Traffic count data downloaded from GDOT public map here: https://gdottrafficdata.drakewell.com/publicmultinodemap.aspRetrieved Annual Statistics Reports: "All Station AADT and Truck Percent Statistics." Mapped by Lat/Long field.Retrieved and rehosted for staff use and overlay on city maps on 12/14/2018."The Georgia Department of Transportation’s Traffic Analysis and Data Application (TADA!) website presents data collected from the Georgia Traffic Monitoring Program located on the public roads in Georgia. The Website uses a dynamic mapping interface to allow the User to access data from the map as well as in a variety of report, graph, and data export formats."

Georgia_Average_Annual_Daily_Trafffic_2022: Traffic data for selected Georgia road segments between 2020-222. Data obtained from GDOT in 2022 and updated in late 2023. Data attributes include AADT (average annual daily traffic), single-unit truck AADT, combo-unit truck AADT, peak % single-unit AADT, peak % combo-unit AADT. https://www.dot.ga.gov/DS/DataRegional Traffic Counts 2019-2022: This layer shows traffic counts in the greater Chattanooga region compiled by ESRI. Traffic counts are widely used by departments of transportation for highway funding or planning purposes.GaRoad Network Truck 2020: Traffic data for selected Georgia road segments in 2020. Data obtained from GDOT in May 2022. Data attributes include AADT (average annual daily traffic), single-unit truck AADT, combo-unit truck AADT, peak % single-unit AADT, peak % combo-unit AADT. https://www.dot.ga.gov/DS/DataTN Road Network Traffic 2022: Traffic data for selected TN road segments in 2020-2022. Data obtained from TDOT in May 2022 and updated in late 2023. Data attributes include AADT (average annual daily traffic), single-unit truck AADT, combo-unit truck AADT, peak % single-unit AADT, peak % combo-unit AADT.

This layer contains points that highlight traffic counting locations with their associated traffic count observations over several years since 2013 within the City of Johns Creek, GA.Data Note: In 2020, only one direction of traffic was recorded for locations #19, 28, and 34. In the data contained in this layer, that single direction count was duplicated to make year to year comparisons more accurate.

Important Note: This item is in mature support as of June 2023 and will be retired in December 2025.This layer shows traffic counts in the United States in a multiscale map. Traffic counts are widely used for site selection by real estate firms and franchises. Traffic counts are also used by departments of transportation for highway funding. This map is best viewed at large scales where you can click on each point to access up to five different traffic counts over time. At medium to small scales, comparisons along major roads are possible. The Business Basemap has been added to provide context at medium and small scales. It shows the location of businesses in the United States and helps to understand where and why traffic counts are collected and used. The pop-up is configured to display the following information:The most recent traffic countThe street name where the count was collectedThey type of count that was taken. See the methodology document for definitions of count types such as AADT - Average Annual Daily Traffic. Traffic Counts seasonally adjusted to represent the average day of the year. AADT counts represent counts taken Sunday—Saturday.A graph displaying up to five traffic counts taken at the same location over time. Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

The evaluation uses three methodologies to rigorously evaluate the causal impact of the program on outcomes. The first is a difference-in-difference methodology, whereby the project roads are matched to a set of similar comparison roads where no intervention has taken place. These comparison roads are chosen from a number of potential candidates using a propensity score matching technique. The difference-in-difference analysis thus compares traffic counts as well as socioeconomic outcomes for residents of communities located near the project roads to those of residents of communities located near the comparison roads. Secondly, the evaluation incorporates a continuous treatment approach. Project impact is modeled in a dose-response framework, so that communities nearer the project roads are assumed to experience greater impacts than those more distant. Finally, the evaluation estimates a matched difference-indifference model, using propensity score matching to improve the comparability between the treatment and comparison groups. Combining these three approaches allows for results from each to be compared in order to ensure a robust set of findings that is not dependent on the assumptions of one particular modeling approach.

Abstract

The evaluation uses three methodologies to rigorously evaluate the causal impact of the program on outcomes. The first is a difference-in-difference methodology, whereby the project roads are matched to a set of similar comparison roads where no intervention has taken place. These comparison roads are chosen from a number of potential candidates using a propensity score matching technique. The difference-in-difference analysis thus compares traffic counts as well as socioeconomic outcomes for residents of communities located near the project roads to those of residents of communities located near the comparison roads. Secondly, the evaluation incorporates a continuous treatment approach. Project impact is modeled in a dose-response framework, so that communities nearer the project roads are assumed to experience greater impacts than those more distant. Finally, the evaluation estimates a matched difference-indifference model, using propensity score matching to improve the comparability between the treatment and comparison groups. Combining these three approaches allows for results from each to be compared in order to ensure a robust set of findings that is not dependent on the assumptions of one particular modeling approach.

Geographic coverage

The Samtskhe-Javakheti region

Analysis unit

Individuals, households

Universe

To collect the data, enumerators travelled to each settlement and worked with local authorities to identify a small group of individuals who were identified as knowledgeable about conditions in the settlement.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the first round used the 2002 Census to identify a sampling frame of 732 settlements around either the project or comparison roads, of which 690 were surveyed. The sample size was increased for the second and third rounds, which conducted surveys in all settlements that met at least one of the following criteria: settlements along the SJ Road; settlements along comparison roads where traffic counts are conducted; settlements included in the Integrated Household Survey (IHHS) that the evaluation uses to evaluate household-level outcomes, and any other village that was included in the baseline. The second and third rounds each included 960 settlements.

Our approach to selecting the comparison roads uses the technique of Propensity Score Matching (PSM) to identify eight comparison road segments to be included in the analysis. The comparison roads were selected from an inventory of 117 road segments for which data on a variety of characteristics was available from RDMED, the Georgian government roads agency. Our application of PSM in this case is to estimate a logistic regression model of the probability that a road is part of the treatment group as a function of observable characteristics. We then calculate the predicted probability (or propensity score) that a road segment is part of the treatment group for each of the eight treatment roads and 117 potential comparison roads using these estimated regression coefficients. Finally, each of the eight treatment roads is matched to a single comparison road for which the propensity score is the closest in value from among the 117 potential comparison roads.

This layer contains all aspects of the physical traffic signal infrastructure present in the City of Johns Creek, GA.Traffic signal heads: actual traffic signals in the CityLight sign locations: locations of all lighted signsSignal masts and wires: locations of all mast arms and wires that hold traffic signal heads and other signagePedestrian controls and signals: locations of all pedestrian controls and signalsTraffic cameras: locations of all traffic camerasSignal cabinets: locations of all signal cabinets containing ITS infrastructureTraffic counting locations: locations of all traffic counting infrastrucutureTraffic signal pucks: locations of all traffic pucks that have been installed

Average weekly volume of cars for selected traffic count sites

Shodno Zakonu o putevima, Uprava za saobraćaj, kao organ uprave koji vrši upravljanje državnim putevima (magistralnim i regionalnim), između ostalog vrši i poslove upravljanja saobraćajem i organizovanja i obavljanja brojanja vozila na putevima za koje je nadležna. Uprava za saobraćaj je uspostavila Sistem automatskog brojanja saobraćaja koji čine uređaji i oprema 61 automatskog brojača saobraćaja koji su postavljeni na ukupno 59 brojačkih lokacija. Na saobraćajnicama bulevarskog tipa (dvije kolovozne trake fizički odvojene razdjelnim ostrvom, svaka kolovozna traka ima po dvije saobraćajne trake) instaliraju se dva uređaja na jednom zajedničkom stubu u razdjelnom ostrvu.