Development of Breakdown Probability Models and Heavy Vehicle Passenger Car Equivalents for Rural Freeway Work Zones

2020 ◽  
Vol 2674 (12) ◽  
pp. 144-154
Author(s):  
Nicholas L. Jehn ◽  
Rod E. Turochy
Keyword(s):  
Discharge Rate ◽  
Risk Tolerance ◽  
Work Zone ◽  
Work Zones ◽  
Probability Models ◽  
Passenger Car ◽  
Breakdown Probability ◽  
Freeway Work Zones ◽  
The Impact ◽  
Lane Closures

With nearly nine million lane-miles of public roadway and an economy driven by the automobile, interruptions to normal traffic operations for construction and maintenance are inevitable in the U.S.A., but the substantial safety and mobility impacts associated with queueing at freeway lane closures are mitigable. The current freeway work zone capacity methodology in the 6th edition of the Highway Capacity Manual is a vast improvement over historical guidance but still approaches the issue differently than research suggests agencies and practitioners should. Namely, a capacity defined by the mean queue discharge rate is deterministic and fails to account for the stochastic nature of traffic flow and breakdown. These core issues were addressed in this research by developing a methodology for obtaining probabilistic estimates of rural freeway work zone capacity from simulated data in PTV Vissim. Results for a two-to-one lane closure were presented as a series of breakdown probability distributions to demonstrate the viability of this methodology. The data indicated that the impact of trucks on freeway capacity is exacerbated in the presence of lane closures and led to the development of work zone capacity-based passenger car equivalents. Such a procedure may be extended to freeway facilities exhibiting different geometric, traffic, and environmental characteristics and utilized by agencies to make data-driven, risk tolerance-based planning, design, and operations decisions at freeway work zones.

Application of LiDAR and Connected Vehicle Data to Evaluate the Impact of Work Zone Geometry on Freeway Traffic Operations

2018 ◽  
Vol 2672 (16) ◽  
pp. 1-13 ◽  
Author(s):  
Michelle M. Mekker ◽  
Yun-Jou Lin ◽  
Magdy K. I. Elbahnasawy ◽  
Tamer S. A. Shamseldin ◽  
Howell Li ◽  
...  
Keyword(s):  
Case Studies ◽  
Work Zone ◽  
Extensive Literature ◽  
Vehicle Speed ◽  
Connected Vehicle ◽  
Traffic Operations ◽  
Work Zones ◽  
Geometric Data ◽  
Vehicle Data ◽  
The Impact

Extensive literature exists regarding recommendations for lane widths, merging tapers, and work zone geometry to provide safe and efficient traffic operations. However, it is often infeasible or unsafe for inspectors to check these geometric features in a freeway work zone. This paper discusses the integration of LiDAR (Light Detection And Ranging)-generated geometric data with connected vehicle speed data to evaluate the impact of work zone geometry on traffic operations. Connected vehicle speed data can be used at both a system-wide (statewide) or segment-level view to identify periods of congestion and queueing. Examples of regional trends, localized incidents, and recurring bottlenecks are shown in the data in this paper. A LiDAR-mounted vehicle was deployed to a variety of work zones where recurring bottlenecks were identified to collect geometric data. In total, 350 directional miles were covered, resulting in approximately 360 GB of data. Two case studies, where geometric anomalies were identified, are discussed in this paper: a short segment with a narrow lane width of 10–10.5 feet and a merging taper that was about 200 feet shorter than recommended by the Manual on Uniform Traffic Control Devices. In both case studies, these work zone features did not conform to project specifications but were difficult to assess safely by an inspector in the field because of the high volume of traffic. The paper concludes by recommending the use of connected vehicle data to systematically identify work zones with recurring congestion and the use of LiDAR to assess work zone geometrics.

Machine Learning Approach to Forecast Work Zone Mobility using Probe Vehicle Data

2020 ◽  
Vol 2674 (9) ◽  
pp. 157-167
Author(s):  
Mohsen Kamyab ◽  
Stephen Remias ◽  
Erfan Najmi ◽  
Sanaz Rabinia ◽  
Jonathan M. Waddell
Keyword(s):  
Machine Learning ◽  
Traffic Congestion ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Work Zone ◽  
Data Sources ◽  
Work Zones ◽  
Probe Vehicle ◽  
Vehicle Data ◽  
Lane Closures

The aim of deploying intelligent transportation systems (ITS) is often to help engineers and operators identify traffic congestion. The future of ITS-based traffic management is the prediction of traffic conditions using ubiquitous data sources. There are currently well-developed prediction models for recurrent traffic congestion such as during peak hour. However, there is a need to predict traffic congestion resulting from non-recurring events such as highway lane closures. As agencies begin to understand the value of collecting work zone data, rich data sets will emerge consisting of historical work zone information. In the era of big data, rich mobility data sources are becoming available that enable the application of machine learning to predict mobility for work zones. The purpose of this study is to utilize historical lane closure information with supervised machine learning algorithms to forecast spatio-temporal mobility for future lane closures. Various traffic data sources were collected from 1,160 work zones on Michigan interstates between 2014 and 2017. This study uses probe vehicle data to retrieve a mobility profile for these historical observations, and uses these profiles to apply random forest, XGBoost, and artificial neural network (ANN) classification algorithms. The mobility prediction results showed that the ANN model outperformed the other models by reaching up to 85% accuracy. The objective of this research was to show that machine learning algorithms can be used to capture patterns for non-recurrent traffic congestion even when hourly traffic volume is not available.

Evaluation of Interstate Work Zone Mobility using Probe Vehicle Data and Machine Learning Techniques

2019 ◽  
Vol 2673 (2) ◽  
pp. 811-822 ◽  
Author(s):  
Mohsen Kamyab ◽  
Stephen Remias ◽  
Erfan Najmi ◽  
Kerrick Hood ◽  
Mustafa Al-Akshar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Work Zone ◽  
Third Party ◽  
Machine Learning Techniques ◽  
Work Zones ◽  
Vehicle Data ◽  
Gps Devices ◽  
Future Work ◽  
The Impact

According to the Federal Highway Administration (FHWA), US work zones on freeways account for nearly 24% of nonrecurring freeway delays and 10% of overall congestion. Historically, there have been limited scalable datasets to investigate the specific causes of congestion due to work zones or to improve work zone planning processes to characterize the impact of work zone congestion. In recent years, third-party data vendors have provided scalable speed data from Global Positioning System (GPS) devices and cell phones which can be used to characterize mobility on all roadways. Each work zone has unique characteristics and varying mobility impacts which are predicted during the planning and design phases, but can realistically be quite different from what is ultimately experienced by the traveling public. This paper uses these datasets to introduce a scalable Work Zone Mobility Audit (WZMA) template. Additionally, the paper uses metrics developed for individual work zones to characterize the impact of more than 250 work zones varying in length and duration from Southeast Michigan. The authors make recommendations to work zone engineers on useful data to collect for improving the WZMA. As more systematic work zone data are collected, improved analytical assessment techniques, such as machine learning processes, can be used to identify the factors that will predict future work zone impacts. The paper concludes by demonstrating two machine learning algorithms, Random Forest and XGBoost, which show historical speed variation is a critical component when predicting the mobility impact of work zones.

scholarly journals Risk Factors in Work Zone Safety Events: A Naturalistic Driving Study Analysis

2019 ◽  
Vol 2673 (1) ◽  
pp. 379-387 ◽  
Author(s):  
Nipjyoti Bharadwaj ◽  
Praveen Edara ◽  
Carlos Sun
Keyword(s):  
Risk Factors ◽  
Driver Behavior ◽  
Work Zone ◽  
Crash Risk ◽  
Work Zones ◽  
Binary Logistic Regression Model ◽  
Work Zone Safety ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving ◽  
The Impact

Identification of crash risk factors and enhancing safety at work zones is a major priority for transportation agencies. There is a critical need for collecting comprehensive data related to work zone safety. The naturalistic driving study (NDS) data offers a rare opportunity for a first-hand view of crashes and near-crashes (CNC) that occur in and around work zones. NDS includes information related to driver behavior and various non-driving related tasks performed while driving. Thus, the impact of driver behavior on crash risk along with infrastructure and traffic variables can be assessed. This study: (1) investigated risk factors associated with safety critical events occurring in a work zone; (2) developed a binary logistic regression model to estimate crash risk in work zones; and (3) quantified risk for different factors using matched case-control design and odds ratios (OR). The predictive ability of the model was evaluated by developing receiver operating characteristic curves for training and validation datasets. The results indicate that performing a non-driving related secondary task for more than 6 seconds increases the CNC risk by 5.46 times. Driver inattention was found to be the most critical behavioral factor contributing to CNC risk with an odds ratio of 29.06. In addition, traffic conditions corresponding to Level of Service (LOS) D exhibited the highest level of CNC risk in work zones. This study represents one of the first efforts to closely examine work zone events in the Transportation Research Board’s second Strategic Highway Research Program (SHRP 2) NDS data to better understand factors contributing to increased crash risk in work zones.

Field Evaluation of Late Merge Traffic Control in Work Zones

2005 ◽  
Vol 1911 (1) ◽  
pp. 33-41 ◽  
Author(s):  
Andrew G. Beacher ◽  
Michael D. Fontaine ◽  
Nicholas J. Garber
Keyword(s):  
Field Test ◽  
Traffic Control ◽  
Positive Response ◽  
Field Evaluation ◽  
Field Studies ◽  
Work Zone ◽  
Work Zones ◽  
Short Term ◽  
Term Field ◽  
Lane Closures

The traffic control strategy of the late merge in work zones was devised to improve flow and safety at work zone lane closures. Although some states have put the strategy into practice, only a handful of short-term field studies have formally evaluated its effectiveness. Additional field studies were necessary to assess the efficacy of the strategy and its proper deployment. This paper documents the results of a field test of the late merge traffic control conducted over several months. The late merge strategy was evaluated by comparing its effectiveness with that of traditional plans for work zone lane closures. The field test was conducted on a primary route in Tappahannock, Virginia, at a two-to-one lane closure. Results showed that throughput increased, but the increase was not statistically significant. Likewise, time in queue decreased, but the decrease was not statistically significant. These results were much less dramatic than those of other studies. Possible reasons for this disparity include different driver populations, road types, vehicle mixes, and site-specific characteristics. Despite limited improvements in throughput and time in queue, more drivers were in the closed lane, a positive response to the late merge signs.

Crash Testing and Evaluation of Work Zone Traffic Control Devices

1998 ◽  
Vol 1650 (1) ◽  
pp. 45-54 ◽  
Author(s):  
King K. Mak ◽  
Roger P. Bligh ◽  
Lewis R. Rhodes
Keyword(s):  
Traffic Control ◽  
Evaluation Criteria ◽  
Work Zone ◽  
Work Zones ◽  
Impact Performance ◽  
Crash Testing ◽  
Control Devices ◽  
Traffic Control Devices ◽  
Under Construction ◽  
The Impact

Safety of work zones is a major area of concern since it is not always possible to maintain a level of safety comparable to that of a normal highway not under construction. Proper traffic control is critical to the safety of work zones. However, traffic control devices themselves may pose a safety hazard when impacted by errant vehicles. The impact performance of many work zone traffic control devices is mostly unknown, and little, if any, crash testing has been conducted in accordance with guidelines set forth in NCHRP Report 350. The Texas Department of Transportation (TxDOT) has, in recent years, sponsored a number of studies at the Texas Transportation Institute to assess the impact performance of various work zone traffic control devices, including plastic drums and sign substrates, temporary and portable sign supports, plastic cones, vertical panels, and barricades. The results, findings, conclusions, and recommendations are presented for temporary and portable sign supports, plastic drums, sign substrates for use with plastic drums, traffic cones, and vertical panels, whereas those for barricades are covered elsewhere. Most of the work zone traffic control devices satisfactorily met the evaluation criteria set forth in NCHRP Report 350 and are recommended for field implementation. However, some of the devices failed to perform satisfactorily and are not recommended for field applications. The results from these studies are being incorporated into the TxDOT barricade and construction standard sheets for use in work zones.

Traffic impact assessment and mitigation strategies for disruptions

2018 ◽  
Author(s):  
◽  
Yohan Chang
Keyword(s):  
Prediction Models ◽  
Management Strategies ◽  
Mitigation Strategies ◽  
Work Zone ◽  
Learning Approaches ◽  
Work Zones ◽  
Work Activity ◽  
Traffic Conditions ◽  
Traffic Impact ◽  
The Impact

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] This dissertation research focuses on modeling traffic conditions affected by disruptive events such as work zones, incidents, and hurricanes. Using a combination of field data and simulation experiments, this research tried to address the relationship between disruptive events and their impact on traffic conditions and driver behavior. The first half of the dissertation assesses the impact of work zones. First, a data-driven assessment of the traffic impact of work zones using different data sources was conducted. A tool was developed for practitioners to estimate the delay and travel times of planned work zones. Second, traffic flow and speed prediction models were developed for work zones in order to assist with the better scheduling of work activity. Machine learning approaches were used to develop the prediction models. In addition to work zone effects, the effects of another special event, baseball gameday conditions, were also studied and traffic prediction models were developed. Third, using naturalistic driving study data, classification algorithms categorized work zone events into crashes, nearcrashes, and baseline conditions. In the second half of the dissertation, the focus shifts to the effect of emergency on evacuation. Two chapters in this section present the results of different traffic management strategies -- 1) contraflow crossover and ramp closure optimization and 2) reservation-based intersection control in connected and autonomous vehicle environment.

Calibration of Vissim Models for Rural Freeway Lane Closures: Novel Approach to the Modification of Key Parameters

2019 ◽  
Vol 2673 (5) ◽  
pp. 574-583 ◽  
Author(s):  
Nicholas L. Jehn ◽  
Rod E. Turochy
Keyword(s):  
Traffic Flow ◽  
Discharge Rate ◽  
The United States ◽  
Simulation Software ◽  
Work Zone ◽  
Analysis Tool ◽  
Time Headway ◽  
Novel Approach ◽  
Microsimulation Models ◽  
Lane Closures

The definition of freeway work zone capacity has been a topic of debate for several decades, leaving agencies with limited guidance on predicting the behavior of traffic flow at given volumes for various work zone configurations. The methodology presented in the recently published 6th edition of the Highway Capacity Manual (HCM) is a substantial improvement over historical guidance and provides estimates of the mean queue discharge rate under a variety of prevailing site conditions. However, it is limited by the fact that its outputs are deterministic, while traffic flow and breakdown are stochastic phenomena. Recently, well-calibrated microsimulation models have shown promise as a freeway work zone traffic analysis tool, but most guidance is focused on site-specific modeling. This research aimed to address these shortcomings by presenting a novel approach to developing and calibrating generalizable microsimulation models for rural freeway lane closures in Vissim, a traffic simulation software package developed by the PTV Group. Specifically, it was determined that such models may best replicate field conditions at rural freeway work zones when time headway is described by a field-measured distribution and truck characteristics are representative of the United States (U.S.) fleet. The results suggested that the default desired acceleration for heavy trucks should be set between 2 and 3 ft/s2 and that separate time headway distributions should be constructed for passenger cars and trucks. The methodology presented herein may be extended to obtain stochastic estimates of capacity for sites exhibiting a variety of geometric, traffic, and environmental characteristics.

Multicontextual Machine-Learning Approach to Modeling Traffic Impact of Urban Highway Work Zones

2017 ◽  
Vol 2645 (1) ◽  
pp. 184-194 ◽  
Author(s):  
Junseo Bae ◽  
Kunhee Choi ◽  
Jeong Ho Oh
Keyword(s):  
Machine Learning ◽  
Traffic Flow ◽  
Local Governments ◽  
Ad Hoc ◽  
Work Zone ◽  
Work Zones ◽  
Highway Infrastructure ◽  
Traffic Impact ◽  
State And Local ◽  
The Impact

Impact assessments of highway construction work zones (CWZs) are mandated for all federally funded highway infrastructure improvement projects. However, most existing approaches are ad hoc or project specific, so they are incapable of being benchmarked for any particular spatial region. A novel multicontextual approach to modeling the traffic impact of urban highway CWZs is proposed and tested in this paper. The proposed approach is unique because it models the impact of CWZ operations through a multicontextual quantitative method using big data for improved accuracy. In this study, a machine-learning technique was adopted to predict long-term traffic flow rates and the corresponding truck percentages. With the use of these predicted values, stereotypical patterns of traffic volume-to-capacity ratios were created for typical urban nighttime closures. Third-order curve-fitting models to achieve potential work zone travel time delays in heavily trafficked large urban cores were then developed and validated. This study will greatly help state and local governments and the general traveling public in major cities know the potential traffic flow resulting from construction and thereby facilitate progress on highway improvement projects with the better-informed work zone traffic flow and thus improve safety and mobility in and between CWZs.

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