Browsing by Subject "Traffic signals"

Now showing 1 - 8 of 8
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    Access to Destinations: Arterial Data Acquisition and Network-Wide Travel Time Estimation (Phase II)
    (Minnesota Department of Transportation, 2010-03) Davis, Gary A.; Hourdos, John; Xiong, Hui; Morris, Ted
    The objectives of this project were to (a) produce historic estimates of travel times on Twin-Cities arterials for 1995 and 2005, and (b) develop an initial architecture and database that could, in the future, produce timely estimates of arterial traffic volumes and travel times. Our Phase I field study indicated that on arterial links where both the demand traffic volume and the signal timing are known, model-based estimates of travel time that are on average within 10% of measured values can be obtained. Phase II of this project then focused on applying this approach to the entire Twin Cities arterial system. The Phase II effort divided into three main subtasks: (1) updating estimates of demand traffic volume obtained from a transportation planning model to make them consistent with available volume measurements, (2) collecting information on traffic signal locations in the Twin Cities and compiling this into a geographic database, and (3) combining the updated traffic volumes and signal information to produce link-by-link peak-period travel time estimates. The traffic volume update took as inputs the predicted volumes generated by a traffic assignment model and measured average annual daily traffic from automatic traffic recorders, and gave as output updated estimates of the traffic volumes for links lacking automatic traffic recorders. A request to state, county and municipal agencies in the seven-county metro area produced Information on approximately 2,900 traffic signals. Estimated arterial travel times for the morning and afternoon peak periods for 1995 and 2005 were then computed and sent to other components of the Access to Destinations effort.
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    Deploy and Test a Smartphone-Based Accessible Traffic Information System for the Visually Impaired
    (Minnesota Department of Transportation, 2020-10) Liao, Chen-Fu; Davis, Brian
    An increasing number of Accessible Pedestrian Signals (APS) have been installed at new or upgraded intersections to assist people with vision impairment to navigate streets. For un-signalized intersections and intersections without APS, people with vision impairment have to rely on their own orientation and mobility skills to gather necessary information to navigate to their destinations. Previously, a smartphone-based accessible pedestrian system was developed to support wayfinding and navigation for people with vision impairment at both signalized and un-signalized intersections. A digital map was also created to support the wayfinding app. This system allows a visually impaired pedestrian to receive signal timing and intersection geometry information from a smartphone app for wayfinding assistance. A beacon using Bluetooth Low Energy (BLE) technology helps to identify a pedestrian's location when he or she travels in a GPS-unfriendly environment. A network of Bluetooth beacons ensures that correct traffic information is provided to the visually impaired at the right location. This project leverages the previous work by installing the system at a number of intersections in downtown Stillwater, Minnesota, where MnDOT operates the signalized intersections. In this study, researchers interface with the traffic controllers to broadcast traffic signal phasing and timing (SPaT) information through a secured and private wireless network for visually impaired users. The aim is to test the smartphone-based accessible system and evaluate the effectiveness and usefulness of the system in supporting wayfinding and navigation while the visually impaired travel through signalized and un-signalized intersections.
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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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    Evaluation of a Central Traffic Signal System and Best Practices for Implementation
    (Minnesota Department of Transportation., 2019-03) Parikh, Gordon; Hourdos, John
    Detailed Intersection Control Information (ICI), including timing, phasing, geometric, and demand attributes, is an increasingly important resource for researchers, consultants, and private sector companies for many applications, including development of traffic models and technologies such as vehicle information or automation systems. While this information has historically been difficult to distribute due to variations in the availability and format across the numerous jurisdictions that operate signals, recent trends toward increased use of Central Traffic Signal Control Systems (CTSCSs) have made creation of a unified, standardized system for organizing ICI more feasible. To help work toward this, in this project researchers interviewed and surveyed signal operation engineers and transportation modelers throughout Minnesota to learn how different jurisdictions manage information relating to their signals and how this information is used for operations and planning. With this information, researchers developed a comprehensive Unified Set of Intersection Control Information (U-ICI) that contains all the information required to describe the control of an intersection in a format that is readable by both humans and machines. Along with this, researchers evaluated the availability of this information and the feasibility of using existing CTSCS applications to store this information. While the researchers conclude that it is not feasible to use these applications to store all of the U-ICI, the applications will likely make the process of implementing and populating such a system easier. Though some information may be contained in formats that will require manual effort to digitize, the up-front effort to do so will be a worthwhile pursuit.
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    A real-time performance measurement system for arterial traffic signals
    (2008-08) Ma, Wenteg
    Performance monitoring for arterial traffic control and management system is an area of emerging focus in the United States. To properly study traffic flow at signalized intersections, both arrival/departure traffic flow data and associated signal status data are required. Although many existing signal control systems are capable of generating data to support performance assessment, most do not make it "easy" for the managing agencies to prioritize improvements and plan for future needs. Indeed, the 2005 Traffic Signal Operation Self Assessment Survey indicated that the majority of agencies involved in the operation and maintenance of traffic signal systems do not monitor or archive traffic system performance data in an effort to improve their operation. Therefore, despite studies having shown that the benefits of investments in improved signal timing outweigh the costs by 40:1 or more, signal retiming is often not repeated frequently enough to account for rapidly changing traffic patterns, largely due to the expense of manual data collection and performance measurements. The need to address the above problems inspired this research. The goal is to develop a real-time arterial performance measurement system, which can automatically collect and archive high-resolution traffic signal data, and build a rich list of performance measures. The objectives of this doctoral research are two-fold: (1) to develop a system for high-resolution traffic signal data collection, archival, and preprocessing; and (2) to develop a set of methodologies that can measure traffic signal performance, including queue length, delay and level of service (LOS) for individual intersections and travel time and number of stops for an arterial corridor. In this research, a system for high resolution traffic signal data collection is successfully built. The system, named as SMART-SIGNAL (Systematic Monitoring of Arterial Road Traffic and Signals), is an arterial data collection and performance measurement system, which simultaneously collects "event-based" high-resolution traffic data from multiple intersections and generates arterial performance measures in real time. In the SMART-SIGNAL system, a complete history of traffic signal control, including all signal events such as vehicle actuations on detectors and signal phase changes, is archived and stored. Using the collected "event" data, mathematical models are built to calculate intersection and arterial performance measures. A time-dependent queue length estimation model is proposed that can handle long queues under both under-saturated and over-saturated conditions. The model examines the changes in signal detector's occupancy profile within a cycle, and derives queue length by identifying traffic flow pattern changes during the queue discharging process. A turning movement proportion estimation model is also offered in this thesis. Detector counts from surrounding intersections are used to calculate right turning traffic for the subject intersection. An innovative algorithm is proposed in this research for arterial performance measurement by tracing virtual probe vehicles from origin to destination. One of three maneuvers: acceleration, deceleration or no-speed-change, is selected based on the current traffic states of the virtual probe. The step-by-step maneuver calculation stops until the virtual probe "arrives" at the destination, and various arterial performance measures, including travel time, can thus be estimated. An interesting property of the proposed model is that travel time estimation errors can be self-corrected with the signal status data, because the differences between a virtual probe vehicle and a real probe can be reduced when both of them meet the red signal phase. The virtual probe mimics regular travel behaviors of arterial drivers and thus can be treated as a representative vehicle traversing the arterial. The SMART-SIGNAL data collection system has been installed on an 11-intersections arterial corridor along France Avenue in Hennepin County, Minnesota since February 2007. Event-based signal data are being collected in a 24/7 mode and then immediately archived in the SMART-SIGNAL system, thus yielding a tremendous amount of field data available for research. The field study shows that the proposed mathematical models can generate accurate time-dependent queue lengths, travel times, numbers of stops, and other performance measures under various traffic conditions.
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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.
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    Using a Smartphone App to Assist the Visually Impaired at Signalized Intersections
    (Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota, 2012-08) Liao, Chen-Fu
    The blind and Visually Impaired (VI) rely heavily on walking and public transit for their transportation needs. A major challenge for this population is safe crossing of intersections. As a result of the American with Disabilities Act (ADA), Accessible Pedestrian Signal (APS) systems at signalized intersections have improved significantly since 2000. However, these systems still have shortcomings for both users and municipalities, and new approaches are needed to adequately serve pedestrians with low vision. As part of our ongoing effort to develop a prototype Mobile Accessible Pedestrian Signal (MAPS) application for the blind and VI, we interviewed ten blind and lowvision people to better understand what types of information they use at intersection crossings and to identify information types that could assist them. With these survey results, a MAPS prototype was developed that provides signal and intersection geometry information to Smartphone users at signalized intersections. User interaction is via simple tactile input (single or double-tap) and Text-To-Speech (TTS) technology. A MAPS prototype was developed and tested to evaluate the functionalities of providing signal and orientation information to the visually impaired travelers at signalized intersections. This proposal will build upon the developed MAPS and investigate how blind and low-vision individuals gain their spatial knowledge surrounding an intersection and how the MAPS can be used to support their decision-making strategy at intersection crossings.
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    Using Detailed Signal and Detector Data to Investigate Intersection Crash Causation
    (Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota, 2013-01) Davis, Gary A.; Chatterjee, Indrajit
    Traffic crashes may not always result in severe or fatal injuries, but they can still have nontrivial impacts on system performance, particularly during heavy traffic conditions. One way toward reducing the frequency of such incidents is to first identify the necessary circumstances that resulted in the collision. However, road crashes, particularly intersection related crashes, are complex phenomenon and often result from different combinations of causal factors. Recently, methods for recording high-resolution arterial traffic data have been developed, and it is important for traffic safety engineers to explore such high-resolution data to understand the causes of crashes. In this research one such integrated event based system, known as SMART SIGNAL, which collects and stores detailed loop detector and signal activity, was used to identify the events leading to a crash or a potential crash and illuminate the mechanisms by which traffic conditions and driver decisions interact to produce those events. Two specific event types, a signal violation crash and vehicle pedestrian crash, were evaluated. For the signal violation crash study, SMART SIGNAL data were used to identify the incident and the vehicles involved in the crash. It was then shown how high-resolution data could support a traditional reconstruction of this crash. For vehicle pedestrian interactions, detector and signal activity data were used to predict pedestrian crash risk in the absence of clearance interval at three signalized intersections. A simulation-based method was used to first estimate crash probabilities, and then a counterfactual approach to calculate the probability of the absence of the all-red phase as a necessary condition for the occurrence of the crash provided an alternate estimate of crash-reduction factors for the all-red phase.