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Dr. Daniel Rodríguez Román
Departamento de Ingeniería Civil y Agrimensura, Universidad de Puerto Rico, Mayagüez, PO Box 9000, PR 00682, USA

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Research Keywords & Expertise

0 Network Design
0 Environmental Sustainability
0 Transportation Equity
0 Travel demand management
0 Road pricing

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Journal article
Published: 16 October 2019 in Transportation Research Part D: Transport and Environment
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The introduction of area-based pricing schemes is often motivated by both urban congestion and pollution concerns. Existing discrete network optimization models for the design of area pricing schemes focus primarily on travel-related objectives, such as maximizing social welfare measures based on travel costs. In this paper, an area pricing problem is proposed that explicitly accounts for both travel- and environmentally-oriented objectives to optimally define charging boundaries and tolling levels. The environmental objective is formulated from an equity perspective. Specifically, it is assumed that regional planners are interested in minimizing inequality in the levels of pollutant encountered by individuals as they perform their daily activities. Here, pollutant exposure is specified in terms of agent-level intake of pollutants. In addition, it is assumed that pricing schemes must reduce pollutant concentrations in the region below an established threshold. A network-based activity model is presented as an approach for modeling the changes in travelers’ mobility behavior and activity patterns in response to pricing schemes. A surrogate-based optimization approach is proposed to solve the area pricing problem, as it is likely that, in practice, this design problem would be computationally costly. The proposed algorithm uses a geometric representation of the charging boundary. New procedures for generating candidate boundary locations are presented, which include the use of surrogate-based methods to screen for feasible, non-dominated solutions prior to their evaluation via the computationally expensive models. The proposed model and solution heuristic are tested using the Chicago Sketch Network and a smaller test network.

ACS Style

Daniel Rodriguez-Roman; Mahdieh Allahviranloo. Designing area pricing schemes to minimize travel disutility and exposure to pollutants. Transportation Research Part D: Transport and Environment 2019, 76, 236 -254.

AMA Style

Daniel Rodriguez-Roman, Mahdieh Allahviranloo. Designing area pricing schemes to minimize travel disutility and exposure to pollutants. Transportation Research Part D: Transport and Environment. 2019; 76 ():236-254.

Chicago/Turabian Style

Daniel Rodriguez-Roman; Mahdieh Allahviranloo. 2019. "Designing area pricing schemes to minimize travel disutility and exposure to pollutants." Transportation Research Part D: Transport and Environment 76, no. : 236-254.

Journal article
Published: 07 May 2019 in Transportation Research Part D: Transport and Environment
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A surrogate-based solution heuristic is presented for single-objective cordon and area-based road pricing problems that consider environmental constraints. In the proposed algorithm, surrogate models are constructed using geometric representations of charging boundaries. A surrogate model is defined here as a simple approximation to computationally expensive models used to simulate road users’ response to pricing. The surrogates are employed as part of a screening procedure to select the most promising candidate schemes for evaluation by potentially time-consuming models. Departing from previous elastic demand-based formulations of congestion charging problems, this study utilizes a set of objective functions that can be easily integrated with commonly used travel demand models. Environmental considerations are introduced to the pricing problem in the form of pollutant concentration constraints. Two constraint handling strategies are presented to account for the pollutant concentration constraints in the solution heuristics. Numerical tests were conducted to explore the surrogate models’ predictive accuracy and their degree of correlation with the model outputs. On average, the surrogate predictions exhibited relatively good correlation with model outputs (correlation coefficients greater than 0.70). Additionally, a sample application of the proposed problem and methods is presented for illustrative purposes. The tests examined the relative performance of the proposed algorithm, the diversity of the design solutions generated, and the impact of the pricing schemes on pollutant concentration in the hypothetical study area.

ACS Style

Daniel Rodriguez-Roman; Stephen G. Ritchie. Surrogate-based optimization for the design of area charging schemes under environmental constraints. Transportation Research Part D: Transport and Environment 2019, 72, 162 -186.

AMA Style

Daniel Rodriguez-Roman, Stephen G. Ritchie. Surrogate-based optimization for the design of area charging schemes under environmental constraints. Transportation Research Part D: Transport and Environment. 2019; 72 ():162-186.

Chicago/Turabian Style

Daniel Rodriguez-Roman; Stephen G. Ritchie. 2019. "Surrogate-based optimization for the design of area charging schemes under environmental constraints." Transportation Research Part D: Transport and Environment 72, no. : 162-186.

Journal article
Published: 03 December 2018 in Transportation Research Part A: Policy and Practice
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The toll design problem (TDP) provides a quantitative approach to the design of road pricing schemes. Its practical use, however, can be computationally challenging if the formulated TDP requires time-consuming computer models to evaluate candidate designs, especially if such designs must account for multiple objectives. For TDPs to be of practical relevance to the real-world planning of sustainable transportation networks, efficient TDP solution heuristics must be developed. To this end, two surrogate-based solution heuristics for multi-objective TDPs are proposed in this paper. Surrogate-based optimization uses simple approximations to computationally expensive models in order to accelerate the discovery of good solutions. The general search strategy of the proposed heuristics is as follows. In each iteration of the heuristics, a pool of candidate pricing schemes with unique sets of tolling locations and associated tolling levels is generated. From this pool of designs, the heuristics use the surrogate models to screen for solutions that are expected to be nondominated and that meet a specified selection criterion. Then, these promising designs are evaluated by the computationally expensive models, and the outputs obtained from these evaluations are used to update the surrogate models. Both heuristics repeat this general process until a maximum number of iterations are completed, at which point the best TDP solutions are returned. In addition to the solution heuristics, this paper presents a transportation network paradox that highlights how transportation network interventions intended to reduce traffic emissions could have unintended effects on a population’s exposure to pollutants. The paradox also is used to illustrate the practical complexity of accounting for environmental inequality objectives, as well as the relevance of multi-objective analysis approaches to transportation planning. Formulations of multi-objective TDPs that consider both travel and pollutant exposure-related objectives are also presented, including the objectives of reducing human intake of vehicle-generated air pollutants and of minimizing environmental inequality. The Sioux Falls and Chicago Sketch networks were used in tests that examined the relative performance of the heuristics, as well as the characteristics of pricing configurations obtained under different budget constraints. Among other results, the tests show that a pricing configuration could decrease total pollutant intake and environmental inequality, while at the same time producing an increase in pollutant concentrations in a significant number of pollutant receptor points.

ACS Style

Daniel Rodriguez-Roman; Stephen G. Ritchie. Surrogate-based optimization for multi-objective toll design problems. Transportation Research Part A: Policy and Practice 2018, 137, 485 -503.

AMA Style

Daniel Rodriguez-Roman, Stephen G. Ritchie. Surrogate-based optimization for multi-objective toll design problems. Transportation Research Part A: Policy and Practice. 2018; 137 ():485-503.

Chicago/Turabian Style

Daniel Rodriguez-Roman; Stephen G. Ritchie. 2018. "Surrogate-based optimization for multi-objective toll design problems." Transportation Research Part A: Policy and Practice 137, no. : 485-503.

Journal article
Published: 01 March 2018 in Safety Science
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Measures to improve highway safety can affect travel times, just like measures to reduce travel times can affect highway safety. For this reason, the models used in the process of allocating funds across different highway improvement projects should simultaneously consider the safety and travel time effects of the project alternatives. In this paper, an optimization model is presented for the joint selection and design of highway safety and travel time improvement projects. The model is formulated as a bi-objective, mixed-integer optimization problem with constraints on project costs and on the types of improvement combinations admissible at project sites. By incorporating travel behavior models within the optimization process, the model accounts for the potential network-level effects of highway improvement schemes. Given that the model systems needed in this process are time-consuming, a genetic algorithm is proposed that utilizes surrogate models to accelerate the discovery of good solutions to the presented optimization model. In this algorithm, the surrogate models are used to generate computationally inexpensive approximations to computationally expensive functions that quantify a decision-maker’s safety and travel time objectives. Like the problem formulation, the proposed heuristic can be employed in conjunction with computer-based travel demand models commonly used by transportation planning agencies. An illustrative application of the model and its solution heuristic is presented using a hypothetical planning scenario in Southwest Puerto Rico. Besides illustrating the application of the model, the example was used to test the surrogates’ predictive accuracy and the impact of different parameter values on the algorithm’s performance.

ACS Style

Daniel Rodriguez-Roman. A surrogate-assisted genetic algorithm for the selection and design of highway safety and travel time improvement projects. Safety Science 2018, 103, 305 -315.

AMA Style

Daniel Rodriguez-Roman. A surrogate-assisted genetic algorithm for the selection and design of highway safety and travel time improvement projects. Safety Science. 2018; 103 ():305-315.

Chicago/Turabian Style

Daniel Rodriguez-Roman. 2018. "A surrogate-assisted genetic algorithm for the selection and design of highway safety and travel time improvement projects." Safety Science 103, no. : 305-315.