This PDF contains additional information on the dataset groups and datasets in ModE-Sim and the variables therein.

No description was included in this Dataset collected from the OSF

Occitania Transport Mode (OCC-TM) is a mobility dataset collected using a smartphone application developed as part of Vilagil research project. This application passively collects GPS positions and accelerometer signals from the smartphone. This study focuses on data collected by a 25-year-old male user. This user then added a label corresponding to the mode of transportation (walk, still, car, bus, bike, train or metro) of each observation point. The smartphone used for collection is a Samsung Galaxy A32 with the Android 11 operating system.

The dataset data was collected by a single user in a discontinuous manner from July 26, 2022 to August 10, 2022. This user moved around the Occitania region in the south of France (between Toulouse and Montpellier), noting for each trip the start time, end time, and mode of transportation used.

The .zip file contains two folders:

• raw_data: raw accelerometer, location and label data in .csv format

• processed_data: feature dataset in .csv format

Please cite the paper below in your publications if it helps your research:

TODO

Proportion of school aged children in full time education travelling to school by the mode of travel that they usually use. Mode of transport is defined as six modes: cars, including vans and taxis, car share, public transport, walking, cycling, and other.

Commute mode is tracked by the American Community Survey (ACS) by asking respondents to provide the means of transportation usually used to travel the longest distance to work the prior week. A follow-up question asks about vehicle occupancy when "car, truck, van" is selected. This dataset tracks the sum of all individuals not selecting "car, truck, van" with one person in it. Transportation professionals often group travel modes into "single-occupancy vehicles" (SOV) and "non-single-occupancy vehicles" (non-SOV) because SOVs are a less efficient use of roadway and environmental resources. It also shows the share of modes that are classified as non-SOV.

In 2015, passenger vehicles was the mode of transportation that consumed the largest volume of biofuels in the world, at around 58 million barrels of oil equivalent. These projections were made under the sustainable development scenario* indicated that this tendency will remain similar, however other modes of transportation are expected increase or entirely begin consuming biofuels, such as shipping and aviation.

Mode is the Modular DeFi L2 building the Superchain alongside Optimism

This dataset contains travel times, distances and fare information for six modes of transport between taxi zone pairs in New York City as well as evaluated citywide impacts in travel times and mileage from the choice simulation model. The travel times, fares and distances are retrieved from Google Maps and HERE Maps APIs as well as ride sharing mobile apps for a weekday morning rush hour (8am-11am). Uncertainty is incorporated in the data by calculating the metrics for 5 random origin-destination pairs for each taxi zone pair and measuring the average and standard deviation.

The project built a city-wide mode-choice simulation model in NYC capable of assessing the mode-shift related to ride-sharing services and other intervention scenarios such as Manhattan Congestion surcharge.

The 'data_description.docx' file contains relevant information about the files.

The mode structure fully describes a light field and contains the information about the source of light without a direct access to the source. Here we offer the tool to extract this information from the measured photon number resolved (PNR) distribution. We present a software package aimed at simulating photon-number probability distributions of a range of naturally occurring classical and non-classical states of light. This software can generate arbitrary probability distributions based on the known mode structure of a light field. It also can solve the reverse problem, i.e. reconstructing the mode structure of a light field based on a given probability distribution.

In 2022, almost ** percent of workers in the U.S. commuted by personal vehicle on their own. During the same year, only *** percent of Americans workers traveled to work using public transportation. Meanwhile, around ** percent of U.S. Americans worked from home.

Detailed Token trading volume metrics and analytics for Mode Network, including historical data and trends.

These files allow for replication for the mode study results presented in our article. The main datafile is allmodes.dta, but govpartydata.dta is also used by several of the do-files to judge correct answers for one knowledge question. And cpstoappend.dta is necessary for replicating the analysis in Figure 1 and the Appendix. There are four do-files total: tables2_thru_6_replication.do runs the analysis that appears in the Tables 2-6 cpscomparison_replication.do runs the analysis that appears in the Figure 1 and the Appendix. fig2_replication.do runs the analysis that appears in Figure 2. figs_3_4_replication.do runs the analyses that appear in Figures 3 and 4.

This paper examines the effects of survey mode on patterns of survey response, paying special attention to the conditions under which mode effects are more or less consequential. We use the Youth Participatory Politics survey, a study administered either online or over the phone to 2,920 young people. Our results provide consistent evidence of mode effects. The internet sample exhibits higher rates of item non-response and “no opinion” responses, and considerably lower levels of differentiation in the use of rating scales. These differences remain even after accounting for how respondents selected into the mode of survey administration. We demonstrate the substantive implications of mode effects in the context of items measuring political knowledge and racial attitudes. We conclude by discussing the implications of our results for comparing data obtained from surveys conducted with different modes, and for the design and analysis of multi-mode surveys.

Detailed Active addresses (daily) metrics and analytics for Mode Network, including historical data and trends.

This table shows passenger kilometres for modes of transport including passenger cars, buses, rail, air, and other. Bus and rail passenger kilometres values are trend estimates - subject to later revision when final data becomes available. BITRE modelling uses data from a range of sources to provide a consistent time series of Australian passenger travel (PKM). Vehicles not classified to passenger cars, buses, rail or air are included in ‘other transport mode’ (Table T 3.1). The other transport mode represents primarily non–freight use of light commercial vehicles (with contributions from motorcycles, non–business use of trucks and ferries).

Harvesting Mode

## Overview

Harvesting Mode is a dataset for object detection tasks - it contains Tomatoes annotations for 1,575 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The feedback archive contains additional information on the data assimilation process in the ModE-RA reanalysis product. It contains the metadata and information about preparation and assimilation of all observations considered in each assimilation step. It allows to track the impact of each observation on the reanalysis. A detailed overview on the meaning of the variables provided in the feedback archive can be found in the additional info file at the experiment level.

An app depicting the mode share break down of the number individuals who travel to work and by which mode of transportation from census tracts & consolidated traffic zones.Application is comprised of 6 applications & 5 Maps; 1. HRM Mode Share - Base AppApp - https://hrm.maps.arcgis.com/apps/webappviewer/index.html?id=7fc75ddc204d48bb9e47fdaa17a8e8b6Map - https://hrm.maps.arcgis.com/home/webmap/viewer.html?webmap=5af6050417f24ba1a161d2a7769a55c12. HRM Mode Share - WalkingApp - https://hrm.maps.arcgis.com/apps/webappviewer/index.html?id=9e46cf5f1b5e4eb3ae4e8c66267366eaMap - https://hrm.maps.arcgis.com/home/webmap/viewer.html?webmap=72e8cc6e4f324a4a83456624474e41f33. HRM Mode Share - Auto PassengersApp - https://hrm.maps.arcgis.com/apps/webappviewer/index.html?id=786572992718405faf9647df67af415aMap - https://hrm.maps.arcgis.com/home/webmap/viewer.html?webmap=8f0d2acb33ae4b8cabb149a5325a3c284. HRM Mode Share - Auto DriversApp - https://hrm.maps.arcgis.com/apps/webappviewer/index.html?id=56ff4e4f08644004b8e7978051320a90Map - https://hrm.maps.arcgis.com/home/webmap/viewer.html?webmap=4dbe33aa38f34938928b33c1bc0317615. HRM Mode Share - Public TransitApp - https://hrm.maps.arcgis.com/apps/webappviewer/index.html?id=18071c8ea0d24749a726126d8accf7d1Map - https://hrm.maps.arcgis.com/home/webmap/viewer.html?webmap=e84ab594feda493e85b8d2a256f9aaae6. HRM Mode Share - BicyclingApp - https://hrm.maps.arcgis.com/apps/webappviewer/index.html?id=2568a7ac95b744fa8c43aff13c0ac427Map - https://hrm.maps.arcgis.com/home/webmap/viewer.html?webmap=ba7277ba059a41939a1c2d09973741d2

The modes of transportation chart illustrates residents' most popular choices when it comes to getting around the area.