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During the 20th century, transportation programs were focused on the development of the basic infrastructure for the transportation networks. In the 21st century, the focus has shifted to management and operations of these networks. Transportation network reliability measure plays an important role in judging the performance of the transportation system and in evaluating the impact of new Intelligent Transportation Systems (ITS) deployment. The measurement of transportation network travel time reliability is imperative for providing travelers with accurate route guidance information. It can be applied to generate the shortest path (or alternative paths) connecting the origins and destinations especially under conditions of varying demands and limited capacities. The measurement of transportation network reliability is a complex issue because it involves both the infrastructure and the behavioral responses of the users. Also, this subject is challenging because there is no single agreed-upon reliability measure. This dissertation developed a new method for estimating the effect of travel demand variation and link capacity degradation on the reliability of a roadway network. The method is applied to a hypothetical roadway network and the results show that both travel time reliability and capacity reliability are consistent measures for reliability of the road network, but each may have a different use. The capacity reliability measure is of special interest to transportation network planners and engineers because it addresses the issue of whether the available network capacity relative to the present or forecast demand is sufficient, whereas travel time reliability is especially interesting for network users. The new travel time reliability method is sensitive to the users' perspective since it reflects that an increase in segment travel time should always result in less travel time reliability. And, it is an indicator of the operational consistency of a facility over an extended period of time. This initial theoretical effort and basic research was followed by applying the new method to the I-4 corridor in Orlando, Florida. This dissertation utilized a real life transportation data warehouse to estimate travel time reliability of the I-4 corridor. Four different travel time stochastic models: Weibull, Exponential, Lognormal, and Normal were tested. Lognormal was the best-fit model. Unlike the mechanical equipments, it is unrealistic that any freeway segment can be traversed in zero seconds no matter how fast the vehicles are. So, an adjustment of the developed best-fit statistical model (Lognormal) location parameter was needed to accurately estimate the travel time reliability. The adjusted model can be used to compute and predict travel time reliability of freeway corridors and report this information in real time to the public through traffic management centers. Compared to existing Florida Method and California Buffer Time Method, the new reliability method showed higher sensitivity to geographical locations, which reflects the level of congestion and bottlenecks. The major advantages/benefits of this new method to practitioners and researchers over the existing methods are its ability to estimate travel time reliability as a function of departure time, and that it treats travel time as a continuous variable that captures the variability experienced by individual travelers over an extended period of time. As such, the new method developed in this dissertation could be utilized in transportation planning and freeway operations for estimating the important travel time reliability measure of performance. Then, the segment length impacts on travel time reliability calculations were investigated utilizing the wealth of data available in the I-4 data warehouse. The developed travel time reliability models showed significant evidence of the relationship between the segment length and the results accuracy. The longer the segment, the less accurate were the travel time reliability estimates. Accordingly, long segments (e.g., 25 miles) are more appropriate for planning purposes as a macroscopic performance measure of the freeway corridor. Short segments (e.g., 5 miles) are more appropriate for the evaluation of freeway operations as a microscopic performance measure. Further, this dissertation has explored the impact of relaxing an important assumption in reliability analysis: Link independency. In real life, assuming that link failures on a road network are statistically independent is dubious. The failure of a link in one particular area does not necessarily result in the complete failure of the neighboring link, but may lead to deterioration of its performance. The "Cause-Based Multimode Model" (CBMM) has been used to address link dependency in communication networks. However, the transferability of this model to transportation networks has not been tested and this approach has not been considered before in the calculation of transportation networks' reliability. This dissertation presented the CBMM and applied it to predict transportation networks' travel time reliability that an origin demand can reach a specified destination under multimodal dependency link failure conditions. The new model studied the multi-state system reliability analysis of transportation networks for which one cannot formulate an "all or nothing" type of failure criterion and in which dependent link failures are considered. The results demonstrated that the newly developed method has true potential and can be easily extended to large-scale networks as long as the data is available. More specifically, the analysis of a hypothetical network showed that the dependency assumption is very important to obtain more reasonable travel time reliability estimates of links, paths, and the entire network. The results showed large discrepancy between the dependency and independency analysis scenarios. Realistic scenarios that considered the dependency assumption were on the safe side, this is important for transportation network decision makers. Also, this could aid travelers in making better choices. In contrast, deceptive information caused by the independency assumption could add to the travelers' anxiety associated with the unknown length of delay. This normally reflects negatively on highway agencies and management of taxpayers' resources.

Transit travel time and operating speed influence service attractiveness, operating cost, system efficiency and sustainability. The Tri-County Metropolitan Transportation District of Oregon (TriMet) provides public transportation service in the tri-county Portland metropolitan area. TriMet was one of the first transit agencies to implement a Bus Dispatch System (BDS) as a part of its overall service control and management system. TriMet has had the foresight to fully archive the BDS automatic vehicle location and automatic passenger count data for all bus trips at the stop level since 1997. More recently, the BDS system was upgraded to provide stop-level data plus 5-second resolution bus positions between stops. Rather than relying on prediction tools to determine bus trajectories (including stops and delays) between stops, the higher resolution data presents actual bus positions along each trip. Bus travel speeds and intersection signal/queuing delays may be determined using this newer information. This thesis examines the potential applications of higher resolution transit operations data for a bus route in Portland, Oregon, TriMet Route 14. BDS and 5-second resolution data from all trips during the month of October 2014 are used to determine the impacts and evaluate candidate trip time models. Comparisons are drawn between models and some conclusions are drawn regarding the utility of the higher resolution transit data. In previous research inter-stop models were developed based on the use of average or maximum speed between stops. We know that this does not represent realistic conditions of stopping at a signal/crosswalk or traffic congestion along the link. A new inter-stop trip time model is developed using the 5-second resolution data to determine the number of signals encountered by the bus along the route. The variability in inter-stop time is likely due to the effect of the delay superimposed by signals encountered. This newly developed model resulted in statistically significant results. This type of information is important to transit agencies looking to improve bus running times and reliability. These results, the benefits of archiving higher resolution data to understand bus movement between stops, and future research opportunities are also discussed.

Wiener Linien’s tram network is one of the largest and busiest of its kind worldwide. Maintaining and improving the Level of Service (LoS) is one of the major tasks of the operations division. To direct these improvements efficiently to lines and sections of lines in need, tram line performance needs to be assessed. In this master thesis a Python-based model is developed to assess tram line performance using ideal operational constraints. Furthermore, the model is capable of computing the energy consumption for this optimal case. The tool computes Undisturbed Optimal Travel Times (UOTTs) which serve as a benchmark for tram line performance. Therefor it builds on track alignment data (curves and radii, gradients and switches), speed restriction data and a set of optimal parameters (no traffic, constant acceleration, etc.). Furthermore, train resistance, curve speeds and vehicle type are taken into consideration. These parameters are investigated and selected based on literature studies, interviews with employees of Wiener Linien, as well as field tests. Finally, the model results are compared to real world data for evaluation. A discussion of the results regarding further applications and improvements is performed. / Spårvagnsnätet i Wiener Linien är ett av de största och mest trafikerade i sitt slag i världen. Att underhålla och förbättra servicenivån är en av de viktigaste uppgifterna för driftsavdelningen. För att dessa förbättringar ska kunna riktas effektivt till de linjer som mest behöver det måste spårvagnarnas prestanda bedömas. I detta exjobb utvecklas en Python-baserad modell för att bedöma spårvagnslinjens prestanda med hjälp av ideala trafikparametrar. Dessutom kan modellen beräkna energiförbrukningen för dessa optimala scenarier.Verktyget beräknar Ostörda Optimala Restider (UOTTs) som blir referenser för spårvagnslinjernas prestanda. Metoden bygger på spårutformning (kurvor och radier, lutningar och växlar), hastighetsbegränsningar, och en uppsättning optimala parametrar (ingen vägtrafik, konstant acceleration osv.). Dessutom tas hänsyn till rullmotstånd, hastighet i kurvor och fordonstyp. Dessa parametrar undersöks och väljs ut genom litteraturstudier, intervjuer med anställda vid Wiener Linien samt fälttester.Modellresultaten jämförs med uppmätta restidsdata för utvärdering av den dagliga driften. En diskussion av ytterligare tillämpningar och förbättringar förs.

Congestion is a world-wide problem in transportation. One major reason is random interruptions. The traffic network is inherently stochastic, and strong dependencies exist among traffic quantities, e.g., travel time, traffic speed, link volume. Information in stochastic networks can help with adaptive routing in terms of minimizing expected travel time or disutility. Routing in such networks is different from that in deterministic networks or when stochastic dependencies are not taken into account. This dissertation addresses the optimal routing problems, including the optimal a priori path problem and the optimal adaptive routing problem with different information scenarios, in stochastic and time-dependent networks with explicit consideration of the correlations between link travel time random variables. There are a number of studies in the literature addressing the optimal routing problems, but most of them ignore the correlations between link travel times. The consideration of the correlations makes the problem studied in this dissertation difficult, both conceptually and computationally. The optimal path finding problem in such networks is different from that in stochastic and time-dependent networks with no consideration of the correlations. This dissertation firstly provides an empirical study of the correlations between random link travel times and also verifies the importance of the consideration of the spatial and temporal correlations in estimating trip travel time and its reliability. It then shows that Bellman's principle of optimality or non-dominance is not valid due to the time-dependency and the correlations. A new property termed purity is introduced and an exact label-correcting algorithm is designed to solve the problem. With the fast advance of telecommunication technologies, real-time traffic information will soon become an integral part of travelers' route choice decision making. The study of optimal adaptive routing problems is thus timely and of great value. This dissertation studies the problems with a wide variety of information scenarios, including delayed global information, real-time local information, pre-trip global information, no online information, and trajectory information. It is shown that, for the first four partial information scenarios, Bellman's principle of optimality does not hold. A heuristic algorithm is developed and employed based on a set of necessary conditions for optimality. The same algorithm is showed to be exact for the perfect online information scenario. For optimal adaptive routing problem with trajectory information, this dissertation proves that, if the routing policy is defined in a similar way to other four information scenarios, i.e., the trajectory information is included in the state variable, Bellman's principle of optimality is valid. However, this definition results in a prohibitively large number of the states and the computation can hardly be carried out. The dissertation provides a recursive definition for the trajectory-adaptive routing policy, for which the information is not included in the state variable. In this way, the number of states is small, but Bellman's principle of optimality or non-dominance is invalid for a similar reason as in the optimal path problem. Again purity is introduced to the trajectory-adaptive routing policy and an exact algorithm is designed based on the concept of decreasing order of time.

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As concerns about climate change increase, many people are calling for reductions in the use of fossil fuels and encouraging a shift to more sustainable and less polluting transportation modes. Cities and urban areas are more concerned because their population currently comprises over half of the world's population. Sustainable transportation modes such as cycling, walking, and use of public transit and electric vehicles can benefit the environment in many ways, including a reduction in toxic greenhouse gas (GHG) emissions and noise levels. In order to enhance the trend of using sustainable modes of transportation, tools, measures, and planning techniques similar to those used for vehicular transportation need to be developed. In this dissertation, we consider four problems in the context of different sustainable modes of transportation, namely, cycling, rail, public transit, and ridesharing. We develop different models to predict bike travel times for use in bike share systems (BSSs) using random forest (RF), least square boosting (LSBoost), and artificial neural network (ANN) techniques. We also use cycling Global Positioning System (GPS) data collected from 10 people (3 females and 7 males) to study cyclists' acceleration/deceleration behavior. Moreover, we develop a continuous rail transit simulator (RailSIM) intended for multi-modal energy-efficient routing applications. Finally, we propose a dynamic trip planning system that integrates ridesharing and public transit. The work done in this dissertation can help encouraging more people to move to more sustainable modes of transportation. / Doctor of Philosophy / As concerns about climate change increase, many people are calling for reductions in the use of fossil fuels and encouraging a shift to more sustainable and less polluting transportation modes. Cities and urban areas are more concerned because their population currently comprises over half of the world's population. Sustainable transportation modes such as cycling, walking, and use of public transit and electric vehicles can benefit the environment in many ways, including a reduction of toxic greenhouse gas (GHG) emissions and noise levels. In order to enhance the trend of using sustainable modes of transportation, tools, measures, and planning techniques similar to those used for vehicular transportation need to be developed. In this dissertation, we consider four problems in the context of different sustainable modes of transportation, namely, cycling, rail, public transit, and ridesharing. We develop different models to predict bike travel times in bike share systems (BSSs) using machine learning techniques. We also use cycling Global Positioning System (GPS) data collected from 10 people (3 females and 7 males) to study cyclists' acceleration/deceleration behavior. Moreover, we develop a continuous rail transit simulator (RailSIM) intended for multi-modal energy-efficient routing applications. Finally, we propose a dynamic trip planning system that integrates ridesharing and public transit. The work done in this dissertation can help encouraging more people to move to more sustainable modes of transportation.

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.

Not too long ago, commercial supersonic aircraft flights were part of the air transportation system. In the 1970's we had the Russian-built Tupolev Tu-144 and the BAC/Aerospatiale Concorde, the latest being tin operation for 27 years. The work documented in this dissertation focused on the viability of bringing back supersonic aircraft as a transportation mode. Throughout three years, Virginia Tech and a team from NASA have been combining efforts to develop a model capable of predicting future air travel demand for supersonic vehicles. The model can predict future supersonic commercial services and allows aircraft designers from NASA to optimize aircraft performance and characteristics by maximizing the potential air travel demand. The final product of this study was the development of the Low-Boom Supersonic Aircraft Model (LBSAM). The development progress took three years to be completed, and during each year, a version of the model with the preliminary predictions was made available to NASA. Each of the three versions of the model predicts future supersonic commercial services. What differentiates each version is the data, method, and aircraft type/design implemented; the latest version of the model is more realistic and provides a higher number of functionalities. The first version of the model predicted the possible supersonic commercial service for three aircraft types: each with two variations. An 18-seat, 40-seat, and 60-seat low-boom and non-low-boom aircraft were analyzed. The second version of the model analyzed a 20-seat and 40-seat low-boom, non-low-boom aircraft with restrictions and non-low-boom aircraft without restrictions. The latest version of the model tries to estimate potential demand for a 43-seat and a 52-seat supersonic low-boom aircraft design. The low-boom concept refers to the implementation of technology that reduces the loudness of a sonic boom. A non-low-boom concept refers to an aircraft flying faster than Mach 1 with the technology's implementation that reduces the loudness of a sonic boom. The final results suggest that for a 52-seat LBSA, the potential worldwide demand is as follows. • 33.4 million seats worldwide. Assuming an overland range of 3,200 nm., an overland Mach 1.7, and an overland fuel scale factor of 0.98. • 772 aircraft needed worldwide. Assuming an overland range of 2,800 nm., an overland Mach 1.7, and an overland fuel scale factor of 0.90. • 1,032 one-way OD pairs where LBSA can operate. Assuming an overland range of 2,800 nm., an overland Mach 1.7, and an overland fuel scale factor of 0.90. The LBSAM is mainly driven by the cost per passenger mile values calculated for each one-way Origin-Destination (OD) pair. Additional uncertainties in the model include the market share and annual aircraft utilization. The market share refers to the percent of the demand that will switch from current subsonic commercial services to commercial supersonic services. During the three-year work, we considered a market share of 50% and 100%. Aircraft utilization refers to the number of hours that the airline will be able to use the aircraft. The majority of the projections were based on a 3,500-hour aircraft utilization. / Doctor of Philosophy / Not too long ago, commercial supersonic aircraft flights were part of the air transportation system. An aircraft flying faster than the speed of sound is known as an aircraft flying at supersonic speed. Current commercial aircraft fly at subsonic speed. Subsonic speed refers to aircraft flying at a speed lower than the speed of sound. In the 1970's we had the Russian-built Tupolev Tu-144 and the BAC/Aerospatiale Concorde, the latest being tin operation for 27 years. The work documented in this dissertation focused on the viability of bringing back supersonic aircraft as a transportation mode. Throughout three years, Virginia Tech and a team from NASA have been combining efforts to develop a model capable of predicting future air travel demand for supersonic vehicles. The model can predict future supersonic commercial services and allows aircraft designers from NASA to optimize aircraft performance and characteristics by maximizing the potential air travel demand. The purpose of this dissertation effort is to provide a better understanding of what could be the potential commercial demand for supersonic flight in the near future. We consider all the benefits and characteristics of supersonic flight and studied in detail what percentage of the travelers might be willing to migrate from the current subsonic market to the supersonic market. We estimated this ratio by studying the spending behavior of passengers in the current market. How much more are passengers willing to pay to save time? We can infer how much travelers value their time by comparing direct flights versus flights with an intermediate stop. The results show that a demand of 33.4 million seats could be reached by the year 2040. The supersonic market would consist of more than one thousand one-way origin-destination pairs worldwide, and more than seven hundred supersonic aircraft are expected to satisfy the forecast demand.