An investigation of location effect on trip production using artificial neural networks

As traffic congestion exuberates and new roadway construction is severely constrained because of limited availability of land, high cost of land acquisition, and communities' opposition to the building of major roads, new solutions have to be sought to either make roadway use more efficient or reduce travel demand. There is a general agreement that travel demand is affected by land use patterns. However, traditional aggregate four-step models, which are the prevailing modeling approach presently, assume that traffic condition will not affect people's decision on whether to make a trip or not when trip generation is estimated. Existing survey data indicate, however, that differences exist in trip rates for different geographic areas. The reasons for such differences have not been carefully studied, and the success of quantifying the influence of land use on travel demand beyond employment, households, and their characteristics has been limited to be useful to the traditional four-step models. There may be a number of reasons, such as that the representation of influence of land use on travel demand is aggregated and is not explicit and that land use variables such as density and mix and accessibility as measured by travel time and congestion have not been adequately considered. This research employs the artificial neural network technique to investigate the potential effects of land use and accessibility on trip productions. Sixty two variables that may potentially influence trip production are studied. These variables include demographic, socioeconomic, land use and accessibility variables. Different architectures of ANN models are tested. Sensitivity analysis of the models shows that land use does have an effect on trip production, so does traffic condition. The ANN models are compared with linear regression models and cross-classification models using the same data. The results show that ANN models are better than the linear regression models and cross-classification models in terms of RMSE. Future work may focus on finding a representation of traffic condition with existing network data and population data which might be available when the variables are needed to in prediction.

An ethnopharmacological approach to multidrug -resistant Staphylococcus aureus: Evaluation of Italian plants used in the traditional healing of skin disease

One-third of botanical remedies from southern Italy are used to treat skin and soft tissue infections (SST's). Methicillin-resistant Staphylococcus aureus (MRSA), a common cause of SSTIs, is responsible for increased morbidity and mortality from infections. Therapeutic options are limited by antibiotic resistance. Many plants possess potent antimicrobial compounds for these disorders. Validation of traditional medical practices is important for the people who rely on medicinal plants. Moreover, identification of novel antibiotics and anti-pathogenic agents for MRSA is important to global healthcare.^ I took an ethnopharmacological approach to understand how Italian medicinal plants used for the treatment of SSTIs affect MRSA growth and virulence. My hypothesis was that plants used in folk remedies for SSTI would exhibit lower cytotoxicity and greater inhibition of bacterial growth, biofilm formation and toxin production in MRSA than plants used for remedies unrelated to the skin or for plants with no ethnomedical application. The field portion of my research was conducted in the Vulture-Alto Bradano area of southern Italy. I collected 104 plant species and created 168 crude extracts. In the lab, I screened samples for activity against MRSA in a battery of bioassays. Growth inhibition was analyzed using broth microtiter assays for determination of the minimum inhibitory concentration. Interference with quorum-sensing (QS) processes, which mediate pathogenicity, was quantified through RP-HPLC of δ-toxin production. Interference with biofilm formation and adherence was assessed using staining methods. The mammalian cytotoxicity of natural products was analyzed using MTT cell proliferation assay techniques.^ Although bacteriostatic activity was limited, extracts from six plants used in Italian folk medicine (Arundo donax, Ballota nigra, Juglans regia, Leopoldia comosa, Marrubium vulgare, and Rubus ulmifolius ) significantly inhibited biofilm formation and adherence. Moreover, plants used to treat SSTI demonstrated significantly greater anti-biofilm activity when compared to plants with no ethnomedical application. QSI activity was evident in 90% of the extracts tested and extracts from four plants ( Ballota nigra, Castanea saliva, Rosmarinus officinalis, and Sambucus ebulus) exhibited a significant dose-dependent response. Some of the plant remedies for SSTI identified in this study can be validated due to anti-MRSA activity.^