Geolocation of road counting points accompanied by annual traffic indicators (all-vehicle TMJA / share of heavy-duty vehicles in percentage / heavy-duty TMJA) - 2023 edition Collection context Knowledge of the total traffic to be flown on a road (all vehicles combined) is essential in order to give the track geometric characteristics commensurate with its importance and the speeds used, to draw up accident reports and to propose route or safety arrangements. It is also essential for calculating the necessary pavement structure in the case of rehabilitation or development projects, based on heavy-duty traffic. Collection method The departmental road network has been divided into homogeneous sections of traffic. In addition to the permanent stations, temporary counts are carried out by the ID teams each year to complete the road traffic base. Annually, nearly 500 meter installations and removals are carried out on a time and means basis. In order to consolidate and make the road traffic base more reliable, it was decided to count traffic from each section at least once every 5 years. The counting frequency is calculated according to predefined criteria. For the points of permanent traffic counting (117 equipment), the collection and exploitation of data can be carried out with hourly steps, the discrimination of vehicles is effective on all these sites (restitution TV/PL or VL/PL). Permanent traffic counting stations can be characterized by the type of data available: . 24 stations are able to render data of flow type with discrimination of vehicles according to length. . 93 stations are able to render data of type flow + speed with discrimination of vehicles according to length. Of these last 93 equipment, a real-time visualization/collection of data is a priori possible, on the other hand it presupposes a permanent connection for each equipment (RTC, GSM or IP Internet). Thus traffic congestion can be visualized/analyzed on these points of the network. An automatic data collection procedure at regular intervals can also be set up to avoid permanent occupancy of BTI, GSM or IP lines. Attributes | Field | Alias | Type | | --- | --- | --- | | objectid | | integer | | route | Road name (long) | char | | pr | Benchmark | integer | | abs | Abscissa | integer | | cumulative | Cumulative | integer | | id_route | Name of the (short) route | char | | pr_deb | PR+ABS Start of counting section | char | | pr_end | PR+ABS End of counting section | char | | category | Category | integer | | section_in | Index Section | char | | id_traffic | Traffic ID | char | | id_site | Site identifier | char | | site_name | Site name | char | | place | Venue | char | | type | Equipment type | char | | cptge_year | Year | integer | | value_type | Type value | char | | mja_tv | Annual Average Daily All Vehicles | integer | | mja_pl | Annual Average Daily Heavy Weights | integer | | p_pl | Percentage Heavy Weight | double | | x | Longitude | double | | y | Latitude | double | For more information, see the metadata on the Isogeo catalogue.

Geolocation of homogeneous traffic sections on the departmental road network of the Loiret accompanied by annual traffic indicators (TMJA all vehicles/Part of heavy goods vehicles as a percentage/TMJA heavy goods vehicles) — Edition 2020

Collection context

Knowledge of the total traffic to be flowed on a road (all vehicles combined) is essential in order to give the track geometric characteristics in relation to its importance and the speeds used, to draw up accident checks and to propose route or safety arrangements. It is also essential to calculate the road structure necessary for rehabilitation or development projects, based on heavy goods traffic.

Collection method

The departmental road network has been divided into homogeneous traffic sections. In addition to permanent stations, temporary counts are carried out by DI teams each year to complete the road traffic base. Each year, nearly 500 meter installation and deposit have been carried out on a time-by-day basis. In order to consolidate and secure the road traffic base, it was decided to count at least once every 5 years the traffic of each section. The counting frequency is calculated according to predefined criteria.

For permanent traffic counting points (117 equipment), data collection and exploitation can be carried out with time steps, vehicle discrimination is effective on all these sites (TV/PL or VL/PL).

Permanent traffic counting stations can be characterised by the type of data available:

. 48 positions are able to return flow-type data with vehicle discrimination according to length.

. 69 stations are able to return data of type flow + speed with discrimination of vehicles according to length.

Of the latter 69, real-time data visualisation/collection is a priori possible, but this requires a permanent connection for each equipment (RTC, GSM or Internet IP).

Thus road congestion can be visualised/analysed at these points of the network.

An automatic data collection procedure at regular intervals may also be set up to avoid permanent occupancy of BTI, GSM or IP lines.

Attributes

| Champ | Alias | Type | | — | — | | — | | ‘ObjectID’ | FID | ‘integer’ | | ‘ROUTE’ | Road name (long) | ‘char’ | | ‘PRD’ | Point Locator Start | ‘integer’ | | ‘ABD’ | Abscisse beginning section | ‘integer’ | | ‘PRF’ | Reference point end section | ‘integer’ | | ‘ABF’ | Abscisse fin section | ‘integer’ | | ‘cumuld’ | Cumul beginning section | ‘integer’ | | ‘CUMULF’ | Cumulus end section | ‘integer’ | | ‘ID_ROUTE’ | Road name (short) | ‘char’ | | ‘PR_CPTGE’ | PR+ABS counting | ‘char’ | | ‘CATEGORIA’ | Category | ‘integer’ | | ‘ID_TRAFIC’ | Traffic ID | ‘char’ | ‘ID_TRAFIC’ | ‘ID_SITE’ | Site ID | ‘char’ | | ‘NOM_SITE’ | Site Name | ‘char’ | | ‘LIEU’ | Location | ‘char’ | ‘LIEU’ | ‘TYPE’ | Equipment type | ‘char’ | ‘TYPE’ | ‘CPTGE_YEAR’ | Year | ‘integer’ | | ‘VALUE_TYPE’ | Value Type | ‘char’ | | ‘MJA_TV’ | Average Annual Daily All Vehicles | ‘integer’ | | ‘MJA_PL’ | Annual Daily Average Heavy Weights | ‘integer’ | | ‘P_PL’ | Percent Heavy Weights | ‘double’ | | ‘SHAPE_LENG’ | ‘double’ | | ‘SECTION_IN’ | Index Section | ‘char’ | ‘SECTION_IN’ | ‘SHAPE.LEN’ | ‘double’ |

For more information, see the metadata on the Isogeo catalog.

Geolocation of road counting points accompanied by annual traffic indicators (ATMJA all vehicles/Percentage share of heavy goods vehicles/HLGVs) – 2022 edition Collection Context Knowledge of the total traffic to be flowed on a road (all vehicles combined) is essential to give the route geometric characteristics in relation to its importance and speeds, to draw up accident assessments and to propose route or safety adjustments. It is also essential to calculate the pavement structure needed in the case of rehabilitation or development projects, based on heavy-duty traffic. Collection method The departmental road network has been divided into homogeneous sections of traffic. In addition to the permanent stations, temporary counts are carried out by the DI teams each year to complete the road traffic base. Annually, nearly 500 metering installations and deposits are carried out on a regular basis. In order to consolidate and maintain the road traffic base, it was decided to count at least once every 5 years the traffic of each section. The counting frequency is calculated according to predefined criteria. For permanent traffic counting points (117 equipment), the collection and exploitation of data can be carried out with hourly steps, the discrimination of vehicles is effective on all these sites (TV/PL or VL/PL restitution). Permanent traffic counting stations can be characterised by the type of data available: . 30 stations are able to return flow-type data with discrimination of vehicles according to length. . 87 stations are able to return speed + speed data with discrimination of vehicles according to length. Of the last 87 equipment, a real-time visualisation/collection of data is a priori possible, on the other hand this requires a permanent connection for each equipment (RTC, GSM or Internet IP). Thus road congestion can be visualised/analysed at these points of the network. An automatic data collection procedure at regular intervals can also be put in place to avoid permanent occupancy of the BTI, GSM or IP protocol lines. Attributes | field | Alias ▲ Type | – | – — | ‘objectID’ | | ‘integer’ | ‘road’ | Road name (long) ⋆ ‘char’ -’ | ‘PR’ | Benchmark ‘integer’ | ‘ABS’ | Abscisse ‘integer’ | ‘cumul’ | Cumul ‘integer’ | ‘id_route’ | Road name (short) ⋆ ‘char’ ⋆ | ‘pr_deb’ | PR+ABS Beginning of the counting section ‘char’ | ‘pr_fin’ | PR+ABS End of the counting section ⋆ ‘char’ ⋆ | ‘categorie’ | Category ▲ ‘integer’ ⋆ | ‘section_in’ | Section Index ▲ ‘char’ — | ‘id_trafic’ | Identifier traffic ▲ ‘char’ ⋆ | ‘id_site’ | Identifier site ▲ ‘char’ ⋆ | ‘name_site’ | Site name ▲ ‘char’ ⋆ | ‘place’ | Place ⋆ ‘char’ -’ | ‘type’ | Type equipment ▲ ‘char’ ⋆ | ‘cptge_year’ | Year ▲ ‘integer’ ⋆ | ‘value_type’ | Type value ▲ ‘char’ ⋆ | ‘mja_tv’ | Average Annual Daily All Vehicles ⋆ ‘integer’ | ‘mja_pl’ | Average Annual Daily Heavy Weight ⋆ ‘integer’ | ‘p_pl’ | Percentage Height Weight ⋆ ‘double’ | ‘x’ | Longitude ▲ ‘double’ | ‘y’ | Latitude ▲ ‘double’ For more information, see the metadata on the Isogeo catalog.

Abstract

The purpose of the 2018 Verification of Crowdsourced Crash Reports survey is to physically verify road traffic crash (RTC) reports that were reported by bystanders in Nairobi, Kenya using Twitter/Ma3Route. Ma3Route is a mobile/web/SMS platform that crowdsources transport data and provides users with information on traffic, RTCs, matatu directions and driving reports. Users post RTC or traffic information to Ma3Route, where Ma3Route then publishes the post on Twitter. In this survey, we sought to verify the accuracy of crowdsourced information as a source of RTC data.

Geographic coverage

Crowdsourced road traffic crash (RTC) reports were verified in Nairobi, Kenya

Analysis unit

Road traffic crash reports

Universe

Road traffic crash reports in Nairobi, Kenya

Kind of data

--

Mode of data collection

Other [oth]

Geolocation of homogeneous traffic sections on the departmental road network of Loiret accompanied by annual traffic indicators (TMJA all vehicles/share of heavy goods vehicles in percentage/TMJA heavy-duty vehicles) – 2022 edition Collection Context Knowledge of the total traffic to be flowed on a road (all vehicles combined) is essential to give the route geometric characteristics in relation to its importance and speeds, to draw up accident assessments and to propose route or safety adjustments. It is also essential to calculate the pavement structure needed in the case of rehabilitation or development projects, based on heavy-duty traffic. Collection method The departmental road network has been divided into homogeneous sections of traffic. In addition to the permanent stations, temporary counts are carried out by the DI teams each year to complete the road traffic base. Annually, nearly 500 metering installations and deposits are carried out on a regular basis. In order to consolidate and maintain the road traffic base, it was decided to count at least once every 5 years the traffic of each section. The counting frequency is calculated according to predefined criteria. For permanent traffic counting points (117 equipment), the collection and exploitation of data can be carried out with hourly steps, the discrimination of vehicles is effective on all these sites (TV/PL or VL/PL restitution). Permanent traffic counting stations can be characterised by the type of data available: . 30 stations are able to return flow-type data with discrimination of vehicles according to length. . 87 stations are able to return speed + speed data with discrimination of vehicles according to length. Of the last 87 equipment, a real-time visualisation/collection of data is a priori possible, on the other hand this requires a permanent connection for each equipment (RTC, GSM or Internet IP). Thus road congestion can be visualised/analysed at these points of the network. An automatic data collection procedure at regular intervals can also be put in place to avoid permanent occupancy of the BTI, GSM or IP protocol lines. Attributes | field | Alias ▲ Type | – | – — | ‘objectID’ | | ‘integer’ | ‘road’ | Road name (long) ⋆ ‘char’ -’ | ‘PRD’ | Point reference start ⋆ ‘integer’ | ‘Abd’ | Abscisse debut section ▲ ‘integer’ | ‘PRF’ | End-section marker ‘integer’ | ‘ABF’ | Abscisse fin section ‘integer’ | ‘cumuld’ | Cumul beginning section ▲ ‘integer’ | ‘cumulf’ | Cumul fine section ‘integer’ | ‘id_route’ | Road name (short) ⋆ ‘char’ ⋆ | ‘pr_cptge’ | PR+ABS counting ▲ ‘char’ ⋆ | ‘categorie’ | Category ▲ ‘integer’ ⋆ | ‘section_in’ | Section index ▲ ‘char’ | ‘id_trafic’ | Identifier traffic ▲ ‘char’ ⋆ | ‘id_site’ | Identifier site ▲ ‘char’ ⋆ | ‘name_site’ | Site name ▲ ‘char’ ⋆ | ‘place’ | Place ⋆ ‘char’ -’ | ‘type’ | Type equipment ▲ ‘char’ ⋆ | ‘cptge_year’ | Year ▲ ‘integer’ ⋆ | ‘value_type’ | Type value ▲ ‘char’ ⋆ | ‘mja_tv’ | Average Annual Daily All Vehicles ⋆ ‘integer’ | ‘mja_pl’ | Average Annual Daily Heavy Weight ⋆ ‘integer’ | ‘p_pl’ | Percentage Height Weight ⋆ ‘double’ | ‘st_length(shape)’ | | ‘double’ ▲ For more information, see the metadata on the Isogeo catalog.

The Road Traffic Collision (RTC) app provides access to location-aware real time travel disruption information for the West Midlands Combined Authority Region. The Dashboard visualises real time information for highways, rail, metro and bus in a fully interactive web-based mapping application, enabling Transport for West Midlands to coordinate a multi modal travel approach, moving people in and out of our region.Access is available for TfWM, its partners and key stakeholders within the region.

To date research investigating how mental health impacts physical recovery following a road traffic crash (RTC) has focused on cohorts with severe injuries. The UQ SuPPORT study aims to study the physical and psychological outcomes of claimants with minor injuries following an RTC under the Queensland common law compulsory insurance scheme. This paper outlines the protocols of this study as a platform for future publications. The 2-year longitudinal cohort study collected interview and survey data from claimants at 6, 12, and 24 months post-RTC. Measures used in the telephone interview included the DSM-IV Composite International Diagnostic Interview for posttraumatic stress disorder, generalized anxiety disorder, major depressive episode, panic attacks, agoraphobia; and self-reported disability (WHO-DAS-II). Quality of life (SF-36v2), alcohol use (AUDIT), social support (MSPSS), quality-adjusted life years (EQ-5D), and return to work outcomes were assessed via postal questionnaires. A total of 382 claimants consented to participate at the beginning of the study, and these participants were approached at each wave. Retention was high (65%). The average age of participants at Wave 1 was 48.6 years, with 65% of the sample sustaining minor injuries (Injury Severity Score=1–3). This study has collected a unique sample of data to investigate recovery patterns of claimants with minor injuries. Future publications will more fully assess the effects of the collected measures on recovery rates 2 years post-RTC.

This dataset is published as Open Data and taken directly from Police Scotland's Road Traffic Collision Dataset.The dataset has been manipulated to show reportable Road Traffic Collisions within the Stirling Council area and is a join between Collision and Casualty data available here, https://www.scotland.police.uk/about-us/how-we-do-it/road-traffic-collision-data/ Supporting information about the data from Police Scotland:Please note that these data are not official statistics.These management information data are not directly comparable with official statistics. The data published here is for recorded collisions from January 1st Data before this date are already available online at the Department for Transport (see below).All data are provisional and should be treated as management information. These data were extracted from Police Scotland internal systems and are published quarterly. Management information are extracted, and will be correct, as at July 2nd; October 2nd; January 2nd; and April 2nd. Historical data are refreshed to show the most current recorded information.During 2019 Police Scotland adopted the CRaSH (Collision Recording and Sharing) data recording and management solution. This is one of the first national IT solutions implemented by Police Scotland, which is also used by over half of the police forces in England and Wales. The system is owned by the Department for Transport, and further information is available on the CRaSH website.Before the introduction of CRaSH provision of STATS19 data (see below) was reliant on collating data from a number of different legacy Force IT systems. Due to differences in data structure and recording practices or processes, these data do not have the same level of consistency as that available from CRaSH. However, RTC data (designated as national statistics) for the period before 2020 are published on the Department for Transport website.Within the United Kingdom, recording of road traffic collision (RTC) data is governed by the STATS19 data collection form, with guidance provided by the STATS20 Manual (Instructions for the completion of road accident reports from non-CRASH sources). Further details and access to these documents is available through the Department for Transport website. COVID-19 : During 2020/21 there were a variety of COVID pandemic restrictions and lockdowns. It is recommended that the primary comparator to use as a baseline is a five-year average. Where this is not available, then a three-year average should be used. Where comparisons are made between 2020/21 and 2019/20 caution should be used when interpreting time series or analytical results.National statistics for RTCs and Casualties are routinely published by Transport Scotland and the Department for Transport.For information on stop and search please visit the stop and search data publication page.

The PSNI produces statistics on injury road traffic collisions (RTCs) that are reported to the Police. These statistics are collected in accordance with the STATS20 guidance from the Department for Transport (DfT) and are comparable with the statistics in Great Britain (GB). Damage only collisions or those collisions resulting in no injuries are excluded from these statistics.