There are currently very few models that estimate diesel and hybrid bus fuel consumption levels. Those that are available either require significant dynamometer data gathering to calibrate the model parameters and also produce a bang-bang control system (optimum control entails maximum throttle and braking input). This thesis extends the Virginia Tech Comprehensive Power-Based Fuel Consumption Model (VT-CPFM) to model diesel buses and develops an application for it. A procedure is developed to calibrate the bus parameters using publicly available data from the Altoona Bus Research and Testing Center. In addition, calibration is also made using in-field bus fuel consumption data. The research presented in this thesis calibrates model parameters for a total of 10 standard diesel buses and 3 hybrid buses from Altoona and 10 buses from Blacksburg Transit. In the case of the Altoona data, the VT-CPFM estimated fuel consumption levels on the Orange County bus cycle dynamometer test produce an average error of 4.7%. The estimation error is less than 6% for all but two buses with a maximum error of 10.66% for one hybrid bus. The VT-CPFM is also validated using on-road fuel consumption measurements that are derived by creating drive cycles from acceleration information producing an average estimation error of 22%. These higher errors are attributed to the errors associated with constructing the in-field drive cycles given that they are not available. In the case of the Blacksburg Transit buses, the calibrated parameters produce a low sum of mean squared error, less than 0.002, and a coefficient of determination greater than 0.93. Finally an application of the VT-CPFM is presented in the form of a dynamic bus scheduling algorithm. / Master of Science

Traffic signal cycle lengths are traditionally optimized to minimize vehicle delay at intersections using the Webster formulation. This thesis includes two studies that develop new formulations to compute the optimum cycle length of isolated intersections, considering measures of effectiveness such as vehicle delay, fuel consumption and tailpipe emissions. Additionally, both studies validate the Webster model against simulated data. The microscopic simulation software, INTEGRATION, was used to simulate two-phase and four-phase isolated intersections over a range of cycle lengths, traffic demand levels, and signal timing lost times. Intersection delay, fuel consumption levels, and emissions of hydrocarbon (HC), carbon monoxide (CO), oxides of nitrogen (NOx), and carbon dioxide (CO2) were derived from the simulation software. The cycle lengths that minimized the various measures of effectiveness were then used to develop the proposed formulations. The first research effort entailed recalibrating the Webster model to the simulated data to develop a new delay, fuel consumption, and emissions formulation. However, an additional intercept was incorporated to the new formulations to enhance the Webster model. The second research effort entailed updating the proposed model against four study intersections. To account for the stochastic and random nature of traffic, the simulations were then run with twenty random seeds per scenario. Both efforts noted its estimated cycle lengths to minimize fuel consumption and emissions were longer than cycle lengths optimized for vehicle delay only. Secondly, the simulation results manifested an overestimation in optimum cycle lengths derived from the Webster model for high vehicle demands. / Master of Science / Traffic signal timings are traditionally designed to reduce vehicle congestion at an intersection. This thesis is based on two studies that develop new formulations to compute the most efficient signal cycle lengths of intersections, considering vehicle fuel consumption and tailpipe emissions. Additionally, both studies validate the Webster model, a model that is traditionally used in traffic signal design. Simulations were run to determine the intersection delay, fuel consumption levels, and emissions of hydrocarbon (HC), carbon monoxide (CO), oxides of nitrogen (NO_x), and carbon dioxide (CO₂) of the study intersections. To account for the random nature of traffic, each simulation scenario was run twenty different times. The cycle lengths that minimized the noted simulation outputs were then used to develop the proposed formulations. The new formulations demonstrated its estimated cycle lengths to minimize fuel consumption and emissions were longer than cycle lengths designed to minimize vehicle congestion. Secondly, the simulation results manifested an overestimation in optimum cycle lengths derived from the Webster model for high vehicle traffic.