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.
•Propose a traffic estimation framework based on Newell’s kinematic wave theory.•Estimate traffic parameters and states simultaneously from Eulerian and Lagrangian data.•Formulate and solve a single ...optimization problem.•The method leads to multiple solutions under absolute steady traffic conditions.•The proposed method yields better results than existing methods.
The traffic state estimation process estimates various traffic states from available data in a road network and provides valuable information for travelers and decision makers to improve both travel experience and system performance. In many existing methods, model parameters and initial states have to be given in order to estimate traffic states, which limits the accuracy of the results as well as their transferability to different locations and times. In this paper, we propose a new framework to simultaneously estimate model parameters and traffic states for a congested road segment based on Newell’s simplified kinematic wave model (Newell, 1993). Given both Eulerian traffic count data and Lagrangian vehicle reidentification data, we formulate a single optimization problem in terms of the initial number of vehicles and model parameters. Then we decouple the optimization problem such that the initial number of vehicles can be analytically solved with a closed-form formula, and the model parameters, including the jam density and the shock wave speed in congested traffic, can be computed with the Gauss-Newton method. Based on Newell’s model, we can calculate individual vehicles’ trajectories as well as the average densities, speeds, and flow-rates inside the road segment. We also theoretically show that the optimization problem can have multiple solutions under absolutely steady traffic conditions. We apply the proposed method to the NGSIM datasets, verifying the validity of the method and showing that this method yields better results in the estimation of average densities than existing methods.
In this study, cellulose acetate (CA) supported Ni/Fe nanoparticles were prepared and the ability of these nanoparticles to remove trichloroethylene (TCE) from water was studied. The effects of TCE ...reduction by the nanoparticles and sorption by the CA support were accounted for separately in the model. CA supported post-coated Ni/Fe nanoparticles were used to investigate the effect of metal particle composition on the observed reduction rate constant. The results show that the metal mass normalized observed reduction rate constant was proportional to the Ni content in the post-coated Ni/Fe nanoparticles in the range of 0–14.3
wt.%. This constant reached a maximum between 14.3 and 21.4
wt.% and decreased with further increase in Ni content. CA supported co-reduced Ni/Fe bimetallic nanoparticles gave poorer performance compared to CA supported post-coated Ni/Fe bimetallic nanoparticles at the same Ni content in Ni/Fe nanoparticles.
Mobile health platforms like smartphone apps that provide clinical guidelines are ubiquitous, yet their long-term impact on guideline adherence remains unclear. In 2016, an antibiotic guidelines app, ...called SCRIPT, was introduced in Auckland City Hospital, New Zealand, to provide local antibiotic guidelines to clinicians on their smartphones.
We aimed to assess whether the provision of antibiotic guidelines in a smartphone app resulted in sustained changes in antibiotic guideline adherence by prescribers.
We analyzed antibiotic guideline adherence rates during the first 24 hours of hospital admission in adults diagnosed with community-acquired pneumonia using an interrupted time-series study with 3 distinct periods post app implementation (ie, 3, 12, and 24 months).
Adherence increased from 23% (46/200) at baseline to 31% (73/237) at 3 months and 34% (69/200) at 12 months, reducing to 31% (62/200) at 24 months post app implementation (P=.07 vs baseline). However, increased adherence was sustained in patients with pulmonary consolidation on x-ray (9/63, 14% at baseline; 23/77, 30% after 3 months; 32/92, 35% after 12 month; and 32/102, 31% after 24 months; P=.04 vs baseline).
An antibiotic guidelines app increased overall adherence, but this was not sustained. In patients with pulmonary consolidation, the increased adherence was sustained.
•Planning models for the design of area pricing schemes are presented.•Models incorporate pollutant concentration constraints in the design problem.•Surrogate-based optimization approach proposed to ...solve design problems.•Geometric representation of charging boundary utilized in heuristic.•Utility of models and heuristics illustrated using the Chicago Sketch Network.
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.
A new hybrid sensor technology integrating existing weigh-in-motion axle configuration data combined with inductive signature data obtained from advanced inductive loop detectors is gaining interest ...due to its potential to provide detailed classification of truck body types as well as anonymous tracking of truck movements on freeways. However, selecting optimal deployment locations for the hybrid sensors has been persistently challenging because implementing new technologies state-wide can demand significant capital investment and logistics preparation. This article investigates two proposed strategies for optimally deploying this new technology on California freeways based on actual truck GPS trajectories: (i) A flow-interception approach to maximize the total amount of net origin-destination flows; and (ii) a truck re-identification approach to maximize insights into origins and destinations of sampled truck trips, as well as routes of those trips. The flow-interception model is capable of selecting locations emphasizing different body types with flow-based weight factors. The truck re-identification model investigates the best locations to identify heavy truck movement by selecting pairwise locations, and is shown to be sensitive to re-identification performance uncertainty.
Learning science in the middle years can be an emotional experience. In this study, we explored ninth-grade students' discrete emotions expressed during science activities in a 9- week unit on ...chemistry. Individual student's emotions were analysed through multiple data sources including classroom videos, interviews, and emotions diaries completed at the end of each lesson. Results from three representative students are presented as cases within a case study. Using a theoretical perspective drawn from theories of emotions founded in sociology, three assertions emerged. First, students experienced frustration when learning new chemistry concepts. Second, frustration was resolved through student-student and teacher-student interactions. Third, frustration was transformed when students were afforded time to revisit new concepts. Furthermore, the teacher's identification of students' emotions enabled differentiation of learning through individualised interactions. Finally, we explain how the temporality of emotions emerged as an important phenomenon and suggest an elaboration to Turner's theorisation of emotions. Author abstract
The genetic structure of Staphylococcus aureus populations sampled from diverse regions of the globe have been the subject of numerous investigations. Here we describe the structure of S. aureus ...populations collected from the Southwest Pacific. Multi-locus sequence typing was performed on 467 isolates obtained from people with nasal colonization or bacteremia in Auckland (NZ), and patients predominantly affected by skin and soft tissue infection in Samoa, Fiji and Tonga. The predominant sequence types (STs) varied between Auckland (ST5), Fiji (ST30), and Samoa (ST1), however, the overall genetic diversity within each region did not differ significantly between locations. Divergent Clonal Complex 75 (CC75) strains were isolated in Auckland and Fiji. When diversity of the Southwest Pacific populations was compared with those previously described from Asia, Europe, North America and Africa no significant differences were detected. With the exception of CC75 strains, the global collection of S. aureus encompasses relatively little diversity, with novel STs arising locally from a small number of widespread lineages.
•Extend Newell’s simplified kinematic wave model with first-in-first-out violation.•Introduce vehicle order variable for first-in-first-out violation (overtaking effects).•Propose a linear model of ...vehicle order.•Present two estimation methods of vehicles’ trajectories based on the new model.•Estimation errors are reduced from 13% to 10% with NGSIM data.
Newell’s simplified kinematic wave model has been widely used in studying traffic dynamics in a road network. However, it cannot account for First-In-First-Out violation among vehicles on multi-lane roads. In this study, we present an extension of the model in the Lagrangian coordinates. In particular, we define a new variable of a vehicle’s order as its corresponding cumulative flows at different times and locations. The new kinematic wave model is consistent at the aggregate level with Newell’s simplified kinematic wave model, but allows for FIFO violation among individual vehicles.
Based on the new model, we then present two algorithms to estimate vehicles’ trajectories from Eulerian data (cumulative flows provided by loop detectors) and Lagrangian data (the vehicle’s entry and exit times provided by reidentification technologies) at two boundaries of a road segment. In the first algorithm, we assume that vehicles follow the First-In-First-Out (FIFO) principle on a multi-lane road, and their orders are constant and equal the average of the entry and exit orders; in the second algorithm, we introduce a linear order-changing model to interpolate a vehicle’s orders at different times according to its entry and exit times. With Next Generation Simulation (NGSIM) datasets, we demonstrate that both algorithms are effective, but the second one, with an estimation error of around 10%, greatly outperforms the first one, with an estimation error of around 13%. This verifies the advantage of incorporating FIFO violation in the new model.
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