Browsing by Subject "Traffic models"

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Cost/Benefit Analysis of Fuel-Efficient Speed Control Using Signal Phasing and Timing (SPaT) Data: Evaluation for Future Connected Corridor Deployment
(Minnesota Department of Transportation, 2023-03) Levin, Michael W.; Sun, Zongxuan; Wang, Shi’an; Sun, Wenbo; He, Suiyi; Suh, Bohoon; Zhao, Gaonan; Margolis, Jacob; Zamanpour, Maziar
The objective of this methodology is to refine the preliminary results from previous work (11% fuel savings for one vehicle, one intersection) to an entire corridor of SPaT signals, with different CV market penetration, and with driver awareness of fuel savings benefits. The research will proceed in three parts. First, several vehicles will be instrumented with DSRC receivers and GPS tracking to record SPaT data and the vehicle trajectories together. Offline, the project team will optimize the speed and powertrain control based on recorded SPaT data, using the recorded vehicle trajectories to identify the constraints of traffic flow. A living lab consisting of a GM car engine loaded by a transient hydrostatic dynamometer will be used to measure the fuel consumption with and without speed control. Second, the project team will conduct traffic flow simulations to study the impacts of higher market penetration on the overall fuel benefits, including the benefits to legacy vehicles which unintentionally use SPaT based speed controls by following CVs. Third, network models will be used to predict changes in route choices as drivers recognize the benefits of fuel savings in the route utility. The numerical predictions of fuel savings will be combined into cost/benefit analyses to inform MnDOT on the future deployment of SPaT on other corridors.
Data-Driven Support Tools for Transit Data Analysis, Scheduling and Planning
(Intelligent Transportation Systems Institute Center for Transportation Studies, 2011-07) Liao, Chen-Fu
Many transit agencies in the U.S. have instrumented their fleet with Automatic Data Collection Systems (ADCS) to monitor the performance of transit vehicles, support schedule planning and improve quality of services. The objective of this study is to use an urban local route (Metro Transit Route 10 in Twin Cities) as a case study and develop a route-based trip time model to support scheduling and planning while applying different transit strategies. Usually, timepoints (TP) are virtually placed on a transit route to monitor its schedule adherence and system performance. Empirical TP time and inter-TP link travel time models are developed. The TP-based models consider key parameters such as number of passengers boarding and alighting, fare payment type, bus type, bus load (seat availability), stop location (nearside or far side), traffic signal and volume that affect bus travel time. TP time and inter-TP link travel time of bus route 10 along Central Avenue between downtown Minneapolis and Northtown were analyzed to describe the relationship between trip travel time and primary independent variables. Regression models were calibrated and validated by comparing the simulation results with existing schedule using adjusted travel time derived from data analyses. The route-based transit simulation model can support Metro Transit in evaluating different schedule plans, stop consolidations, and other strategies. The transit model provides an opportunity to predict and evaluate potential impact of different transit strategies prior to deployment.
Development of Guidelines for Permitted Left-Turn Phasing Using Flashing Yellow Arrows.
(Minnesota Department of Transportation, 2015-06) Davis, Gary A.; Hourdos, John; Moshtagh, Vahid
The objective of this project was to develop guidelines for time-of-day use of permitted left-turn phasing, which can then be implemented using flashing yellow arrows (FYA). This required determining how the risk for left-turn crashes varied as traffic-flow conditions varied during the course of a representative day. This was accomplished by developing statistical models, which expressed the risk of occurrence of a left-turn crash during a given hour as a function of the left-turn demand, the opposing traffic volume, and a classification of the approach with respect to the opposing traffic speed limit, the type of left-turn protection, and whether or not opposing left-turn traffic could obstruct sight distance. The models were embedded in a spreadsheet tool which will allow operations personnel to enter, for a candidate intersection approach, existing turning movement counts, and a classification of the approach with respect to speed limit, turn protection, and sight distance issues and receive a prediction of how the risk of left-turn crash occurrence varies throughout the day, relative to a user-specified reference condition.
Development of Next Generation Simulation Models for the Twin Cities Freeway Metro-Wide Simulation Model—Phase 1
(Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota, 2012-10) Hourdos, John
The collapse of the Interstate 35W Highway Bridge over the Mississippi River in Minneapolis resulted in unexpected loss of life and had serious consequences on mobility and accessibility in the Twin Cities metropolitan area. In response to the network disruption caused by the bridge collapse, a number of traffic restoration projects were proposed and implemented by MnDOT in a very short order. Selection and prioritization of these projects, however, was mainly based on engineering judgment and experience. The only decision-support tool available to traffic engineers was the regional transportation planning model, which is static in nature and decennial. Although such a model is suitable for the evaluation of long-term (in the order of 5 years or longer) transportation investments, it is not appropriate or adequate for short-term (within days or weeks) operational planning in response to a disaster or other emergencies. This was the driving force behind the creation of a comprehensive model of the Twin Cities freeway and major highway system that can support higher levels of traffic simulation resolution. Phase 1, described in this report, of the development of the Twin Cities metro-wide freeway microscopic model covered the importation of the roadway geometry into a microscopic simulator, generation of demand information for the entire model as well as for the calibration of as many as possible individual segments. In total, 1,199 directional kilometers of freeway mainline where included in the model. Including ramps and major highways, the number rises to 2,492 directional kilometers. The demand in the model is generated from 859 zones extracted from the regional planning model.
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.
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.
Modeling stochastic human-driver car following behavior in oscillatory traffic conditions
(Center for Transportation Studies, University of Minnesota, 2021-08) Stern, Raphael; Shang, Mingfeng
Accurately modeling the realistic and unstable traffic dynamics of human-driven traffic flow is crucial to being able to understand how traffic dynamics evolve, and how new agents such as autonomous vehicles might influence traffic flow stability. This work is motivated by a recent dataset that allows us to calibrate accurate models, specifically in conditions when traffic waves arise. Three microscopic car-following models are calibrated using a microscopic vehicle trajectory dataset that is collected with the intent of capturing oscillatory driving conditions. For each model, five traffic flow metrics are constructed to compare the flow-level characteristics of the simulated traffic with experimental data.
Practical Methods for Analyzing Pedestrian and Bicycle Use of a Transportation Facility
(Minnesota Department of Transportation Office of Research Services, 2010-02) Somasundaram, Guruprasad; Morellas, Vassilios; Papanikolopoulos, Nikolaos P.
The objective of the project is to analyze existing technologies used for the process of generating counts of bicycles and pedestrians in transportation facilities such as walk and bicycle bridges, urban bicycle routes, bicycle trails etc. The advantages and disadvantages of each existing technology which is being applied to counting has been analyzed and some commercially available products were listed. A technical description of different methods that were considered for vision based object recognition is also mentioned along with the reasons as to why such methods were overlooked for our problem. Support Vector Machines were used for classification based on a vocabulary of features built using interest point detectors. After finalizing the software and hardware, five sites were picked for filming and about 10 hours of video was acquired in all. A portion of the video data was used for training and the remainder was used for testing the algorithm’s accuracy. Results of counts are provided and an interpretation of these results is provided in this report. Upon detailed analysis the reasons for false counts and undercounting in some cases have been identified and current work concerns dealing with these issues. Changes are being made to the system to improve the accuracy with the current level of training and make the system available for practitioners to perform counting.
Real-Time Traffic Prediction for Advanced Traffic Management Systems: Phase I
(Intelligent Transportation Systems Institute, University of Minnesota, 1995-10) Davis, Gary A.; Stephanedes, Yorgos J.; Kang, Jeong-Gyu
It has been recommended that Advanced Traffic Management Systems (ATMS) must work in real-time, must respond to and predict changes in traffic conditions, and must included areawide detection surveillance. To support such ATMS, this project developed a tractable, stochastic model of freeway traffic flow and travel demand which satisfies three primary objectives. First, the model should generate real-time estimates of traffic state variables from loop detector data, which can in turn be used as time-varying initial conditions for more comprehensive simulation models, such as KRONOS or FREESIM. Second, the model should generate its own predictions of mainline and off-ramp traffic volumes, as well as calculate the expected error associated with these predictions, thus supporting the use of both deterministic and stochastic optimization for determining traffic management actions. Third, the model should be capable of full on-line implementation, in that it should be capable of estimating required parameters from traffic detector data. The basic model was developed by combining ideas from the theory of Markov population processes with a new for the relationship between traffic flow and density, producing a stochastic version of a simple-continuum model. Kalman filtering was then applied to the basic model to develop algorithms for (1) estimating from loop detector counts the traffic density in freeway sections broken down by destination off-ramp, (2) predicting main-line and off-ramp traffic volumes from given on-ramp volumes and, (3) computing adaptive estimates of the freeway's origin destination matrix from loop detector counts. Monte Carlo simulation tests were used to evaluate three different methods for off-line estimation of model parameters, as well as to assess the accuracy of the density estimates and volume predictions. The results indicated that the estimation and prediction model tends to be robust with respect to the parameter estimation scheme, and that the model generates a reasonable characterization of estimation and prediction uncertainty. Limited tests with field data tended to confirm the simulation results, and to emphasize the importance of real-time estimation of freeway origin-destination matrices in generating accurate predictions.
TH-36 Full Closure Construction: Evaluation of Traffic Operations Alternatives
(Minnesota Department of Transportation, 2010-01) Hourdos, John; Hong, Feili
According to the 2007 Urban Mobility Report, $78 billion was lost due to congestion on urban roadways. Many urban corridors around the country experience demand that is close to or greater than the available capacity. Although most agree that the transportation system has matured and that we will not build ourselves out of congestion, existing infrastructure still often requires expansion. Such expansion in an already developed system most likely does not involve new roadway construction but results in existing roadway upgrades. Such roadways normally already serve considerable demand, a fact that increases the importance of the impact to the roadway users, estimated as Road User Costs (RUCs), and raises safety concerns both for the driving public as well as for the people working on reconstruction projects. New construction methods like Full Road Closure claim to reduce RUCs as well as reduce capital costs. This project follows the first large-scale Full Closure in Minnesota in an attempt to learn from the experience and propose the most appropriate tools and methodologies for planning, staging, and executing the construction. For the latter, three traffic analysis tools are selected for estimating RUCs due to the construction project. Their effort and data requirements, as well as their accuracy is evaluated and compared to the empirical, engineering-judgment-based, method used by Mn/DOT.
Understanding the Use of Non-Motorized Transportation Facilities
(Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota, 2012-07) Lindsey, Greg; Hoff, Kristopher; Hankey, Steve; Wang, Xize
Traffic counts and models for describing use of non-motorized facilities such as sidewalks, bike lanes, and trails are generally unavailable. Because transportation officials lack the data and tools needed to estimate use of facilities, their ability to make evidence-based choices among investment alternatives is limited. This report describes and assesses manual and automated methods of counting non-motorized traffic; summarizes counts of cyclists and pedestrians in Minneapolis, Minnesota; develops scaling factors to describe temporal patterns in non-motorized traffic volumes; validates models for estimating traffic using ordinary least squares and negative binomial regressions; and estimates bicycle and pedestrian traffic volumes for every street in Minneapolis. Research shows that automated counters are sufficiently accurate for most purposes. Automated counter error rates vary as a function of type of technology and traffic mode and volume. Across all locations, mean pedestrian traffic (51/hour) exceeded mean bicycle traffic (38/hour) by 35 percent. One-hour counts were highly correlated with 12-hour "daily" counts. Significant correlates of non-motorized traffic vary by mode and include weather (temperature, precipitation), neighborhood socio-demographics (household income, education), built environment characteristics (land use mix), and street (or bicycle facility) type. When controlling for these factors, bicycle traffic, but not pedestrian traffic, increased over time and was higher on streets with bicycle facilities than without (and highest on off-street facilities). These new models can be used to estimate non-motorized traffic where counts are unavailable and to estimate changes associated with infrastructure improvements.

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