Civil & Environmental Engineering
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Item A Smart Traffic Incident Management (TIM) System for Estimating Highway Incident Duration and Impacts with and without Surveillance Sensors(2024) Huang, Yen-Lin; Chang, Gang-Len G.L.C; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Highway incidents are major contributors to traffic congestion, causing significant delays for daily roadway users and reducing the reliability and productivity of transportation systems. To mitigate the negative impacts of these incidents and quickly restore highway operations, it is crucial for highway agencies to implement an efficient incident management system. However, providing the public with real-time information about the impacts of incidents at a desired level of precision is challenging due to the complexities involved in obtaining sufficient data and understanding the intricate relationships among the factors that influence these impacts. To address this challenge, this study proposes a Smart Traffic Incident Management (TIM) system that delivers robust and reliable information on estimated clearance durations, resultant queue lengths, time-varying traffic information, and traffic detouring volumes from freeways to adjacent arterials. This initiative aims to improve the effectiveness of incident response, thereby enhancing the resilience and functionality of the transportation network. The proposed system consists of four primary modules. Module 1 aims to robustly predict incident clearance duration through the proposed Knowledge Transferability Analysis (KTA) model, featuring its automated process for assessing, selecting, and transferring existing prediction rules from pre-existing Incident Duration Prediction Models (IDPM). This strategic utilization obviates the necessity for integrating field operators' expertise in formulating prediction rules, thereby alleviating the dependency on an ample volume of incident records for prediction rules calibration. The evaluation results, using I-70 in Maryland for the case study, have demonstrated the effectiveness of the proposed KTA model in not only expediting the development process of an IDPM but also improving the resulting accuracy of the prediction rules. Module 2 endeavors to robustly predict incident queue length by introducing the Real-time Incident Queue Prediction (R-IQP) system. This system's principal model enhances the formulations for queue propagation dynamics by incorporating the influences of incoming drivers' perceptions and responses to progressively constrained traffic conditions. Additionally, two supplementary models are proposed to precisely estimate flow rates, leveraging probe speed information, to accommodate different surveillance environments characterized by varying levels of data availability. The evaluations of the proposed R-IQP system with both the field-collected data and the well-calibrated simulator’s data have proven the capability of the R-IQP system on predicting time-varying queue lengths for incidents with various clearance durations and types of lane blockage statuses. Module 3 introduces a traffic flow model specifically designed for traffic incident management. The proposed Incident-oriented METANET (I-METANET) enhances the widely used METANET model with three key improvements: 1) reflecting the merging behaviors incurred by incidents and their significant yet diminishing effects on speed propagation over upstream segments; 2) incorporating the simultaneous effects of upstream traffic flows and downstream incident-induced queue waves on the speed of a subject segment; and 3) integrating the combined effects of ramp-flow weaving maneuvers and the presence of incident queues on traffic conditions at interchange segments. The proposed I-METANET model, calibrated and validated using field data from I-4 in Florida, has demonstrated its effectiveness in predicting time-varying speeds and flow rates over roadway segments during incident clearance periods. Module 4 focuses on assessing the impact of freeway incidents on nearby local roads. To achieve this, the study developed a Real-time Detour Volume Estimation (R-DVE) system, designed to estimate the volume of traffic diverted from the freeway mainline to its adjacent arterials, even when freeway traffic sensors are unavailable. This system leverages a set of offline speed-flow models developed in Module 2 to estimate traffic flow using probe speed data as input. Additionally, the proposed R-DVE incorporates a Quality Assessment Mechanism (QAM) that integrates a robust customized dynamic speed-flow relations (CDSFR) developed in Module 3 to continually examine the applicability of the estimated flow rates and update the offline speed-flow models. The performance evaluation, based on real-world incident cases on I-95 in Maryland, demonstrated that the R-DVE system can accurately estimate real-time detouring volumes, highlighting its practical applicability. The proposed Smart Traffic Incident Management (TIM) system, delineated through its comprehensive modules, embodies several key features aimed at enhancing incident management efficacy, including 1) providing a systematic decision-making framework for incident clearance duration prediction, particularly valuable for highways lacking sufficient incident records to calibrate prediction rules; 2) incorporating predicted clearance duration to generate timely estimates of incident queue length, with the adaptive capability particularly beneficial for highways under varying levels of traffic sensor availability; 3) predicting time-varying traffic information to faciltiate better incident management and responsive strategies; 4) generating real-time estimates of detour volume originating from each interchange within the impact area, facilitating the execution of appropriate responsive operations contributing to efficient incident management; and 5) exhibiting a dynamic nature by updating estimated information when additional data become available or when there are changes in traffic dynamics or incident clearance operation to ensure the continuous relevance and accuracy of the provided information.Item A DATA-DRIVEN FRAMEWORK FOR THE PREDICTION OF NON-RECURRENT TRAFFIC CONGESTION RECOVERY TIME ON FREEWAYS(2024) Kabiri, Aliakbar; Haghani, Ali; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This study introduces a comprehensive approach aimed at improving the management of incident durations. It delves into enhancing traffic incident management by integrating diverse incident datasets, including Maryland State Police incident data and Coordinated Highways Action Response Team (CHART) incident data, to improve the assessment of traffic incident durations. The dissertation employs spatial and temporal thresholds to explore matching different incident datasets and identifies discrepancies between various incident reports. The dissertation also explores methodologies for estimating traffic recovery times of each incident, utilizing historical data and pre-incident conditions as baselines to establish normal traffic conditions. A novel framework is introduced to estimate non-recurrent traffic congestion recovery time, revealing that many incidents recover faster than their reported clearance times. In these cases, traffic flow returns to normal conditions quickly.Further, the study examines predictive modeling for traffic recovery time, highlighting the Random Forest model's effectiveness among various machine learning algorithms. This model's superiority, based on precision, recall, and F1-scores, underlines its potential in accurately predicting traffic incident recovery time categorized as short-duration, medium-duration, and long-duration incidents. In particular, the random forest model results in a precision of 0.7 for short-duration incidents, 0.3 for medium-duration incidents, and 0.5 for long-duration incidents. For instance, the precision of 0.5 for long-duration incidents indicates that half of the cases predicted as long-duration incidents are indeed long-duration incidents. Key predictors such as link-level vehicle volume, clearance time, response time, and number of lanes closed are identified, providing valuable insights for traffic management strategies. This dissertation underscores the importance of data-driven approaches in traffic incident management, aiming to enhance the efficiency of transportation systems through accurate prediction and estimation of incident recovery times.Item Examination of US Transportation Public-Private Partnership Experience: Performance and Market(2024) Zhang, Kunqi; Cui, Qingbin; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Worldwide, public-private partnership (P3) project performance and benefits accrued to market participants are understudied. Focusing on the US, this dissertation examines the country’s transportation P3 experience through three empirical studies comparing P3 to design-bid-build (DBB), the traditional delivery method. Throughout, the Information Source for Major Projects database, built by a University of Maryland team in which the author led the data collection effort, served as the data source. In the first study, the researchers examined P3 cost and time performance using piecewise linear growth curve modeling, recognizing that past cross-sectional studies had produced mixed results. With 133 major transportation projects, the longitudinal analysis confirmed P3’s time performance advantage and efficiency diffusion effecting cost savings in DBB, where efficiency diffusion was a new term describing the spillover and internalization of technical and managerial innovations inducing an efficient outcome. The second study used social network analysis to investigate collaboration patterns among different types of players in the P3 market (i.e., public sponsors, special purpose vehicles, investors, lenders, advisors, contractors, and professional service firms). With 135 projects and 1009 organizations, data found that both P3 and DBB networks are small worlds. Exponential random graph modeling revealed that practicing in the DBB market helps firms participate in P3 projects and that large firms (vis-à-vis small/medium-sized firms) are not privileged. The third study, further exploring the P3 market, focused on the Disadvantaged Business Enterprise (DBE) program. Administered by the US Department of Transportation, the program promotes the participation of small, disadvantaged firms in federal-aid projects. Linear regressions on 134 contracts showed that P3 associates with higher DBE goals in terms of percentage of dollars to be awarded to DBEs, whereas the delivery method does not affect the actual attainment. Overall, the findings justify continued policy support towards P3 implementation.Item Econometric Evaluation of Transportation Policies: Decarbonization and Electrification(2024) Burra, Lavan Teja; Cirillo, Cinzia; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The transportation sector, one of the largest contributors to global energy-related emissions, is undergoing a major transition. Governments worldwide are implementing stringent fuel economy and emissions standards, promoting the adoption of electric vehicles--a key technology for decarbonizing the transport sector--through various policy measures. This dissertation contains four chapters, studying the effects of such policies implemented across major vehicle markets and evaluating their effectiveness, with a particular focus on the electrification of light-duty passenger vehicle fleet. The first chapter explores whether multi-car households shift mileage to the most fuel-efficient car in response to increasing driving costs, which carries implications for designing effective fuel economy standards. The second chapter investigates the potential interaction between purchase subsidies given to consumers in buying electric vehicles (EVs) and expanding the public charging network. The third chapter focuses on the effectiveness of purchase subsidies for EV buyers and quantifies the free-rider share, given that this is a commonly employed policy measure worldwide. The final chapter explores the differential effects of level 2 and level 3 chargers, as well as the distributional impacts of public charging network on driving EV uptake across various demographic groups and built environment characteristics. Overall, the chapters in this dissertation employ travel survey data, longitudinal and big data analysis, causal identification, optimal policy design, counterfactual simulations, and a combination of data and economic reasoning to glean insights on the effectiveness and equitable aspects of policies aiming to decarbonize and electrify the transportation sector.Item EQUITY ISSUES IN ELECTRIC VEHICLE ADOPTION AND PLANNING FOR CHARGING INFRASTRUCTURE(2024) Ugwu, Nneoma; Niemeier, Deb; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Electric Vehicles (EVs) offer a sustainable solution to fossil fuel dependency and environmentalpollution from conventional vehicles, crucial for mitigating climate change. However, low market penetration among minority and low-income communities raises equity and environmental justice concerns. This dissertation examines EV adoption and charging station access disparities in Maryland, focusing on sociodemographic factors such as race and income. To address the lack of minority representation in existing EV research surveys, we conducted anonline survey targeting people of color (POC) and low-to-moderate-income households. We received 542 complete responses. Ordinal regression models were used to analyze factors influencing EV interest. We then performed a cumulative accessibility study of EV infrastructure in Maryland. Pearson correlation analysis was used to show the relationship between charging station accessibility and sociodemographics. Population density showed a strong positive correlation (0.87) with charging deployment. We found that Baltimore City, had the highest population density and the highest concentration of EV charging in Maryland. We conducted a case study of Baltimore City’s EV infrastructure investments and policy efforts. Charging stations were categorized based on speed, network, access, and facility type. Spatial analysis andZero-Inflated Poisson (ZIP) regression models at the block group level were employed to investigate the disparities in EV charging infrastructure distribution within the City across minority and non-minority communities. Our findings show substantial disparities in EV perceptions between POC and Whitecommunities. The survey revealed that POC were more than twice more likely than White respondents to indicate that the availability of charging stations affects their interest in EV adoption, while the case studies revealed that POC populations are less likely to have access to EV infrastructure, necessitating targeted investment in charging options and subsidies in these communities. Our study also found the need for policies fostering residential charging station deployment, particularly in minority communities. To ensure equitable EV adoption, strategic investments in economically disadvantaged and rural areas beyond centralized regions are vital. This study informs evidence-based policies prioritizing accessibility, equity, and inclusivity in promoting a cleaner and sustainable transportation landscape.Item A Planning Model For Flexible-route Freight Deliveries in Rural Areas Based on Adjusted Tour Length Estimations(2023) Li, Zheyu; Schonfeld, Paul; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Addressing the issue of delivery efficiency and transportation service quality in rural areas, this thesis presents an analysis of total cost of delivery services in regions with low demand density and low road network density. It focuses on designing a cost-effective and efficient freight delivery system, which is crucial for promoting a vibrant rural economy. A flexible-route service model is developed, aiming to improve farm products and other deliveries by optimizing the service zone size and frequency to minimize the average cost per delivered package. The model is tailored for a potential truck operation scenario in the central Appalachian region, serving as a representative case study, with a general formulation of total cost that can be adapted to similar cases elsewhere. Considering the influence of dead-end roads in rural area, this study presents an adjusted formulation of length estimation for Traveling Salesman Problem (TSP) tours based on the literature review and regression on multiple graphs with road network, and develops a mathematical formulation of total cost, integrating operation and user costs, supported by reasonable assumptions and system constraints. The results from the baseline study suggest that one truck can serve a large service area by exceeding the maximum working hours constraint. This observation is made without considering the potential expansion into a multi-zone system, which might be necessary due to the combined factors of road network complexity and the perishability of farm products. The results from our sensitivity analysis show that a system with a single large truck will have the lowest average cost per package when demand is low. Considering an actual road network, this study also explores the possibility of combining the flexible-route delivery service with self-deliveries and the extension of Vehicle Routing Problem (VRP) with maximum working hour constraint. The study concludes with suggested future research directions in this important domain.Item Analysis of Transfer Coordination in Flexible-Route Bus Services(2023) Yang, Tao; Schonfeld, Paul; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Urban residents are often confronted with transportation predicaments. The inconvenience caused by the conventional fixed-route bus system has led to excessive reliance on private cars, worsening traffic congestion and air pollution. However, a flexible bus route can provide passengers with a convenient, expedient, cost-effective commuting option. This thesis studies a flexible bus system with Many-to-One (M-1) and Many-to-Many (M-M) demand patterns, comprising multiple rectangular residential zones and a central terminal. The total cost of flexible route bus service is modeled and modified for coordinated and uncoordinated headway conditions. Among them, the demand between each service zone and the central terminal, and the demand among service zones are analyzed to optimize headways in order to minimize total system cost. Finally, the sensitivity analyses are conducted to explore the impact of parameter changes on the results. The comparison of baseline and sensitivity analysis results shows that more benefits can be achieved when coordinating headways under low-demand conditions.Item EXPLORING TEMPORAL AND SPATIAL VARYING IMPACTS ON COMMUTE TRIP CHANGE DUE TO COVID-19(2023) Saleh Namadi, Saeed; Niemeier, Deb; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)COVID-19 has deeply affected people’s daily life and travel behaviors. This study uses large-scale mobile device location data at the U.S. county level in the DMV area to reveal the impacts of demographic and socioeconomic variables on commute trip change. The study investigates the contribution of these variables to the temporal and spatial varying impacts on commuter trips. It reflects the short and long-term impact of COVID-19 on travel behavior via linear regression and geographically weighted regression models. The results indicate that commute trips decreased with more white-collar jobs, while blue-collar sectors demonstrated the opposite effect. Unexpectedly, elderly individuals, who were highly vulnerable to COVID-19, negatively correlated with decreased commute trips. Moreover, in the DMV area, counties with a higher proportion of Democratic voters also showed a negative correlation with reduced commute trips. Notably, the pandemic's impact on commuting behaviors was global at the onset of COVID-19. Still, the effects exhibited local correlations as the pandemic evolved, suggesting a geographical impact pattern.Item SELECTION AND SCHEDULING OF INTERRELATED NETWORK IMPROVEMENT PROJECTS UNDER UNCERTAINTIES(2023) Wu, Fei; Schonfeld, Paul M.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The analysis of improvements in transportation networks is complicated by the interdependence of those improvements. Changes in links or nodes tend to shift traffic flows. Hence, the benefits of changes in any network component depend on what changes are made at what time in other components. There may also be synergies in the costs of implementing network changes. Methods are needed for selecting and scheduling interrelated network changes under uncertainties regarding demand, costs, implementation times and other factors. The proposed research focuses on optimizing the selection and schedule of interrelated network projects for enhancing a network’s performance under various uncertainties. Two multi-level models are formulated for analyzing problems on two types of transportation networks: rail freight networks in Problem 1 and road networks in Problem 2. For rail freight networks, the proposed tri-level model jointly optimizes short-term post-disruption restoration schedules and long-term network development schedules. Its lower level assigns capacitated freight flows to minimize total hourly cost, and its middle level optimizes the restoration sequence for the minimized cumulative cost increment (excess) during the restoration process under a given disruption scenario. At the upper level, given probabilistic disruption scenarios, network improvement projects are selected from a given set and sequenced to minimize the sum of construction cost and cumulative expected excess over the planning horizon. For road networks, the lower level of the proposed bi-level model performs user-equilibrium (UE) traffic assignment using the Frank-Wolfe (F-W) algorithm. The upper-level model first generates multiple scenarios with samples from the multivariate distribution of multiple correlated uncertain parameters. For a given long-term network improvement plan, an expected present value (PV) of cumulative system travel time cost over the planning horizon plus construction costs of implemented projects is obtained after computing this discounted sum separately under each generated scenario. To minimize this expected PV, the upper level optimizes the improvement plan with a specified selection and sequence of projects. In both problems, any sequence of restoration or improvement actions is mapped to a unique schedule under the rules based on binding constraints of resources, budget, and required work time. The planning horizon is segmented into short sub-periods based on the improvement schedule to approximate the effects of demand growth and cost discounting. A genetic algorithm (GA) with its operators is customized for optimizing restorations and improvement plans. For road networks, with the internal budget supply based on a fraction of the travel time cost, a set of methods is proposed for determining budget-ready times of projects. The model also allows the use of buses as a mode competing with cars in road networks, and the iteration of mode shares is integrated with the lower-level F-W traffic assignment. In Problem 1, the proposed model is demonstrated with short-term and long-term numerical cases in a small demand-loaded network. In the short-term problem, the optimized restoration sequences with different numbers of available work teams are obtained by the GA, whose solutions to relatively small problems are shown to be globally optimal through exhaustive enumeration. The restoration itineraries and schedules of work teams along with corresponding changes in hourly cost are shown in figures. For a small long-term problem, the selection, sequence, and schedule of projects are optimized by exhaustive enumeration. Sensitivity analyses show that the availability of more work teams greatly reduces cumulative expected excess and thus justifies implementing fewer improvement projects. With a longer planning horizon, lower construction costs, and a higher interest rate, more improvements are favored. Evaluations of cumulative expected excess with probabilistic growth rates of demand are provided. The model is also tested on a larger network where the long-term improvement plan is optimized by the GA. The quality of its solution is statistically verified. In Problem 2, 50 scenarios are generated by a quasi-Monte-Carlo sampling method. Demand growth rate, external budget supply, and the multiplier of required construction time are three correlated uncertainties whose effects are analyzed. The selection and sequencing of improvement projects are optimized by the GA. When introducing buses in the network, two operation modes are considered: using dedicated bus lanes and sharing roads with cars. The iteration of bus shares shows that the bus operation is more desirable with dedicated lanes, and that the potential demand from bus passengers greatly affects that desirability. In sensitivity analyses the factors favoring the implementation of projects include a higher demand level, a higher value of travel time, lower construction costs, and a higher demand growth rate. Across the scenarios, the demand growth rate and the external budget supply directly affect the PVC and last completion time (LCT), respectively. Modifying correlation coefficients of uncertain parameters has slight impact on the minimized PVC, but greatly affects the correlation between PVC and LCT for a given improvement sequence.Item REAL-TIME DISPATCHING AND REDEPLOYMENT OF HETEROGENEOUS EMERGENCY VEHICLES FLEET WITH A BALANCED WORKLOAD(2023) Fang, Chenyang; Haghani, Ali; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The emergency management service (EMS) system is a complicated system that tries to coordinate each system component to provide a quick response to emergencies. Different types of vehicles cooperate to finish the tasks under unified command. The EMS system tries to respond quickly to emergency calls and communicate with each department to balance the resources and provide maximal coverage for the whole system. This work aims to develop a highly efficient model for the EMS system to assist the coordinator in making the dispatching and relocation decisions simultaneously. Meanwhile, the model will make a route decision to provide the vehicle drivers with route guidance. In the model, heterogenous emergency vehicle fleets consisting of police vehicles, Basic Life Support (BLS) vehicles, Advanced Life Support (ALS) vehicles, Fire Engines, Fire Trucks, and Fire Quants are considered. Moreover, a coverage strategy is proposed, and different coverage types are considered according to the division of vehicle function. The model tries to provide maximal coverage by advanced vehicles under the premise of ensuring full coverage by basic vehicles. The workload balance of the vehicle crews is considered in the model to ensure fairness. A mathematical model is proposed, then a numerical study is conducted to test the model's performance. The results show that the proposed model can perform well in large-scale problems with significant demands. A comprehensive analysis is conducted on the real-case historical medical data. Then a discrete event simulation system is built. The framework of a discrete event simulation model can mimic the evolution of the entire operation of an emergency response system over time. Finally, the proposed model and discrete event simulation system are applied to the real-case historical medical data. Three different categories of performance measurements are collected, analyzed, and compared with the real-case data. A comprehensive sensitivity analysis is conducted to test the ability of the model to handle different situations. The final results illustrate that the proposed model can improve overall performance in various evaluation metrics.