Book Description

The demand for transportation and travel is a major focus for designing a highway. The different socioeconomic characteristics of many regions often stimulate the need for highways to be constructed or expanded. This research aims at developing a systematic methodology that is able to comprehensively formulate the socioeconomic factors in highway construction and expansion. While much awareness has been seen in recent years towards socioeconomic trends and their relation to induced demand, these characteristics have not been effectively incorporated into highway planning and design models. Low-income neighborhoods and communities are often impacted when new highways and expressways are built. In this dissertation research an optimization model is developed to determine the impact of highway alignments on low-income areas. A low-income cost formulation is developed to determine the extent of impact of new highway construction on low-income families. The optimization model is connected to a Genetic Algorithm (GA) from a previously developed highway alignment optimization model, to generate an optimal alignment. A Geographic Information System (GIS) in conjunction with a GA is used to model the socioeconomic characteristics. The GA is used because of their effectiveness in searching through complex spaces with large numbers of local optima. A multiple regression analysis is also performed to estimate the Annual Average Daily Traffic (AADT), which is an input to the optimization model. Penalty costs are chosen and are used to penalize alignments crossing low-income areas. Multiple values for the penalty costs are used to test the sensitivity of generated alignments. Various case studies and sensitivity analyses are performed to test the robustness of the developed methodology. The results of each run for each case study are compared with results from a previously developed highway alignment optimization model. The results indicate that alignments can be generated that avoid low-income properties, but they require high penalty costs. The results also show that extensive research must be conducted on developing a penalty cost that is suitable for avoiding low-income communities. Finally, the results of the case studies demonstrate that one can select different highway routes depending on the relative weights of penalty costs. -- Abstract.