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Due to budget constraints, most of the traffic signals in the US are retimed once every 2-5 years. Despite that, traffic delay increases 3-5% per year with outdated timing plans. It would be desirable to reduce the signal retiming costs by automating all or a portion of the manual process. This research takes one step forward in this direction. In this project, we develop a performance visualization and fine-tuning tool for arterial traffic signal systems, aimed at reducing the labor costs for signal retiming. Using high-resolution event-based data from the SMART-Signal system, a set of easy-to-use algorithms are developed to refine traffic signal systems. Specifically, a framework is developed to diagnose operational problems regarding cycle lengths, green splits and offsets. Then, algorithms for offsets and green splits fine-tuning are proposed. To fine-tune offsets, a practical procedure to construct time space diagram (TS-Diagram) to visualize the progression quality on arterials is proposed and validated. For green splits, an adjusted measure of effectiveness (MOE), the utilized green time (UGT), is proposed for performance evaluation. Moreover, a practical procedure for time of day (TOD) transitions is also developed to generate optimal timing plan schedules. Field case studies and simulation experiments are carried out to illustrate and validate the proposed algorithms. The algorithms could be used during the retiming process to help agencies reduce labor costs, or to periodically refine traffic signal systems for coordinated arterials.

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.

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.

This project explores the possibility of using high-resolution traffic signal data to evaluate intersection safety. Traditional methods using historical crash data collected from infrequently and randomly occurring vehicle collisions can require several years to identify potentially risky situations. By contrast; the proposed method estimates potential traffic conflicts using high-resolution traffic signal data collected from the SMART-Signal system. The potential conflicts estimated in this research include both red-light running events; when stop-bar detectors are available; and crossing (i.e. right-angle) conflicts. Preliminary testing based on limited data showed that estimated conflict frequencies were better than AADT for predicting frequencies of angle crashes. With additional validation this could provide a low-cost and easy-to-use tool for traffic engineers to evaluate traffic safety performance at signalized intersections.

MnDOT has already deployed an extensive infrastructure for Active Traffic Management (ATM) on I-35W and I-94 with plans to expand on other segments of the Twin Cities freeway network. The ATM system includes intelligent lane control signals (ILCS) spaced every half mile over every lane to warn motorists of incidents or hazards on the roadway ahead. This project developed two separate systems that can identify lane-specific shockwave or queuing conditions on the freeway and use existing ILCS to warn motorists upstream for rear-end collision prevention. The two systems were field tested at two locations in the ATM equipped network that have a high frequency of rear-end collisions. These locations experience significantly different traffic-flow conditions; allowing for the development and testing of twodifferent approaches to the same problem. The I-94 westbound segment in downtown Minneapolis is known for its high crash rate due torapidly evolving shockwaves while the I-35W southbound segment north of the TH-62 interchange experiences longstanding queues extending into the freeway mainline. The Minnesota Traffic Observatory developed the I-94 Queue Warning system while the University of Michigan; under contract; developed the I-35W system. Prior to the I-94 installation; based on data collected in 2013; there were 11.9 crashes per VMT and 111.8 near crashes per VMT. In the first three months of the system's deployment; event frequency reduced to 9.34 crashes per million vehicle miles of travel (MVMT) and 51.8 near crashes per MVMT; a 22% decrease in crashes and a 54% decrease in nearcrashes. The I-35W system did not undergo a similarly thorough evaluation; but for most of the lane segments involved; it showed that queue warning messages help reduce the speed variance near the queue locations and the speed difference between upstream and downstream locations. This also implicated a satisfactory level of compliance rate from travelers.

Starting from 1993, MnDOT annually prepares a Metro Freeway System Congestion Report to document congested segments of the freeway system. However, a similar congestion report for the arterial system has not been developed, mainly due to lack of ability for automatic traffic signal data collection and performance measurement. In this project, based on the archived high-resolution traffic data from four major arterials equipped with the SMART-Signal system in the metro area, i.e., Trunk Highway 13, Trunk Highway 55, Trunk Highway 7 and Trunk Highway 10, we developed an innovative approach to generate an arterial traffic congestion report for the MnDOT Metro District. Results show that the main approaches of the four signalized corridors operate very efficiently during AM and PM peaks, but larger delays happen at side streets in general.

This research proposes a framework for real-time emergency evacuation management. The researchers first developed and proposed a theoretical framework for adaptive system control, which involved control updating based on real-world traffic data. They next addressed computation challenges by constructing a heuristic framework for real-time computations of evacuee routing strategies and intersection control via officer deployment.

Most of the rural or suburban high-speed isolated intersections have higher traffic volumes on the major approach compared to the minor approach. The vehicle platoons on major approach are often forced to stop frequently due to conflicting calls placed by few vehicles on the minor approach. To overcome this issue, platoon-priority signal control systems have been developed to progress platoons efficiently at these intersections in a number of previous studies. In addition, Advance Warning Flashers (AWF) are used at these intersections to provide advance warning of end of green to the motorists. The conventional method uses trailing overlap green which holds the green for a fixed time after gap-out. This trailing overlap green replaces the existing dilemma-zone protection provided by loop detectors and also increases delay on the minor approach. Recently, Advance Warning of End of Green System (AWEGS) has been developed to provide advance warning at these intersections without having to hold green after gap-out. The purpose of this research study is to develop and evaluate the benefits of an integrated system which provides platoon-priority, advance warning of end-of-green, and also dilemma-zone protection at the end-of-green for rural high-speed isolated intersections. Cabinet-in the-loop tests were performed using a real world scenario. These study results showed 50 percent reduction in delay and stops on the major approach with platoons. It was found that the total intersection delay and stops were reduced by as much as 20 percent. The system was also successful in providing advance warning to the motorists by predicting gap-outs 7 to 8 seconds earlier in the majority of the cases.

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.

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.