The Southeast Michigan Operational Data Environment (SEMI-ODE) is a real-time data acquisition and distribution software system that processes vehicle and infrastructure data collected from sources such as the Southeast Michigan testbed Situational Data Clearinghouse (SDC) and the Situational Data Warehouse (SDW), along with other non-connected vehicle sources of data. The ODE offers four core functions to supply tailored and custom-requested data from the SEMI Testbed to subscribing client software applications. The core functions are: 1) Valuation (V), 2) Integration (I), 3) Sanitization (S) (also called de-identification), and 4) Aggregation (A). These four VISA functions are critical to the field test as they enable the subscribing emulated applications to receive data tailored to support their operation. These functions also serve to increase the general usability of the data being generated in the SEMI Test Bed. This legacy dataset was created before data.transportation.gov and is only currently available via the attached file(s). Please contact the dataset owner if there is a need for users to work with this data using the data.transportation.gov analysis features (online viewing, API, graphing, etc.) and the USDOT will consider modifying the dataset to fully integrate in data.transportation.gov.

Background: Motorcycle riding as a means of daily transportation in Southeast Asia, especially Jakarta, Indonesia, manifests the struggle to use the road. This struggle - associated with congestion, irregularity, and lack of control - can lead to physical harm or material loss. Studies on deviant behavior related to motorcycle use mainly discuss motorcycle gangs and traffic violations. This paper intends to explicate how the context of this struggle informs the different meanings of the deviant behavior of motorcyclists in daily traffic life. It employs concepts of innovation and ritualism, learning, harm, visuality, and secret deviance to shatter the prevailing understanding of motorcyclist traffic behavior. Methods: This study chooses T.B. Simatupang Street, one of the busiest roads in Jakarta, as a research location. A mixed method is used to examine the context of the struggle, first qualitatively by utilizing visual data collected through (a) direct video recordings on the road and (b) aerial drone video recordings. Both recordings captured images of motorcyclist behavior considered deviant and can cause harm, such as stopping illegally, clamoring while cutting lanes and other vehicles, and slipping between two automobiles. Then quantitatively, it conducts a survey to collect data on motorcyclists' experiences. Included in the survey were questions with images captured from the direct video recording footage to collect responses toward motorcyclist riding behavior. Findings: The survey findings show that responses toward images of motorcyclist behavior—concerning harm—show a lack of understanding of driving safety and traffic rules, and some consider it as just the daily routine of motorcyclists. Conclusion: Therefore, the visuality of motorcyclist traffic violation is learned through innovation (driving recklessly) or ritualism (daily habit) as a struggle that is ironically visibly secretly upheld by fellow motorcyclists. Novelty/Originality of this Study: This study offers a novel perspective by framing motorcyclists' behavior in Jakarta's traffic as a form of adaptive struggle rather than mere deviance or rule breaking. It uniquely utilizes visual criminology to argue that what is often seen as traffic violations may be better understood as contextual adaptations to urban congestion, thereby challenging prevailing interpretations of motorcyclist behavior.

This collection contains map data often used as base layers for hydrologic and geographic analysis, organized by these categories: - Addresses and Boundaries (Texas address points, counties, Councils of Government boundaries, Texas Dept of Public Safety districts and regions) - Hydrology (streams, gages, dams, catchments, watersheds) - Transportation (Texas roads, railways, bridges, low water crossings)

The Addresses, Transportation and Dams datasets are for Texas only, but the remaining Hydrology data covers an area of 39 HUC6 basins around the Harvey zone across southeast Texas, Louisiana, Mississippi and Arkansas.

These data layers generally date to 2015-2016, so could be considered reasonably representative of the base layers at the time of Hurricane Harvey.

The Texas Address and Base Layers Story Map referenced here [1] is an interactive web app supported by Esri ArcGIS Online, that provides visualization and access to specific data layers for Texas only.

One other base layer is the Social Vulnerability Index (SVI) developed by the U.S. Centers for Disease Control (CDC). This is used by the emergency response community to anticipate areas where social support systems are weaker, and residents may be more likely to need help.

References [1] Texas Address and Base Layers Story Map [https://www.hydroshare.org/resource/6d5c7dbe0762413fbe6d7a39e4ba1986/]

This is the 28th study in a series of annual surveys on road safety conducted since 1981. The study is conducted by Statistics Sweden on behalf of the the Swedish National Road Administration and aims to identify and analyse traffic safety statistics. Since 2010 the study is conducted on behalf of the Swedish Transport Administration.

The study is is done by a questionnaire directed to a stratified sample of the population. The study from 2009 had a 60 percent response rate and a sample of 11 096 randomly selected people aged between 15 and 84 years.

The questions in the study include, for example, traffic behaviour and traffic patterns; attitudes to road safety and traffic rules, the relation between alcohol and traffic and child safety in traffic. Both motor traffic, bicycle and foot traffic is concerned. A total of 29 questions are used, including descriptive questions, estimation questions and attitude questions.

Purpose:

The survey aims to identify and analyse traffic safety statistics.

This is the first study in a series of annual surveys on road safety. The study is conducted by Statistics Sweden on behalf of the Traffic Safety Board, which had the overall responsibility for road safety in Sweden in 1981.

The study aims to identify and analyse traffic safety statistics. This is done by a written questionnaire directed to a stratified sample of the population. The study from 1981 had a 79.4 percent response rate and a sample of 3 060 randomly selected people aged between 15 and 74 years.

The questions in the study include, for example, traffic behaviour and traffic patterns; attitudes to road safety and traffic rules, the relation between alcohol and traffic and child safety in traffic. Both motor traffic, bicycle and foot traffic is concerned. A total of 22 questions are used, including descriptive questions, estimation questions and attitude questions.

Purpose:

The survey aims to identify and analyse traffic safety statistics.

This dataset is a highly versatile and precisely annotated large-scale dataset of smartphone sensor data for multimodal locomotion and transportation analytics of mobile users.The dataset comprises 7 months of measurements, collected from all sensors of 4 smartphones carried at typical body locations, including the images of a body-worn camera, while 3 participants used 8 different modes of transportation in the southeast of the United Kingdom, including in London.In total 28 context labels were annotated, including transportation mode, participant’s posture, inside/outside location, road conditions, traffic conditions, presence in tunnels, social interactions, and having meals.The total amount of collected data exceed 950 GB of sensor data, which corresponds to 2812 hours of labelled data and 17562 km of traveled distance. The potential applications arising from this dataset include:Machine-learning systems to automatically recognize modes of transportations from mobile phone dataRoad condition analysis and recognitionTraffic conditions analysis and recognition.Assessment of Google’s activity and transportation recognition API in comparison to custom algorithmsProbabilistic mobility modellingActivity recognition (e.g. automatic detection of eating and drinking)Novel localization techniques using dynamic fusion of sensorsRadio signal propagation analsisImage-based activity and transportation mode recognition The current recommended publication regarding the dataset is [1]. The current recommended publication regarding the application which was used to collect the dataset is [2].[1] H. Gjoreski, M. Ciliberto, L. Wang, F. J. Ordoñez Morales, S.Mekki, S.Valentin, D. Roggen, “The University of Sussex-Huawei Locomotion and Transportation Dataset for Multimodal Analytics with Mobile Devices”, In IEEE Access, 2018[2] M. Ciliberto, F. J. Ordoñez Morales, H. Gjoreski, D. Roggen, S.Mekki, S.Valentin. “High reliability Android application for multidevice multimodal mobile data acquisition and annotation.” In ACM Conference on Embedded Networked Sensor Systems. ACM, 2017.We recommend to refer to the dataset as follows in your publications:Use at least once the complete name: “The University of Sussex-Huawei Locomotion and Transportation Dataset” or “The Sussex-Huawei Locomotion and Transportation Dataset“. You may introduce the acronym of the dataset as well: “The University of Sussex-Huawei Locomotion and Transportation (SHL) Dataset“.Subsequently, you may refer to the dataset with its acronym: “The SHL Dataset“.

Sweden SE: Logistics Performance Index: 1=Low To 5=High: Quality of Trade and Transport-Related Infrastructure data was reported at 4.270 NA in 2016. This records an increase from the previous number of 4.094 NA for 2014. Sweden SE: Logistics Performance Index: 1=Low To 5=High: Quality of Trade and Transport-Related Infrastructure data is updated yearly, averaging 4.110 NA from Dec 2007 (Median) to 2016, with 5 observations. The data reached an all-time high of 4.270 NA in 2016 and a record low of 4.030 NA in 2010. Sweden SE: Logistics Performance Index: 1=Low To 5=High: Quality of Trade and Transport-Related Infrastructure data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Sweden – Table SE.World Bank.WDI: Transportation. Data are from Logistics Performance Index surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. 2009 round of surveys covered more than 5,000 country assessments by nearly 1,000 international freight forwarders. Respondents evaluate eight markets on six core dimensions on a scale from 1 (worst) to 5 (best). The markets are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. Details of the survey methodology are in Arvis and others' Connecting to Compete 2010: Trade Logistics in the Global Economy (2010). Respondents evaluated the quality of trade and transport related infrastructure (e.g. ports, railroads, roads, information technology), on a rating ranging from 1 (very low) to 5 (very high). Scores are averaged across all respondents.; ; World Bank and Turku School of Economics, Logistic Performance Index Surveys. Data are available online at : http://www.worldbank.org/lpi. Summary results are published in Arvis and others' Connecting to Compete: Trade Logistics in the Global Economy, The Logistics Performance Index and Its Indicators report.; Unweighted average;

This is the sixth survey in a series of surveys measuring and following up the general public's experiences and usage of Swedish roads, and looking into how the products and services of the National Road Administration are received by the users. Questions on the respondent's experiences as a driver and usage of vehicles includes holding of driving license, number of miles driven by car during the last 12 months, size of car driven, car equipment, how often the respondent had driven different vehicles during the last 12 months, how dependent the respondent is on a car, if the car usage will increase or decrease within the next five years, and reasons for changes in car usage. One section of the questionnaire is dedicated to roads; how often the respondent uses different kinds of roads, and how they are graded by the respondents, in which fields the National Road Administration has to increase or decrease their efforts, the importance of a number of road administrative issues, and on how it works today. The respondents have to give their opinion on the importance of dealing with a number of public issues, such as environmental pollution, care of elderly and children, the budget deficit, unemployment, health care etc. For a number of risks in traffic the respondents have to state how dangerous they count them to be and they also have to state how much different things, such as traffic accidents, serious illness, burglary, street violence etc., worries them. They also have to give their opinion on different threats against the environment and to tell what they are doing themselves to save the environment. The respondents have to give their opinion on the National Road Administration in general, and how it is dealing with road safety issues and environmental issues. Other questions deal with the knowledge and usage of different channels for information on road and weather conditions. A number of questions deals with the respondent's knowledge of traffic offences leading to withdrawal of the driving licence. Background information includes gender, age, place of living, size of household, number of children under the age of 16, and if the respondent works as professional driver.