Spatial-temporal mining of archived automatic vehicle location (AVL) data for transit service planning applications

An Automatic Vehicle Location (AVL) system is a computer-based vehicle tracking system that is capable of determining a vehicle's location in real time. As a major technology of the Advanced Public Transportation System (APTS), AVL systems have been widely deployed by transit agencies for purposes such as real-time operation monitoring, computer-aided dispatching, and arrival time prediction. AVL systems make a large amount of transit performance data available that are valuable for transit performance management and planning purposes. However, the difficulties of extracting useful information from the huge spatial-temporal database have hindered off-line applications of the AVL data. ^ In this study, a data mining process, including data integration, cluster analysis, and multiple regression, is proposed. The AVL-generated data are first integrated into a Geographic Information System (GIS) platform. The model-based cluster method is employed to investigate the spatial and temporal patterns of transit travel speeds, which may be easily translated into travel time. The transit speed variations along the route segments are identified. Transit service periods such as morning peak, mid-day, afternoon peak, and evening periods are determined based on analyses of transit travel speed variations for different times of day. The seasonal patterns of transit performance are investigated by using the analysis of variance (ANOVA). Travel speed models based on the clustered time-of-day intervals are developed using important factors identified as having significant effects on speed for different time-of-day periods. ^ It has been found that transit performance varied from different seasons and different time-of-day periods. The geographic location of a transit route segment also plays a role in the variation of the transit performance. The results of this research indicate that advanced data mining techniques have good potential in providing automated techniques of assisting transit agencies in service planning, scheduling, and operations control. ^

An exergy based engineering and economic analysis of sustainable building

To achieve the goal of sustainable development, the building energy system was evaluated from both the first and second law of thermodynamics point of view. The relationship between exergy destruction and sustainable development were discussed at first, followed by the description of the resource abundance model, the life cycle analysis model and the economic investment effectiveness model. By combining the forgoing models, a new sustainable index was proposed. Several green building case studies in U.S. and China were presented. The influences of building function, geographic location, climate pattern, the regional energy structure, and the technology improvement potential of renewable energy in the future were discussed. The building’s envelope, HVAC system, on-site renewable energy system life cycle analysis from energy, exergy, environmental and economic perspective were compared. It was found that climate pattern had a dramatic influence on the life cycle investment effectiveness of the building envelope. The building HVAC system energy performance was much better than its exergy performance. To further increase the exergy efficiency, renewable energy rather than fossil fuel should be used as the primary energy. A building life cycle cost and exergy consumption regression model was set up. The optimal building insulation level could be affected by either cost minimization or exergy consumption minimization approach. The exergy approach would cause better insulation than cost approach. The influence of energy price on the system selection strategy was discussed. Two photovoltaics (PV) systems—stand alone and grid tied system were compared by the life cycle assessment method. The superiority of the latter one was quite obvious. The analysis also showed that during its life span PV technology was less attractive economically because the electricity price in U.S. and China did not fully reflect the environmental burden associated with it. However if future energy price surges and PV system cost reductions were considered, the technology could be very promising for sustainable buildings in the future.

Effect of child care subsidies on price and quality of care for low -income families

This dissertation examines the effect of regulations, resource and referral agencies, and subsidies on price and quality of care in child care centers. This research is based on a carefully developed conceptual framework that incorporates the factors affecting the demand and supply of child care. The first step in developing this framework is sketching out the structural equations. The structural equations help us understand the underlying behavior of individuals and firms making a decision. The exogenous variables are vector of attributes relating to family characteristics, child characteristics, regulations, subsidy, community characteristics and prices of inputs. Based on the structural equations, reduced form equations are estimated to find the effect of each of the exogenous variables on each of the endogenous variables. Reduced form equations help us answer public policy questions. The sample for this study is from the 1990 Profile of Child Care Settings (PCCS) data in which 2,089 center based programs were interviewed.^ Child/Staff Ratio (Group Level). Results indicate that among subsidies, only the state subsidy per child in poverty has a significant effect on the child/staff ratio at the group level. Presence of resource and referral agencies also increase the child/staff ratio at the group level. Also when the maximum center group size regulation for 25-36 months becomes more stringent, the child/staff ratio at the group level decreases.^ Child/Staff Ratio (Center Level). When the regulations for the maximum child/staff ratio for age groups 13-24 months and 37-60 months become lax, the child/staff ratio for the center increases. As the regulation for maximum group size for infants becomes stringent, the child/staff ratio decreases. An interesting finding is that as the regulations for maximum group size for age groups 13-24 months and 25-36 months become stringent, the child/staff ratio for the center increases. Another significant finding is that when a center is located in a rural area the child/staff ratio is significantly lower.^ Center Weighted Average Hourly Fees. Maximum group size regulations for age groups 25-36 months and 37-60 months have a negative effect on center hourly fee. Maximum child staff regulations for age groups 13-24 months and 37-60 months have a negative effect on center hourly fee. Maximum child staff regulations for age groups 0-12 months and 25-36 months have a positive effect on center hourly fee. Findings also indicate that the center average hourly price is lower when there is a resource and referral agency present. Cost adjusted prekindergarten funds and JOBS child care subsidies have a negative effect on average hourly fee. Cost adjusted social services block grant and state subsidy per child in poverty have a positive effect on the average hourly price. A major finding of this dissertation is the interaction of subsidy and regulatory variables.^ Another major finding is that child/staff ratio at the group level is lower when there is an interaction between geographic location and nature of center sponsorship. ^

An Exergy Based Engineering and Economic Analysis of Sustainable Building

To achieve the goal of sustainable development, the building energy system was evaluated from both the first and second law of thermodynamics point of view. The relationship between exergy destruction and sustainable development were discussed at first, followed by the description of the resource abundance model, the life cycle analysis model and the economic investment effectiveness model. By combining the forgoing models, a new sustainable index was proposed. Several green building case studies in U.S. and China were presented. The influences of building function, geographic location, climate pattern, the regional energy structure, and the technology improvement potential of renewable energy in the future were discussed. The building’s envelope, HVAC system, on-site renewable energy system life cycle analysis from energy, exergy, environmental and economic perspective were compared. It was found that climate pattern had a dramatic influence on the life cycle investment effectiveness of the building envelope. The building HVAC system energy performance was much better than its exergy performance. To further increase the exergy efficiency, renewable energy rather than fossil fuel should be used as the primary energy. A building life cycle cost and exergy consumption regression model was set up. The optimal building insulation level could be affected by either cost minimization or exergy consumption minimization approach. The exergy approach would cause better insulation than cost approach. The influence of energy price on the system selection strategy was discussed. Two photovoltaics (PV) systems – stand alone and grid tied system were compared by the life cycle assessment method. The superiority of the latter one was quite obvious. The analysis also showed that during its life span PV technology was less attractive economically because the electricity price in U.S. and China did not fully reflect the environmental burden associated with it. However if future energy price surges and PV system cost reductions were considered, the technology could be very promising for sustainable buildings in the future.

Effect of child care subsidies on price and quality of care for low -income families

This dissertation examines the effect of regulations, resource and referral agencies, and subsidies on price and quality of care in child care centers. This research is based on a carefully developed conceptual framework that incorporates the factors affecting the demand and supply of child care. The first step in developing this framework is sketching out the structural equations. The structural equations help us understand the underlying behavior of individuals and firms making a decision. The exogenous variables are vector of attributes relating to family characteristics, child characteristics, regulations, subsidy, community characteristics and prices of inputs. Based on the structural equations, reduced form equations are estimated to find the effect of each of the exogenous variables on each of the endogenous variables. Reduced form equations help us answer public policy questions. The sample for this study is from the 1990 Profile of Child Care Settings (PCCS) data in which 2,089 center based programs were interviewed. Child/Staff Ratio (Group Level): Results indicate that among subsidies, only the state subsidy per child in poverty has a significant effect on the child/staff ratio at the group level. Presence of resource and referral agencies also increase the child/staff ratio at the group level. Also when the maximum center group size regulation for 25-36 months becomes more stringent, the child/staff ratio at the group level decreases. Child/Staff Ratio (Center Level): When the regulations for the maximum child/staff ratio for age groups 13-24 months and 37-60 months become lax, the child/staff ratio for the center increases. As the regulation for maximum group size for infants becomes stringent, the child/staff ratio decreases. An interesting finding is that as the regulations for maximum group size for age groups 13-24 months and 25-36 months become stringent, the child/staff ratio for the center increases. Another significant finding is that when a center is located in a rural area the child/staff ratio is significantly lower. Center Weighted Average Hourly Fees: Maximum group size regulations for age groups 25-36 months and 37-60 months have a negative effect on center hourly fee. Maximum child staff regulations for age groups 13-24 months and 37-60 months have a negative effect on center hourly fee. Maximum child staff regulations for age groups 0-12 months and 25-36 months have a positive effect on center hourly fee. Findings also indicate that the center average hourly price is lower when there is a resource and referral agency present. Cost adjusted prekindergarten funds and JOBS child care subsidies have a negative effect on average hourly fee. Cost adjusted social services block grant and state subsidy per child in poverty have a positive effect on the average hourly price. A major finding of this dissertation is the interaction of subsidy and regulatory variables. Another major finding is that child/staff ratio at the group level is lower when there is an interaction between geographic location and nature of center sponsorship.

Estimation of average annual daily traffic (AADT) using geographically weighted regression (GWR) method and geographic information system (GIS)

Annual average daily traffic (AADT) is important information for many transportation planning, design, operation, and maintenance activities, as well as for the allocation of highway funds. Many studies have attempted AADT estimation using factor approach, regression analysis, time series, and artificial neural networks. However, these methods are unable to account for spatially variable influence of independent variables on the dependent variable even though it is well known that to many transportation problems, including AADT estimation, spatial context is important. ^ In this study, applications of geographically weighted regression (GWR) methods to estimating AADT were investigated. The GWR based methods considered the influence of correlations among the variables over space and the spatially non-stationarity of the variables. A GWR model allows different relationships between the dependent and independent variables to exist at different points in space. In other words, model parameters vary from location to location and the locally linear regression parameters at a point are affected more by observations near that point than observations further away. ^ The study area was Broward County, Florida. Broward County lies on the Atlantic coast between Palm Beach and Miami-Dade counties. In this study, a total of 67 variables were considered as potential AADT predictors, and six variables (lanes, speed, regional accessibility, direct access, density of roadway length, and density of seasonal household) were selected to develop the models. ^ To investigate the predictive powers of various AADT predictors over the space, the statistics including local r-square, local parameter estimates, and local errors were examined and mapped. The local variations in relationships among parameters were investigated, measured, and mapped to assess the usefulness of GWR methods. ^ The results indicated that the GWR models were able to better explain the variation in the data and to predict AADT with smaller errors than the ordinary linear regression models for the same dataset. Additionally, GWR was able to model the spatial non-stationarity in the data, i.e., the spatially varying relationship between AADT and predictors, which cannot be modeled in ordinary linear regression. ^

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.