32 research outputs found

    Mitigating Initialization Bias in Transportation Modeling Applications

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    Traffic simulation model is a useful tool to evaluate real world transportation solutions in a risk free environment. Traffic simulation model requires some form of initialization before their outputs can be considered meaningful. Models are typically initialized in a particular, often “empty” state and therefore must be “warmed-up” for an unknown amount of simulation time before reaching a “quasi-steady-state” representative of the systems’ performance. The portion of the output series influenced by the arbitrary initialization is referred to as the initial transient and is a widely recognized problem in other areas, but less emphasized in the transportation application. After reviewing methods of accounting for the initial transient bias, this paper selects and evaluates three techniques; two popular methods from the general simulation field, Welch’s and MSER method, and one from the current state of the practice in the transportation application, Volume Balancing. VISSIM models were created to compare the selected methods. After presenting the results of each method, advantages and criticisms of each are discussed as well as issues that arose during the implementation. It is hoped that this paper informs the current practice in transportation application as to how to account for the initial transient in order to continue facilitating meaningful and reliable results

    Probabilistic Fatigue Life Updating for Railway Bridges Based on Local Inspection and Repair

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    Railway bridges are exposed to repeated train loads, which may cause fatigue failure. As critical links in a transportation network, railway bridges are expected to survive for a target period of time, but sometimes they fail earlier than expected. To guarantee the target bridge life, bridge maintenance activities such as local inspection and repair should be undertaken properly. However, this is a challenging task because there are various sources of uncertainty associated with aging bridges, train loads, environmental conditions, and maintenance work. Therefore, to perform optimal risk-based maintenance of railway bridges, it is essential to estimate the probabilistic fatigue life of a railway bridge and update the life information based on the results of local inspections and repair. Recently, a system reliability approach was proposed to evaluate the fatigue failure risk of structural systems and update the prior risk information in various inspection scenarios. However, this approach can handle only a constant-amplitude load and has limitations in considering a cyclic load with varying amplitude levels, which is the major loading pattern generated by train traffic. In addition, it is not feasible to update the prior risk information after bridges are repaired. In this research, the system reliability approach is further developed so that it can handle a varying-amplitude load and update the system-level risk of fatigue failure for railway bridges after inspection and repair. The proposed method is applied to a numerical example of an in-service railway bridge, and the effects of inspection and repair on the probabilistic fatigue life are discussed.ope

    Respirable Dust Monitoring in Construction Sites and Visualization in Building Information Modeling Using Real-time Sensor Data

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    Construction activities, involving cutting, drilling, and grinding of materials, often produce toxic respirable dust that can cause fatal diseases and illnesses. To protect workers from breathing excessive amounts of respirable dust at job sites, superintendents should continuously monitor the level of respirable dust in workspaces and make timely interventions for overexposed workers. However, current practices of respirable dust monitoring have critical drawbacks, and superintendents cannot accurately estimate workers’ exposures to respirable dust or make prompt decisions to protect the workers. Therefore, there is a need for real-time air dust monitoring that can be deployed ubiquitously at a construction site and be integrated as part of daily construction management. In this research, we developed a real-time dust monitoring system that comprises a network of low-cost mobile dust sensors and visualization in building information modeling (BIM). Single-board computers and dust sensors were integrated as field deployment units. Inaccurate sensors were calibrated automatically on the basis of an accurate ground truth sensor. A BIM-based visualization system was developed to present the data collected from dust sensors in real time. A prototype system was developed and tested in a controlled environment

    Optimal decision making in post-hazard bridge recovery strategies for transportation networks after seismic events

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    In this study, optimal post-hazard bridge recovery strategies were proposed for transportation networks under seismic conditions. To predict the performance of the transportation network, a robust performance measure, total system travel time (TSTT), was employed, and an artificial neural network (ANN)-based surrogate model was developed to enable an accelerated Monte Carlo analysis. In addition, a sensitivity analysis based on the benefit-cost ratio was proposed to support optimal decision making immediately after an earthquake. To demonstrate the proposed methodology, an actual transportation network in South Korea was adopted, and a network map was reconstructed based on geographic information system (GIS) data. A surrogate model for network performance evaluation was constructed using training data generated based on historical earthquake epicenters. In addition, the damage ratio and required recovery days according to the damage states of bridges were employed to perform network recovery analysis. For the numerical analysis, a limited budget was set for each scenario, and the recovery and damage curve were compared with existing priority strategy. The numerical results showed that the priority strategy of bridge restoration determined through the benefit-cost analysis generated a faster recovery curve and significantly reduced the damage, as compared to existing strategy. Therefore, it is concluded that the proposed methodology enables optimal decision making and also helps risk management that can minimize the economic damage

    Vision Technology Based Traffic Safety Analysis Using Signal Data

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    Recent vision technology allows traffic engineers to analyze traffic safety based on image processing applications. Vehicle trajectory data including vehicle position and speed are extracted and converted into traffic conflicts related variables. These variables are analyzed to find correlations with crash data and geometric design variables and included in the statistical analysis process to evaluate the safety of intersections. In this paper, signal timing information data is included in the analysis process to investigate if there are any correlations with traffic conflict data. For example, traffic conflicts happen more frequently in a certain movement at certain signal time phase. The goal of this paper is to develop a method for vision technology-based traffic safety analysis process using traffic signal data to identify crash prone movement and signal to time at a given intersection. The proposed technique is demonstrated in real-world video data collected in an intersection. This paper is expected to provide more insight and technique in traffic safety evaluation

    Feasibility of LoRa for Smart Home Indoor Localization

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    With the advancement of low-power and low-cost wireless technologies in the past few years, the Internet of Things (IoT) has been growing rapidly in numerous areas of Industry 4.0 and smart homes. With the development of many applications for the IoT, indoor localization, i.e., the capability to determine the physical location of people or devices, has become an important component of smart homes. Various wireless technologies have been used for indoor localization includingWiFi, ultra-wideband (UWB), Bluetooth low energy (BLE), radio-frequency identification (RFID), and LoRa. The ability of low-cost long range (LoRa) radios for low-power and long-range communication has made this radio technology a suitable candidate for many indoor and outdoor IoT applications. Additionally, research studies have shown the feasibility of localization with LoRa radios. However, indoor localization with LoRa is not adequately explored at the home level, where the localization area is relatively smaller than offices and corporate buildings. In this study, we first explore the feasibility of ranging with LoRa. Then, we conduct experiments to demonstrate the capability of LoRa for accurate and precise indoor localization in a typical apartment setting. Our experimental results show that LoRa-based indoor localization has an accuracy better than 1.6 m in line-of-sight scenario and 3.2 m in extreme non-line-of-sight scenario with a precision better than 25 cm in all cases, without using any data filtering on the location estimates

    Spatial Analysis of Subway Ridership: Rainfall and Ridership

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    In-vehicle congestion of the urban railway system is the most important indicator to reflect the operation state of the urban railway. To provide the good service quality of urban railway, the crowdedness of the urban railway should be managed appropriately. The weather is one of the critical factors for the crowdedness. That is because even though the crowdedness of the urban railway is the same, passengers feel more uncomfortable in rainy weather condition. Indeed if specific sections and stations suddenly are concentrated excessive demand, it will lead far more serious problem. Therefore, this study analysis the relationship between the number of urban railway passenger and rainfall intensity in Seoul metropolitan subway system and then conducts the spatial analysis to deduct passenger demand patterns. This study is expected to be useful base study in order to manage the congestion at the urban railway station effectively by considering the different rainfall intensity

    Analysis of Macroscopic Traffic Network Impacted by Structural Damage to Bridges from Earthquakes

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    Highway systems play a key role in providing mobility to society, especially during emergency situations, including earthquakes. Bridges in highway systems are susceptible to damage from earthquakes, causing traffic capacity loss leading to a serious impact on surrounding areas. To better prepare for such scenarios, it is important to estimate capacity loss and traffic disruptions from earthquakes. For this purpose, a traffic-capacity-analysisbased methodology was developed to model the performance of a transportation network immediately following an earthquake using a macroscopic multi-level urban traffic planning simulation model EMME4. This method employs the second order linear approximation (SOLA) traffic assignment and calculates total system travel time for various capacity loss scenarios due to bridge damage from earthquakes. It has been applied to Pohang City in Korea to evaluate the performance of traffic networks in various situations. The results indicate a significant increase in travel time and a decrease in travel speed as the intensity of an earthquake increases. However, the impact on traffic volume varies depending on the bridges. It is assumed that the location of the bridges and traffic routing patterns might be the main reason. The results are expected to help estimate the impact on transportation networks when earthquakes cause traffic capacity loss on bridges

    Risk and sensitivity quantification of fracture failure employing cohesive zone elements

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    Many structures are subjected to the risk of fatigue failure. For their reliability-based design, it is thus important to calculate the probability of fatigue failure and assess the relative importance of the involved parameters. Although various studies have analyzed the fatigue failure, the stage of fracture failure has been less focused. In particular, the risk analysis of fracture failure needs to be conducted considering its importance in actual structures. This article proposes a new probabilistic framework for the risk and sensitivity analysis of structural fatigue failure employing cohesive zone elements. The proposed framework comprises three steps, namely finite element analysis using cohesive zone elements, response surface construction, and risk and sensitivity analysis of fatigue failure, which require several mathematical techniques and algorithms. The proposed framework is tested by applying it to an illustrative example, and the corresponding analysis results of fracture failure probability with different threshold values of a limit-state function are presented. In addition, the sensitivities of failure risk with respect to the statistical parameters of random variables are presented and their relative importance is discussed

    Online ad hoc distributed traffic simulation with optimistic execution

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    As roadside and in-vehicle sensors are deployed under the Connected Vehicle Research program (formerly known as Vehicle Infrastructure Integration initiative and Intellidrive), an increasing variety of traffic data is becoming available in real time. This real time traffic data is shared among vehicles and between vehicles and traffic management centers through wireless communication. This course of events creates an opportunity for mobile computing and online traffic simulations. However, online traffic simulations require faster than real time running speed with high simulation resolution, since the purpose of the simulations is to provide immediate future traffic forecast based on real time traffic data. However, simulating at high resolution is often too computationally intensive to process a large scale network on a single processor in real time. To mitigate this limitation an online ad hoc distributed simulation with optimistic execution is proposed in this study. The objective of this study is to develop an online traffic simulation system based on an ad hoc distributed simulation with optimistic execution. In this system, data collection, processing, and simulations are performed in a distributed fashion. Each individual simulator models the current traffic conditions of its local vicinity focusing only on its area of interest, without modeling other less relevant areas. Collectively, a central server coordinates the overall simulations with an optimistic execution technique and provides a predictive model of traffic conditions in large areas by combining simulations geographically spread over large areas. This distributed approach increases computing capacity of the entire system and speed of execution. The proposed model manages the distributed network, synchronizes the predictions among simulators, and resolves simulation output conflicts. Proper feedback allows each simulator to have accurate input data and eventually produce predictions close to reality. Such a system could provide both more up-to-date and robust predictions than that offered by centralized simulations within a single transportation management center. As these systems evolve, the online traffic predictions can be used in surface transportation management and travelers will benefit from more accurate and reliable traffic forecast.PhDCommittee Chair: Hunter, Michael; Committee Member: Fujimoto, Richard; Committee Member: Laval, Jorge; Committee Member: Leonard, John; Committee Member: Rodgers, Michae
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