles traveled, total delay, average network speed, and standard deviation of network speed were all examined.

Valuation of strategic network flexibility in development of toll road projects

A reliable, cost‐effective and safe transportation system is essential to economic growth. To keep pace with demands for network capacity, revenue‐generating projects are increasingly being used to complement the current procurement practices and lessen the pressure on public finances. In such transportation networks where there exists a mix of free access links and links with user fees, network interconnectivity is an important component of project valuation. A bilevel stochastic recourse model for valuating network flexibility is formulated. A key component of the model is consideration of network‐based managerial flexibility in context of the upper level project valuation objective and the lower level network user equilibrium solution under demand uncertainty. The results from a test network, for which a closed form solution is possible, indicate that the value of network flexibility directly depends on initial network conditions, variance in future travel demand and toll pricing decisions.

Optimization Framework for Bicycle Network Design

This paper presents a new formulation for the network design problem as it relates to retrofitting existing roadway infrastructure for bicycles. The goal of the problem is, for a minimum cost, to connect all origin-destination pairs with paths where each roadway segment and intersection meets or exceeds a lower bound on its bicycling level of service. The length of each optimal path is constrained to be no greater than a given upper bound, which is expressed as a function of shortest path length. Experimental analysis on the Austin, Texas downtown region shows that a systems approach will yield different results than an approach that separately considers connecting each pair of origins and destinations, and that placing an upper bound on the amount of deviation from the shortest path will impact the design decisions. Model parameters, although the defaults are based on existing research, should be calibrated based on local data. Variants on the formulation are provided that allow for a trade-off between optimality and computational efficiency.

Demand Profiling for Dynamic Traffic Assignment by Integrating Departure Time Choice and Trip Distribution

One challenge in dynamic traffic assignment (DTA) modeling is estimating the finely disaggregated trip matrix required by such models. In previous work, an exogenous time distribution profile for trip departure rates is applied uniformly across all origin-destination (O-D) pairs. This article develops an endogenous departure time choice model based on an arrival time penalty function incorporated into trip distribution, which results in distinct demand profiles by O-D pair. This yields a simultaneous departure time and trip choice making use of the time-varying travel times in DTA. The required input is arrival time preferences, which can be disaggregated by O-D pair and may be easier to collect through surveys than the demand profile. This model is integrated into the four-step planning process with feedback, creating an extension of previous frameworks which aggregate over time. Empirical results from a network representing Austin, Texas indicate variation in departure time choice appropriate to the arrival time penalties and travel times. Our model also appears to converge faster to a dynamic trip table prediction than a time-aggregated coupling of DTA and planning, potentially reducing the substantial computation time of combined planning models that solve DTA as a subproblem of a feedback process.

Analysis of factors influencing bicycle-vehicle interactions on urban roadways by ordered probit regression

A study of the impacts of roadway environment, motorist behavior, and bicyclist behavior on bicyclist–motorist interactions was based on video footage of traffic movements during peak commuting hours at four locations in Austin, Texas. The study developed three unique models of ordered probit regression that describe bicyclist lateral location, bicyclist–motorist interaction movement, and bicyclist–motorist lateral interaction distance. This structure of discrete choice model was used for the first time to address bicycle–vehicle interactions and offered more meaningful results because it used a latent measure of bicyclists’ and motorists’ mutual acceptance and comfort level sharing a roadway. It was demonstrated that adding “sharrows” and “Share the Road” signage promoted safer interactions on narrow urban roadways, whereas simply widening travel lanes and adding on-street parking did not necessarily improve bicyclist–motorist interactions.