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.

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.

scholarly journals Evaluation of Two Improved Schemes at Non-Aligned Intersections Affected by a Work Zone with an Entropy Method

2020 ◽  
Vol 12 (14) ◽  
pp. 5494
Author(s):  
Yang Shao ◽  
Zhongbin Luo ◽  
Huan Wu ◽  
Xueyan Han ◽  
Binghong Pan ◽  
...  
Keyword(s):  
Traffic Management ◽  
Control Strategies ◽  
Management Strategies ◽  
Entropy Method ◽  
Work Zone ◽  
Work Zones ◽  
Research Results ◽  
The Road ◽  
Traffic Operation ◽  
The Impact

The impact of work zones on traffic is a common problem encountered in traffic management. The reconstruction of roads is inevitable, and it is necessary and urgent to reduce the impact of the work zone on the operation of traffic. There are many existing research results on the influence of highway work zones, including management strategies, traffic flow control strategies, and various corresponding model theories. There are also many research results on the impacts of urban road and subway construction on traffic operation, including construction efficiency, economic impact, and travel matrix. However, there are few studies concerning the choice of work zone location, and most previous studies have assumed that the work zone choice was scientific and reasonable. Therefore, it is reasonable to choose the location of the work zone and to assess whether there is room for improvement in the road form of the work zone, but this remains a research gap. Therefore, we studied a seven-lane main road T-intersection in Xi’an, China, and investigated a work zone located at this intersection that caused a road offset, leading to the non-aligned flow of main traffic. We designed two road improvement schemes and multiple transition schemes, used VISSIM software to evaluate the traffic operation of the two schemes, and used the entropy method to choose the suitability of the two schemes under different conditions. According to the results, in the best case, the driving time, delay, and number of stops are reduced by 44%, 66%, and 92%.

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.

scholarly journals Prediction of Eye, Hair and Skin Color in Admixed Populations of Latin America

2020 ◽  
Author(s):  
Sagnik Palmal ◽  
Kaustubh Adhikari ◽  
Javier Mendoza-Revilla ◽  
Macarena Fuentes-Guajardo ◽  
Caio C. Silva de Cerqueira ◽  
...  
Keyword(s):  
Native American ◽  
Skin Color ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Skin Pigmentation ◽  
Genetic Ancestry ◽  
Learning Approaches ◽  
Latin Americans ◽  
Eye Color ◽  
The Impact

AbstractWe report an evaluation of prediction accuracy for eye, hair and skin pigmentation based on genomic and phenotypic data for over 6,500 admixed Latin Americans (the CANDELA dataset). We examined the impact on prediction accuracy of three main factors: (i) The methods of prediction, including classical statistical methods and machine learning approaches, (ii) The inclusion of non-genetic predictors, continental genetic ancestry and pigmentation SNPs in the prediction models, and (iii) Compared two sets of pigmentation SNPs: the commonly-used HIrisPlex-S set (developed in Europeans) and novel SNP sets we defined here based on genome-wide association results in the CANDELA sample. We find that Random Forest or regression are globally the best performing methods. Although continental genetic ancestry has substantial power for prediction of pigmentation in Latin Americans, the inclusion of pigmentation SNPs increases prediction accuracy considerably, particularly for skin color. For hair and eye color, HIrisPlex-S has a similar performance to the CANDELA-specific prediction SNP sets. However, for skin pigmentation the performance of HIrisPlex-S is markedly lower than the SNP set defined here, including predictions in an independent dataset of Native American data. These results reflect the relatively high variation in hair and eye color among Europeans for whom HIrisPlex-S was developed, whereas their variation in skin pigmentation is comparatively lower. Furthermore, we show that the dataset used in the training of prediction models strongly impacts on the portability of these models across Europeans and Native Americans.

A Methodology for the Sport Industry to Capture Public Perceptions and Responses in the Time of COVID-19

2021 ◽  
Author(s):  
Yen-Yao Wang ◽  
Tawei (David) Wang ◽  
Kyunghee Yoon
Keyword(s):  
Crisis Management ◽  
Management Strategies ◽  
Professional Sports ◽  
Public Perceptions ◽  
Learning Approaches ◽  
Sports Industry ◽  
Sport Industry ◽  
Hidden Patterns ◽  
The Impact ◽  
Sports Activities

The COVID-19 pandemic has had an unprecedented impact on the sports industry, affecting from professional sports activities to the 2020 Summer Olympics. It has wreaked havoc on the sports calendar, causing a number of events to be canceled or postponed. This study proposes a methodology by which the sports industry can assess public perceptions and responses in social media to gain important insights that can be used to craft effective crisis management strategies. Using machine learning approaches in order to extract hidden patterns in tweets could assist practitioners in creating and implementing crisis communication strategies for mitigating the impact of COVID-19.

Capacity for North Carolina Freeway Work Zones

1996 ◽  
Vol 1529 (1) ◽  
pp. 27-34 ◽  
Author(s):  
Karen K. Dixon ◽  
Joseph E. Hummer ◽  
Ann R. Lorscheider
Keyword(s):  
North Carolina ◽  
Flow Behavior ◽  
Work Zone ◽  
Site Location ◽  
Work Zones ◽  
Work Activity ◽  
Rural And Urban ◽  
Speed Flow ◽  
Work Area ◽  
Rural Sites

Work zone capacity values for rural and urban freeways without continuous frontage roads were defined and determined. Data were collected using Nu-Metrics counters and classifiers at 24 work zones in North Carolina. The research included analysis of speed-flow behavior, evaluation of work zone sites based on lane configuration and site location, and determination of the location within the work zone where capacity is lowest. It was shown that the intensity of work activity and the type of study site (rural or urban) strongly affected work zone capacity. The data suggested that the location where capacity is reached is also variable based on the intensity of work. For heavy work in a two-lane to one-lane work zone configuration, the capacity values proposed at the active work area are approximately 1,200 vehicles per hour per lane for rural sites and 1,500 vehicles per hour per lane for urban sites. It is recommended that two distinct volumes be used when queue behavior in a freeway work zone is analyzed. The collapse from uninterrupted flow (designated work zone capacity) and the lower queue-discharge volume both should be considered.

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.

scholarly journals Evaluating Impacts of Overloaded Heavy Vehicles on Freeway Traffic Condition by a Novel Multi-Class Traffic Flow Model

2018 ◽  
Vol 10 (12) ◽  
pp. 4694 ◽  
Author(s):  
Xiang Wang ◽  
Po Zhao ◽  
Yanyun Tao
Keyword(s):  
Traffic Flow ◽  
Flow Model ◽  
Management Strategies ◽  
Maximum Speed ◽  
Heavy Vehicles ◽  
Fundamental Diagram ◽  
Traffic Condition ◽  
Traffic Flow Model ◽  
Traffic Conditions ◽  
The Impact

Overloaded heavy vehicles (HVs) have significant negative impacts on traffic conditions due to their inferior driving performance. Highway authorities need to understand the impact of overloaded HVs to assess traffic conditions and set management strategies. We propose a multi-class traffic flow model based on Smulders fundamental diagram to analyze the influence of overloaded HVs on traffic conditions. The relationship between the overloading ratio and maximum speed is established by freeway toll collection data for different types of HVs. Dynamic passenger car equivalent factors are introduced to represent the various impacts of overloaded HVs in different traffic flow patterns. The model is solved analytically and discussed in detail in the appendices. The model validation results show that the proposed model can represent traffic conditions more accurately with consideration for overloaded HVs. The scenario tests indicate that the increase of overloaded HVs leads to both a higher congestion level and longer duration.

Effectiveness of Predictive Weather-Related Active Transportation and Demand Management Strategies for Network Management

2017 ◽  
Vol 2667 (1) ◽  
pp. 71-87 ◽  
Author(s):  
Zihan Hong ◽  
Hani S. Mahmassani ◽  
Xiang Xu ◽  
Archak Mittal ◽  
Ying Chen ◽  
...  
Keyword(s):  
Traffic Management ◽  
Demand Management ◽  
Management Strategies ◽  
Weather Conditions ◽  
Active Transportation ◽  
Operational Conditions ◽  
Traffic Conditions ◽  
Adverse Weather ◽  
Traffic Performance ◽  
The Impact

This paper presents the development, implementation, and evaluation of predictive active transportation and demand management (ATDM) and weather-responsive traffic management (WRTM) strategies to support operations for weather-affected traffic conditions with traffic estimation and prediction system models. First, the problem is defined as a dynamic process of traffic system evolution under the impact of operational conditions and management strategies (interventions). A list of research questions to be addressed is provided. Second, a systematic framework for implementing and evaluating predictive weather-related ATDM strategies is illustrated. The framework consists of an offline model that simulates and evaluates the traffic operations and an online model that predicts traffic conditions and transits information to the offline model to generate or adjust traffic management strategies. Next, the detailed description and the logic design of ATDM and WRTM strategies to be evaluated are proposed. To determine effectiveness, the selection of strategy combination and sensitivity of operational features are assessed with a series of experiments implemented with a locally calibrated network in the Chicago, Illinois, area. The analysis results confirm the models’ ability to replicate observed traffic patterns and to evaluate the system performance across operational conditions. The results confirm the effectiveness of the predictive strategies tested in managing and improving traffic performance under adverse weather conditions. The results also verify that, with the appropriate operational settings and synergistic combination of strategies, weather-related ATDM strategies can generate maximal effectiveness to improve traffic performance.

Sign in / Sign up

Export Citation Format

Share Document