Travel times, operating speeds, and service reliability influence costs and service attractiveness. This paper outlines an approach to quantify how these metrics change after a modification of roadway design or transit routes using archived transit data. The Tri-County Metropolitan Transportation District of Oregon (TriMet), Portland's public transportation provider, archives automatic vehicle location (AVL) data for all buses as part of their bus dispatch system (BDS). This research combines three types of AVL data (stop event, stop disturbance, and high-resolution) to create a detailed account of transit behavior; this probe data gives insights into the behavior of transit as well as general traffic. The methodology also includes an updated approach for confidence intervals estimates that more accurately represent of range of speed and travel time percentile estimates. This methodology is applied to three test cases using a month of AVL data collected before and after the implementation of each roadway change. The results of the test cases highlight the broad applicability for this approach to before-and-after studies.

Traveler information systems play a significant role in most travelers’ daily trips. These systems assist travelers in choosing the best routes to reach their destinations and possibly select suitable departure times and modes for their trips. Connected Vehicle (CV) technologies are now in the pilot program stage. Vehicle-to-Infrastructure (V2I) communications will be an important source of data for traffic agencies. If this data is processed properly, then agencies will be able to better determine traffic conditions, allowing them to take proper countermeasures to remedy transportation system problems under different conditions. This research focuses on developing methods to assess the potential of utilizing CV data to support the traveler information system data collection process. The results from the assessment can be used to establish a timeline indicating when an agency can stop investing, at least partially, in traditional technologies, and instead rely on CV technologies for traveler information system support. This research utilizes real-world vehicle trajectory data collected under the Next Generation Simulation (NGSIM) program and simulation modeling to emulate the use of connected vehicle data to support the traveler information system. NGSIM datasets collected from an arterial segment and a freeway segment are used in this research. Microscopic simulation modeling is also used to generate required trajectory data, allowing further analysis, which is not possible using NGSIM data. The first step is to predict the market penetration of connected vehicles in future years. This estimated market penetration is then used for the evaluation of the effectiveness of CV-based data for travel time and volume estimation, which are two important inputs for the traveler information system. The travel times are estimated at different market penetrations of CV. The quality of the estimation is assessed by investigating the accuracy and reliability with different CV deployment scenarios. The quality of volume estimates is also assessed using the same data with different future scenarios of CV deployment and partial or no detector data. Such assessment supports the identification of a timeline indicating when CV data can be used to support the traveler information system.

The need for a better understanding of journey to work behavior has never before been so important. Many transportation corridors are functioning at unacceptable levels of service and many at times to their capacity. This phenomenon is more pronounced during peak period when majority of the population is making their work trip. This research builds on the recent developments in structural equations modeling technique for identifying the socio-demographic influences on the commute behavior of the population in Florida. Towards this purpose a series of five structural equations models are estimated using aggregate level data from Census 2000. Each of these models has a set of journey to work characteristics that are observed for their behavior against prevalent sociodemographic characteristics. The journey to work characteristics identified are exhaustively studied for various relationships to the socio-demographic characteristics. The model estimation led to the identification of relations between various journey to work characteristics and the socio-demographic characteristics at the Census Tract level. Some of the results obtained supported other studies performed earlier. It is hoped that the findings of this research would broaden the horizon in understanding journey to work behavior of the population of Florida.

Pollutants follow various subsurface and surface water pathways from sources within a catchment to its outlet and may cause detrimental effects on downstream water quality and ecosystems. Along their different transport pathways through a catchment, pollutants may be attenuated subject to different physical and biogeochemical processes. In this thesis, physical process effects on such catchment-scale pollutant transport and attenuation, resulting coastal pollutant loading and its efficient abatement are investigated. For this purpose, pollutant transport-attenuation is modeled both generically using a Lagrangian Stochastic Advective-Reactive (LaSAR) approach and site specifically for the Swedish Norrström basin using the GIS-based dynamic nitrogen transport-attenuation model POLFLOW. Furthermore, the role of such modeling for catchment-scale pollutant abatement is also investigated by use of economic optimization modeling. Results indicate that appropriate characterization of catchment-scale solute transport and attenuation processes requires accurate quantification of the specific solute pathways from different sources in a catchment, through the subsurface and surface water systems of the catchment, to the catchment outlet. The various physical processes that act on solute transported along these pathways may be quantified appropriately by use of relevant solute travel time distributions for each water subsystem that the pathways cross through the catchment. Such distributions capture the physical solute travel time variability from source to catchment outlet and its effects on reactive pollutant transport. Results of this thesis show specifically that neglect of such physical solute travel time variability in large-scale models of nitrogen transport and attenuation in catchments may yield misleading model estimates of nitrogen attenuation rates. Results for nitrogen abatement optimization in catchments further indicate that inefficient solutions for coastal nitrogen load reduction may result from simplifying physical transport assumptions made in different catchment-scale nitrogen transport-attenuation models. Modeling of possible future nitrogen management scenarios show also that slow nitrogen transport and reversible mass transfer processes in the subsurface water systems of catchments may greatly delay and temporally redistribute coastal nitrogen load effects of inland nitrogen source abatement over decades or much longer. Achievement of the national Swedish environmental objective to reduce the anthropogenic coastal nitrogen loading by 30% may therefore require up to a 40% reduction of both point sources, for achieving a fast coastal load response, and diffuse sources, for maintaining the coastal load reduction also in the long term. / QC 20100908

There are many different types of automatic data collection technologies that have been used in transportation system applications such as pneumatic tubes, radar, video cameras, inductive loops detectors, wireless toll tags, and global positioning systems (GPS). Nevertheless, there are still multiple examples of important and helpful transportation system data that still require manual data collection. In this research, the automatic transportation system data collection capabilities are expanded by enhancements in the use of wireless communications technology. In recent years, smartphones and electronic peripherals with wireless communication capabilities have become very popular. Many of these electronic devices include a Bluetooth or Wi-Fi wireless radio, whose presence in a vehicle can be used as a vehicle identifier. With wireless on-board devices available now and in the future, this research explores how roadside data collection units (DCUs) communicating with on-board devices can be used for the automated data collection of important road system data such as intersection performance data. To this end, two approaches for wirelessly collecting vehicle movement over a short road segment were explored. One approach utilized the collection and triangulation of wireless signal strength data, and demonstrated the capabilities and limitations of this approach. The second approach focused on developing methods for utilizing wireless signal strength data for vehicle point detection and identification. The vehicle point detection methods developed were applied to collect travel time data over signalized arterial roads, and to collect intersection delay data for a three way stop controlled intersection. The results from these case studies indicate a significant advantage in the proposed data collection system over the existing data collection approaches presented in the literature. / Graduation date: 2013

Recent advances in travel demand modeling have concentrated on adding behavioral realism by focusing on an individual’s activity participation. And, to account for trip-chaining, tour-based methods are largely replacing trip-based methods. Alongside these advances and innovations in dynamic traffic assignment (DTA) techniques, however, time-of-day (TOD) modeling remains an Achilles’ heel. As congestion worsens and operators turn to variable road pricing, sensors are added to networks, cell phones are GPS-enabled, and DTA techniques become practical, accurate time-of-day forecasts become critical. In addition, most models highlight tradeoffs between travel time and cost, while neglecting variations in travel time. Research into stated and revealed choices suggests that travel time variability can be highly consequential. This dissertation introduces a method for imputing travel time variability information as a continuous function of time-of-day, while utilizing an existing method for imputing average travel times (by TOD). The methods employ ordinary least squares (OLS) regression techniques, and rely on reported travel time information from survey data (typically available to researchers), as well as travel time and distance estimates by origin-destination (OD) pair for free-flow and peak-period conditions from network data. This dissertation also develops two models of activity timing that recognize the imputed average travel times and travel time variability. Both models are based in random utility theory and both recognize potential correlations across time-of-day alternatives. In addition, both models are estimated in a Bayesian framework using Gibbs sampling and Metropolis-Hastings (MH) algorithms, and model estimation relies on San Francisco Bay Area data collected in 2000. The first model is the continuous cross-nested logit (CCNL) and represents tour outbound departure time choice in a continuous context (rather than discretizing time) over an entire day. The model is formulated as a generalization of the discrete cross-nested logit (CNL) for continuous choice and represents the first random utility maximization model to incorporate the ability to capture correlations across alternatives in a continuous choice context. The model is then compared to the continuous logit, which represents a generalization of the multinomial logit (MNL) for continuous choice. Empirical results suggest that the CCNL out-performs the continuous logit in terms of predictive accuracy and reasonableness of predictions for three tolling policy simulations. Moreover, while this dissertation focuses on time-of-day modeling, the CCNL could be used in a number of other continuous choice contexts (e.g., location/destination, vehicle usage, trip durations, and profit-maximizing production). The second model is a bivariate multinomial probit (BVMNP) model. While the model relies on discretization of time (into 30-minute intervals), it captures both key dimensions of a tour’s timing (rather than just one, as in this dissertation’s application of the CCNL model), which is important for tour- and activity-based models of travel demand. The BVMNP’s ability to capture correlations across scheduling alternatives is something no existing two-dimensional choice models of tour timing can claim. Both models represent substantial contributions for continuous choice modeling in transportation, business, biology, and various other fields. In addition, the empirical results of the models evaluated here enhance our understanding of individuals’ time-of-day decisions. For instance, average travel time and its variance are estimated to have a negative effect on workers’ utilities, as expected, but are not found to be that practically relevant here, probably because most workers are rather constrained in their activity scheduling and/or work hours. However, correlations are found to be rather strong in both models, particularly for home-to-work journeys, suggesting that if models fail to accommodate such correlations, biased application results may emerge. / text

Flourishing economy, rapid industrialization and increasing trend of motorization have been shaping societies in the developing countries like India in an unprecedented manner.Infrastructure backlog amid such rapid growth in all imaginable directions has heavily exacerbated the urban transport crisis in these countries by alarming increase in vehicular travel demand, road fatalities, and environmental pollution. To address urban transport challenges, the necessary development and implementation of effective transport planning and policies have generally lagged in the developing countries compared to that seen in the developed countries due to several constraints including resource constraints, knowledge constraints, institutional constraints and so on. However, in the recent past, with the rapid development seen by several emerging economies and the explosive growth in transportation infrastructure investment, there is a growing interest in the development and implementati on of advanced travel demand modeling systems in developing countries. But lack of necessary research and exploration of travel behavior in a developing country context has left very limited knowledge for us to understand the extent of applicability of these advanced theories and methodologies in a different socio-cultural perspective. Assessing the practical relevance of the subject, this research adopts a comprehensive approach to explore the activity engagement pattern and time use behavior from a developing country standpoint. To accomplish this goal, a series of empirical and analytical studies are performed on a household travel survey data set available from Thane Metropolitan Area in India. The study also introduces new concepts and facilitates enhancements of existing modeling methodologies in the field of travel behavior and time use research. The study results provide very insightful findings and plausible interpretations consistent with a developing country perspective reco gnizing a wide spectrum of differences and similarities in activity patterns and time use behavior between a developed and a developing country. Specified model structures are meaningfully able to incorporate various socio-cultural and institutional constraints and reflected sensitivity to the behavioral variability between the contexts suggesting that advanced analytical techniques may be satisfactorily applied on the data set from developing countries which may contribute important ingredients in the development of advanced activity-based model system in the countries like India.

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This dissertation presents a study on velocity modeling using ocean-bottom seismometer data (OBS) collected in 2004 and 2006 on the western Scotian slope. Gas hydrate and free gas concentrations and their distribution along the Scotian margin were derived based on the velocity results modeled with two different OBS data sets. A strong velocity increase (140-300 m/s) associated with gas hydrate was modeled for a depth of 220 m below seafloor (bsf). At the base of that high velocity zone (330 mbsf) the velocity decreases with 50-130 m/s. This depth is associated with the depth of the bottom-simulating reflector (BSR) observed in previous 2-D seismic reflection data. The gas hydrate concentrations (2-18 %) based on these velocities were calculated with an effective medium model. The velocity modeling shows that a sparser OBS spacing (~ 1 km) reveals more velocity uncertainties and smaller velocity contrasts than a denser (100 m) spaced OBS array. The results of the travel-time inverse modeling are applied in a waveform inverse modeling with OBS data in the second part of the thesis. The modeling tests were performed to obtain information on OBS instrument spacings necessary to detect low-concentration gas hydrate occurrences. The model runs show that an increase in instrument spacing leads to an increasing loss of model smoothness. However, large instrument spacings (>500 m) are beneficial for covering a wide target region with only using a few instruments, but decreasing the lateral resolution limits of the subsurface targets. In general half of the instrument spacing defines the lower boundary for the lateral width of the target structure. Waveform modeling with the 2006 OBS data has shown that low frequencies (<8 Hz) in the source spectrum are necessary to recover the background velocity of the model. The starting model derived from travel-time inversion of the 2006 data is not close enough to the true model. Thus the first-arrival waveforms do not match within half a cycle. Modeling with a starting frequency of 8 Hz and and applying data with a low signal-to-noise ratio (1.25) introduces artifacts into the final model result without updating the velocity. / Graduate

Over the past decade, the popularity of regional bus services has grown in large North American cities owing to more people living in suburban areas and commuting to the Central Business District to work every day. Estimating journey time for regional buses is challenging because of the low frequencies and long commuting distances that typically characterize such services. This research project developed a mathematical model to estimate regional bus travel time using artificial neural networks (ANN). ANN outperformed other forecasting methods, namely historical average and linear regression, by an average of 35 and 26 seconds respectively. The ANN results showed, however, overestimation by 40% to 60%, which can lead to travellers missing the bus. An operational strategy is integrated into the model to minimize stakeholders’ costs when the model’s forecast time is later than the scheduled bus departure time. This operational strategy should be varied as the commuting distance decreases.