Traffic congestion has continued to grow as a result of urbanization, which is associated with an increase in car ownership. As a way to improve the efficiency of the transportation system, emerging technologies including Connected Automated Vehicles (CAVs), loop detectors, Advanced Traveler Information Systems (ATISs), and Advanced Public Transportation Systems (APTSs) are being deployed. One of the successful techniques that has demonstrated benefits for system users, operators and agencies is Transit Signal Priority (TSP). TSP favors transit vehicles in the allocation of green times at traffic signals. A successful deployment of TSP depends on different factors including the prediction of various components of transit vehicle travel times to predict when a vehicle would arrive at a traffic signal. Current TSP state-of-the-art and state-of-practice disregards the impact of bus stops, transit vehicle characteristics, driver, and the prevailing traffic conditions on the predicted arrival time of transit vehicles at traffic signals. Considering these factors is important the success of TSP hinges on the ability to predict transit vehicle arrival times at traffic signals in order to provide these vehicles with priority service. The main contribution of this research effort relates to the modeling of the various components of transit vehicle travel times. This model explicitly captures the impact of passengers, drivers and vehicle characteristics on transit vehicle travel times thus providing better models for use in various transit applications, including TSP. Furthermore, the thesis presents a comprehensive understanding of the determinants of each travel time component. In essence, the determinants of each component, the stochasticity in these determinants and the correlation between them are explicitly modeled and captured. To achieve its contribution, the study starts by improving the current state-of-the-art and state-of-practice transit vehicle boarding/alighting (BA) models by explicitly accounting for the different factors that impact BA times while ensuring a relatively generalized formulation. Current formulations are specific for the localities and bus configurations that they were developed for. Alternatively, the proposed BA time model is independent of the transit vehicle capacity and transit vehicle configuration (except for the fact that it is only valid for two-door buses – a separate door for alighting and boarding the bus) and accounts for the number of on-board passengers, boarding and alighting passengers. The model also captures the stochasticity and the correlation between the model coefficients with minimum computational requirements. Next the model was extended to capture the bus driver and vehicle impacts on the transit vehicle delay in the vicinity of bus stops, using a vehicle kinematics model with maximum speed and acceleration constraints to model the acceleration/deceleration delay. The validation of the model was done using field data that cover different driving conditions. Results of this work found that the proposed formulation successfully integrated the human and vehicle characteristics component in the model and that the new formulation improves the estimation of the total delay that transit vehicles experience near bus stops. Finally, the model was extended to estimate the time required to merge into the adjacent lane and the time required to traverse a queue upstream of a traffic signal. The final part of this study models the bus arrival time at traffic signal using shockwave and prediction model in a connected environment. This section aims to model the transit vehicle arrival time at traffic signal considering the impact of signal timing and the prevailing traffic conditions. In summary, the proposed model overcomes the current state-of-the-art models in the following ways: 1) it accounts for the vehicle capacity and the number of on-board passengers on bus BA times, 2) it captures the stochasticity in the bus stop demand and the associated BA times, 3) it captures the impact of the traffic in modeling the delay at a bus stop , 4) it incorporates the driver and vehicle impact by modeling the acceleration and deceleration time, and 5) it uses shockwave analysis to estimate bus arrival times through the use of emerging technology data. Through statistical modeling and evaluation using field and simulated data, the model overcomes the current state-of practice and state-of art transit vehicle arrival time models. / Doctor of Philosophy / Traffic congestion grows rapidly causing increment in travel time, reducing travel time reliability, and reducing the number of public transportation riders. Using the Advanced Public Transportation Systems (APTS) technology with Advanced Traveler Information Systems (ATISs) helps in improving transportation network travel time by providing real-time travel information. One of the successful techniques that has demonstrated benefits for system users, operators and agencies is Transit Signal Priority (TSP). A successful deployment of TSP depends on different factors including the prediction of various components of transit vehicle travel times to predict when a vehicle would arrive at a traffic signal. Current TSP state-of-the-art and state-of-practice disregards the impact of bus stops, transit vehicle characteristics, driver, and the prevailing traffic conditions on the predicted arrival time of transit vehicles at traffic signals. The difficulty of modeling the various determinants of the transit vehicle travel time as explicit variables rather than include some of them are implicitly modeled due to two main reasons. First, there are various significant factors affecting estimating the transit vehicle arrival time including; the passenger demand at bus stop, driver characteristics, vehicle characteristics and the adjacent prevailing traffic conditions. Second, the stochasticity and the fluctuation nature of each variables as they differ spatiotemporally. The research presented in this thesis provides a comprehensive investigation of the determinants of different transit vehicle travel time components of the transit vehicle arrival time at traffic signal leading to a better implementing of TSP. This study was initiated due to the noticeable overlooking of the different factors including human and vehicle behavior in the current state-of-practice and state-of-art which, as a result, fails to capture and incorporate the impact of these components on the implementing of TSP.

This thesis examines the marginal cost of congested travel to a variety of businesses by observing time spent in congestion and estimating excess labor costs based upon the relevant value of time. The fleets in the scoping study represented commercial deliveries of goods and services, government agencies, and transit systems. Observations on limited-access expressways within the 13-county Atlanta metropolitan region were used in the analysis. Vehicles were monitored by using a passive GPS assembly that transmitted speed and location data in real-time to an off-site location. Installation and operation during the observation period required no interaction from the driver. Over 217 hours of good freeway movement during 354 vehicle-days was recorded. Rates of delay, expressed as a unit of lost minutes per mile traveled, were calculated by taking the difference in speeds observed during congestion from an optimal free-flow speed of 45 mph and dividing that by the distance traveled per segment. The difference between the 50th and 95th percentile delay rates was used as the measure for travel unreliability. Daily average values of extra time needed per fleet vehicle to ensure on-time arrivals were derived, and the median buffer across all fleets was 1.65 hours of added time per vehicle. Weekly marginal costs per fleet vehicle were estimated by factoring in the corresponding driver wages or hourly operation costs (for transit fleets). Equivalent toll rates were calculated by multiplying the 95th percentile delay rate by the hourly costs. The equivalent toll per mile traveled was representative of an equal relationship between the marginal costs of congestion experienced and a hypothetical state of free-flow travel (under first-best rules of marginal cost pricing). The median equivalent toll rates across all fleets was $0.43 per mile for weekday mornings, $0.13 per mile for midday weekdays, $0.53 per mile for afternoon weekdays and $0.01 per mile for weekday nights and weekends.