Browsing by Author "Hu, Heng"

Now showing 1 - 5 of 5
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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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    Improving Traffic Signal Operations for Integrated Corridor Management
    (Minnesota Department of Transportation, 2013-07) Liu, Henry X.; Hu, Heng
    The Integrated Corridor Management (ICM) approach has drawn increasingly more attention in recent years because it is believed to be a promising tool to mitigate urban traffic congestion. In this project, a maximum flow based control model was first developed to handle oversaturated traffic conditions at signalized arterials. Based on the arterial control model, an integrated control model was proposed to manage network congestion. Through diversion control, the model aims to fully utilize the available capacity along parallel routes. The impact of the diversion traffic is considered, especially for signalized arterials, so that traffic congestion on the diversion route can be reduced or eliminated by proper adjustment of signal timings. This model does not rely on time-dependent traffic demand as model inputs and it is ready to be implemented at typical parallel traffic corridors where the standard detection system is available. The performance of the proposed model was tested using microscopic traffic simulation in the I-394 and TH 55 corridor in Minneapolis, Minnesota. The results indicate that the proposed model can significantly reduce network congestion.
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    Improving traffic signal performance using high-resolution data
    (2013-12) Hu, Heng
    Traffic congestion has become a more and more severe problem for metropolitan areas all over the world. Although lots of effort has been devoted to improve the operation on major corridors, how to efficiently and effectively manage traffic based on the existing infrastructure is still a challenging task, and the task seems even more challenging for signalized arterials due to lack of traffic monitoring and data collection system. This research aims to improve the traffic signal performance based on the collected high-resolution traffic signal data and the derived performance measures by the SMART-Signal system developed at the University of Minnesota. In particular, this research focuses on the following three areas: 1) Optimize offsets to reduce congestion Traditionally, offset optimization for coordinated traffic signals fails to consider the stochastic nature of field traffic. Using the archived high-resolution traffic signal data, in this research, we develop an arterial offset optimization model which will take two well-known problems with vehicle-actuated signal coordination into consideration: the early return to green problem and the uncertain intersection queue length problem. To account for the early return to green problem, we introduce the concept of conditional distribution of the green start times for the coordinated phase. To handle the uncertainty of intersection queue length, we adopt a scenario-based approach that generates optimal offsets using a series of traffic demand scenarios as the input to the optimization model. Both the conditional distributions of the green start times and traffic demand scenarios can be obtained from the archived high-resolution traffic signal data. Under different traffic conditions, queues formed by side-street and main-street traffic are explicitly considered in the derivation of intersection delay. The objective of this model is to minimize total delay for the main coordinated direction and at the same time it considers the performance of the opposite direction. The results from the field implementation show that the proposed model can reduce travel delay of coordinated direction significantly without compromising the performance of the opposite approach. 2) Manage oversaturated signal arterials.Under oversaturated traffic conditions, signal timings need to be adjusted accordingly in order to alleviate the detrimental impacts caused by oversaturation. In this research, our focus is to mitigate two types of detrimental effects, signal phase failure with residual queue and downstream queue spillover. Building upon the previous work on the oversaturation severity indices, a maximum-flow based approach to manage oversaturated intersections is developed. The proposed model maximizes the discharging capacity along oversaturated routes, while satisfying the constraints on available green times. We show that a simple forward-backward procedure (FBP) can be used to obtain the optimal solution to the maximum flow model. The forward process aims to increase green time to mitigate oversaturation, therefore improve the throughput for the oversaturated approach; and the backward process aims to gate the traffic at some intersections to prevent residual queues and downstream queue spill-back when the available green time is insufficient. The algorithm is tested using a microscopic traffic simulation model for an arterial network in the City of Pasadena, CA. The results indicate the model can effectively and efficiently reduce oversaturation and improve system performance.3) Mange integrated corridors An integrated control model is proposed to manage traffic congestion along a freeway and a parallel signalized arterial. This model focuses on freeway diversion, which aims to utilize available capacities along parallel arterial routes to reduce network congestion. The potential impact of the diverting traffic to the performance of the arterial route is considered in this research and the maximum flow based signal control model is utilized to manage congestion on the arterial route. The integrated control model does not need the time-dependent traffic demand information as most of previous approaches do and it is suitable for online applications because of its low computation burden. The model is tested using the microscopic traffic simulation in the I-394 and TH 55 corridor in Minneapolis, MN. The results indicate that the model can effectively and efficiently reduce network congestion.
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    Research Implementation of the SMART SIGNAL System on Trunk Highway (TH) 13
    (Minnesota Department of Transportation, 2013-02) Liu, Henry X.; Zheng, Jianfeng; Hu, Heng; Sun, Jie
    In our previous research, the SMART-SIGNAL (Systematic Monitoring of Arterial Road Traffic and Signals) system that can collect event-based traffic data and generate comprehensive performance measures has been successfully developed by the University of Minnesota. In this research, a new set of interfaces are developed for SMART-SIGNAL system including new prototypes of data collection unit (DCU) and refined web-based user interface. To collect high resolution event-based traffic data including both vehicle detector actuation event and signal phase change event, two types of DCUs are designed, the TS-1 DCU and TS-2 DCU for corresponding traffic signal cabinet. TS-1 DCU connects with TS-1 cabinet using pin to pin interface, and the TS-2 DCU interfaces directly with SDLC bus within TS-2 cabinet. The DCUs uses high performance microcontroller modules, and are compact and easy to install. Both DCUs are designed to be vender independent add-on module for traffic cabinet, and can be used as flexible solution to enhance data collection by agencies. The refined web-based user interface features various performance measures to public users, such as Level of Service (LOS), queue length, travel time and intersection delays. The new set of interfaces have been deployed with the SMART-SIGNAL system at 13 intersections along Trunk Highway (TH) 13 in Burnsville, MN.
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    SMART-Signal Phase II: Arterial Offset Optimization Using Archived High-Resolution Traffic Signal Data
    (Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota, 2013-04) Liu, Henry; Hu, Heng
    Traditionally, offset optimization for coordinated traffic signals is based on average travel times between intersections and average traffic volumes at each intersection, without consideration of the stochastic nature of field traffic. Using the archived high-resolution traffic signal data, in this project, we developed a data-driven arterial offset optimization model that will address two well-known problems with vehicle-actuated signal coordination: the early return to green problem and the uncertain intersection queue length problem. To account for the early return to green problem, we introduce the concept of conditional distribution of the green start times for the coordinated phase. To handle the uncertainty of intersection queue length, we adopt a scenario-based approach that generates optimization results using a series of traffic-demand scenarios as the input to the offset optimization model. Both the conditional distributions of the green start times and traffic demand scenarios can be obtained from the archived high-resolution traffic signal data. Under different traffic conditions, queues formed by side-street and main-street traffic are explicitly considered in the derivation of intersection delay. The objective of this model is to minimize total delay for the main coordinated direction and at the same time it considers the performance of the opposite direction. Due to model complexity, a genetic algorithm is adopted to obtain the optimal solution. We test the performance of the optimized offsets not only in a simulated environment but also in the field. Results from both experiments show that the proposed model can reduce travel delay of coordinated direction significantly without compromising the performance of the opposite approach.