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.

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.

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.

scholarly journals The Impact of the Economic Crisis on Social Policy in Interwar Poland

2021 ◽  
Vol 66 (4) ◽  
pp. 373-387
Author(s):  
Paweł Grata
Keyword(s):  
Social Policy ◽  
Economic Crisis ◽  
Local Governments ◽  
Ad Hoc ◽  
Social Issues ◽  
Public Funding ◽  
Multidimensional Analysis ◽  
Effective Response ◽  
State And Local ◽  
The Impact

Abstract The aim of this paper is to determine how the economic crisis, and the ad hoc attempts made by the authorities to counteract it, affected the Polish society and, above all, to prepare a multidimensional analysis of how the crisis impacted systemic changes in the Polish social policy. The author will examine both positive changes that followed the relevant global trends and also negative changes that resulted from the developmental lag, the country’s economic situation and especially the generally low involvement of the state via public funding in activities that were part of the state’s social policy. Assessment of Polish social policy during the crisis must be ambiguous. On the one hand, systemic changes in social policy introduced as a result of the economic collapse can be clearly distinguished, on the other hand, however, the severity of the crisis visibly affected many activities of state and local governments in the social sphere. These activities posed unsuccessful attempts to rescue the situation in the labour market. Additionally, an effective response to deepening poverty was missing, amendments adopted to labour legislation were disadvantageous to employees, and finally fundamental sacrifices in terms of social policy were made as the lawmakers passed the Unification Act during the crisis. Polish social policy was unable, for a number of reasons, to essentially redefine its approach to addressing the numerous social issues it had to face.

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.

How Does a Reduction in Mandated Medicaid Spending Affect Local Fiscal Behaviors? Evidence from New York State

2021 ◽  
Vol 49 (4) ◽  
pp. 495-547
Author(s):  
Yusun Kim
Keyword(s):  
New York ◽  
Local Governments ◽  
Social Insurance ◽  
New York State ◽  
Property Tax ◽  
Effective Property ◽  
Medicaid Spending ◽  
State And Local ◽  
Sudden Decrease ◽  
The Impact

In 2005, New York (NY) state capped the growth of county-level Medicaid spending, which abruptly decreased counties’ Medicaid outlay in both relative and absolute terms. This study exploits this discontinuity in county Medicaid outlay to estimate the impact of the relief mandate policy on county budgets and property tax levies. It bridges a gap in the public finance literature by addressing local government responses to a sudden decrease in the outlay of a large mandatory spending category. We find a compositional change but no income effect on non-Medicaid spending. However, the policy reduced the effective property tax rate significantly by 6.6 to 8.1 percent on average among affected NY counties after the enactment of the policy relative to control counties. This study advances our understanding of local fiscal responses to an intergovernmental fiscal policy that changes how state and local governments share the costs of a large public social insurance program.

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.

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 The impact of household connection to public network wastewater systems: regulatory impact assessment

2019 ◽  
Vol 79 (6) ◽  
pp. 1060-1070
Author(s):  
Bruno Eustaquio de Carvalho ◽  
Samuel Alves Barbi Costa ◽  
Rui Cunha Marques ◽  
Oscar Cordeiro Netto
Keyword(s):  
Impact Assessment ◽  
Urban Areas ◽  
Local Governments ◽  
Federal State ◽  
Public Network ◽  
Policy Tool ◽  
Regulatory Impact ◽  
State And Local ◽  
The Impact ◽  
Regulatory Impact Assessment

Abstract Brazil faces a severe lack of wastewater coverage. Even in urban areas, wastewater is directly disposed of in watercourses without any treatment for a large part of the population. Although the federal, state, and local governments have invested in water and wastewater services (WWS), the expected results have not been achieved. To overcome this problem, the present paper provides an opportunity to observe an ex-ante regulatory impact assessment (RIA) as a policy tool in Brazil. The regulatory policy options will be appraised through the multiple criteria decision analysis (MCDA) according to the following objectives: (i) protect the customers with respect to social aspects; (ii) safeguard the economic, operational and infrastructure sustainability; and (iii) protect the environment. The results show that by making decisions based on evidence, policy makers should reduce the households not connected to wastewater services by 75% and for that they should incur BRL 33 million to the year 2023. Hence, the extra revenues to be obtained with these new connections are capable of making a surplus estimated as BRL 42 million for the same period. This study promotes the use of RIA as a rational, robust and transparent decision framework by the regulatory agencies worldwide.

The politics of redistribution in local governments: the effect of gender representation on welfare spending in California counties

2014 ◽  
Vol 34 (2) ◽  
pp. 269-301 ◽  
Author(s):  
Sanghee Park
Keyword(s):  
Local Governments ◽  
State Legislature ◽  
Local Economy ◽  
Gender Representation ◽  
Home Rule ◽  
Political Preference ◽  
Non Profit ◽  
Welfare Spending ◽  
State And Local ◽  
The Impact

AbstractThis research explores the impact of gender representation at the state and local levels on redistributive choices. This research also examines whether female officeholders moderate the impact of the local economy and institution on welfare spending. Hypotheses are tested across 58 counties in California over ten years, between 2001 and 2010. According to the fixed effect models, women in state legislature had a positive effect on local welfare spending, while women on county boards had no significant effect. However, a positive moderating effect of women on county boards during economic hardship was found. Three categories of control variables include institutional factors, such as the introduction of Proposition 1A and county home rule; political factors, such as the political preference of each county’s residents and strength of non-profit organisations; and socio-economic factors, such as intergovernmental revenue, unemployment rate and demographics. Counties with more intergovernmental revenue and supporters of Democratic presidential candidates are likely to spend more on welfare services.

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