Requisitos para el desempeño del cargo.

Bi-objective optimization model was utilized to minimize both the average and standard deviation of travel time. Two separate models were developed to gain Pareto frontiers corresponding to drive time and ride time-based optimal locations. Minimizing the standard deviation requires reducing the variability in travel time dataset and hence facilitating remote users. Similarly, locating an intervention center by minimizing the average time ensures efficiency by decreasing the total travel time needed for patients to reach that facility. The bi- objective model suggests only solution points that are Pareto optimal. These solution points are called Pareto frontiers and characterize the bounds of what can be considered bi-optimal in the siting of a health facility. Each frontier indicates a location from which it is impossible to reallocate the intervention center in a way that improves one objective without reducing the acceptability of the alternate criterion.

A line of Pareto frontiers can be established by connecting all solution points. Each point along that line represents a unique model parameterization. As Pareto optimality identifies multiple optimal solutions, it allows the decision makers to investigate differences among the solutions and make an informed choice among varying combinations of assessment criteria.

Fig. 3 and 4 show each model derived Pareto frontier. The drive time and transit time-based optimization yielded a set of 235 and 275 pixels on the transportation network, respectively. The minimum average (DA and TA) and minimum standard deviation (i.e., DS and TS) values bound

the Pareto frontier lines obtained from two different models. Three other Pareto frontiers from each model are shown for discussion purposes. D1, D2, and D3 are three compromised solutions

at the median and quartile values between DA and DS; T1, T2, and T3 are three other compromised

35 Figure 8. The Pareto frontiers of the drive time based bi-objective optimization.

36 For example, location D2 can be reached by an average travel time of 5.20 minutes with a

standard deviation value of 1.96 minutes. Although the average travel time is increased by 23.81% when compared to location DA, the standard deviation is decreased by 1.06. When

compared to location DS, the standard deviation of travel time is increased by 41.01%, but the

average travel time is reduced by 2.61 minutes. Similarly, location T2 reduces the standard

deviation of transit time by 1.09 minutes when compared to location TA by increasing the

average travel time by 15.59%. T2 lessens the average travel time by 2.19 minutes by conceding

only 11.94% increase in standard deviation when comparedto location TS.

4.3 Multimodal Optimization

Multimodal optimization of the locations that have already been optimized for a single mode of transportation ensures a balance of equity and efficiency among a combined client set of transit riders and drivers of personal vehicles. The spatial bounding box of frontier solutions of each transportation mode is shown in fig. 5 (i, ii). Fig. 5 (iii) exhibits the common area between optimal drive time and transit time bounds. Fig. 5 (iv) provides a larger scale view of the road sections that are equitably and efficiently optimized for both travel modes. Siting an STI

intervention facility along any of these road sections would ensure better access to remote repeat users as well as users in particular high-density areas irrespective of their modal share.

37 Figure 10. Locations or road sections optimally accessible by (i) drive, (ii) transit and (iii) both (iv) Blow-up of multimodal accessible road sections that are equitably and efficiently optimized.

38

5. Discussion

Improving spatial access to health facilities is recognized as an important component of reducing the prevalence of disease and achieving better health outcomes. Multimodal accessibility

estimation and optimization can play a vital role in this respect. The novel and disaggregated nature of this study allow to consider individual’s travel time from distinct household to facility locations, thus helps to address the inherent mismatch between popular statistical methods of significant density detection and the reality of individuals located on a street network or constrained by a particular transportation modality.

Cost raster based optimization not only offers the opportunity to compare different solutions but also paves the way for understanding how this approach may help identify potential locations that could provide better accessibility than the current facility location. For example, the existing facility in Kalamazoo County (see fig. 5) is located an average of 7.44 minutes away from drivers living at hypothetical household locations, with a standard deviation of 3.23 minutes, but location D2 offers a more accessible location by minimizing the average drive time by 2.24

minutes and standard deviation by 1.27 minutes. This facility houses many programs and services quite apart from STI testing, so the purpose of this paper is not to recommend the relocation of the current facility. Instead, we present a case study of how equity and efficiency of facility placement can be quantified and compared for any number of at-risk populations. The advantage of Pareto solutions is that the analysis can be tailored to a range of populations and objectives. Decision makers with experience in a particular area with a predetermined client base may have specific objectives that are dependent on the geographical distribution of targeted population, socio-economic characteristics, the magnitude of travel time variability and so on, which vary in space and time. This study does not focus on quantifying the preference based on the aforementioned factors, rather offers a set of geocomputational tools to the decision makers for assessing multiple locations.

Network problems are generally considered to be better represented and modeled in vector data models. Common vector network modeling characteristics such as constraints on intersections, non-planar roadways (overpasses), one-way streets, and the like, are difficult or impossible to consider in raster analysis and certainly pose some limitations to the results of this

39 study. However, raster analysis of network problems enable the researcher to make use of

techniques such as stacked approach, which are not easily duplicated in vector analysis. Vector analysis also does not lend itself to paired optimality in a setting with virtually no limitations on candidate locations. It is the goal of this paper to present options outside of the standard regimen of vector solutions to network problems.

Another limitation of this case study is that the coincidence of optimization is due to chance, as well as circumstances unique to this particular transportation network for this local area. In general, the area bounded by the minimum average and minimum standard deviation location for each frontier would represent the constraints to intervention location. The extremum frontier values represent the bounds of the ‘spatial frontier’ or area of potential locations. The size of this area becomes, then, a usable metric by which to measure transit accessibility with respect to accessibility by private vehicle as it will vary by proximity of optimal accessibility and not with city size. By extension, the relative size of this area with respect to the total area of the

jurisdiction or total population served can be used by decision makers to quantitatively assess the determinants of intervention site selection within this region.

From a public health policy perspective, equal access to health care is considered one of the most important parameters to address health equity (Oliver & Mossialos, 2004). At a time when socioeconomic disparities are prevalent, multi-modal transportation models can provide insight into the constraints and challenges met by individuals across a spectrum of transportation options including dial-a-ride services, light rail, city bus, a personal vehicle and active transportation options such as cycling and walking. This heuristic approach increases the sophistication of accessibility measurement by quantifying the spatial scope of optimization for specific public health problems and at-risk populations. Additionally, by presenting temporally-aware and spatially disaggregated accessibility metrics, this paper introduces a set of tools that offer efficient as well as more equitable solutions.

40

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