This dataset contains the current estimated speed for about 1250 segments covering 300 miles of arterial roads. For a more detailed description, please go to https://tas.chicago.gov, click the About button at the bottom of the page, and then the MAP LAYERS tab. The Chicago Traffic Tracker estimates traffic congestion on Chicago’s arterial streets (nonfreeway streets) in real-time by continuously monitoring and analyzing GPS traces received from Chicago Transit Authority (CTA) buses. Two types of congestion estimates are produced every ten minutes: 1) by Traffic Segments and 2) by Traffic Regions or Zones. Congestion estimate by traffic segments gives the observed speed typically for one-half mile of a street in one direction of traffic. Traffic Segment level congestion is available for about 300 miles of principal arterials. Congestion by Traffic Region gives the average traffic condition for all arterial street segments within a region. A traffic region is comprised of two or three community areas with comparable traffic patterns. 29 regions are created to cover the entire city (except O’Hare airport area). This dataset contains the current estimated speed for about 1250 segments covering 300 miles of arterial roads. There is much volatility in traffic segment speed. However, the congestion estimates for the traffic regions remain consistent for relatively longer period. Most volatility in arterial speed comes from the very nature of the arterials themselves. Due to a myriad of factors, including but not limited to frequent intersections, traffic signals, transit movements, availability of alternative routes, crashes, short length of the segments, etc. speed on individual arterial segments can fluctuate from heavily congested to no congestion and back in a few minutes. The segment speed and traffic region congestion estimates together may give a better understanding of the actual traffic conditions.

Annual average daily traffic is the total volume for the year divided by 365 days. The traffic count year is from October 1st through September 30th. Very few locations in California are actually counted continuously. Traffic Counting is generally performed by electronic counting instruments moved from location throughout the State in a program of continuous traffic count sampling. The resulting counts are adjusted to an estimate of annual average daily traffic by compensating for seasonal influence, weekly variation and other variables which may be present. Annual ADT is necessary for presenting a statewide picture of traffic flow, evaluating traffic trends, computing accident rates. planning and designing highways and other purposes.Traffic Census Program Page

The census count of vehicles on city streets is normally reported in the form of Average Daily Traffic (ADT) counts. These counts provide a good estimate for the actual number of vehicles on an average weekday at select street segments. Specific block segments are selected for a count because they are deemed as representative of a larger segment on the same roadway. ADT counts are used by transportation engineers, economists, real estate agents, planners, and others professionals for planning and operational analysis. The frequency for each count varies depending on City staff’s needs for analysis in any given area. This report covers the counts taken in our City during the past 12 years approximately.

AADT represents current (most recent) Annual Average Daily Traffic on sampled road systems. This information is displayed using the Traffic Count Locations Active feature class as of the annual HPMS freeze in January. Historical AADT is found in another table. Please note that updates to this dataset are on an annual basis, therefore the data may not match ground conditions or may not be available for new roadways. Resource Contact: Christy Prentice, Traffic Forecasting & Analysis (TFA), http://www.dot.state.mn.us/tda/contacts.html#TFA

Check other metadata records in this package for more information on Annual Average Daily Traffic Locations Information.


Link to ESRI Feature Service:

Annual Average Daily Traffic Locations in Minnesota: Annual Average Daily Traffic Locations

Estimation results of modified Bass model using patent citations and web search traffic.

Dataset 1 (AXA collisions 2015–2019) was curated and used to evaluate the effect of two road traffic regulations implemented in Mexico City in 2015 and 2019 on collisions using an interrupted time series analysis. Collisions data came from insurance collision claims (January 2015 to December 2019). The dataset contains 8 variables: year (anio_n), week (semana), count of total collisions per week (c_total), count of collisions resulting in injury per week (c_p_lesion), binary variable to identify the 2015 intervention (limit), binary variable to identify the 2019 intervention (limit1), the number of weeks from baseline (time), an estimate of the number of insured vehicles per week (veh_a_cdmx). Dataset 2 (Road traffic deaths 2013–2019) was curated and used to evaluate the effect of two road traffic regulations implemented in Mexico City in 2015 and 2019 on mortality using an interrupted time series analysis. Mortality data came from vital registries collated by the Mexican Institute for Geography and Statistics, INEGI, (January 2013 to December 2019). The dataset contains 7 variables: year (anio_ocur), week (semana), count of traffic-related deaths per week (def_trans), binary variable to identify the 2015 intervention (limit), binary variable to identify the 2019 intervention (limit1), the number of weeks from baseline (time) and an estimate of the Mexico City population per week (pob_tot_p). Methods Dataset 1 arises from publicly available data on insurance-reported collisions published on the website of the International Institute for Data Science (see reference below). The data were collected by claims adjusters from the company AXA at the site of the collision using an electronic device. These data were available for public use from January 2015 to December 2019 and include information on individual collisions and their characteristics: date the collision occurred, location (coordinates and adjuster reported location), type of vehicle involved and whether there were injuries or deaths. Data were processed and cleaned, mapping collisions, and keeping only those georeferenced within Mexico City boundaries as well as coded to Mexico City in the reported location variable. We then summed the number of collisions per week and merged it with data on an estimate of the number of insured registered vehicles per week (using information from registered vehicles and proportion of insured vehicles from the Mexican Association of Insurance companies). Two more variables were created, one that identifies the week when the intervention came into effect and another variable to number the weeks since baseline. This dataset contains all the necessary information to conduct the interrupted time series analysis for total collisions and collisions resulting in injuries. Dataset 2: mortality data were validated and reported by INEGI (see reference below) from death certificates filed mainly by the Health Sector, using the International Classification of Disease, 10th Revision (ICD-10) for diagnosis codes. We used data from January 2013 to December 2019 and included deaths with the following ICD-10 codes: V02-V04 (.1-.9), V09, V092, V09.3, V09.9, V12-V14 (.3-.9), V19.4-V19.6, V19.9, V20-V28 (.3-.9), V29, V30-V39, V40-V79 (.4-.9), V80.3-V80.5, V81.1, V82.1, V82.1, V83-V86 (.0-.3), V87-V89.2 and V89.9. We summed the number of traffic-related deaths per week and merged it with data on an estimate of the total population in Mexico City per week (see refs below). Two more variables were created, one that identifies the week when the intervention came into effect and another variable to number the weeks since baseline. This dataset contains all the necessary information to conduct the interrupted time series analysis for road traffic deaths. References to original data:

Instituto Internacional de Ciencia de Datos. Datos AXA de Percances Viales [Internet]. 2020 [July 2021]. Available from: https://i2ds.org/datos-abiertos/. Instituto Nacional de Geografía y Estadística. Parque Vehicular [Internet]. 2019 [July 2021]. Available from: https://www.inegi.org.mx/temas/vehiculos/default.html#Tabulados. Dirección Ejecutiva de Líneas de Negocio área de Automóviles. Sistema Estadístico del Sector Asegurador del ramo Automóviles SESA 2018. Mexico City: Asociación Mexicana de Instituciones de Seguro, 2020. Instituto Nacional de Geografía y Estadística. Mortalidad [Internet]. 2020 [July 2021]. Available from: https://www.inegi.org.mx/programas/mortalidad/default.html#Datos_abiertos.

World Health Organisation. ICD-10 Version:2010 [Internet]. 2010 [July 2021]. Available from: https://icd.who.int/browse10/2010/en. Consejo Nacional de Población. Proyecciones de la Población de México y de las Entidades Federativas, 2016-2050 [Internet]. 2018 [July 2021]. Available from: https://datos.gob.mx/busca/dataset/proyecciones-de-la-poblacion-de-mexico-y-de-las-entidades-federativas-2016-2050.

Sweden consistently ranked low compared to other countries worldwide when it comes to crypto adoption based on transaction volume. This is according to estimates based on web traffic toward individual websites that are used to perform cryptocurrency transactions. Sweden did not have a specific main area in which it preferred to use crypto. Initial estimates showcased an interest in *** payments, but this ranking continuously declined as more research released.