Identification and Analysis of Queue Spillovers in City Street Networks

We propose a methodology for identifying queue spillovers in city street networks with signalized intersections using data from conventional surveillance systems, such as counts and occupancy from loop detectors. The key idea of the proposed methodology is that when spillovers from a downstream link block vehicle departures from the upstream signal line, queues discharge at rates smaller than the saturation flow. The application of the methodology on an arterial site and the comparison with field data show that it consistently identifies spillovers in urban networks with signal-controlled intersections. The method is extended to account for the variations in vehicle lengths. We also investigate the significant effect of spillovers in congestion and show that a macroscopic diagram that connects spillovers with vehicle density exists in large-scale congested urban networks

Queue Profile Estimation in Congested Urban Networks with Probe Data

Queues at signalized intersections are the main cause of traffic delays and travel time variability in urban networks. In this article, we propose a method to estimate queue profiles that are traffic shockwave polygons in the time-space plane describing the spatiotemporal formation and dissipation of queues. The method integrates the collective effect of dispersed probe vehicle data with traffic flow shockwave analysis and data mining techniques. The proposed queue profile estimation method requires position and velocity data of probe vehicles; however, any explicit information of signal settings and arrival distribution is indispensable. Moreover, the method captures interdependencies in queue evolutions of successive intersections. The significance of the proposed method is that it is applicable in oversaturated conditions and includes queue spillover identification. Numerical results of simulation experiments and tests on NGSIM field data, with various penetration rates and sampling intervals, reveal the promising and robust performance of the proposed method compared with a uniform arrival queue estimation procedure. The method provides a thorough understanding of urban traffic flow dynamics and has direct applications for delay analysis, queue length estimation, signal settings estimation, and vehicle trajectory reconstruction

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

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

Formation and Propagation of Local Traffic Jam

Large scale traffic congestion often stems from local traffic jam in single road or intersection. In this paper, macroscopic method was used to explore the formation and propagation of local traffic jam. It is found that (1) the propagation of traffic jam can be seen as the propagation of traffic signal parameters, that is, virtual split and virtual green time; (2) for a road with endogenous flow, entrance location influences the jam propagation. With the same demand (upstream links flow and entrance flow), the upstream got more influence; (3) when a one-lane road is thoroughly congested, virtual signal parameters everywhere are the same as that at stop line; for a basic road, the virtual signals work in a cooperative manner; (4) phase sequence is one important parameter that influences traffic performances during peak hour where spill back of channelization takes place. The same phase plan for left-turn flow and through flow would be preferred; (5) signal coordination plays an important role in traffic jam propagation and hence effective network signal parameters should be designed to prevent jam from propagation to the whole network. These findings would serve as a basis for future network traffic congestion control

Is the Repeal of Net Neutrality a Necessary Evil? An Empirical Analysis of Net Neutrality and Cybercrime

While net neutrality guarantees equal access to the Internet and online content, it serves as a limiting factor in identifying and tracking criminal activities in cyberspace by ensuring that data packet is transmitted with equal priority, irrespective of its source and content. Exploiting a natural experiment in which net neutrality policies were officially repealed in 2018 in the United States, this study examines the impact of net neutrality on the occurrence of cybercrime. Our findings suggest that the repeal of net neutrality is negatively associated with the occurrence of malicious code and content in an attempt to compromise computer systems (e.g., malware and ransomware). In contrast, we do not find any significant relationship with cybercrime victimization, and cybercrime that may subsequently occur in compromised systems (e.g., data breaches and denial-of-service attacks). This study provides novel insights into the role of net neutrality and open Internet toward the preventive cybersecurity paradigm