Application of intervention analysis to evaluate the impacts of special events on freeways

In China in particular, large, planned special events (e.g., the Olympic Games, etc.) are viewed as great opportunities for economic development. Large numbers of visitors from other countries and provinces may be expected to attend such events, bringing in significant tourism dollars. However, as a direct result of such events, the transportation system is likely to face great challenges as travel demand increases beyond its original design capacity. Special events in central business districts (CBD) in particular will further exacerbate traffic congestion on surrounding freeway segments near event locations. To manage the transportation system, it is necessary to plan and prepare for such special events, which requires prediction of traffic conditions during the events. This dissertation presents a set of novel prototype models to forecast traffic volumes along freeway segments during special events. Almost all research to date has focused solely on traffic management techniques under special event conditions. These studies, at most, provided a qualitative analysis and there was a lack of an easy-to-implement method for quantitative analyses. This dissertation presents a systematic approach, based separately on univariate time series model with intervention analysis and multivariate time series model with intervention analysis for forecasting traffic volumes on freeway segments near an event location. A case study was carried out, which involved analyzing and modelling the historical time series data collected from loop-detector traffic monitoring stations on the Second and Third Ring Roads near Beijing Workers Stadium. The proposed time series models, with expected intervention, are found to provide reasonably accurate forecasts of traffic pattern changes efficiently. They may be used to support transportation planning and management for special events.

The Influence of Psychological Predictors and Cognitive Behavioral Stress Management Intervention on Antiretroviral Therapy (ART) Adherence Among HIV-Positive Female Haitian Alcohol Users

Purpose: Over half the HIV-infected persons in the Caribbean, the second most HIV-impacted region in the world, live in Haiti. Using secondary data from a parent study, this research assessed the effects of psychological and social factors on antiretroviral therapy (ART) adherence among Haitian, HIV-positive, female alcohol users. Theoretical Foundation and Research Questions: Using the Theory of Planned Behavior/Reasoned Action and the Information, Motivation, Behavior skills model as guiding theoretical frameworks, the study examined the effectiveness of an adapted cognitive behavioral stress management (CBSM-A) intervention in improving ART adherence. The effect of psychological factors (depression, anxiety, beliefs about medicine, and social support), social factors (stigma, relationship status, and educational attainment), and alcohol on adherence to ART was assessed. Methods: The sample consisted of 116 female ART patients who were randomly assigned to the CBSM-A intervention or the wait-list control group. Participants completed intervention sessions as well as pre- and post-test assessments. Analyses of variance, t-tests, and point biserial correlations were used to test hypotheses. Results: Surprisingly, ART adherence rates significantly decreased for both groups combined [F (1, 108) = 8.79, p = .004]; there was no significant difference between the intervention and control groups with regard to the magnitude of change between baseline and post assessment. On average, depression decreased significantly among participants in the CBSM-A group only [(t (62) = 5.54, p < .001)]. For both groups combined, alcohol use significantly decreased between baseline and post-assessment [(F (1, 78) = 34.70, p < .001)]; there was no significant difference between the intervention and control groups with regard to the magnitude of change between baseline and post-assessment. None of the variables were significantly correlated with ART adherence. Discussion: Adherence to ART did not improve in this sample, nor were any of the variables significantly associated with adherence. The findings suggest that additional supportive and psychological services may be needed in order to promote higher adherence to ART among HIV-positive females. More research may be needed on this sample; a focus on mental health issues, partner conflict, family and sexual history may allow for better targeting and more successful interventions.

Application of Intervention Analysis to Evaluate the Impacts of Special Events on Freeways

In China in particular, large, planned special events (e.g., the Olympic Games, etc.) are viewed as great opportunities for economic development. Large numbers of visitors from other countries and provinces may be expected to attend such events, bringing in significant tourism dollars. However, as a direct result of such events, the transportation system is likely to face great challenges as travel demand increases beyond its original design capacity. Special events in central business districts (CBD) in particular will further exacerbate traffic congestion on surrounding freeway segments near event locations. To manage the transportation system, it is necessary to plan and prepare for such special events, which requires prediction of traffic conditions during the events. This dissertation presents a set of novel prototype models to forecast traffic volumes along freeway segments during special events. Almost all research to date has focused solely on traffic management techniques under special event conditions. These studies, at most, provided a qualitative analysis and there was a lack of an easy-to-implement method for quantitative analyses. This dissertation presents a systematic approach, based separately on univariate time series model with intervention analysis and multivariate time series model with intervention analysis for forecasting traffic volumes on freeway segments near an event location. A case study was carried out, which involved analyzing and modelling the historical time series data collected from loop-detector traffic monitoring stations on the Second and Third Ring Roads near Beijing Workers Stadium. The proposed time series models, with expected intervention, are found to provide reasonably accurate forecasts of traffic pattern changes efficiently. They may be used to support transportation planning and management for special events.