These datasets include the analysis year inputs and outputs from the Air Quality Conformity Analysis approved in June 2024. The horizon year is 2050 and reflects the policies and projects adopted in the ON TO 2050 Regional Comprehensive Plan.The air quality analysis is completed twice annually, in the second quarter and the fourth quarter. The data associated with the analysis is named based on the year the analysis was completed and the quarter it was completed. Therefore, the files in these datasets are referred to as c24q2 data.The analysis years for this conformity cycle include 2019, 2025, 2030, 2035, 2040, and 2050. We associate scenario numbers with the analysis years as shown below. You will notice the scenario numbers 100–700 referenced in many of the filenames or in headers within the files.Analysis year scenario numbers:2019 | 1002025 | 2002030 | 3002035 | 4002040 | 5002050 | 700Links to download data files:Travel Demand Model Data (c24q2) - 2019 BaseTravel Demand Model Data (c24q2) - 2025 ForecastTravel Demand Model Data (c24q2) - 2030 ForecastTravel Demand Model Data (c24q2) - 2035 ForecastTravel Demand Model Data (c24q2) - 2040 ForecastTravel Demand Model Data (c24q2) - 2050 Forecast

Great Smoky Mountains National Park Travel Time: This reference is for 3 travel time model rasters, with time from Park Headquarters, Twin Creeks Science Center, and Oconaluftee Visitor Center modeled separately. These rasters are Travel Time Cost Surface Models (TTCSM) created by NPS I&M staff, and models estimated travel time from starting locations to any point wihtin GRSM. Each model is for a different starting location: Park Headquarters (HQ), Twin Creeks Science Center (TWCR), and Oconaluftee Visitor Center (OVC). The models are created using the NPS TTCSM toolbox in ArcGIS, and raster values are in minutes traveled (rounded down to nearest minute). The resolution is 10M. The following assumptions are made by the modeling tool: 1. Travel starts at the given starting point, and then follows roads, trails, and off trail travel to reach any point. 2. No travel can occur on terrain with a slope of over 40 degrees. Areas on slopes over 40 degrees, or which cannot be reached without traversing said slopes, will return a NoData Value. 3. Travel values time out at 640 minutes (10 Hours, 40 minutes). After this, a NoData value is given. Generally, this only occurs in areas of signfiicantly steep slopes and dense understory vegetation. 4. Travel speed on roads is set as the legal speed limit. This model does allow travel on Administrative Roads, at a speed of 10 MPH. 5. Travel speed on trail is a function of slope, and of a user-defined speed reduction. For this model, GRSM public trails have no travel reduction, and un-maintained trails have a 50% travel reduction. 6. Off-trail travel speed is a function of slope, and a user-defined reduction of speed due to land cover. For the purpose of this model, areas with HIGH level of understory veg and/or significant wind damage are reduced 90%. Those with a medium level of understory vegetation reduced 66%, and those with light-to-no understory vegetation are reduced 50%. 7. The model allows for boat travel across Fonana Lake at a speed of 15 MPH, and access to administrative roads across the lake. Park streams do not effect the model, except that several user-defined high volume streams are set to a 90% time reduction (this eliminates the model from following large streams, where topography is often flatter). 8. Maximum walking speed is 3 mph, which can only occur on flat ground with no travel speed reductions. The model assumes no fatigue. Speed is reduced as slope increases and as various ground conditions are encountered. 9. Travel through non-park property is generally not allowed (except in a few instances of right-of-ways). Travel on non-park roads is allowed though, where they are included in model.

These datasets include the analysis year inputs and outputs from the Air Quality Conformity Analysis approved in January 2025. The horizon year is 2050 and reflects the policies and projects adopted in the ON TO 2050 Regional Comprehensive Plan.The air quality analysis is completed twice annually, in the second quarter and the fourth quarter. The data associated with the analysis is named based on the year the analysis was completed and the quarter it was completed. Therefore, the files in these datasets are referred to as c24q4 data.The analysis years for this conformity cycle include 2019, 2025, 2030, 2035, 2040, and 2050. We associate scenario numbers with the analysis years as shown below. You will notice the scenario numbers 100–700 referenced in many of the filenames or in headers within the files.Analysis year scenario numbers:2019 | 1002025 | 2002030 | 3002035 | 4002040 | 5002050 | 700Links to download data files:Travel Demand Model Data (c24q4) - 2019 BaseTravel Demand Model Data (c24q4) - 2025 ForecastTravel Demand Model Data (c24q4) - 2030 ForecastTravel Demand Model Data (c24q4) - 2035 ForecastTravel Demand Model Data (c24q4) - 2040 ForecastTravel Demand Model Data (c24q4) - 2050 ForecastFor additional information, see the travel demand model documentation.

Travel regions are not necessarily defined by political or administrative boundaries. For example, in the Schengen region of Europe, tourists can travel freely across borders irrespective of national borders. Identifying transboundary travel regions is an interesting problem which we aim to solve using mobility analysis of Twitter users. Our proposed solution comprises collecting geotagged tweets, combining them into trajectories and, thus, mining thousands of trips undertaken by twitter users. After aggregating these trips into a mobility graph, we apply a community detection algorithm to find coherent regions throughout the world. The discovered regions provide insights into international travel and can reveal both domestic and transnational travel regions.

The datasets in this zip file are in support of FHWA-JPO-16-379, Analysis, Modeling, and Simulation (AMS) Testbed Development and Evaluation to Support Dynamic Mobility Applications (DMA) and Active Transportation and Demand Management (ATDM) Programs - calibration Report for Phoenix Testbed : Final Report. The compressed zip file totals 1.1 GB in size. The zip file have been uploaded as-is; no further documentation was supplied by NTL, excepted as noted: All located .docx files were converted to .pdf document files which are an archival format. These .pdfs were then added to the zip file alongside the original .docx files. The initial zip file presented to NTL contained uncompressed datasets and duplicative zip files of the files. In order to make the overall size of the this zip file more manageable, duplicative files were deleted. The zip file can be unzipped using any zip compression/decompression software. This zip file contains files in the following formats: .pdf document files which can be read using any pdf reader; .cvs text files which can be read using any text editor; .docx document files which can be read in Microsoft Word and some other word processing programs; .txt text files which can be opened with any text editor; .xlsx spreadsheet files which can be read in Microsoft Excel and some other spreadsheet programs; .cfg computer configuration files; .db database files, which can be opened with many database programs; .rif raster image files, these files may have been created by the Corel Painter image editing application, a proprietary software program, although other image programs may open the files [software requirements]. These files were last accessed in 2017.

This zip file contains files of data to support FHWA-JPO-16-370, Analysis, Modeling, and Simulation (AMS) Testbed Development and Evaluation to Support Dynamic Mobility Applications (DMA) and Active Transportation and Demand Management (ATDM) Programs - San Mateo Testbed Analysis Plan : Final Report. Zip size is 1.5 GB. The files have been uploaded as-is; no further documentation was supplied by NTL. All located .docx files were copied to .pdf document files which are an archival format. These .pdfs were then added to the zip file alongside the original .docx files. The attached zip files can be unzipped using any zip compression/decompression software. These zip file contains files in the following formats: .pdf document files which can be read using any pdf reader; .docx document files which may be opened with Microsoft Word or some other open source document editors; .xlsx spreadsheet files which may be opened with Microsoft Excel or some other open source spreadsheet editors; .syn files are a proprietary file format for signal timing plans which are provided in the Synchro Model given as “El Camino Real Synchro.syn” and can be opened using Trafficware Synchro, which may require users to purchase a license or software (for more information go to http://www.trafficware.com/); .csv data files, an open format, which may be opened with any text editor or in many spreadsheet applications; .db generic database files, often associated with thumbnail images in the Windows operating environment; .rbc files, which are scripts written in Rembo-C, which can be opened in a text editor, but require a server with Rembo installed to run the scripts; .vap audio files which will require special audio editing software to manipulate; .dll dynamically linked files for Windows program operations; .layx, a file type on which we could not locate reliable information; and .inpx files, a file type on which we could not locate reliable information [software requirements]. These files were last accessed in 2017. Files were accessed in 2017. Data will be preserved as is.

The datasets in this zip file are in support of Intelligent Transportation Systems Joint Program Office (ITS JPO) report FHWA-JPO-16-385, "Analysis, Modeling, and Simulation (AMS) Testbed Development and Evaluation to Support Dynamic Mobility Applications (DMA) and Active Transportation and Demand Management (ATDM) Programs — Evaluation Report for ATDM Program," and FHWA-JPO-16-389, "Analysis, Modeling, and Simulation (AMS) Testbed Development and Evaluation to Support Dynamic Mobility Applications (DMA) and Active Transportation and Demand Management (ATDM) Programs : Evaluation Report for the San Diego Testbed : Draft Report". The files in this zip file are specifically related to the San Diego Testbed. The compressed zip files total 3.17 GB in size. The files have been uploaded as-is; no further documentation was supplied by NTL. Direct download of data zip file: https://doi.org/10.21949/1500873 All located .docx files were converted to .pdf document files which are an open, archival format. These pdfs were then added to the zip file alongside the original .docx files. These files can be unzipped using any zip compression/decompression software. This zip file contains files in the following formats: .pdf document files which can be read using any pdf reader; .cvs text files which can be read using any text editor; .txt text files which can be read using any text editor; .docx document files which can be read in Microsoft Word and some other word processing programs; . xlsx spreadsheet files which can be read in Microsoft Excel and some other spreadsheet programs; .dat data files which may be text or multimedia; as well as GIS or mapping files in the following formats: .mxd, .dbf, .prj, .sbn, .shp., .shp.xml; which may be opened in ArcGIS or other GIS software. [software requirements] These files were last accessed in 2017.

For regional transportation planning purposes only.Representative analysis areas used as inputs for network analysis task of modeling travel demand of vehicles and trucks within the San Bernardino County subregion of the larger Southern California region. Their attributes are as of September 2012. Analysis areas and

attributes developed and adopted by Southern California Regional Governments (SCAG) and refined by third-party consultant for modeling within San Bernardino County by SBCTA using the San Bernardino County Transportation Analysis Model (SBTAM). Data was received and acknowledged by SBCTA Board of Directors September 2012 as source for local jurisdictions/agencies for regional transportation planning purposes.Visit the following web address to see data in use:gosbcta.com/planning-sustainability/?category=transportation-management-analysis : SBTAM information site

About

The Synthetic Sweden Mobility (SySMo) model provides a simplified yet statistically realistic microscopic representation of the real population of Sweden. The agents in this synthetic population contain socioeconomic attributes, household characteristics, and corresponding activity plans for an average weekday. This agent-based modelling approach derives the transportation demand from the agents’ planned activities using various transport modes (e.g., car, public transport, bike, and walking).

This open data repository contains four datasets:

(1) Synthetic Agents,

(2) Activity Plans of the Agents,

(3) Travel Trajectories of the Agents, and

(4) Road Network (EPSG: 3006)

(OpenStreetMap data were retrieved on August 28, 2023, from https://download.geofabrik.de/europe.html, and GTFS data were retrieved on September 6, 2023 from https://samtrafiken.se/)

The database can serve as input to assess the potential impacts of new transportation technologies, infrastructure changes, and policy interventions on the mobility patterns of the Swedish population.

Methodology

This dataset contains statistically simulated 10.2 million agents representing the population of Sweden, their socio-economic characteristics and the activity plan for an average weekday. For preparing data for the MATSim simulation, we randomly divided all the agents into 10 batches. Each batch's agents are then simulated in MATSim using the multi-modal network combining road networks and public transit data in Sweden using the package pt2matsim (https://github.com/matsim-org/pt2matsim).

The agents' daily activity plans along with the road network serve as the primary inputs in the MATSim environment which ensures iterative replanning while aiming for a convergence on optimal activity plans for all the agents. Subsequently, the individual mobility trajectories of the agents from the MATSim simulation are retrieved.

The activity plans of the individual agents extracted from the MATSim simulation output data are then further processed. All agents with negative utility score and negative activity time corresponding to at least one activity are filtered out as the ‘infeasible’ agents. The dataset ‘Synthetic Agents’ contains all synthetic agents regardless of their ‘feasibility’ (0=excluded & 1=included in plans and trajectories). In the other datasets, only agents with feasible activity plans are included.

The simulation setup adheres to the MATSim 13.0 benchmark scenario, with slight adjustments. The strategy for replanning integrates BestScore (60%), TimeAllocationMutator (30%), and ReRoute (10%)— the percentages denote the proportion of agents utilizing these strategies. In each iteration of the simulation, the agents adopt these strategies to adjust their activity plans. The "BestScore" strategy retains the plan with the highest score from the previous iteration, selecting the most successful strategy an agent has employed up until that point. The "TimeAllocationMutator" modifies the end times of activities by introducing random shifts within a specified range, allowing for the exploration of different schedules. The "ReRoute" strategy enables agents to alter their current routes, potentially optimizing travel based on updated information or preferences. These strategies are detailed further in W. Axhausen et al. (2016) work, which provides comprehensive insights into their implementation and impact within the context of transport simulation modeling.

Data Description

(1) Synthetic Agents

This dataset contains all agents in Sweden and their socioeconomic characteristics.

The attribute ‘feasibility’ has two categories: feasible agents (73%), and infeasible agents (27%). Infeasible agents are agents with negative utility score and negative activity time corresponding to at least one activity.

File name: 1_syn_pop_all.parquet

Column

Description

Data type

Unit

PId

Agent ID

Integer

-

Deso Zone code of Demographic statistical areas (DeSO)1

String

kommun

Municipality code

Integer

marital

Marital Status (single/ couple/ child)

String

sex

Gender (0 = Male, 1 = Female)

Integer

age

Age

Integer

HId

A unique identifier for households

Integer

HHtype

Type of households (single/ couple/ other)

String

HHsize

Number of people living in the households

Integer

num_babies

Number of children less than six years old in the household

Integer

employment Employment Status (0 = Not Employed, 1 = Employed)

Integer

studenthood Studenthood Status (0 = Not Student, 1 = Student)

Integer

income_class Income Class (0 = No Income, 1 = Low Income, 2 = Lower-middle Income, 3 = Upper-middle Income, 4 = High Income)

Integer

num_cars Number of cars owned by an individual

Integer

HHcars Number of cars in the household

Integer

feasibility

Status of the individual (1=feasible, 0=infeasible)

Integer

1 https://www.scb.se/vara-tjanster/oppna-data/oppna-geodata/deso--demografiska-statistikomraden/

(2) Activity Plans of the Agents

The dataset contains the car agents’ (agents that use cars on the simulated day) activity plans for a simulated average weekday.

File name: 2_plans_i.parquet, i = 0, 1, 2, ..., 8, 9. (10 files in total)

Column

Description

Data type

Unit

act_purpose

Activity purpose (work/ home/ school/ other)

String

-

PId

Agent ID

Integer

-

act_end

End time of activity (0:00:00 – 23:59:59)

String

hour:minute:seco

nd

act_id

Activity index of each agent

Integer

-

mode

Transport mode to reach the activity location

String

-

POINT_X

Coordinate X of activity location (SWEREF99TM)

Float

metre

POINT_Y

Coordinate Y of activity location (SWEREF99TM)

Float

metre

dep_time

Departure time (0:00:00 – 23:59:59)

String

hour:minute:seco

nd

score

Utility score of the simulation day as obtained from MATSim

Float

-

trav_time

Travel time to reach the activity location

String

hour:minute:seco

nd

trav_time_min

Travel time in decimal minute

Float

minute

act_time

Activity duration in decimal minute

Float

minute

distance

Travel distance between the origin and the destination

Float

km

speed

Travel speed to reach the activity location

Float

km/h

(3) Travel Trajectories of the Agents

This dataset contains the driving trajectories of all the agents on the road network, and the public transit vehicles used by these agents, including buses, ferries, trams etc. The files are produced by MATSim simulations and organised into 10 *.parquet’ files (representing different batches of simulation) corresponding to each plan file.

File name: 3_events_i.parquet, i = 0, 1, 2, ..., 8, 9. (10 files in total)

Column

Description

Data type

Unit

time

Time in second in a simulation day (0-86399)

Integer

second

type

Event type defined by MATSim simulation*

String

person

Agent ID

Integer

link

Nearest road link consistent with the road network

String

vehicle

Vehicle ID identical to person

Integer

from_node

Start node of the link

Integer

to_node

End node of the link

Integer

• One typical episode of MATSim simulation events: Activity ends (actend) -> Agent’s vehicle enters traffic (vehicle enters traffic) -> Agent’s vehicle moves from previous road segment to its next connected one (left link) -> Agent’s vehicle leaves traffic for activity (vehicle leaves traffic) -> Activity starts (actstart)

(4) Road Network

This dataset contains the road network.

File name: 4_network.shp

Column

Description

Data type

Unit

length

The length of road link

Float

metre

freespeed

Free speed

Float

km/h

capacity

Number of vehicles

Integer

permlanes

Number of lanes

Integer

oneway

Whether the segment is one-way (0=no, 1=yes)

Integer

modes

Transport mode

String

from_node

Start node of the link

Integer

to_node

End node of the link

Integer

geometry

LINESTRING (SWEREF99TM)

geometry

metre

Additional Notes

This research is funded by the RISE Research Institutes of Sweden, the Swedish Research Council for Sustainable Development (Formas, project number 2018-01768), and Transport Area of Advance, Chalmers.

Contributions

YL designed the simulation, analyzed the simulation data, and, along with CT, executed the simulation. CT, SD, FS, and SY conceptualized the model (SySMo), with CT and SD further developing the model to produce agents and their activity plans. KG wrote the data document. All authors reviewed, edited, and approved the final document.

The 2017/2018 Regional Travel Survey (RTS) collected demographic and travel information from a randomly selected representative sample of households in the National Capital Region Transportation Planning Board (TPB) jurisdictions and adjacent areas, which comprise the TPB model region. It is the primary source of observed data to estimate, calibrate, and validate the regional travel demand model. The model in turn is used for the travel forecasting and air quality conformity analysis of the region’s long-range transportation plan as well as to support other key program activities. The survey data is also used for analyzing regional travel trends and provides a comprehensive picture of travel patterns in the region. The RTS captured information on household, person, and vehicle characteristics in the recruitment survey, and actual observed trip information in a one-day travel diary, which household members recorded details of every trip taken on their assigned travel day.The Regional Transportation Data Clearinghouse (RTDC) Regional Travel Survey (RTS) Tabulations were prepared by TPB staff to provide an online resource for the RTS data to be used by practitioners, researchers, and other stakeholders. The RTDC RTS Tabulations share the standard 2017/2018 Regional Travel Survey tabulations from the RTS which include the household, person, vehicle, and trip files. The purpose of the RTDC RTS Tabulations is to provide descriptive summaries of the data items from these files. These are first level tabulations of the RTS dataset that can be quickly pulled “off-the-shelf” when needed. Note that no cross tabulations are included in the RTDC RTS Tabulations. The user can perform customized tabulations and cross tabulations by requesting the RTS Public File.File DetailsThe RTDC_RTS_Tabulations.zip file contains the RTDC RTS Tabulations Matrix (RTDC RTS Tabulations Matrix.xlsx) that includes the tabulation variable, tabulation description, RTS source file, along with the corresponding file names. Tabulations were prepared for the entire RTS universe, in addition to County/Independent City Jurisdiction, Subregional Area, Activity Centers and Equity Emphasis Areas. For tabulations by Subregional Area, Activity Centers, and Equity Emphasis Areas, “Elsewhere” refers to outside of the TPB Planning Region but within the RTS Universe; almost all of these records are within the TPB Modeled Area. The tabulation files contain two standard data structures: 1) Universe Tabulations; 2) Jurisdiction, Subregional Area, Activity Centers, and Equity Emphasis Area Tabulations.There are two sets of RTDC RTS Tabulations contained in the following folders: 1) ‘All Records’ which includes all records in the RTS universe; and 2) ‘NotAscertRemoved’ which removed ‘not ascertained’ records before the tabulations were generated. Users should exercise discretion in determining which set of tabulations to use when conducting their analysis.Please see the Regional Travel Survey (RTS)- 'About the RTDC RTS Tabulations' Documentation for further details about the contents of this ZIP file. For more information about the RTS, please visit the RTS webpage. Should you have further questions about these tabulations or the RTS in general, please contact Ken Joh.

This dataset describes the distribution (in number of tracers) of downstream travel distances calculated from field-measured PIT-tagged particles, two-dimensional hydrodynamic modeling and probabilistic modeling in the Ain River channel over the periods 2013-2014, 2013-2015 and 2013-2017.

The 2017/2018 Regional Travel Survey (RTS) collected demographic and travel information from a randomly selected representative sample of households in the National Capital Region Transportation Planning Board (TPB) jurisdictions and adjacent areas, which comprise the TPB model region. It is the primary source of observed data to estimate, calibrate, and validate the regional travel demand model. The model in turn is used for the travel forecasting and air quality conformity analysis of the region’s long-range transportation plan as well as to support other key program activities. The survey data is also used for analyzing regional travel trends and provides a comprehensive picture of travel patterns in the region. The RTS captured information on household, person, and vehicle characteristics in the recruitment survey, and actual observed trip information in a one-day travel diary, which household members recorded details of every trip taken on their assigned travel day.From October 2017 through December 2018, the Regional Travel Survey (RTS) collected information on demographic and travel behavior characteristics of persons living in households in the metropolitan Washington region and adjoining jurisdictions. Under the oversight of COG/TPB, the survey was conducted by a nationally recognized transportation survey research firm, Resource Systems Group, Inc. (RSG). Previous COG/TPB regional household surveys for the Washington area were conducted in 1968, 1987/1988, 1994, and 2007/2008. This document describes the technical approach used for the RTS. It provides a brief overview of the survey methodology. Additional information about the survey methodology, including the questionnaire design, survey sampling, survey administration, targeted outreach, and survey response can be found in the final report prepared by RSG (Appendix A). Due to the complexity of multi-modal travel patterns in the National Capital Region, review and editing of the RTS data files was performed internally by staff familiar with travel patterns in the region. This report is primarily focused on the post-survey data processing and survey expansion performed by COG/TPB staff. Appendices also contain file format and file frequency tables for the final public release files.For more information about the RTS, please visit the RTS webpage.To download the RTS Tabulations, please visit the Regional Travel Survey (RTS) Tabulations page.The RTS Public File is also available by request.

Travel Zones (TZs) are the spatial unit of geography defined by Transport Performance and Analytics (TPA), a business unit within Transport for NSW (TfNSW). The TZ spatial layer is applied to data sources used by TfNSW for transport modelling and analysis, including the Household Travel Survey and the Census 2016 Journey to Work data. The Australian Bureau of Statistics (ABS) Statistical Area boundaries form the foundation of the TZ. Generally, a TZ is larger than a Statistical Area Level 1 or Mesh Block, both ABS geography definitions. The ABS Statistical Areas are based on population counts whereas TZ boundaries are defined using population, employment, housing and transport infrastructure. TZs are designed to have standardised trip generation levels across all zones. This causes zones to be different sizes across the metropolitan area. As with many other spatial boundaries, TZs tend to be small in areas with high land-use densities and larger in areas of lower density. This dataset now includes a CSV file mapping the Transit Stop Number (TSN) to the Travel Zone (TZ16). It captures the stop name, suburb and coordinates. Travel Zone Explorer is an interactive map where you can search for Travel Zones (TZ) and find out the current and future population in occupied private dwellings by age and sex.

The Transport Performance and Analytics (TPA) produces travel forecasts using the Strategic Travel Model (STM). This model is a world class tool that projects travel patterns in the Sydney Greater Metropolitan Area under different land use, transport and pricing scenarios. It can be used to test alternative settlement, employment and transport policies, to identify likely future capacity constraints, or to determine potential usage levels of proposed new transport infrastructure or services.

The STM is built largely in the EMME transport modelling software. It is comprised of a series of models and processes that attempt to replicate, in a simplified manner, people’s travel choices and behaviour under a given scenario. The STM combines our understanding of travel behaviour with likely population and employment size and distribution, and likely road and public transport networks and services to estimate future travel under different strategic land use and transport scenarios.

The STM produces travel forecasts by origin (2,690) and destination (2,690) STM zones for:

• The Sydney Greater Metropolitan Area which includes the Sydney Statistical Division, Newcastle Statistical Subdivision and Illawarra Statistical Division.

• 5 yearly intervals from the latest Census year up to a 35-year horizon

• 9 travel modes: Car driver, Car passenger, Rail, Bus, Light rail, Ferry, Bike, Walk and Taxi

• 7 purposes: Work, Business, Primary/Secondary/Tertiary education, Shopping, Other

• 24 hour, average workday (Monday to Friday excluding public holidays)

• am/pm peak, interpeak and evening travel

Envestnet®| Yodlee®'s Tourism Transaction Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

ABSTRACT This paper addresses the Traveling Salesman Problem with Priority Prizes (TSPPP), an extension of the classical TSP in which the order of the node visits is taken into account in the objective function. A prize p ki is received by the traveling salesman when node i is visited in the k-th order of the route, while a travel cost c ij is incurred when the salesman travels from node i to node j . The aim of the TSPPP is to find the maximum profit n-node tour. The problem can be seen as a TSP variant with a more general objective function, aiming at solutions that in some way consider the quality of customer service and the delivery priorities and costs. A natural representation for the TSPPP is here grounded in the point of view of Koopmans and Beckmann approach, according to which the problem is seem as a special case of the quadratic assignment problem (QAP). Given the novelty of this TSP variant, we propose different mixed integer programming models to appropriately represent the TSPPP, some of them based on the QAP. Computational experiments are also presented when solving the MIP models with a well-known optimization software, as well as with a tabu search algorithm.

The travel time map was generated using the Pedestrian Evacuation Analyst model from the USGS. The travel time analysis uses ESRI's Path Distance tool to find the shortest distance across a cost surface from any point in the hazard zone to a safe zone. This cost analysis considers the direction of movement and assigns a higher cost to steeper slopes, based on a table contained within the model. The analysis also adds in the energy costs of crossing different types of land cover, assuming that less energy is expended walking along a road than walking across a sandy beach. To produce the time map, the evacuation surface output from the model is grouped into 1-minute increments for easier visualization. The times in the attribute table represent the estimated time to travel on foot to the nearest safe zone at the speed designated in the map title. The bridge or nobridge name in the map title identifies whether bridges were represented in the modeling or whether they were removed prior to modeling to estimate the impact on travel times from earthquake-damaged bridges.

Tourist Images

## Overview

Tourist Images is a dataset for object detection tasks - it contains Objects annotations for 689 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).

1. “MODELOGIT.csv” file includes the information of choice behavior for urban travelers who have responded from the national household travel survey.
2. The data is composed of 8 modes alternative set incorporating socio-demographic information of each respondent

Travel Zones (TZs) are the spatial unit of geography for Transport for NSW (TfNSW). The TZ spatial layer is applied to data sources used by TfNSW for transport modelling and analysis, including the Travel Zone Projections and key transport models such as the Strategic Travel Model (STM).

The Australian Statistical Geography Standard (ASGS) Edition 3 boundaries provided by the Australian Bureau of Statistics (ABS) form the foundation of the Travel Zone geography. Generally, a TZ is an aggregation of whole ABS Mesh Blocks. The ASGS are based on population counts, whereas TZ boundaries are defined using population, employment, housing and transport infrastructure, with consideration for planned future changes in land use. Some of the State’s greenfield growth areas have deviated from using whole Mesh Blocks. Instead, Department of Planning, Housing and Infrastructure (DPHI) growth area precincts have been used to create more functional TZs in those areas (for example, the Aerotropolis).

TZs are designed to have standardised trip generation levels across all zones. This causes zones to be different sizes across NSW. As with many other spatial boundaries, TZs tend to be small in areas with high land-use densities and larger in areas of lower density.

As areas and transport infrastructure change over time, TfNSW creates new Travel Zone geography in line with each ABS Census of Population and Housing, the latest being 2021.

Below you can download spatial files of the Travel Zone 2021 (TZ21) geography, the TZ21 fact sheet, as well as concordance tables for various geographies to TZ21 and vice versa.