Utilizing shockwave theory and deep learning to estimate intersection traffic flow and queue length based on connected vehicle data.

The development of Connected Vehicles (CV) facilitates the use of trajectory data to estimate queue length and traffic volume at signalized intersections. The previously proposed models involved additional information that may require conducting different types of data collection. Also, most models need higher market penetration rate or more than a vehicle per cycle to provide adequate estimation. This is mainly because a significant number of the estimation models utilized only queued vehicles. However, the state of motion in non-queued vehicles, particularly slowed-down vehicles, provides much information about the traffic characteristics. There is a lack of exploiting the information from slowed-down vehicles in identifying the last queued vehicle to improve the estimation models. The importance of this work is to propose a cycle-by-cycle queue length and traffic volume estimation models by utilizing the slowed-down vehicles. It proposes a sophisticated model to estimate the queue length and traffic volume from connected vehicles with low market penetration rate (MPR) by utilizing shockwave theory and deep learning technique (artificial neural network). The work starts with establishing a relationship between the slowed-down vehicles and last queued vehicles based on shockwave theory and traffic flow dynamics. Then, the queue estimation algorithm is modeled based on the capacity state and deep learning technique. The traffic volume algorithm modeled is based on the queue length information. Four experiments were conducted to investigate the performance of the queue length and traffic volume estimation models on dataset from simulation environment and real-world data. The queue length results of the simulation experiment demonstrated an adequate mean absolute percentage error (MAPE) of 13.44%, which means an accuracy of 86.56%. The highest MAPE was 19.16% (80.84% accuracy) and the lowest was 7.36% (92.64%). The results of the queue length algorithm applied on real-world data demonstrated an MAPE of 21.97% (78.03% accuracy). The performance of the traffic volume algorithm on simulation data demonstrated an excellent MAPE of 11.8% (88.2% accuracy). The performance of the algorithm based on real-world data from demonstrated an MAPE of 23.57% (76.43% accuracy). Although the previous models can provide similar accuracy rates, they require higher MPR. With the low MPR of 10%, the proposed models revealed an adequate estimation accuracy of the cycle-by-cycle queue length and traffic volume

Real-time Traffic State Assessment using Multi-source Data

The normal flow of traffic is impeded by abnormal events and the impacts of the events extend over time and space. In recent years, with the rapid growth of multi-source data, traffic researchers seek to leverage those data to identify the spatial-temporal dynamics of traffic flow and proactively manage abnormal traffic conditions. However, the characteristics of data collected by different techniques have not been fully understood. To this end, this study presents a series of studies to provide insight to data from different sources and to dynamically detect real-time traffic states utilizing those data. Speed is one of the three traffic fundamental parameters in traffic flow theory that describe traffic flow states. While the speed collection techniques evolve over the past decades, the average speed calculation method has not been updated. The first section of this study pointed out the traditional harmonic mean-based average speed calculation method can produce erroneous results for probe-based data. A new speed calculation method based on the fundamental definition was proposed instead. The second section evaluated the spatial-temporal accuracy of a different type of crowdsourced data - crowdsourced user reports and revealed Waze user behavior. Based on the evaluation results, a traffic detection system was developed to support the dynamic detection of incidents and traffic queues. A critical problem with current automatic incident detection algorithms (AIDs) which limits their application in practice is their heavy calibration requirements. The third section solved this problem by proposing a selfevaluation module that determines the occurrence of traffic incidents and serves as an autocalibration procedure. Following the incident detection, the fourth section proposed a clustering algorithm to detect the spatial-temporal movements of congestion by clustering crowdsource reports. This study contributes to the understanding of fundamental parameters and expands the knowledge of multi-source data. It has implications for future speed, flow, and density calculation with data collection technique advancements. Additionally, the proposed dynamic algorithms allow the system to run automatically with minimum human intervention thus promote the intelligence of the traffic operation system. The algorithms not only apply to incident and queue detection but also apply to a variety of detection systems

Real-time estimation of lane-based queue lengths at isolated signalized junctions

In this study, we develop a real-time estimation approach for lane-based queue lengths. Our aim is to determine the numbers of queued vehicles in each lane, based on detector information at isolated signalized junctions. The challenges involved in this task are to identify whether there is a residual queue at the start time of each cycle and to determine the proportions of lane-to-lane traffic volumes in each lane. Discriminant models are developed based on time occupancy rates and impulse memories, as calculated by the detector and signal information from a set of upstream and downstream detectors. To determine the proportions of total traffic volume in each lane, the downstream arrivals for each cycle are estimated by using the Kalman filter, which is based on upstream arrivals and downstream discharges collected during the previous cycle. Both the computer simulations and the case study of real-world traffic show that the proposed method is robust and accurate for the estimation of lane-based queue lengths in real time under a wide range of traffic conditions. Calibrated discriminant models play a significant role in determining whether there are residual queued vehicles in each lane at the start time of each cycle. In addition, downstream arrivals estimated by the Kalman filter enhance the accuracy of the estimates by minimizing any error terms caused by lane-changing behavior.postprin

Traffic State Estimation Using Probe Vehicle Data

Traffic problems are becoming a burden on cities across the world. To prevent traffic accidents, mitigate congestion, and reduce fuel consumption, a critical step is to have a good understanding of traffic. Traditionally, traffic conditions are monitored primarily by fixed-location sensors. However, fixed-location sensors only provide information about specific locations, and the installation and maintenance cost is very high. The advances in gls{gps}-based technologies, such as connected vehicles and ride-hailing services, provide us an alternative approach to traffic monitoring. While these types of gls{gps}-equipped probe vehicles travel on the road, a vast amount of trajectory data are being collected. As probe vehicle data contain rich information about traffic conditions, they have drawn much attention from both researchers and practitioners in the field of traffic management and control. Extensive literature has studied the estimation of traffic speeds and travel times using probe vehicle data. However, as for queue lengths and traffic volumes, which are critical for traffic signal control and performance measures, most of the existing estimation methods based on probe vehicles can hardly be implemented in practice. The main obstacle is the low market penetration of probe vehicles. Therefore, in this dissertation, we aim to develop probe vehicle based traffic state estimation methods that are suitable for the low penetration rate environment and can potentially be implemented in the real world. First, we treat the traffic state in each location and each time point independently. We focus on estimating the queues forming at isolated intersections under light or moderate traffic. The existing methods often require prior knowledge of the queue length distribution or the probe vehicle penetration rate. However, these parameters are not available beforehand in real life. Therefore, we propose a series of methods to estimate these parameters from historical probe vehicle data. Some of the methods have been validated using real-world probe vehicle data. Second, we study traffic state estimation considering temporal correlations. The correlation of queue lengths in different traffic signal cycles is often ignored by the existing studies, although the phenomenon is commonly-observed in real life, such as the overflow queues induced by oversaturated traffic. To fill the gap, we model such queueing processes and observation processes using a hidden Markov model (gls{hmm}). Based on the gls{hmm}, we develop two cycle-by-cycle queue length estimation methods and an algorithm that can estimate the parameters of the gls{hmm} from historical probe vehicle data. Lastly, we consider the spatiotemporal correlations of traffic states, with a focus on the estimation of traffic volumes. With limited probe vehicle data, it is difficult to estimate traffic volumes accurately if we treat each location and each time slot independently. Noticing that traffic volumes in different locations and different time slots are correlated, we propose to find the low-rank representation of traffic volumes and then reconstruct the unknown values by fusing probe vehicle data and fixed-location sensor data. Test results show that the proposed methods can reconstruct the traffic volumes accurately, and they have great potential for real-world applications. In summary, this thesis systematically studies traffic state estimation based on probe vehicle data. Some of the proposed methods have been implemented in real life. We expect the methods to be implemented on an even larger scale and help transportation agencies solve more real-world traffic problems.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155289/1/zhaoyann_1.pd

Evaluation and Refinement of Minnesota Queue Warning Systems

(c) 1003325 (wo) 84In previous research, investigators developed and deployed two warning systems, including MN-QWARN, which was installed on a segment of Interstate 94 (I-94) with a historically high crash rate. This system targeted shockwaves or initial stages of congestion based on the understanding that not all congestion events create high risks for crashes. Certain traffic conditions can be unsafe even if they don\u2019t result in queueing. A brief evaluation of MN-QWARN, which has been operating since 2016, suggested that the warning system reduced crashes by 22%. Seeing the opportunity to leverage the historical traffic data on that highway segment, MnDOT was interested in learning more about the performance of MN-QWARN and the transferability of the queue warning system

A Lane-based Predictive Model of Downstream Arrival Rates in a Queue Estimation Model Using a Long Short-Term Memory Network

In this study, we develop a mathematical framework to predict cycle-based queued vehicles at each individual lane using a deep learning method - the long short-term memory (LSTM) network. The key challenges are to decide the existence of residual queued vehicles at the end of each cycle, and to predict the lane-based downstream arrivals to calculate vertical queue lengths at individual lanes using an integrated deep learning method. The primary contribution of the proposed method is to enhance the predictive accuracy of lane-based queue lengths in the future cycles using the historical queuing patterns. A major advantage of implementing an integrated deep learning process compared to the previously Kalman-filter-based queue estimation approach (Lee et al., 2015) is that there is no need to calibrate the co-variance matrix and tune the gain values (parameters) of the estimator. In the simulation results, the proposed method perform better in only straight movements and a shared lane with left turning movements

Statistical and simulation methods for evaluating stationary and mobile work zone impacts

In 2014, nearly 10% of overall congestion on freeways was due to the presence of work zones (WZs), equivalent to 310 million gallons of fuel loss (FHWA, 2017a). In terms of safety, in the US, every 5.4 minutes, a WZ related crash occurred in 2015 (96,626 crashes annually) (FHWA, 2017b). Maintenance work involves both Stationary Work Zones (SWZs) and Mobile Work Zones (MWZs). There are many analytical and simulation-based tools available for analyzing the traffic impacts of SWZs. However, the existing traffic analysis tools are not designed to appropriately model MWZs traffic impacts. This study seeks to address this gap in existing knowledge through the use of data from MWZs to develop and calibrate traffic impact analysis tools. This objective is accomplished through data fusion from multiple sources of MWZ, probe vehicle and traffic detector data. The simulation tool VISSIM is calibrated for MWZs using information extracted from videos of MWZ operations. This is the first study that calibrated the simulation based on real driving behavior behind a MWZ. The three recommended calibration parameters are safety reduction factor of 0.7, minimum look ahead distance of 500 feet and the use of smooth closeup option. These calibration values can be used to compare MWZ scenarios. Also, the data collection framework and calibration methodology designed in this study could be used in future research. The operational analysis concluded that a moving work activity lasting one hour or more are suggested to be done when traffic volumes are under 1400 veh/hr/ln, and preferably under 1000 veh/hr/ln, due to the drastic increase in the number of conflicts. In addition, three data driven models were developed to predict traffic speed inside a MWZ: a linear regression model and two models that used Multi-Gene Genetic Programming (MGGP). The second objective is to develop models and tools for safety assessment of stationary work zones. In the WZ safety literature, few studies have quantified the safety impact of SWZ and almost no quantitative study assessing MWZ safety impact. Using Missouri data, this study introduces 20 new crash prediction models for SWZs on freeways, expressways, rural two lane highways, urban multi-lane highways, arterials, ramps, signalized intersections, and unsignalized intersections. All the models except freeway SWZs are proposed for the first time in the literature. The mentioned SWZ models are implemented in a user-friendly spreadsheet tool which automatically selects the most appropriate model based on user input. The tool predicts crashes by severity, and computes the crash costs. For MWZs, there is no crash data available to develop crash prediction models. Thus, this dissertation analyzed conflict measures as a surrogate for safety impacts of MWZs. Conflict measures were generated from the trajectories of traffic simulation model. The safety trade-off plots between conflicts and combination of MWZ's duration and traffic volume were introduced. A transportation agency can use these plots to determine, for example, if they should conduct a MWZ for a short duration when the volume is high or for a longer duration when the volume is lower.Includes bibliographical reference

Traffic modeling, estimation and control for large-scale congested urban networks

Part I of the thesis investigates novel urban traffic state estimation methods utilizing probe vehicle data. Chapter 2 proposes a method to integrate the collective effect of dispersed probe data with traffic kinematic wave theory and data mining techniques to model the spatial and temporal dynamics of queue formation and dissipation in arterials. The queue estimation method captures interdependencies in queue evolutions of successive intersections, and moreover, the method is applicable in oversaturated conditions and includes a queue spillover statistical inference procedure. Chapter 3 develops a travel time reliability model to estimate arterial route travel times distribution (TTD) considering spatial and temporal correlations between traffic states in consecutive links. The model uses link-level travel time data and a heuristic grid clustering method to estimate the state structure and transition probabilities of a Markov chain. By applying the Markov chain procedure, the correlation between states of successive links is integrated and the route-level TTD is estimated. The methods in Part I are tested with various probe vehicle penetration rates on case studies with field measurements and simulated data. The methods are straightforward in implementation and have demonstrated promising performance and accuracy through numerous experiments. Part II studies network-level modeling and control of large-scale urban networks. Chapter 4 is the pioneer that introduces the urban perimeter control for two-region urban cities as an elegant control strategy to decrease delays in urban networks. Perimeter controllers operate on the border between the two regions, and manipulate the percentages of transfer flows between the two regions, such that the number of trips reaching their destinations is maximized. The optimal perimeter control problem is solved by the model predictive control (MPC) scheme, where the prediction model and the plant (reality) are formulated by macroscopic fundamental diagrams (MFD). Chapter 5 extends the perimeter control strategy and MFD modeling to mixed urban-freeway networks to provide a holistic approach for large-scale integrated corridor management (ICM). The network consists of two urban regions, each one with a well-defined MFD, and a freeway, modeled by the asymmetric cell transmission model, that is an alternative commuting route which has one on-ramp and one off-ramp within each urban region. The optimal traffic control problem is solved by the MPC approach to minimize total delay in the entire network considering several control policies with different levels of urban-freeway control coordination. Chapter 6 integrates traffic heterogeneity dynamics in large-scale urban modeling and control to develop a hierarchical control strategy for heterogeneously congested cities. Two aggregated models, region- and subregion-based MFDs, are introduced to study the effect of link density heterogeneity on the scatter and hysteresis of MFD. A hierarchical perimeter flow control problem is proposed to minimize the network delay and to homogenize the distribution of congestion. The first level of the hierarchical control problem is solved by the MPC approach, where the prediction model is the aggregated parsimonious region-based MFD and the plant is the subregion-based MFD, which is a more detailed model. At the lower level, a feedback controller tries to maximize the network outflow, by increasing regional homogeneity

Artificial intelligence enabled automatic traffic monitoring system

The rapid advancement in the field of machine learning and high-performance computing have highly augmented the scope of video-based traffic monitoring systems. In this study, an automatic traffic monitoring system is proposed that deploys several state-of-the-art deep learning algorithms based on the nature of traffic operation. Taking advantage of a large database of annotated video surveillance data, deep learning-based models are trained to track congestion, detect traffic anomalies and tabulate vehicle counts. To monitor traffic queues, this study implements a Mask region-based convolutional neural network (Mask R-CNN) that predicts congestion using pixel-level segmentation masks on classified regions of interest. Similarly, the model was used to accurately extract traffic queue-related information from infrastructure mounted video cameras. The use of infrastructure-mounted CCTV cameras for traffic anomaly detection and verification is further explored. Initially, a convolutional neural network model based on you only look once (YOLO), a popular deep learning framework for object detection and classification is deployed. The following identification model, together with a multi-object tracking system (based on intersection over union -- IOU) is used to search for and scrutinize various traffic scenes for possible anomalies. Likewise, several experiments were conducted to fine-tune the system's robustness in different environmental and traffic conditions. Some of the techniques such as bounding box suppression and adaptive thresholding were used to reduce false alarm rates and refine the robustness of the methodology developed. At each stage of our developments, a comparative analysis is conducted to evaluate the strengths and limitations of the proposed approach. Likewise, IOU tracker coupled with YOLO was used to automatically count the number of vehicles whose accuracy was later compared with a manual counting technique from CCTV video feeds. Overall, the proposed system is evaluated based on F1 and S3 performance metrics. The outcome of this study could be seamlessly integrated into traffic system such as smart traffic surveillance system, traffic volume estimation system, smart work zone management systems, etc.by Vishal MandalIncludes bibliographical reference