Civil & Environmental Engineering Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2753
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Item MULTIMODAL TRAVEL BEHAVIOR ANALYSIS AND MONITORING AT METROPOLITAN LEVEL USING PUBLIC DOMAIN DATA(2019) PENG, BO; Zhang, Lei; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Travel behavior data enable the understanding of why, how, and when people travel, and play a critical role in travel trend monitoring, transportation planning, and policy decision support. Conventional travel behavior data collection methods such as the National Household Travel Survey (NHTS) have been the primary source of travel behavior information for transportation agencies. However, the relatively high cost of traditional travel surveys often prohibits frequent survey cycles (currently once every 5-10 years). With decision makers increasingly requesting recent and up-to-date information on multimodal travel trends, establishing a sustainable and timely travel monitoring program based on available data sources from the public domain is in order. This dissertation developed advanced data processing, expansion, fusion and analysis methods and integrated such methods with existing public domain data into a comprehensive model that allows transportation agencies to track monthly multimodal travel behavior trends, e.g., mode share, number of trips, and trip frequency, at the metropolitan level. Advanced data analytical methods are developed to overcome significant challenges for tracking monthly travel behavior trends of different modes. The proposed methods are tailored to address different challenges for different modes and are flexible enough to accommodate heterogeneous spatial and temporary resolutions and updating schedules of different data sources. For the driving mode, this dissertation developed reliable methods for estimates of local road VMT, various temporal adjustment factors, truck percentage factors, average vehicular occupancy, and average trip length based on additional data from the Travel Monitoring Analysis System and the most recent regional household travel survey to translate HPMS data into monthly number of vehicular and person driving trips for a metropolitan area. For the transit mode, this dissertation collectively exhausted detailed transit network geo-data to complement NTD and developed advanced geo-analysis and statistical methods tailored to the service network of different types of operators to accurately and reliably allocate ridership data to the metropolitan area of interest, and to allocate annual ridership data to each month. The data for non-motorized is even more sparse, although the local government has growing interests and efforts on collecting such data. A two-step statistical model is developed to derive the trend for non-motorized modes and then integrating such trends with base-year number of trips number from most recent household travel survey conducted in the metropolitan areas of interest. Based on the number of trips by modes estimated using the proposed methods, the monthly trend in mode share can be timely estimated and continuously monitored over time for the first time in the literature using public domain data only. The dissertation has demonstrated that it is feasible to develop a comprehensive model for multimodal travel trend monitoring and analysis by integrating a wide range of traffic and travel behavior data sets of multiple travel modes. Based on findings, it can be concluded that the proposed public-domain databases and data processing, expansion, fusion and analysis methods can provide a reliable way to monitor the month-to-month multimodal travel demand at the metropolitan level across the U.S.Item A Q-LEARNING BASED INTEGRATED VARIABLE SPEED LIMIT AND HARD SHOULDER RUNNING CONTROL TO REDUCE TRAVEL TIME AT FREEWAY BOTTLENECK(2019) Zhou, Weiyi; Zhang, Lei; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)To increase traffic mobility and safety, several types of active traffic management (ATM) strategies, such as variable speed limit (VSL) and hard shoulder running (HSR), are implemented in many countries. While all kinds of ATM strategies show promise in releasing traffic congestion, many studies indicate that stand-alone strategies have very limited capability. This paper proposes an integrated VSL and HSR control strategy based on a reinforcement learning (RL) technique, Q-learning (QL). The proposed strategy bridges a direct connection between the traffic flow data and the ATM control strategies via intensive self-learning processes thus reduces the need for human knowledge. A typical congested interstate highway, I-270 in Maryland, U.S. is simulated using a dynamic traffic assignment (DTA) model to evaluate the proposed strategy. Simulation results indicated that the integrated strategy outperforms the stand-alone strategies and traditional feedback-based VSL strategy in mitigating congestions and reducing travel time on the freeway corridor.Item Development of a Traffic Incident Management Support System(2019) Won, Minsu; Chang, Gang-Len; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Highway incidents, primary contributors to traffic congestion, often cause not only significant delays to the daily roadway users, but also the reliability of transportation systems and even the productivity of the supply-chains of some vital industry sectors. To mitigate the impacts of such incidents and to recover the performance of highway systems as safely and quickly as possible, it is essential that the responsible highway agencies shall operate an efficient system to detect an incident, estimate the required clearance duration, assess the resulting traffic impacts, and then take necessary control actions. To do so, the most critical task is to have a reliable estimate of a detected incident’s impacts. However, providing the information of time-varying incident impacts to the general public at the desirable level of accuracy is a challenging task due to difficulties in having sufficient data and the complex relations between key factors contributing to the incident impacts. The purpose of this study is to develop a traffic incident management (TIM) support system which is capable of providing robust and reliable information with respect to the estimated clearance duration and capacity drop of a detected incident and its temporal as well as spatial evolution of traffic impact patterns. The proposed incident management support system consists of two main components, one for estimating the incident duration and the other for computing the resulting capacity drop of the roadway segment plagued by the incident. The first component servers to provide a robust incident duration estimate, using several specially-designed methods to effectively tackle the unique distribution patterns of the incident duration data and their complex correlations among contributing factors, which includes classification model, continuous model, and supplemental rules to first produce an initial interval estimate, and then a point estimate along with the outlier information. The second component is designed to estimate the additional roadway capacity reduction (i.e., capacity drop) due to the lane blockages by incidents and the response operations, allowing the control center operators to assess the spatial and temporal incident impacts on the highway network, and take necessary control actions in a timely manner. The proposed TIM support system has the following key features: 1) providing the initial estimate of incident duration, based on limited data available at the early stage of incident responses and operations; 2) updating the estimated incident duration with a specially-design process and models when more data become available; 3) implementing an integrated estimation methodology to circumvent the variances due to the unique characteristics associated with recorded incident data (e.g., highly skewed distribution, complex correlations among the explanatory variables, mixed qualitative and quantitative variables, and heteroscedasticity); 4) generating the estimated additional capacity reduction for the highway segment plagued by the lane-closure and lane-changing activities during the incident clearance operations using a reliable and trackable analytical model; and 5) providing a convenient and effective computation process to estimate time-varying incident impacts, such as queues and delays, in the highway network for real-time applications.Item Copula Based Population Synthesis and Big Data Driven Performance Measurement(2019) Kaushik, Kartik; Cirillo, Cinzia; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Transportation agencies all over the country are facing fiscal shortages due to the increasing costs of management and maintenance of facilities. The political reluctance to increase gas taxes, the primary source of revenue for many government transportation agencies, along with the improving fuel efficiency of automobiles sold to consumers, only exacerbate the financial dire straits. The adoption of electric vehicles threatens to completely stop the inflow of money into federal, state and regional agencies. Consequently, expansion of the network and infrastructure is slowly being replaced by a more proactive approach to managing the use of existing facilities. The required insights to manage the network more efficiently is also partly due to a massive increase in the type and volume of available data. These data are paving the way for network-wide Intelligent Transportation Systems (ITS), which promises to maximize utilization of current facilities. The waves of revolutions overtaking the usual business affairs of transportation agencies have prompted the development and application of various analytical tools, models and and procedures to transportation. Contributions to this growth of analysis techniques are documented in this dissertation. There are two main domains of transportation: demand and supply, which need to be simultaneously managed to effectively push towards optimal use of resources, facilities, and to minimize negative impacts like time wasted in delays, environmental pollution, and greenhouse gas emissions. The two domains are quite distinct and require specialized solutions to the problems. This dissertation documents the developed techniques in two sections, addressing the two domains of demand and supply. In the first section, a copula based approach is demonstrated to produce a reliable and accurate synthetic population which is essential to estimate the demand correctly. The second section deals with big data analytics using simple models and fast algorithms to produce results in real-time. The techniques developed target short-term traffic forecasting, linking of multiple disparate datasets to power niche analytics, and quickly computing accurate measures of highway network performance to inform decisions made by facility operators in real-time. The analyses presented in this dissertation target many core aspects of transportation science, and enable the shared goal of providing safe, efficient and equitable service to travelers. Synthetic population in transportation is used primarily to estimate transportation demand from Activity Based Modeling (ABM) framework containing well-fitted behavioral and choice models. It allows accurate verification of the impacts of policies on the travel behavior of people, enabling confident implementation of policies, like setting transit fares or tolls, designed for the common benefit of many. Further accurate demand models allow for resilient and resourceful planning of new or repurposing existing infrastructure and assets. On the other hand, short-term traffic speed predictions and speed based reliable performance measures are key in providing advanced ITS, like real-time route guidance, traveler awareness, and others, geared towards minimizing time, energy and resource wastage, and maximizing user satisfaction. Merging of datasets allow transfer of data such as traffic volumes and speeds between them, allowing computation of the global and network-wide impacts and externalities of transportation, like greenhouse gas emissions, time, energy and resources consumed and wasted in traffic jams, etc.Item CONTINUOUS CHOICE MODELS FOR TIME-OF-DAY CHOICE MODELING APPLICATIONS(2019) Ghader, Sepehr; Zhang, Lei; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)I propose a series of tools to model travelers’ time-of-day choice in continuous time. The models discussed in this dissertation can help advancing time-of-day modeling of trips or activities and produce demand with fine time resolution. These models are a good fit for dynamic traffic assignment and they can be applied for policy evaluation, travel management, and real-time applications. I first present the Continuous Logit (CL) model as the originator of a variety of discrete and continuous choice models and shed light on the relationship between some of the available choice models and CL by showing how these models can be seen as approximations to the CL. I also demonstrate how different approximation techniques can lead to new forms of choice models. I conduct Monte Carlo experiments to study the magnitude of error in the approximated models. These experiments can help the reader better understand the implications of various approximation and discretization schemes for time-of-day modeling. Due to the limits of CL in modeling correlations, I introduce and formulate the AutoRegressive Continuous Logit (ARCL) as a novel continuous class of choice models capable of representing correlations across alternatives in the continuous spectrum. I formulate this model by considering two approaches: combining a discrete-time autoregressive process of order one with the CL model, and combining a continuous-time autoregressive process with the CL model. ARCL is the only Random Utility Maximization-based continuous choice model, besides the Continuous Cross-Nested Logit (CCNL), able to handle correlations across alternatives in the continuous spectrum. I extend the continuous time-of-day modeling to multi-dimensional case by introducing a framework to model the joint choice of arrival to an activity and departure from the activity. Each choice is modeled in continuous time using CCNL. I use Copula to capture the correlation between the two dependent choices. Copula can model the correlation structure without knowing the actual bivariate distribution function. With its multidimensionality and ability to capture different sorts of correlations and model demand in fine time resolution, the introduced framework can provide a sufficient tool for the time-of-day component of various travel demand models.Item DEVELOPMENT OF AN INTEGRATED RIDE-SHARED MOBILITY-ON-DEMAND (MOD) AND PUBLIC TRANSIT SYSTEM(2019) XU, LIU; Haghani, Ali; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The Mobility-on-Demand (MOD) services, like the ones offered by Uber and Lyft, are transforming urban transportation by providing more sustainable and convenient service that allows people to access anytime and anywhere. In most U.S. cities with sprawling suburban areas, the utilization of public transit for commuting is often low due to lack of accessibility. Thereby the MOD system can function as a first-and-last-mile solution to attract more riders to use public transit. Seamless integration of ride-shared MOD service with public transit presents enormous potential in reducing pollution, saving energy, and alleviating congestion. This research proposes a general mathematical framework for solving a multi-modal large-scale ride-sharing problem under real-time context. The framework consists of three core modules. The first module partitions the entire map into a set of more scalable zones to enhance computational efficiency. The second module encompasses a mixed-integer-programming model to concurrently find the optimal vehicle-to-request and request-to-request matches in a hybrid network. The third module forecasts the demand for each station in the near future and then generates an optimized vehicle allocation plan to best serve the incoming rider requests. To ensure its applicability, the proposed model accounts for transit frequency, MOD vehicle capacity, available fleet size, customer walk-away condition and travel time uncertainty. Extensive experimental results prove that the proposed system can bring significant vehicular emission reduction and deliver timely ride-sharing service for a large number of riders. The main contributions of this study are as follows: • Design of a general framework for planning a multi-modal ride-sharing system in cities with under-utilized public transit system; • Development of an efficient real-time algorithm that can produce solutions of desired quality and scalability and redistribute the available fleet corresponding to the future demand evolution; • Validation of the potential applicability of the proposed system and quantitatively reveal the trade-off between service quality and system efficiency.Item EFFECTS OF DRIVERLESS VEHICLES ON THE COMPETITIVENESS OF BUS TRANSIT SERVICES(2019) Liu, Shiyi; Schonfeld, Paul; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The advent of driverless vehicles, including automobiles and buses, may considerably affect the competitiveness and ridership of public transportation services in negative as well as positive ways. Since driverless vehicles may be widely used in the fairly near future, public transit operators and transportation planners should prepare to deal with their anticipated effects. In this thesis the author (1) formulate modular optimization models for both human-driven and automated bus services with fixed routes as well as flexible routes, (2) develop preliminary quantitative assessments of those effects, showing that without drivers, competitiveness of public transportation compared to private transportation decreases; (3) conduct sensitivity analyses to explore how changes in input parameters affect the results; and (4) identify insights in which transit operators, transportation planners and other transportation system stakeholders may use in effectively adapting to the introduction of driverless vehicles.Item Solving the integrated school bell time, and bus routing and scheduling optimization problem under the deterministic and stochastic conditions(2019) Wang, Zhongxiang; Haghani, Ali; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The school bus planning problem (SBPP) has drawn significant attention in research and practice because of its importance in pupil transportation. The major task of the SBPP is to simultaneously optimize the school bell times, the routing plan (a set of trips) and the scheduling plan (the assignment of buses to serve these trips) while maintaining the minimum level-of-service requirements with the objective that the total number of buses and the total vehicle time are both minimized. Many subproblems of the SBPP have been well studied, but the integrated problem lacks much research due to its complexity. A Mixed Integer Programming (MIP) model is proposed for the integrated SBPP. A novel decomposition method is developed to solve the model. It distinguishes itself from the literature with the consideration of trip compatibility in the routing stage, which is a piece of essential information in the following scheduling stage. This ‘look ahead’ strategy finds a new balance between the model integration and decomposition, which solves the problem efficiently as a decomposed problem but with the high solution quality as the integrated model. Three heuristic algorithms are proposed to solve the deterministic SBPP with the trip compatibility. Then, two mathematical programming models and a Column Generation-based algorithm are proposed for the SBPP under traffic congestion and stochastic travel time in a real uncertain world. These innovative algorithms incorporate the merits of the Simulated Annealing, Tabu Search, Insertion Algorithm, and Greedy Randomized Adaptive Search Procedure and gain the computational power that the existing methods do not have. The experiments are conducted on randomly generated datasets, benchmark problems, and real-world cases. The results show that the proposed models and algorithms outperform the state-of-the-art method in all test problems by up to 25%. In a real-world case study, after the bell time adjustment, up to 41% of current buses can be saved with even better service with respect to the higher punctuality and shorter student ride time.Item Optimal Reassignment of Flights to Gates Focusing on Transfer Passengers(2019) Pternea, Moschoula; Haghani, Ali; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation focuses on the optimal flight-to-gate assignment in cases of schedule disruptions with a focus on transfer passengers. Disruptions result from increased passenger demand, combined with tight scheduling and limited infrastructure capacity. The critical role of gate assignment, combined with the scarcity of models and algorithms to handle passenger connections, is the main motivation for this study. Our first task is to develop a generalizable multidimensional assignment model that considers the location of gates and the required connection time to assess the success of passenger transfers. The results demonstrate that considering gate location is critical for assessing of the success of a connection, since transfer passengers contribute significantly to total cost. We then explore the mathematical programming formulation of the problem. First, we compare different state-of-art mathematical formulations, and identify their underlying assumptions. Then, we strengthen our time-index formulation by introducing valid inequalities. Afterwards, we express the cost of passenger connections using an aggregating formulation, which outperforms the quadratic formulation and is consistently more efficient than network flow formulations when the cost of successful connections is considered. In the last part of the dissertation, we embed the formulation in an MIP-based metaheuristic framework using Variable Neighborhood Search with Local Branching (VNS-LB). We explore the key notion of a solution neighborhood in the context of gate assignment, given that transfer passengers are our main consideration. Our implementation produces near-optimal results in a low amount of time and responds reasonably to sensitivity analysis in operating parameters and external conditions. Furthermore, VNS-LB is shown to outperform the Local Branching heuristic in terms of solution quality. Finally, we propose a set of extensions to the algorithm which are shown to improve the quality of the final solution, as well as the progress of the optimization procedure as a whole. This dissertation aspires to develop a versatile tool that can be adapted to the objectives and priorities of practitioners, and to provide researchers with an insight of how the features of a solution are reflected in the mathematical formulation. Every idea relying on these principles should be a promising path for future research.Item Mechanisms for Trajectory Options Allocation in Collaborative Air Traffic Flow Management(2018) Mohanavelu Umamagesh, Prithiv Raj; Lovell, David J; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Flight delays are primarily due to traffic imbalances caused by the demand for airspace resource exceeding its capacity. The capacity restriction might be due to inclement weather, an overloaded air traffic sector, or an airspace restriction. The Federal Aviation Administration (FAA), the organization responsible for air traffic control and management in the USA, has developed several tools known as Traffic Management Initiatives (TMI) to bring the demand into compliance with the capacity constraints. Collaborative Trajectory Option Program (CTOP) is one such tool that has been developed by the FAA to mitigate the delay experienced by flights. Operating under a Collaborative Decision Making (CDM) environment, CTOP is considered as the next step into the future of air traffic management by the FAA. The advantages of CTOP over the traditional the TMIs are unequivocal. The concerns about the allocation scheme used in the CTOP and treatment of flights from the flight operators/airlines have limited its usage. This research was motivated by the high ground delays that were experienced by flights and how the rerouting decisions were made in the current allocation method used in a CTOP. We have proposed four alternative approaches in this thesis, which incorporated priority of flights by the respective flight operator, aimed at not merely reducing an individual flight operator’s delay but also the total delay incurred to the system. We developed a test case scenario to compare the performances of the four proposed allocation methods against one another and with the present allocation mechanism of CTOP.