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ItemEmergency Vehicle Preemption Strategies using Machine Learning to Optimize Traffic Operations(Georgia Institute of Technology, 2023-04-30) Roy, SomdutEmergency-Response-Vehicles (ERVs) operate with the purpose of saving lives and mitigating property damage. Emergency-response Vehicle Preemption (EVP) is implemented to provide the right-of-way to ERVs by displaying the green indications along the ERV route. Two EVP strategies were developed as part of this effort. First, a strategy was developed, defined as “Dynamic-Preemption” (DP), that utilizes Connected-Vehicle (CV) technology to detect, in real time, the need for preemption prior to the ERV reaching the vicinity of an intersection. The DP strategy is based on several generalized traffic demand and simplified traffic flow assumptions. Second, a machine learning approach was utilized to develop an EVP call strategy that sought to (1) preemptively clear queues at intersections prior to ERV arrival, (2) create a "delay-free" path for the ERV, and (3) minimize excess delay to the conflicting traffic in the event of an EVP call. The ML approach utilizes currently available vehicle detection data streams and is trained based on simulated EVP scenarios. Existing field strategies and the developed strategies were tested under varying scenarios, on a simulated signalized corridor testbed. It was observed that the proposed methodologies showed tangible improvement over the existing baseline algorithms for EVP, both in terms of ERV travel time and delay to the conflicting movements. In summary, this research is expected to lay the foundation for use of novel computational approaches in solving the EVP problem in traffic ecosystems with limited CV penetration, with the aid of microsimulation.
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ItemDevelopment of a real-time connected corridor data-driven digital twin and data imputation methods(Georgia Institute of Technology, 2020-07-09) Saroj, Abhilasha JairamSmart cities -- equipped with connected infrastructure -- receive significant real-time traffic data. The simulation platform developed in this research leverages these high-frequency connected data streams to derive meaningful insights on the current traffic state by providing near real-time corridor performance measures. A data-driven traffic simulation model, i.e. digital twin, capable of providing environmental and traffic performance measures in near real-time is developed for a connected corridor. The data streams driving the developed simulation model are traffic volumes and signal indications. The research demonstrates the feasibility of the overall connected corridor simulation approach. In addition, investigation of the real-time data streams from the connected corridor revealed the presence of data gaps. Such data gaps can impact the simulation generated performance measures. This research investigates the sensitivity of simulated performance measures to data loss and data imputations developed to infill the detector stream gaps. The impact of data stream gaps on the simulated performance measures is seen, in part, to be dependent on the combination of intersection approaches experiencing data loss. This combination effect can be attributed to both the vehicle volumes observed at these approaches and the ability of the approaches to process additional vehicles. The corridor location of the intersection approaches that have missing data, as well as the travel path of interest, also influence performance measure accuracy. The research demonstrates that to successfully leverage real-time high frequency connected corridor data streams for (near) real-time applications, it is crucial to develop data imputation methodologies that can both learn from historically available data and adapt to recent data trends. In this research, a Long Short Term Memory Recurrent Neural Network layers approach, modeling univariate and multivariate time series data, is developed for data imputation. Experiments are conducted to compare the performance of the univariate and multivariate models and to investigate the impact of these imputation approaches on the simulation performance measures. The findings show the potential advantages of using a multivariate model approach for imputations over a univariate model under atypical traffic conditions. Results also suggest better performance of the univariate model to impute missing data under typical traffic conditions. Future work includes additional development of the model using increased training and validation data along with hyper parameter tuning to increase robustness of the model performance.
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ItemModeling the impact of road grade on vehicle operation, vehicle energy consumption, and emissions(Georgia Institute of Technology, 2018-08-08) Liu, HaobingMotor vehicle emissions and their impacts on local air pollutant concentrations are a primary concern in cities. Properly quantifying energy and emissions is the key step in identifying the major sources of air pollution, evaluating whether transportation activities are consistent with air quality goals, and providing decision makers with reference for implementation of new policies for sustainable development. Mathematical models are commonly used to predict vehicle energy consumption and emissions. Vehicle-specific power (VSP) is widely used in such models to evaluate engine load, and it is represented as a function of vehicle mass, vehicle dynamic parameters (rolling/drag coefficient), driving behavior (speed and acceleration) and road conditions (gravitational acceleration and road gradient). In the U.S. Environmental Protection Agency’s (USEPA’s) MOVES (MOtor Vehicle Emission Simulator) model, speed and VSP levels are tied to vehicle energy consumption and emission rates. Detailed and accurate speed-acceleration joint distributions (SAJDs, also known as Watson plots) can be used to reflect onroad activity required for calculating the distribution of activities in MOVES VSP and speed bins, and thus for estimating vehicle energy consumption and emissions. Road grade is also a critical variable that affects engine operations, as uphill grades require that the engine perform additional work against gravity in the direction of vehicle motion (while downhill grades obtain an energy benefit). Real-world vehicle speed and acceleration can be easily collected using low-cost global positioning system (GPS) data loggers, on-board diagnostics (OBD) system data loggers, and smartphones apps. But, The effect of road grade is usually ignored in emission modeling. On the other hand, very little attention has been paid to the interaction between real-world road grade and onroad activity patterns and the resulting impact on energy use and emissions. However, road grade is expected to impact vehicle operations due to drivers’ response to uphill and downhill driving, or due to vehicle mechanical performance. It is currently unclear that how speed and accelerations vary across different road grade levels, and how the interaction of driver behavior and road grade affect engine power, energy consumption, and emissions modeling. This study is directed at answering two issues: 1): how road grade impacts vehicle speed and acceleration distributions, and how such distributions vary across vehicle types, roadway types, traffic conditions, etc., and 2): how significant the impact of integrating grade interactions is with respect to energy, emissions, and air quality modeling.
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ItemOptimal Ramp Metering of Freeway Corridors(Georgia Institute of Technology, 2015-11-19) Chilukuri, Bhargava RamaRamp meters have been used for congestion management on freeways since the 1960s to maximize freeway capacity by controlling on-ramp flows. Traditionally, the focus has been to develop rule-based algorithms and optimal control case studies. This led to a host of algorithms and methods which cannot be proven to provide an optimal control and the case studies does not provide a systematic understanding of the characteristics of optimal control and its influence on traffic dynamics. Moreover, optimal is not easy to achieve in practice due to the limited storage on the on-ramps. Towards this end, this dissertation systematically studies the optimality conditions for the case of unlimited storage and spatiotemporal evolution of control and its corresponding traffic dynamics on freeway and ramps under queue constraint, carefully taking the traffic dynamics into account. A Kinematic Wave model of the freeway-ramps system is optimized for minimal total delay. The optimality conditions for the case of unlimited ramp storage are studied using Moskowitz functions that provide several interesting insights for different scenarios, including the case of limited storage. This dissertation shows that the current problem posed as a nonlinear coupled PDE system with a nonlinear merge model cannot be solved analytically. This study also shows that the discrete-time nonlinear formulation solved with simulation-based optimization does not converge in reasonable time. To overcome this, the problem is reposed as a LP formulation that includes capacity drop. For discrete formulation, this study develops an error-free solution to the KW model with a source term that enhanced the quality of the numerical solution. This study identifies four distinct regions in the state surface with distinct metering patterns. Explicit modeling of ramps enabled correlating the initialization and termination times of the metering patterns with the evolution of traffic dynamics on the freeways and ramps. Using these results, this dissertation presents a hybrid isolated ramp metering algorithm that outperforms existing methods.
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ItemEvaluating comprehension of temporary traffic control(Georgia Institute of Technology, 2015-11-18) Greenwood, Aaron T.There are over 5 million reported motor vehicle collisions annually in the United States, and while crash rates and fatality rates have declined in the past decades, rates in work zones are disproportionately high. There are strict standards for evaluating the crashworthiness of temporary traffic control devices, but not for evaluating drivers’ comprehension of existing or novel device deployments. This dissertation presents a series of three experiments evaluating driver comprehension for existing and novel traffic control devices conducted in a work zone setting. This evaluation is further expanded by decomposing the task of comprehending traffic control into the three subtasks of detection, localization, and identification. Methods are proposed for conducting a computer-based experiment with still image stimuli to measure participant performance at each of these subtasks. Next, procedures for categorizing localization responses and accounting for variation in participants physical responses are explored. Lastly, an application of Item Response Theory toward the evaluation and comparison of participant comprehension is demonstrated. It is hoped that these methods and procedures can be used by future researchers and experimenters to compare novel temporary traffic control devices and systems to inform future design.
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ItemMethods To evaluate the effectiveness of certain surrogate measures to assess safety of opposing left-turn interactions(Georgia Institute of Technology, 2014-07-01) Peesapati, Lakshmi NarasimhamHighway safety evaluation has traditionally been performed using crash data. However crash data based safety analysis has limitations in terms of timeliness and efficiency. Previous studies show that the use of surrogate safety data allows for earlier evaluation of safety in comparison to the significantly longer time horizon required for collecting crash data. However, the predictive capability of surrogate measures is an area of ongoing research. Previous studies have often resulted in inconsistent findings in the relationship between surrogates and crashes, one of the primary reasons being inconsistent definitions of a conflict. This study evaluated the effectiveness of certain surrogate measures (Acceleration-Deceleration profile, intersection entering speed of through vehicles, and Post Encroachment Time (PET)) in assessing the safety of opposing left-turn interactions at 4-legged signalized intersections by collection of time resolved video from eighteen selected intersections throughout Georgia. Overall, this research demonstrated that surrogate measures can be effective in safety evaluation, specifically demonstrating the use of PET as a surrogate for crashes between left-turning vehicles and opposing through vehicles. The analysis of data found that the selected surrogate threshold is critical to the effectiveness of any surrogate measure. For example, the required PET threshold was found to be as low as 1 second to identify high crash intersections, significantly lower than the commonly reported 3 second threshold. Non-parametric rank analysis methods and generalized linear modeling techniques were used to model PET with other intersection and traffic characteristics to demonstrate the degree to which these surrogates can be used to identify potential high-crash intersections without resorting to a crash history. Finally, the effectiveness of PET and its assistance to decision makers is also been demonstrated through an example that helped find errors in reported crash data.
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ItemOnline ad hoc distributed traffic simulation with optimistic execution(Georgia Institute of Technology, 2012-07-03) Suh, WonhoAs 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.
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ItemReal-time estimation of arterial performance measures using a data-driven microscopic traffic simulation technique(Georgia Institute of Technology, 2012-06-06) Henclewood, Dwayne AnthonyTraffic congestion is a one hundred billion dollar problem in the US. The cost of congestion has been trending upward over the last few decades, but has experienced slight decreases in recent years partly due to the impact of congestion reduction strategies. The impact of these strategies is however largely experienced on freeways and not arterials. This discrepancy in impact is partially linked to the lack of real-time, arterial traffic information. Toward this end, this research effort seeks to address the lack of arterial traffic information. To address this dearth of information, this effort developed a methodology to provide accurate estimates of arterial performance measures to transportation facility managers and travelers in real-time. This methodology employs transmitted point sensor data to drive an online, microscopic traffic simulation model. The feasibility of this methodology was examined through a series of experiments that were built upon the successes of the previous, while addressing the necessary limitations. The results from each experiment were encouraging. They successfully demonstrated the method's likely feasibility, and the accuracy with which field estimates of performance measures may be obtained. In addition, the method's results support the viability of a "real-world" implementation of the method. An advanced calibration process was also developed as a means of improving the method's accuracy. This process will in turn serve to inform future calibration efforts as the need for more robust and accurate traffic simulation models are needed. The success of this method provides a template for real-time traffic simulation modeling which is capable of adequately addressing the lack of available arterial traffic information. In providing such information, it is hoped that transportation facility managers and travelers will make more informed decisions regarding more efficient management and usage of the nation's transportation network.
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ItemDevelopment and evaluation of advanced traveler information system (ATIS) using vehicle-to-vehicle (V2V) communication system(Georgia Institute of Technology, 2010-01-15) Kim, Hoe KyoungThis research develops and evaluates an Advanced Traveler Information System (ATIS) model using a Vehicle-to-Vehicle (V2V) communication system (referred to as the GATIS-V2V model) with the off-the-shelf microscopic simulation model, VISSIM. The GATIS-V2V model is tested on notional small traffic networks (non-signalized and signalized) and a 6X6 typical urban grid network (signalized traffic network). The GATIS-V2V model consists of three key modules: vehicle communication, on-board travel time database management, and a Dynamic Route Guidance System (DRGS). In addition, the system performance has been enhanced by applying three complementary functions: Autonomous Automatic Incident Detection (AAID), a minimum sample size algorithm, and a simple driver behavior model. To select appropriate parameter ranges for the complementary functions a sensitivity analysis has been conducted. The GATIS-V2V performance has been investigated relative to three underlying system parameters: traffic flow, communication radio range, and penetration ratio of participating vehicles. Lastly, the enhanced GATIS-V2V model is compared with the centralized traffic information system. This research found that the enhanced GATIS-V2V model outperforms the basic model in terms of travel time savings and produces more consistent and robust system output under non-recurrent traffic states (i.e., traffic incident) in the simple traffic network. This research also identified that the traffic incident detection time and driver's route choice rule are the most crucial factors influencing the system performance. As expected, as traffic flow and penetration ratio increase, the system becomes more efficient, with non-participating vehicles also benefiting from the re-routing of participating vehicles. The communication radio ranges considered were found not to significantly influence system operations in the studied traffic network. Finally, it is found that the decentralized GATIS-V2V model has similar performance to the centralized model even under low flow, short radio range, and low penetration ratio cases. This implies that a dynamic infrastructure-based traffic information system could replace a fixed infrastructure-based traffic information system, allowing for considerable savings in fixed costs and ready expansion of the system off of the main network corridors.
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ItemAdaptive traffic control effect on arterial travel time charateristics(Georgia Institute of Technology, 2009-11-16) Wu, Seung KookAn arterial traffic control system influences the travel time characteristics of a corridor, including the average corridor travel time and the travel time reliability. However, reliability measures have typically been outside of the focus of arterial control system performance evaluation studies. To assess the effectiveness of arterial traffic control performance evaluation studies are normally limited to average measures of travel time, speed, or delay. As an advanced traffic management system, adaptive traffic control has been developed to address real time demand variability. Thus, an evaluation of the adaptive traffic control system based on reliability may be as important as evaluation based on average travel time or delay. In addition, arterial control systems may also affect the performance of side street traffic as well as arterial corridor traffic. The performance of side street traffic is another measure that should be used in the assessment of the effectiveness of any arterial traffic control system. Finally, an arterial's operational performance often changes throughout a day and over the arterial length. Thus, a system-wide measure that reflects the range of observed operations is needed to thoroughly assess the performance. Given these issues the goal of this research is the development of procedures to evaluate adaptive traffic control's effect on arterial characteristics such as travel time distribution, reliability, side street performance, and system-wide performance. The developed procedures were applied to the evaluation of an adaptive traffic control system, SCATS (Sydney Coordinated Adaptive Traffic System) in Cobb County, Georgia that replaced a semi-actuated coordinated control system. After the procedures were applied, it was found that SCATS produced a less extreme shape of travel time distribution, possibly due to the adaptive feature, but that it did not make statistically significant changes in the selected overall analysis measures. Also, it was found that the results of the performance evaluation can vary depending on the measures selected or the study period and location.