Browsing by Author "Liu, Hongchao"
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Item Analysis of the effects of demographic and driver behavior variables on traffic safety and crash prediction(2015-05) Kumfer, Wesley J.; Liu, Hongchao; Senadheera, Sanjaya; Won, Moon C.Traffic safety is a major concern for transportation engineers. Motor vehicle crashes, in addition to severely diminishing the efficacy of a roadway, also significantly impact quality of life. Motor vehicle crashes are one of the major causes of injury and death in the United States, and although trends seem to indicate that crashes are decreasing annually, engineers must still be actively engaged in working to reduce the total number of crashes and to mitigate the severity of collisions. Researchers agree that the greatest contributing factor to motor vehicle crashes is human error. Humans, whether through inherent driving behaviors and cultural attitudes or through simple negligence, are prone to make mistakes that can have devastating effects on roadways. Extensive research has shown that various behaviors and attitudes significantly impact traffic safety, but a comprehensive understanding of the human component of the crash causation equation is limited. Therefore, the purpose of this dissertation is to address the lack of knowledge regarding how humans affect crashes and to indicate how the practice must evolve in order to reach the goal of zero roadway fatalities. This dissertation provides a comprehensive examination of the human impact on traffic safety, particularly on fatal crashes. First, demographic trends in the United States are examined to establish a basis for the human impact. Second, a binary logistic regression analysis is conducted to identify how different driver behavior and demographic factors lead to different crash types. Third, forecast models for those crash types are built using significant demographic and driver variables and historical data. Last, a glimpse at how human attitudes and ethics will affect the transportation system of the future is provided. This dissertation provides both a comprehensive and an analytical approach to understanding the complex relationship between drivers and traffic safety, particularly in regards to fatal crashes.Item Data-driven modeling and transportation data analytics(2014-05) Wei, Dali; Liu, Hongchao; Senadheera, Sanjaya; Surles, James; Matis, Timothy I.Data has become increasingly important in transportation research. Unfortunately, existing traffic models, though developed and practiced for decades, are not data driven and therefore inherently incapable of analyzing modern traffic data from multiple sources with different time resolution and spatial coverage. A new paradigm centered on data-driven theories needs to be established, which can fully exploit and leverage traffic data toward new insights on traffic dynamics, accurate traffic forecast and effective traffic control. This dissertation focuses on investigations of advanced data-driven methods and developing mathematical models and solution algorithms for analyzing multimodal transportation data from a single source, multiple sources, and large networks. As studies on big transportation data are still in their very early stages, a key objective of this research is to explore suitable modeling and computing methods to address the fundamental problems of transportation data. To this end, various types of traffic data, including microscopic vehicle trajectory data, macroscopic velocity data from multiple sensors, and network-wide Floating Car data were studied. Specifically, this dissertation concentrates on the following three topics: • Single source data: analysis of asymmetric driving behavior using high resolution trajectory data The access to high-resolution vehicle trajectory data opened up new avenues for understanding and modeling both micro- and macroscopic traffic phenomena. A novel data-driven algorithm was developed based on the kernel machine to extract driving patterns from trajectory data, which avoids subjective biases in traditional physical models. A particular focus has been paid to analyzing the asymmetry phenomena in driving behavior. The study successfully proved the existence of significant asymmetry between deceleration and acceleration and revealed its impacts to macroscopic traffic flow characteristics. New findings have revealed a strong connection between the asymmetric driving behavior and prominent macroscopic traffic phenomena including congestion propagation and recovery. • Multiple source data: traffic projection by fusing stationary and mobile data Due to the advancement of sensing technologies, traffic data are emerging from multiple sources with different temporal and spatial characteristics and varying types of errors. A challenging task in analysis and modeling of multi-source transportation data lies in the combination of floating data from mobile sensors with data from stationary sensors such as loop detectors. To assimilate both stationary and mobile data to estimate highway traffic, a Gaussian process model is developed with a novel covariance function to integrate fundamental features of the congestion propagation within a Bayesian framework. Field experiments with data from U.S. 880 proved the model’s capability of providing reliable and accurate traffic estimation and prediction with a variety of information. • Large network data: impact of service refusal to urban taxicab system using Floating Car Data The true challenge of big transportation data lies in the techniques and modeling approaches for analyzing multimodal data from large transportation networks. With the knowledge obtained from traffic data analysis, in-depth investigation of network level and multimodal transportation data becomes possible. In traffic data analysis, the focus has been placed on the demand side only, and our goal is to develop methodologies and modeling approaches to accurately reproduce traffic flow profiles and estimate traffic dynamics. A unique problem in network level, multimodal transportation analysis, however, is both the demand and supply sides need to be considered and the result usually relates to policy making. The data to be analyzed in this section is operational data in the taxicab market, which goes well beyond the engineering arena. The technical models are extended to include social and economic components in addition to engineering analysis. A partial differential equation system with a sigmoid function was developed to address the impacts of service refusal to the demand-supply equilibrium of a taxicab system. From the combined approach of data-driven and network analysis, new insights have been gained, which lead to promising policy recommendations against this unpleasant phenomena. Centered on the technical issues and challenges with regard to the data-driven modeling approach (versus the traditional physical modeling methods), and the development and application of advanced analytic models to address real-world traffic and transportation problems, this dissertation spans a wide range of topics pertinent to the selected typical problems in traffic flow theory and transportation network analysis. Topics include 1) a self-learning car-following model which uses a pure data-driven approach and produces better results than traditional models, 2) accurate reproduction of traffic flow profile and estimation of the flow dynamics using data from both stationary and floating sensors, and 3) analysis of both engineering and socioeconomic data to solve the service refusal problem in a taxicab market. As pure data-driven methods are still in the early stage, a systematic investigation of the technical issues and methodological approaches pertinent to comprehensive engineering and socioeconomic analysis of transportation data is timely and meaningful. Although it cannot cover every aspect of this promising area, this dissertation is aimed to lay a stone in the foundation of the pure data-driven and self-learning approach in a timely and systematic manner. It contributes to the state of the knowledge by answering the following questions: • Is it possible to use pure self-learning and data-driven methods to develop traffic flow models with the same or an even better level of accuracy of classic theoretical models? What methods are suitable for this approach and what are the technical issues in applying these methods? [Chapter 3] • What are the flaws in existing methods for integration and analysis of data from multiple sources and how to improve these methods? [Chapter 4], and • To make the data-driven approach a complete technical system, it must also be applicable to problems in the socioeconomic area that are fundamental to transportation policy making. How can analysis be extended from the engineering arena, and what are the suitable methodological approaches for analyzing combined engineering and socioeconomic data? [Chapter 5]Item Evaluation of transit signal priority using analytical method(2007-08) Zhang, Jie; Liu, Hongchao; Hart, JerylTransit Signal Priority (TSP) systems have grown in popularity throughout the world£¬ and are regarded as a cost-effective way to solve current congestion in metropolitan areas. Successful deployment of TSP systems requires thorough laboratory evaluation before field implementation. Traffic simulation is a powerful tool in this regard; however, it requires tremendous efforts toward network coding, data collection and model calibration. Besides, simulation models tend to be project specific and the models developed for one project are often discarded upon the completion of that project. In this thesis, it is shown that the impacts of two fundamental TSP strategies (early green and extended green) can be evaluated using an analytical approach. The impacts of the above two strategies on both prioritized and non-prioritized approaches are illustrated both graphically as well as analytically. The validity of the proposed analysis is examined by an example application. A microscopic simulation tool, Vissim4.1, is applied for comparing the results of the analytical method.Item Exploring the fundamentals of using infrastructure-based LiDAR sensors to develop connected intersections(2019-12) Zhao, Junxuan; Liu, Hongchao; Xu, Hao; Shankar, VenkyConnected and autonomous vehicle technologies aim to enable vehicles, pedestrians, roads, and infrastructures to communicate with each other and share vital traffic information through networks. However, in the current mixed traffic stage, on-board sensing systems only focus on a limited detection range around the vehicle, thus non-connected road users such as pedestrians who do not have any communication means are passively protected by the systems. In order to develop a human-in-the-loop connected environment at intersections and proactively protect road users who are not included in the current connected systems, the proposed infrastructure-based LiDAR sensing system can be a feasible solution to this problem due to LiDAR sensors’ capability of scanning objects in three-dimensional space and reporting their locations with great accuracy. Since the methods for on-board LiDAR data processing cannot be directly applied to roadside LiDAR data, it is imperative to investigate the essentials of roadside LiDAR ranging from installation strategies to efficient and effective processing point clouds data. In this dissertation, the author first introduced the characteristics of LiDAR sensors and presented the detection range analysis of roadside LiDAR sensors considering the senor’s built-in features and installation techniques. With the appropriate installation of sensors, a systematic approach to extracting pedestrian and vehicle trajectories from roadside LiDAR data was developed in the order of background filtering, object clustering, vehicle/pedestrian classification, and tracking. An application of extracted pedestrian trajectories was demonstrated by predicting pedestrian crossing intention at intersections using trained deterministic and probabilistic prediction models. This pilot study of infrastructure-based LiDAR sensing systems could be a valuable input for various traffic research, which includes cooperation between vehicles and infrastructures for connected/autonomous vehicle systems, vehicle-to-pedestrian crash reduction, smart traffic signals, etc.Item Louvain-Based Traffic Object Detection for Roadside 4D Millimeter-Wave Radar(2024) Gong, Bowen; Sun, Jinghang; Lin, Ciyun; Liu, Hongchao; Sun, GanghaoObject detection is the fundamental task of vision-based sensors in environmental perception and sensing. To leverage the full potential of roadside 4D MMW radars, an innovative traffic detection method is proposed based on their distinctive data characteristics. First, velocity-based filtering and region of interest (ROI) extraction were employed to filter and associate point data by merging the point cloud frames to enhance the point relationship. Then, the Louvain algorithm was used to divide the graph into modularity by converting the point cloud data into graph structure and amplifying the differences with the Gaussian kernel function. Finally, a detection augmentation method is introduced to address the problems of over-clustering and under-clustering based on the object ID characteristics of 4D MMW radar data. The experimental results showed that the proposed method obtained the highest average precision and F1 score: 98.15% and 98.58%, respectively. In addition, the proposed method showcased the lowest over-clustering and under-clustering errors in various traffic scenarios compared with the other detection methods.Item Online vehicle trajectory extraction based on LiDAR data(2023-12) Zhang, Yibin; Liu, Hongchao; Fedler, Clifford; Xu, Hao; Won, MoonThis comprehensive research explores three distinct approaches for enhancing LiDAR data analysis and vehicle tracking in traffic monitoring and safety assessment applications. The first study introduces a novel background filtering technique, effectively identifying stationary vehicles amidst moving ones and significantly reducing the dataset size to improve computational efficiency. The second study proposes an unsupervised clustering method tailored for roadside LiDAR applications, utilizing a unique approach of converting 3D LiDAR data into a 2D data structure with the aid of LiDAR working principles This innovative approach, comprising a combination of region growing, connected component labeling, and an improved merge process, has proven effective in addressing over-segmentation challenges. The third section discusses the implementation and evaluation of a proposed index method for online vehicle tracking, outperforming the traditional bounding box method in accuracy and computation time. Inspired by the idea of integrating the framework of the widely used Simple Online and Real-time Tracking (SORT) algorithm, the proposed method tracks objects based on centroids and associations, making it well-suited for real-time vehicle tracking applications. One of the most compelling strengths of this unsupervised clustering method is its successful integration with the widely recognized Simple Online and Real-time Tracking (SORT) framework. Worth noting is that this method employs tracking by point, a concept distinct from the commonly used Intersection over Union (IoU) method in SORT. The method demonstrates impressive performance on datasets collected from three intersections. It outperforms the traditional bounding box method in tracking accuracy, achieving higher Multiple Object Tracking Accuracy (MOTA) and lower False Positive (FP) and False Negative (FN) rates. The promising results showcase the potential of the proposed method for real-world implementations in traffic management and safety assessment. The integration of the SORT framework and the concept of tracking by point, along with an unsupervised clustering method with the innovative 2D data structure converted from 3D LiDAR data after background filtering, presents a promising approach for optimizing vehicle tracking in roadside LiDAR applications. The study's significant performance improvements highlight the potential for real-time implementations. With continued research and refinement, the proposed method holds great promise for enhancing vehicle tracking accuracy and contributing to safer and more efficient traffic management systems for road users.Item Optimization of signal timings for Diverging Diamond Interchanges(2014-05) Ostrander, Chad; Liu, Hongchao; Senadheera, SanjayaA Diverging Diamond Interchange (DDI) is a relatively new design of interchange that has several advantages over the standard diamond interchange. Two of the most notable advantages of the DDI are an increased capacity for turning movements and a reduction in conflict point resulting in a safer intersection. Because this design is still in its infancy there are not established methodologies for dealing with the operations of the interchange. Most studies only offer a basic signal timing plan and phasing scheme which are not optimized for efficiency. Therefore, most state DOTs implementing a DDI choose to actuate the interchange and use a basic phasing scheme to optimize it in the field. This works great for off peak hours but causes delay during peak hours. This study applies a more efficient phasing scheme for this complex interchange and proves that optimization software such as Synchro can optimize the interchange with little loss in delay. As well as providing a range of volumes that the DDI can operate within and still be efficient. This approach to optimizing a DDI using Synchro was tested on the proposed location for the first DDI in Texas, located in North Austin at I-35 and RM-1431. The effectiveness of Synchro at optimizing the DDI is tested using Webster’s method of signal timing.Item Proactive safety analysis using roadside LiDAR based vehicle trajectory data(2023-12) Bhattarai, Nischal; Liu, Hongchao; Fedler, Clifford B.; Xu, Hao; Won, MoonThe underlying weaknesses of crash data have led to the shift of traffic safety analysis from reactive to proactive approaches. Using conflicts/near-crashes as crash surrogates is the most common technique to evaluate the safety levels of traffic facilities prior to the occurrence of crashes. However, the existing conflict-based proactive safety measures face several challenges such as complex data collection methods, selection of suitable surrogate indicators and their thresholds, reliability issues of crash-conflict relationships etc. The availability of advance sensor-based technologies has made it easier to capture road-user data at a microscopic level; however, the implementation of these data types towards traffic safety applications is very limited. In this regard, this study uses the emerging, cost-effective LiDAR sensor-based technology to develop a methodology for proactive safety analysis. High resolution microscopic trajectory data collected using infrastructure-based LiDAR sensors is used to identify traffic conflicts as crash surrogates. In addition, bivariate extreme value models are developed using proximity and evasive action based surrogate indicators to predict crash frequencies at signalized intersections. A case study was conducted to implement the developed methodology at 5 signalized intersections of Lubbock, Texas. The results indicate a correlation between the identified conflicts and the crashes, and further provide new insights into the crash risks at the intersections. Overall, the proposed methodology lays ground for using roadside lidar based trajectory data for proactive safety analysis of signalized intersections.Item Quantifying the environmental impacts of selected sustainable transportation policies(2016-12-01) Javid, Roxana; Nejat, Ali; Liu, Hongchao; Hayhoe, KatharineOn-road transportation policies could play a key role in reducing greenhouse gas (GHG) and air pollutant emissions via a wide range of strategies that can be classified into three groups: reduce, avoid, and replace. The objective of this dissertation is to prioritize on-road emission mitigation strategies for policy -level transportation funding allocation and to quantify the environmental impacts of first, High Occupancy Vehicle (HOV) lanes and carpooling as a realization of avoid strategies; and second, Plug-in Electric Vehicles (PEVs) as an example of replace strategies. The resulting models have a wide range of applications in evaluating the effectiveness of each strategy, including the potential to assist policymakers, and transportation planners in optimizing infrastructural investments by identifying regions where the response to a specific policy would be maximized. In chapter II, reduce, avoid, and replace strategies were prioritized based on transportation and climate science professionals’ opinions through Analytical Hierarchy Process (AHP), applied to the cities of Dallas and Lubbock in Texas. The results indicated that reduce strategies had the highest preference score of 40%, followed by avoid strategies with 36% and replace strategies with 24%. In chapter III, we developed a statistical model to relate HOV lanes and other potential factors to carpooling propensity in all 50 U.S. states and the District of Columbia. At the state level, we found HOV lane-kilometers together with higher-than-average gasoline prices to be effective in promoting carpooling. An in-depth analysis of 58 counties in California found that HOV lane-kilometers also positively impact carpooling rates for individual counties. For a hypothetical scenario where existing HOV lane-kilometers in each state are expanded by 0.5 meters for every hour of total daily travel time to work, we found this strategy has the greatest potential to reduce annual carbon dioxide equivalent (CO2e) in the District of Columbia, by 4.5%, followed by Hawaii and New York, and New Jersey. The smallest potential is found in North Dakota. Nationally, 1.83 MMT of CO2e or 0.16% of light duty vehicle emissions would be reduced under this scenario In chapter IV, we attributed PEV adoption rate in 58 California Counties to charging station infrastructure and other potential factors using statistical models. We found charging station per capita to be effective in promoting PEV adoption, particularly among male buyers in households with less number of vehicles available. For a hypothetical scenario where existing charging station infrastructures in each county are expanded by 2 charging station for every one million total daily miles travelled, we found this strategy has the greatest potential to reduce petroleum use, GHG and criteria air pollutant emissions in Modoc County, by 0.06%-0.03%, followed by Sierra, and Mono counties. For 20 counties, including Butte, San Joaquin, and San Francisco counties, the benefit to cost ratio is below one, indicating the incompatibility of the strategy in these counties.Item Real-time diversion model for traffic on evacuation corridors(2011-05) Xu, Hao; Liu, Hongchao; Norville, H. Scott; Won, Moon C.; Senadheera, Sanjaya; Haq, SaifCaused by events such as terrorist attacks and natural disasters, evacuation planning and management has attracted growing interest from researchers, engineers, and governments for protecting people from disasters and complications. Transportation engineers are challenged by moving the extremely large groups of people that are usually associated with an evacuation. Pre-developed plans help evacuation management, but do not guarantee its success. Even carefully laid-out evacuation plans can have a diminished capacity or become ineffective because of a crash or other incident blocking a vital evacuation route. An incident blocking an evacuation corridor results in serious traffic congestion. As queue builds up along the corridor, both travel time and delay significantly increase upstream from the incident location, which leads to extra evacuation time or even a failed evacuation. Diversion, using adjacent arterials to divert stuck traffic, can be a feasible approach to evacuation incident management due to its capability to reduce traffic volume on the freeway and distribute exceeded demand to adjacent arterials. Widely deployed Intelligent Transportation Systems have made real-time traffic data collection much more efficient than before, but it is still difficult to re-route evacuation traffic in real time when precipitating incidents occur. Diversion routing during evacuation is a computationally challenging task because the number of evacuees often far exceeds the capacity, and the transportation networks involved are very large. Based on a review of the literature, two problems regarding diversion routing for traffic on evacuation corridors were raised: 1) lack of a diversion routing model considering resource cost for diversion control and characters of diversion traffic in evacuation; 2) lack of a method for real-time diversion routing in evacuation. For the first problem, a new diversion routing model was developed upon the basic minimum cost network flow model. It considers characters of diversion traffic in evacuation, while the prominent feature of the new model is to take intersection control cost for diversion operation into the route optimization process. The model is more appropriate for identifying practical diversion routes in evacuation than conventional ones. To address the second problem, a method for determining subareas for diversion routing was designed. This method decreases the road network size involved in routing algorithms and significantly reduces computational time, which makes real-time diversion routing feasible during evacuation. By using the two methods together, practicable diversion routes could be obtained in real time to alleviate congestion on evacuation corridors caused by precipitating incidents.Item Real-time traffic signal control for over-saturated networks(Texas Tech University, 2007-12) Chen, Shuaiyu; Liu, Hongchao; Senadheera, Sanjaya; Kobza, John E.Traffic congestion results from many sources including bottlenecks, incidents, work zones, bad weather, special events, and poor signal timing. Optimal control of traffic signals has proven to be one of the most cost-effective ways of relieving congestion in street networks. Previous researches on traffic signal timing have been focused primarily on under-saturated traffic conditions. With increasing traffic demands on existing road networks, more and more intersections are becoming over-saturated during peak hours. There is a strong need to investigate the characteristics of traffic flow in congested street networks and develop signal control systems that are capable of system-wide online signal timing optimization. This dissertation is aimed to fill this gap by developing a real-time online signal control system for optimal signal timing of both single intersections and signalized networks under over-saturated traffic flow conditions. A dynamic linear programming model is first developed for single over-saturated intersections. The proposed model provides a real-time formulation procedure to optimize signal timings under given dynamic traffic demand conditions. The research is then extended to network wide signal timing design. A new concept of vehicle detection, gridlock detection, is developed to provide information on backed up queues from the upstream intersections. A heuristic algorithm is then developed to dynamically search the progression routes for the best coordination among the signals in the network. The purpose of the route searching algorithm is to find the best coordination routes to disperse the over-saturated traffic demands to the network intersections. Then, at each intersection level, a compromise approach is developed to optimize the green time allocations simultaneously according to two operational criteria: delay and queue length management. The control concepts and algorithms are then summarized in a comprehensive network signal timing model, namely, the Compromise Network Queue Control (CNQC) model for practical application. The CNQC model features a computer aided, decentralized, and cycle-by-cycle based controlling approach for network wide signal control of over-saturated intersections.Item Successful bicycle policy guidelines and audit for midsize cities(2011-12) Kumfer, Wesley J.; Liu, Hongchao; Senadheera, SanjayaIn the United States, the bicycle has been a historically underutilized mode of transportation. However, recent political and economic trends have enabled the bicycle to become a much more viable means of transportation that should be integrated into multi-modal transportation systems. This thesis is the culmination of research initially conducted on behalf of the Texas Department of Transportation and carried out to further investigate the claims made in that initial report to TxDOT. This research was conducted to determine how bicycling can be effectively integrated into a multi-modal system and what factors lead to successful bicycling. The research was conducted in three phases. The first was an analysis of bicycling data gathered through an online survey. This produced a series of key findings and recommendations for governments wishing to improve and/or implement bicycling into their transportation systems. The second phase was conducted to further that analysis with statistical methods, including a correlation study. This second phase corroborated the findings of the first phase and produced a set of general guidelines to follow for bicycle policy implementation. The final phase of the research sought to conclude the project by deriving from all the previous findings a usable audit form by which cities can grade their level of bicycle implementation. The goal of this audit is to be a timely addition to current bicycle knowledge; this simple yet powerful tool is easy to use but should be intuitive enough to allow users to see areas in which to improve. This research is a crucial addition to the state of practice for bicycling as a mode of transportation and should provide the foundation for future efforts in promoting bicycling as a vital means of transit.Item Understanding the Morphophysiological Basis and Biomass Allocation Strategies of Legumes Under Water-Deficit Conditions(2023-12) Bhattarai, Bishwoyog; Liu, Hongchao; Fedler, Clifford B.; Xu, Hao; Won, MoonClimate change studies have emphasized the significance of water-deficit stress as one of the most critical abiotic stresses that limit crop growth and productivity. This highlights the need to develop strategies for enhancing plant drought tolerance and ensuring sustainable crop production. Legume crops, such as peanuts (Arachis hypogaea L.), primarily cultivated in regions with warm climates, low rainfall, and sandy soil, and barrel clover (Medicago truncatula), a model legume species grown for forage in Mediterranean region, experiences a limited access to irrigation water. In addition to legume high protein content, which contributes significantly to the nutritional requirements of humans and livestock, legume crops offer a unique advantage through their ability to form a symbiotic association with Rhizobium bacteria, promoting nitrogen fixation and soil health. Therefore, it is crucial to gain a comprehensive understanding of the morphophysiological responses of legumes to water-deficit stress conditions and integrate these responses into legume/crop improvement programs. To achieve this, we conducted a three-year field study with multiple peanut genotypes and a two-year growth chamber study with M. truncatula. These studies assessed leaf- and canopy-level gas exchange, quantified various morphological characteristics, and examined nodulation behaviors, leaf ultrastructure, and biomass allocation under water-limited and well-watered conditions. Leaf-level physiological responses in peanuts under water-deficit conditions revealed that, during the flowering stage, stomatal conductance and transpiration were significantly lower. Chlorophyll fluorescence, maximum carboxylation rate, and electron transport rate were lower during the pod-filling stage. These observations could be attributed to the increasing water requirement during the advancing growth stage, coupled with limited soil moisture availability and irrigation, resulting in moderate to severe water-deficit stress during these specific growth stages. However, genotypes C76-16 and Lariat exhibited higher stomatal conductance, transpiration rate, electron transport, and carboxylation rate, leading to increased net photosynthesis, as well as pod and kernel yield, compared to AG18 and GA-09B. These results suggest that genotype C76-16 was able to sustain leaf-level physiological responses even under water-deficit stress conditions, enabling it to achieve higher yields. Upon investigation of the canopy-level physiological response, nodulation behavior, and biomass allocation, it was observed that the water-deficit condition had a significantly higher root dry weight, a lower nodule count, and decreased root CO2 exchange than the well-watered condition. Similarly, C76-16 also exhibited a greater root dry weight, lower nodule count per 10 cm root sections, and lower root CO2 exchange compared to the rest of the peanut genotypes. This nodule count reduction and root dry weight increase must have benefited the water-deficit stress condition and C76-16 genotype by facilitating greater access to soil moisture and nutrients while allocating a smaller proportion of assimilates toward nodule growth and maintenance. Additionally, under water-deficit, there was a significant reduction in the leaf area index and above ground biomass, resulting in lower net canopy CO2 exchange. Moreover, the above ground biomass allocation ratio to stem:leaf:pod for water-deficit and well-watered conditions was approximately 35:35:30 and 30:30:40, respectively, indicating greater allocation for leaf and stem rather than pod yield in water-deficit and well-water conditions. Similarly, biomass allocation to pod was higher in C76-16 (30:30:40) as compared to AG18 (32:33:35), GA-09B (33:32: 35), and Lariat (35:33:32). Therefore, the balanced allocation of biomass to roots component and above ground parts as shown by C76-16 proved to be advantageous for promoting biomass growth and pod yield under the water-deficit conditions. The study of combinations of nitrogen (N) and mycorrhiza (M) supplements on leaf ultrastructure, leaf-level physiology, and yield of M. truncatula under water-deficit and well-watered conditions revealed that thicker leaves and larger-sized cells were observed under water-deficit condition than in well-watered condition. Also, the N-only supplement exhibited thicker leaves and larger cells than the M-only supplement. This reduction in leaf thickness and smaller-sized cells resulted in increased intercellular spaces, enhancing the stomatal conductance in water-deficit conditions and M-only supplement. The chloroplast's density did not vary among the irrigation conditions; however, larger-sized chloroplasts were observed in the water-deficit conditions. Similarly, larger-sized chloroplasts were observed in N-only and M-only supplements than in control and both N and M supplement combinations. Similarly, net photosynthesis, stomatal conductance, shoot length, and pod per plant were higher in M-only and both N and M supplements compared to the none and N-only supplements. This result indicated that the M supplement enhanced the water-deficit stress tolerance leading to increased physiological activity and pod yield than the none and N-only supplement. Likewise, lower root biomass allocation was observed in the M-only supplement than in the N-only supplement, which could be due to enhanced soil moisture supply by mycorrhiza lowering the need to allocate more for root growth. Similarly, a lower proportion of biomass was allocated to stem and leaf growth while greater for the pod formation in the M-only and both N and M supplements compared to the none and N-only supplement.Item Volume to capacity estimation of signalized road networks for metropolitan transportation planning(2012-12) Fernando, Hiron; Liu, Hongchao; Senadheera, SanjayaTransportation planning has played a critical role in shaping the economic health and quality of life for the general public. It not only provides insight into the mobility of people and goods, but also influences patterns of growth and economic activity. Metropolitan transportation planning is a challenging transportation topic due to the lack of future traffic information, such as evaluation of the capacity sufficiency on large metropolitan road networks with signalized intersections. The Highway Capacity Manual provides methods for analysis of signalized intersections and urban streets for planning; however, these methods need detailed traffic volume inputs and lane configurations at signalized intersections, which are normally not readily available in metropolitan transportation planning. The conventional four step planning process is widely used to forecast directional traffic volumes on particular road segments, but the projected volumes are not enough for analyzing more detailed information such as capacity sufficiency of future road networks. This study discusses the proposed quick v/c ratio estimation method developed by integration of available transportation planning data and characteristics of signalized intersections. This method merges traffic assignment results of the conventional transportation planning procedure to capacity sufficiency estimation. By using the proposed method, transportation planners can estimate capacity sufficiency of future metropolitan road networks through use of readily available data in transportation planning. This method will dramatically decrease the effort required for capacity evaluation of large signalized metropolitan road networks.