1,720,967 research outputs found
A Comprehensive Study on the Effectiveness of Office-based TDM Policies
No full textThe objective of this thesis is to develop an employer-based Transportation Demand Management (TDM) evaluation tool that can be used for evaluating various employer-based TDM policies. The conventional method of evaluating TDM policies has typically been conducting expensive before and after TDM policy implementation surveys. On the contrary, this research used a pre-policy deployment joint Revealed Preference and Stated Preference (RP-SP) survey, where the data were collected to develop a TDM policy sensitive mode choice model, which is packaged into a software system for TDM investment decision support. The evaluation tool (named Off-TET) developed by integrating the mode choice model predicts changes in modal share by integrating all possible effects of single or multiple TDM policies implemented in isolation or combined. While the tool presented in this thesis was developed for the region of Peel, there exist opportunities for the application of this type of analysis across Canada.M.A.S
Tour-based Mode Choice Model in Activity-based Modelling Framework
Full text linkThis thesis presents a tour-based mode choice modelling structure for activity-based travel demand models by exploiting the classical dynamic discrete choice modelling approach. Many activity-based modelling systems rely on either trip-based or ‘simplified’ tour-based mode choice models that in many cases completely overlook the dynamics of mode choice behaviour. To contribute to filling this gap, this thesis applies a systematic approach of investigation to better understand the nature of tour-based mode choices and to propose a parsimonious modelling structure for it.
The first investigation looks into the trip-based mode choice behaviour of post-secondary students commuting to universities in the City of Toronto. The second investigation uses a heteroskedastic dynamic discrete choice model for tour-based mode choices modelling with an empirical investigation of university students’ daily mode choices in Toronto. The third investigation uses a computationally tractable dynamic discrete choice modelling framework for modelling tour-based mode choices. The fourth investigation uses a random utility maximization -based dynamic discrete-continuous modelling approach to capture individuals’ tour-based modes and continuous time-expenditure choice trade-offs in a 24-hour time frame.
The model results reveal that individuals’ sensitivity to travel costs varies, while their sensitivity to travel time remains stable. The empirical model reveals that users of newly introduced mobility services (e.g., Uber, Lyft) tend to have different mode choice patterns and value of travel time savings than non-users of these services. The forward-looking component reveals that availability of the modes for subsequent trips in the tour represent a significant portion of the utility of the current mode choices. In terms of the time-expenditure choice model, it is found that full-time employees and younger individuals tend to leave home earlier than part-time employees and older individuals. It is found that individuals are likely to spend long hours at work or school if they leave home early. Furthermore, individuals are likely to schedule non-mandatory activities, such as shopping, later in the day. The validation and policy evaluation results are promising. While the models proposed here can be easily developed in different regions across North America, opportunities also exist for the application of this type of analysis globally.Ph.D
A Comprehensive Study on the Effectiveness of Office-based TDM Policies
Full text linkThe objective of this thesis is to develop an employer-based Transportation Demand Management (TDM) evaluation tool that can be used for evaluating various employer-based TDM policies. The conventional method of evaluating TDM policies has typically been conducting expensive before and after TDM policy implementation surveys. On the contrary, this research used a pre-policy deployment joint Revealed Preference and Stated Preference (RP-SP) survey, where the data were collected to develop a TDM policy sensitive mode choice model, which is packaged into a software system for TDM investment decision support. The evaluation tool (named Off-TET) developed by integrating the mode choice model predicts changes in modal share by integrating all possible effects of single or multiple TDM policies implemented in isolation or combined. While the tool presented in this thesis was developed for the region of Peel, there exist opportunities for the application of this type of analysis across Canada.M.A.S
Post-secondary Students' Travel Behavior through the Lens of Urban and Rural Contexts
Full text linkThis four-manuscript research investigates the travel behavior of post-secondary students across urban and rural contexts through four interrelated studies. The research addresses gaps in understanding how activity choices, departure time decisions, and active transportation behaviors are shaped by contextual, demographic, and policy factors. Two studies utilize the StudentMoveTO dataset, a detailed activity-travel diary survey from the Greater Toronto and Hamilton Area (GTHA), to model multi-destination trip-based activity type choices and sequential departure time decisions. These models capture interdependencies across multi-destination trips, enabling a more realistic representation of student travel patterns in dense urban environments. The other two studies draw on a custom-designed revealed–stated preference (RP–SP) survey administered to post-secondary students in rural Virginia. This survey incorporates both actual travel behavior and hypothetical choice experiments to assess rural students' mode preferences and the mental health impacts of active transportation under varying infrastructure and service conditions. The research adopted advanced econometric and machine learning approaches to better understand post-secondary students travel behavior. The urban-focused studies employ the dynamic discrete choice models and deep learning architectures Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM) networks, and Transformers to capture sequential decision-making and nonlinear dependencies in activity type and departure time choices. The rural-focused mode choice analysis estimates both RP–SP multinomial logit (MNL) and RP–SP mixed logit models, enabling the combination of actual revealed preference data with stated preference scenarios while also capturing unobserved taste heterogeneity across individuals. The MNL model provides a baseline understanding of average mode choice behavior, whereas the mixed logit model relaxes the independence of irrelevant alternatives (IIA) assumption and accounts for random variations in preferences influenced by rural context, demographics, and travel conditions. The mental health and active transportation study uses principal component analysis (PCA) for dimensionality reduction, followed by Random Forest and other interpretable machine learning methods to identify the most influential factors. This combined methodological framework leverages both behavioral realism and predictive accuracy, bridging traditional econometric analysis with modern data-driven approaches. The findings reveal that student travel decisions are strongly influenced by institutional schedules, socio-demographic characteristics, and built environment features, with notable differences between urban and rural contexts. Sequential modeling shows that earlier departure times for initial trips significantly constrain subsequent activity timing, while rural analyses highlight that infrastructure quality and service availability directly affect both mode choice and perceived mental health benefits of active travel. These insights provide valuable evidence for transportation planners and policymakers seeking to design targeted, context-sensitive strategies that enhance mobility options, support student well-being, and promote sustainable transportation in both urban and rural communities.Doctor of PhilosophyThis four-manuscript research examines how college and university students travel, focusing on the choices they make about activities, timing, travel modes, and the role of active transportation in supporting mental health. The research considers students in both large metropolitan areas and rural communities, recognizing that their opportunities and challenges differ greatly depending on where they live and study. Two of the studies draw on the StudentMoveTO survey from the Greater Toronto and Hamilton Area, a detailed record of students' daily trips and activities. These studies explore how students decide the types of activities they do on their trips throughout the day and when they choose to begin each trip. The findings show that earlier departures often limit later activities, and that travel patterns are shaped by a mix of institutional schedules, personal commitments, and the urban environment. The other two studies are based on a custom survey conducted with post-secondary students in rural Virginia. This survey captures both actual travel behavior and responses to "what if" scenarios about new transportation options, changes in infrastructure, or different travel costs. One study examines how walking and cycling in rural areas relate to students' mental health, revealing that better infrastructure and safer routes can encourage active travel and improve well-being. The other investigates how rural students choose their travel mode, whether driving, carpooling, using public transit, or cycling, and how these decisions respond to service availability, infrastructure quality, and travel costs. By combining real-world travel data with hypothetical scenarios, the research highlights the different ways students adapt their travel in urban and rural settings. The results show that infrastructure, service quality, and travel costs strongly influence mode choice, while active travel can offer important mental health benefits when safe and convenient options are available. These findings provide actionable insights for transportation planners and policymakers, offering strategies to expand mobility, improve student well-being, and promote sustainable travel in both cities and rural areas
Dynamic Connected Automated Vehicle Trajectory and Traffic Signal Timing Optimization
No full textThis dissertation addresses the topic of sustainable transportation in the context of traffic signalized networks by optimizing both vehicle and traffic signal operations. From the vehicle side, the research develops a Green Light Optimal Speed Advisory (GLOSA) system also known as an Eco-Cooperative Adaptive Cruise Control system at intersections (ECO-CACC-I) for fixed and actuated traffic signals, using probabilistic traffic signal switching times to minimize vehicle fuel consumption through a computationally efficient A* minimum path algorithm within a dynamic programming procedure. This system explicitly minimizes vehicle fuel consumption while ensuring vehicle safety by preventing red light violations, hard braking, and excessive jerking. A sensitivity analysis is performed to quantify the impact of uncertainty in traffic signal timing predictions on fuel consumption. The research also extends the ECO-CACC-I system to integrate real-time back-of-the-queue estimation using loop detector and probe vehicle data with trajectory optimization, considering uncertainties in actuated traffic signal timings. The system enhances queue length estimates without relying on historical data and significantly improves upon the sole use of shockwave theory for the estimation of queues. On the infrastructure side, a cycle-free dynamic Decentralized Nash Bargaining (DNB) traffic signal controller is developed to optimize traffic signal operations using traffic stream density predictions, a flexible National Electrical Manufacturers Association (NEMA) phasing scheme, and dynamically adaptable control time steps. The DNB controller is benchmarked against fixed-time, actuated, and reinforcement (RL) machine learning (ML) control methods demonstrating its superior performance and simple algorithmic formulation. Furthermore, a two-stage Kalman filter algorithm is developed to predict traffic states for real-time traffic signal control, with the first stage estimating turning movement counts and the second stage estimating queue sizes and traffic stream density on the intersection approaches. This Kalman filter approach is integrated within the DNB controller to predict and optimize traffic signal timings in real time. The development of these vehicle and infrastructure systems aims to reduce vehicle energy consumption and emissions while improving traffic mobility. The proposed ECO-CACC-I system achieves average fuel savings of 37% and 30% for deterministic and stochastic settings respectively, compared to uninformed drivers. Furthermore, the ECO-CACC-I system demonstrates fuel savings of up to 18.89% when considering queue effects. The DNB traffic signal controller reduces average vehicle delay and queue sizes by up to 54% and 63%, respectively, compared to the state-of-the-practice Webster's pre-timed control. Finally, the analysis of the joint DNB-KF system showed benchmarks for the market penetration levels of connected vehicles that are required to achieve significant system performance.Doctor of PhilosophyThis dissertation addresses the topic of sustainability in transportation systems, where efficient systems for traffic lights and driver-assist systems are introduced. The problem is approached from two sides; 1) from the vehicle side, which represents efficient driver assist systems at several automation levels, and 2) from the infrastructure side, where traffic lights are optimized to accommodate approaching traffic efficiently and in an environmentally friendly manner. These systems aim to improve overall mobility performance by reducing the excessive wait time experienced at urban intersections at peak hours due to traffic congestion, and also reducing vehicle fuel consumption and harmful emissions. From the vehicle side, this research develops a system known as an Eco-Cooperative Adaptive Cruise Control system at intersections (ECO-CACC-I), which provides recommended speeds for drivers and for self-driving cars to minimize their fuel consumption, as well as reduces the delay at traffic lights. This system is designed to work at fixed-time signals, as well as actuated signals, which are widely common in the U.S. Different scenarios have been studied and analyzed in this dissertation to evaluate the system's performance. The research also extends the ECO-CACC-I system to consider surrounding vehicles, so that the system reduces collisions with other vehicles and avoids running red lights, which are a safety hazard. On the infrastructure side, this dissertation provides an enhanced and more reliable version of a traffic light controller, known as a DNB controller. This system provides a more flexible and adaptive traffic control sequence and green durations. The system is compared with currently common traffic light control systems such as fixed-time and actuated control strategies. In addition, this dissertation also develops a Kalman filtering system that aims to estimate and predict traffic measures, required for traffic analysis and traffic signal control. This system is based on data from connected vehicles and stationary sensors. This Kalman filter approach is integrated within the DNB controller to predict and optimize traffic signal timings in real time. The development of these vehicle and infrastructure systems aims to reduce vehicle energy consumption and emissions while improving vehicle mobility. The proposed ECO-CACC-I system achieves significant fuel savings and delay reductions, compared to the base case of uninformed drivers. Finally, the DNB traffic signal controller also results in significant vehicle delay and queue size reductions, compared to commonly used traffic light control methods
Predictive Modeling and Durability Analysis of Low Carbon Concrete Incorporating Recycled Materials
No full textIncreasing environmental demand for low carbon concrete materials has developed significant interest in the use of industrial by-products and recycled materials towards minimizing the environmental impact of concrete production processes. This dissertation investigates the use of recycled concrete aggregate (RCA), coal ash (CA), and quarry by-products (QB) as the substitute materials to produce cementitious composites using a hybrid approach of machine learning meta-analyses combined with experimental verification.
In the first half of the dissertation, a meta-analysis of more than 750 experimental data available in the literature was performed to predict the compressive strength of concrete using RCA. Various machine learning models, such as individual learners and ensemble models, were developed and compared. Among them, the Light Gradient Boosting Machine (LightGBM) provided optimal predictive performance (R² = 0.94), and the most critical variables were related to age, water-to-cement ratio, and fine RCA content. The study also revealed that partially saturated RCA provided optimal strength results, followed by reduction in strength with fully saturated or oven dry RCA. These results provide data-driven insights into the optimization of the RCA concrete mixtures.
The second half of the dissertation focuses on the simultaneous utilization of CA and QB in pastes, mortars, and concretes. Specifically, unconventional CA were considered, including a high sulfur fly ash and a fluidized bed combustion ash, relative to a commercially available coal fly ash. Granite and limestone QB were considered as replacements of commercially available limestone powder. A machine learning approach was used to determine optimal proportions of CA and QB in mortars. This method replaced traditional trial-and-error by leveraging existing data in the literature on using coal fly ash and limestone powder. Five different binder systems with different types and combinations of CA and QB were examined using isothermal calorimetry, flow tests, pore solution composition analysis, and compressive strength tests at different curing ages. Of the above, a ternary blend with portland cement, high sulfur fly ash, and granite QB was found to perform best with improved hydration kinetics, similar pore solution composition, and strength gain reproducibility relative to a control with a conventional coal fly ash and limestone powder. The study emphasizes the role of proportioning and chemical compatibility towards achieving sustainable mortars. These findings indicate that the proposed ML-assisted mix design approach can effectively identify high-performing mortar mixtures using industrial by-products. Though the model targeted for a compressive strength of 40 MPa, only the 100% cement OPC mix attained this value. All the other ML-optimized mixtures had a relatively lower strength, reflecting some compromise between performance and sustainability.
In the final study, the synergistic interaction of CA and QB as partial replacements of cement in concrete was explored, specifically focusing on mechanical properties and durability performance. Constituent samples were evaluated for workability, mechanical properties, fracture properties, and durability properties. Testing proved that the combined usage of CA and QB enhanced the mechanical properties compared to conventional coal fly ash and control limestone powder as well as the long-term strength, in addition to eliminating the drawback of individual use. Compared to the control mix containing limestone, conventional coal fly ash in the CA-QB blends showed improved fracture toughness. Such synergy propels the development of structurally resilient and sustainable concrete mixes. The results confirm that combinations of CA-QB can provide a long-lasting and mechanically robust alternative to conventional cement, especially regarding long-term durability. This combination presents a scalable and circular economy solution for next-generation concrete infrastructure.
The findings of this dissertation offer a multi-faceted solution combining predictive modeling and experimental verification to optimal utilization of recycled and secondary materials in concrete construction. This research facilitates sustainable construction by offering practical solutions to material optimization and performance improvement, opening the door to increased use of low carbon concrete in contemporary infrastructure. Furthermore, it demonstrates the practical potential of mix design with ML assistance and industrial waste recycling to produce sustainable concrete, but its practice application are limited by regional material variability and the lack of long-term field validation.Doctor of PhilosophyTo reduce the environmental impact of construction and preserve natural resources, this dissertation explores the use of recycled and industrial by-products in cementitious composites, specifically recycled concrete aggregate (RCA), coal ash (CA), and quarry by-products (QB), as alternatives to traditional concrete materials. These materials have potential but can be limited by inconsistent performance and lack of reliable mix design methods.
In the first part of the study, a machine learning-based meta-analysis was conducted using 700+ results to predict the compressive strength of RCA concrete. Among various models tested, the Light Gradient Boosting Machine (LightGBM) showed the highest accuracy (R² = 0.94), identifying moisture content, water-to-cement ratio, fine RCA content, and curing age as the most influential factors. This analysis helps guide better use of RCA in concrete by offering data-driven insights.
The second part of the research involved laboratory testing of paste and mortar mixtures using different proportions of CA and QB, specifically unconventional CA. Five combinations were evaluated for hydration behavior, flow, pore solution composition, and strength. Of the combinations, relative to a control mix with conventional CA and limestone powder, a ternary blend with portland cement, high sulfur fly ash, and granite QB demonstrated the most balanced performance, with stable pH levels and consistent strength development. This highlights the importance of chemical compatibility and precise mix design in using CA and QB effectively.
Finally, the third phase examined the performance of CA and QB blends as partial cement replacements in concrete. Tests showed that using both materials together improves fracture toughness, durability, and resistance to chloride penetration compared mixing with control limestone and conventional CA. This synergy provides a promising solution for creating more sustainable and long-lasting concrete structures
Impact of Real-Time Information and Road User Fees on Individuals Mode Choice Decision
No full textThis research investigates the impact of multi-source real-time information and mileage-based user fee (MBUF) on individuals' mode choice behavior. It examines the interaction between MBUF and socio-demographic variables for different trip purposes. This research designs two separate web-based surveys. Each survey has revealed preference (RP) and stated preference (SP) components. The SP components consist of hypothetical scenarios to capture individuals' mode choice behavior based on real-time information and MBUF. The research develops a series of advanced econometric models using the collected survey data to understand the factors influencing individuals' mode choice behavior. The findings indicate that daily parking costs significantly impact individuals' mode choices and tend to discourage driving. Real-time information, such as parking space availability at workplaces and metro stations, encourages people to prefer drive and park & ride modes. Information on road closures and road accidents discourages people from driving, riding as auto-passengers, or taking TNC (Uber/Lyft) for trip purposes. Regarding MBUF, the results reveal that individuals are less likely to prefer motorized modes with the increased rate of MBUF. Full-time workers show more sensitivity towards MBUF for work trips, whereas college students are more sensitive to MBUF for recreational trips. Older adults are more sensitive to MBUF for work trips, and young individuals are more sensitive to MBUF for work and grocery/shopping trips. The results show that increased fuel costs, toll costs, bus fares, and delays reduce the likelihood of driving alone, carpooling, and transit. The findings of this research provide critical insights, supporting the implementation of evidence-based strategies to promote alternative sustainable transportation modes in the presence of real-time information and MBUF.The research work was funded by the National Science Foundation (NSF) (Award \#2200633) and the North Carolina Department of Transportation (NCDOT).Master of ScienceThis research investigates how real-time information and mileage-based user fee (MBUF) influence individuals' mode choice behavior. This research designs two separate web-based surveys. Each survey has revealed preference (RP) and stated preference (SP) components. The SP components consist of hypothetical scenarios to capture individuals' mode choice behavior based on real-time information and MBUF. Using the collected data, the research estimates a series of models to understand the factors influencing mode choice behavior. The findings indicate that daily parking costs significantly impact individuals' mode choices and tend to discourage driving. Real-time information, such as parking space availability at workplaces and metro stations, encourages people to prefer drive and park & ride modes. Information on road closures and road accidents discourages people from driving, riding as auto-passengers, or taking TNC (Uber/Lyft) for trip purposes. Regarding MBUF, the results reveal that individuals are less likely to prefer motorized modes with the increased rate of MBUF. Full-time workers show more sensitivity towards MBUF for work trips, whereas college students are more sensitive to MBUF for recreational trips. Older adults are more sensitive to MBUF for work trips, and young individuals are more sensitive to MBUF for work and grocery/shopping trips. This research is crucial as it provides insights into understanding mode choice behavior and improving road congestion
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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