Browsing by Author "Wu, Xinkai"

Now showing 1 - 6 of 6
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    Development of a Real-Time Arterial Performance Monitoring System Using Traffic Data Available from Existing Signal Systems
    (Minnesota Department of Transportation, 2008-12) Liu, Henry X.; Ma, Wenteng; Wu, Xinkai; Hu, Heng
    Data collection and performance measurement for signalized arterial roads is an area of emerging focus in the United States. As indicated by the results of the 2005 Traffic Signal Operation Self-Assessment Survey, a majority of agencies involved in the operation and maintenance of traffic signal systems do not monitor or archive traffic system performance and thus have limited means to improve their operation. With support from the Transportation Department of Hennepin County, Minneapolis, MN, a system for high resolution traffic signal data collection and arterial performance measurement has been successfully built. The system, named SMART-SIGNAL (Systematic Monitoring of Arterial Road Traffic Signals), is able to collect and archive event-based traffic signal data simultaneously at multiple intersections. Using the event-based traffic data, SMART-SIGNAL can generate timedependent performance measures for both individual intersections and arterials including intersection queue length and arterial travel time. The SMART-SIGNAL system has been deployed at an 11-intersection corridor along France Avenue in south Minneapolis and the estimated performance measures for both intersection queue length and arterial travel times are highly consistent with the observed data.
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    Employment of the Traffic Management Lab for the Evaluation and Improvement of Stratified Metering Algorithm - Phase III
    (Minnesota Department of Transportation, Research Services Section, 2007-05) Liu, Henry; Wu, Xinkai; Michalopoulos, Panos; Hourdos, John
    The evaluation results (done in Phase II) demonstrated that the SZM strategy was generally beneficial. However, they also revealed that freeway performance degraded by reducing the ramp delays. Therefore, it is desired to improve the effectiveness of the current SZM control. There are two objectives in this study. One objective is to improve the control logic of current SZM strategy. This is accomplished through an estimation algorithm for the refined minimum release rate. The simulation results indicate that the improved SZM strategy is very effective in postponing and decreasing freeway congestion while resulting in smoother freeway traffic flow compared to the SZM strategy. The second objective of this project is to improve the current queue size estimation. Depending on the counting error of queue and passage detectors, freeway ramps are classified into three different categories, and different methods are applied respectively for improved queue size estimation. The surveillance video data were recorded and used to verify the improvement of the proposed methods. The results indicate that the proposed methods can greatly improve the accuracy of queue size estimation compared with the current methodology. Also, the proposed method was evaluated by the micro-simulation. The simulation results indicate the performance of freeway mainline is significantly improved. And the total system performance is better than the original SZM control.
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    Employment of the Traffic Management Lab for the Evaluation and Improvement of Stratified Metering Algorithm - Phase IV
    (Minnesota Department of Transportation, 2007-12) Liu, Henry; Wu, Xinkai; Michalopoulos, Panos; Hourdos, John
    Freeway ramp control has been successfully implemented since mid 60's, as an efficient and viable freeway management strategy. However, the effectiveness of any ramp control strategy is largely dependent on optimum parameter values which are preferably determined prior to deployment. This is certainly the case happening to the current Stratified Zone Metering (SZM) strategy deployed in the 260 miles freeway network of Minneapolis - St. Paul metropolitan area. In order to improve the performance of the SZM, which highly depends on the values of more than 20 parameters, this research first proposed a general methodology for site-specific performance optimization of ramp control strategies using a microscopic simulation environment, as an alternative to trial and error field experimentation, and implemented the methodology to the SZM. The testing results show that the new SZM control with site-specific optimum parameter values significantly improves the performance of freeway system compared with the original SZM strategy. Secondly, this research proposed a methodology to explore the common optimum parameter values for the current SZM strategy for the whole Twin Cities freeway system, in order to replace the site-specific optimum values which have minor practical value because of the difficulties in implementation and numerous time-consumption to search the site-specific optimum values for all the freeway sections. The common parameter values are identified applying the Response Surface Methodology (RSM) based on 4 specifically selected freeway sections which can represent all types of freeway sections in Minneapolis-St. Paul metropolitan area.
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    Estimating and Measuring Arterial Travel Time and Delay
    (Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota, 2012-08) Liu, Henry X.; Wu, Xinkai
    To estimate arterial travel time/delay, the key element is to estimate intersection queue length, since travel time, delay, and level of services can be easily derived from queue length information. In this study, we developed a new traffic flow model, named shockwave profile model (SPM), to describe queuing dynamics for congested arterial networks. Taking advantage of the fact that traffic states within a congested link can be simplified as free-flow, saturated, and jammed conditions, the SPM simulates traffic dynamics by analytically deriving the trajectories of four major shockwaves. This model is particularly suitable for simulating congested traffic especially with queue spillover. In the SPM, a novel approach is proposed as part of the SPM, in which queue spillover is treated as either extending a red phase or creating new cycles. Since only the essential features, i.e. queue build-up and dissipation, are considered, the SPM significantly reduces the computational load and improves the numerical efficiency. We further validated the SPM using real-world traffic signal data collected from a major arterial in the Twin Cities. The results clearly demonstrate its effectiveness and accuracy. This model can be applied to estimate arterial travel time and delay and optimize signal timing in real time.
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    Modeling traffic flow on oversaturated arterials.
    (2010-10) Wu, Xinkai
    Traffic congestion is a national issue in the United States and has gotten worse in regions of all sizes. Now, more and more intersections are operated in oversaturated situations where the traffic demand exceeds the capacity of the system. Although a significant amount of literature has been devoted to how to manage oversaturated traffic signal systems, our understanding of the characteristics of oversaturation remains limited, particularly with regard to identification of oversaturation and the transition process from under-saturated condition to oversaturation. It has become increasingly obvious that successful traffic management requires efficient methods to identify and model oversaturated conditions. This research moves towards a better understanding of oversaturation, by 1) providing coherent methodologies to quantify oversaturation and 2) developing a simplified model to describe oversaturation at signalized intersections based on high-resolution traffic signal data collected by the SMART-SIGNAL (Systematic Monitoring of Arterial Road Traffic Signals) system. In particular, the research focuses on the following four areas: 1) Quantification of oversaturation: Traditional definitions of oversaturation are not applicable for existing detection systems. This research circumvents this issue by quantifying the detrimental effects of oversaturation on signal operations, both temporally and spatially. In the temporal dimension, the detrimental effect is characterized by a residual queue at the end of a cycle, which occupies a portion of green time in the next cycle. In the spatial dimension, the detrimental effect is characterized by a downstream spillover, which blocks the traffic and reduces usable green time. From these observations, we derive two types of an oversaturation severity index (OSI): one temporally-based (T-OSI) and one spatially-based (S-OSI). Both T-OSI and S-OSI are designed to yield a ratio between the unusable green time due to detrimental effects and the total available green time in a cycle, using high resolution traffic signal data. T-OSI is quantified by estimating the residual queue length; and S-OSI is quantified by measuring the time period of spillover. Since different types of OSI (T-OSI or S-OSI) point to different underlying causes of oversaturation, this research has the potential to provide guidance for the mitigation strategies of signal oversaturation. 2) Real-time queue length estimation for congested intersections: To quantify T-OSI, this research proposes a novel shockwave-based algorithm to estimate time-dependent queue length even when the signal links are congested with long queues, a situation that the traditional input-output approach for queue length estimation cannot handle. Using high-resolution "event-based" traffic signal data, the new algorithm first identifies traffic state changes; and then applies Lighthill-Whitham-Richards (LWR) shockwave theory to estimate maximum and minimum (i.e. residual) queue length. This algorithm is also applicable for other aspects of arterial performance such as travel time, delay, and level of service. 3) Queue-Over-Detector (QOD): To quantify S-OSI, we study a phenomenon we call Queue-Over-Detector (QOD). QOD occurs when a vehicle stops and rests on a detector for a period of time creating a large occupancy value. This research demonstrates that a main cause of QOD is spillover from downstream intersections. Thus QOD identification can be used to quantify oversaturation in the spatial dimension, i.e. S-OSI. This research also briefly studies the relationship between QOD and the cycle-based arterial fundamental diagram (AFD) by microscopically investigating individual vehicle trajectories derived from event-based data. Results show that proper treatment of QOD results in a stable form of the AFD which clearly identifies three different regimes, under-saturation, saturation, and over-saturation with queue spillovers. Achieving a stable form of the AFD is of great importance for traffic signal control because of its ability to identify traffic states on a signal link. 4). Traffic flow modeling for oversaturated arterials: The culmination of this research project is a simplified traffic flow model for congested arterial networks, which we call the shockwave profile model (SPM). Unlike conventional macroscopic models, in which space is often discretized into small cells for numerical solution, SPM treats each homogeneous road segment with constant capacity as a section; then categorizes the traffic within each section simply as free-flow, saturated, or jammed. Traffic dynamics are analytically described by tracing the shockwave fronts which explicitly separate these three traffic states. SPM is particularly suitable for simulating traffic flow on congested signalized arterials, especially with queue spillover problems. In SPM, queue spillover can be treated as either extending a red light or creating new smaller cycles. Since only the essential features of arterial traffic flow, i.e., queue build-up and dissipation, are considered, SPM significantly simplifies arterial network design and improves numerical efficiency. For these reasons, we fully expect this model to be adopted in real-time applications such as arterial performance prediction and signal optimization.
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    Perception of Waiting Time at Signalized Intersections
    (Transportation Research Board, 2009) Wu, Xinkai; Levinson, David M; Liu, Henry
    Perceived waiting time at signalized intersections differs from the real value, and varies with signal design. The onerousness of delay depends on the conditions under which it is experienced. Using weighted travel time time may contribute to optimal signal control if its use can improve upon assuming that all time is weighted equally by users. This research explores the perception of waiting time at signalized intersections based on the results of an online survey, which directly collected the perceived waiting time and the user ratings of the signal designs of each intersection on an arterial including 3 intersections. Statistically analyzing the survey data suggests the perception of waiting time is a function of the real time; and a quadratic model better can describes relationship. The survey also indicates that there exists a tradeoff between the total waiting time and the individual waiting time of each intersection. It turns out that drivers prefer to split the total waiting time at different intersections at the price of a longer total wait if the difference of the total waiting time of two signal designs is within 30 seconds. The survey data shows that the perceived waiting time, instead of the real waiting time, better explains how users will rate the individual signal designs for both intersections and arterials including multiple intersections.