DESCRIPTION This table contains data on the percent of residents aged 16 years and older mode of transportation to work for ...

SUMMARY This table contains data on the percent of residents aged 16 years and older mode of transportation to work for California, its regions, counties, cities/towns, and census tracts. Data is from the U.S. Census Bureau, Decennial Census and American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Commute trips to work represent 19% of travel miles in the United States. The predominant mode – the automobile - offers extraordinary personal mobility and independence, but it is also associated with health hazards, such as air pollution, motor vehicle crashes, pedestrian injuries and fatalities, and sedentary lifestyles. Automobile commuting has been linked to stress-related health problems. Active modes of transport – bicycling and walking alone and in combination with public transit – offer opportunities for physical activity, which is associated with lowering rates of heart disease and stroke, diabetes, colon and breast cancer, dementia and depression. Risk of injury and death in collisions are higher in urban areas with more concentrated vehicle and pedestrian activity. Bus and rail passengers have a lower risk of injury in collisions than motorcyclists, pedestrians, and bicyclists. Minority communities bear a disproportionate share of pedestrian-car fatalities; Native American male pedestrians experience four times the death rate Whites or Asian pedestrians, and African-Americans and Latinos experience twice the rate as Whites or Asians. More information about the data table and a data dictionary can be found in the About/Attachments section.

ind_id - Indicator ID ind_definition - Definition of indicator in plain language reportyear - Year that the indicator was reported race_eth_code - numeric code for a race/ethnicity group race_eth_name - Name of race/ethnic group geotype - Type of geographic unit geotypevalue - Value of geographic unit geoname - Name of a geographic unit county_name - Name of county that geotype is in county_fips - FIPS code of the county that geotype is in region_name - MPO-based region name; see MPO_County list tab region_code - MPO-based region code; see MPO_County list tab mode - Mode of transportation short name mode_name - Mode of transportation long name pop_total - denominator pop_mode - numerator percent - Percent of Residents Mode of Transportation to Work,
Population Aged 16 Years and Older LL_95CI_percent - The lower limit of 95% confidence interval UL_95CI_percent - The lower limit of 95% confidence interval percent_se - Standard error of the percent mode of transportation percent_rse - Relative standard error (se/value) expressed as a percent CA_decile - California decile CA_RR - Rate ratio to California rate version - Date/time stamp of a version of data

Updates are delayed due to technical difficulties. How many people are staying at home? How far are people traveling when they don’t stay home? Which states and counties have more people taking trips? The Bureau of Transportation Statistics (BTS) now provides answers to those questions through our new mobility statistics. The Trips by Distance data and number of people staying home and not staying home are estimated for the Bureau of Transportation Statistics by the Maryland Transportation Institute and Center for Advanced Transportation Technology Laboratory at the University of Maryland. The travel statistics are produced from an anonymized national panel of mobile device data from multiple sources. All data sources used in the creation of the metrics contain no personal information. Data analysis is conducted at the aggregate national, state, and county levels. A weighting procedure expands the sample of millions of mobile devices, so the results are representative of the entire population in a nation, state, or county. To assure confidentiality and support data quality, no data are reported for a county if it has fewer than 50 devices in the sample on any given day. Trips are defined as movements that include a stay of longer than 10 minutes at an anonymized location away from home. Home locations are imputed on a weekly basis. A movement with multiple stays of longer than 10 minutes before returning home is counted as multiple trips. Trips capture travel by all modes of transportation. including driving, rail, transit, and air. The daily travel estimates are from a mobile device data panel from merged multiple data sources that address the geographic and temporal sample variation issues often observed in a single data source. The merged data panel only includes mobile devices whose anonymized location data meet a set of data quality standards, which further ensures the overall data quality and consistency. The data quality standards consider both temporal frequency and spatial accuracy of anonymized location point observations, temporal coverage and representativeness at the device level, spatial representativeness at the sample and county level, etc. A multi-level weighting method that employs both device and trip-level weights expands the sample to the underlying population at the county and state levels, before travel statistics are computed. These data are experimental and may not meet all of our quality standards. Experimental data products are created using new data sources or methodologies that benefit data users in the absence of other relevant products. We are seeking feedback from data users and stakeholders on the quality and usefulness of these new products. Experimental data products that meet our quality standards and demonstrate sufficient user demand may enter regular production if resources permit.

This table contains information regarding the mobility of the residents of the Netherlands aged 6 or older in private households, so excluding residents of institutions and homes. The table contains the travel behaviour broken down by the proportion of traffic participation and the proportion of participation in public transport divided by personal characteristics, population and gender. These are regular trips on Dutch territory, including domestic holiday mobility and series of calls trips on Dutch territory.

A methodological break has been identified in the 2024 ODiN file. During the analysis of the data from the "Onderweg in Nederland 2024" (ODiN) survey a methodological break was identified. Several changes were made to the survey in 2024, which likely had an unexpected effect on the 2024 ODiN figures. This means that the 2024 results are not readily comparable with those from previous years. For this reason, the 2024 figures are not being updated in the StatLine tables. More information about the methodological break is available in the ODiN 2024 Plausibility Report (see Chapter 4: "Onderwegen in Nederland" (ODiN) 2024 - Plausibility Report). Due to a revision of the ODiN files, the figures by motive for 2018 have been changed as of February 10, 2022, but the total number of motives in 2018 has remained the same. In 2019, the revision sometimes resulted in minor changes in travel time.

Data available from: 2018

Status of the figures: The figures in this table are final.

Changes as of 4 July 2024: The figures for year 2023 are added. Starting with the publication of 2023 data, the figures on people’s background will no longer be available. The data in the table has been replaced by dots. The data on background from previous years are still available.

When will new figures be published? More information will follow in 2026.

Description of the Problem This challenge asks you to build a model that predicts the number of seats that Mobiticket can expect to sell for each ride, i.e. for a specific route on a specific date and time. There are 14 routes in this dataset. All of the routes end in Nairobi and originate in towns to the North-West of Nairobi towards Lake Victoria.

The towns from which these routes originate are:

Awendo Homa Bay Kehancha Kendu Bay Keroka Keumbu Kijauri Kisii Mbita Migori Ndhiwa Nyachenge Oyugis Rodi Rongo Sirare Sori The routes from these 14 origins to the first stop in the outskirts of Nairobi takes approximately 8 to 9 hours from time of departure. From the first stop in the outskirts of Nairobi into the main bus terminal, where most passengers get off, in Central Business District, takes another 2 to 3 hours depending on traffic.

The three stops that all these routes make in Nairobi (in order) are:

Kawangware: the first stop in the outskirts of Nairobi Westlands Afya Centre: the main bus terminal where most passengers disembark All of these points are mapped here.

Passengers of these bus (or shuttle) rides are affected by Nairobi traffic not only during their ride into the city, but from there they must continue their journey to their final destination in Nairobi wherever that may be. Traffic can act as a deterrent for those who have the option to avoid buses that arrive in Nairobi during peak traffic hours. On the other hand, traffic may be an indication for people’s movement patterns, reflecting business hours, cultural events, political events, and holidays.

This is all for you to explore in the data.

Description of the data train_revised.csv (zipped) is the dataset of tickets purchased from Mobiticket for the 14 routes from “up country” into Nairobi between 17 October 2017 and 20 April 2018. This dataset includes the variables: ride_id, seat_number, payment_method, payment_receipt, travel_date, travel_time, travel_from, travel_to, car_type, max_capacity.

test_questions.csv is the dataset on which you will apply your model to estimate number of tickets sold by Mobiticket per unique ride. This dataset contains all of the rides offered on the same 14 routes during the two weeks following train.csv, i.e. 21 April 2018 to 9 May 2018. The variables included in this dataset: ride_id, travel_date, travel_time, travel_from, travel_to, car_type, max_capacity.

sample_submission.csv is a table to provide an example of what your submission file should look like. This table has two columns: ride_id, number_of_ticket.

Uber Movement traffic data can be accessed at movement.uber.com. Data is available for Nairobi through June 2018. (If the data for April-June are not up yet, they will be shortly.) Uber Movement provided historic hourly travel time between any two points in Nairobi. Any tables that are extracted from the Uber Movement platform can be used in your model.

Variables description:

ride_id: unique ID of a vehicle on a specific route on a specific day and time. seat_number: seat assigned to ticket payment_method: method used by customer to purchase ticket from Mobiticket (cash or Mpesa) payment_receipt: unique id number for ticket purchased from Mobiticket travel_date: date of ride departure. (MM/DD/YYYY) travel_time: scheduled departure time of ride. Rides generally depart on time. (hh:mm) travel_from: town from which ride originated travel_to: destination of ride. All rides are to Nairobi. car_type: vehicle type (shuttle or bus) max_capacity: number of seats on the vehicle

This dataset contains 1,000 real-world transport records representing the operational footprint of APSRTC (Andhra Pradesh State Road Transport Corporation) buses. It's designed to support data-driven insights into public transport performance, route planning, and system optimization. Whether you're a data scientist, transport planner, or machine learning enthusiast, this dataset offers a goldmine for experimentation and analysis. 🎯 Use Cases Predictive modeling: Ridership forecasts, revenue prediction, fuel efficiency Exploratory Data Analysis (EDA): Peak travel trends, route utilization Clustering & Segmentation: Bus type or route-based grouping Time Series Analysis: Trend shifts over months/days Decision Support Systems for Transport Management ⚙️ Tools That Work Well Python (Pandas, Seaborn, Matplotlib) Power BI / Tableau for dashboards Scikit-learn or XGBoost for ML modeling Jupyter Notebooks for analysis 🧠 Who Should Use This? Students & researchers working on transportation analytics ML/AI enthusiasts building real-world prediction models Public policy analysts looking into urban mobility Anyone obsessed with bringing order to traffic chaos 🙃 🛡️ License 📢 For educational and non-commercial research use only. This dataset was generated/processed by a student team under APSSDC (Andhra Pradesh State Skill Development Corporation) guidelines.

These surveys were conducted to collect data on travel origins and destinations, trip purposes, and travel characteristics of New York City Transit, Metro-North Railroad, and Long Island Rail Road customers with the aim of upgrading the MTA's travel forecasting tools and gaining a better understanding of how people travel. --LIRR origin-destination survey (2012-14) --Metro-North origin-destination survey (2007) --Metro-North origin-destination survey (2017) --MTA New York City travel survey (2008) --MTA New York City travel survey (2018)

Frequent, reliable transit service is the foundation of a transportation system that empowers all travelers and makes Seattle a truly transit-friendly city. A robust transit network is essential if Seattle is to meet its climate goals and address transportation-related inequities. At its most fundamental level, a transit network is made up of transit infrastructure such as bus lanes, transit signals, and bus stops, often arranged in corridors. The transit service that travels on this infrastructure can be described as a series of routes that connect different parts of a community for a number of hours per day at a certain frequency (the number of trips at a bus stop per hour). SDOT’s vision for the service aspect of the transit network is followed by a vision for transit infrastructure in the sections below. Public input and surveys consistently point to transit frequency as the most critical factor that influences ridership behavior. This fundamental concept directly informs SDOT’s shared vision for a “Frequent Transit Network” (FTN), which builds from the 2016 Transit Master Plan (TMP) and establishes aspirational frequency targets for transit corridors throughout the city. A high-frequency transit network enables people to move through the city with confidence in a timely arrival—and without the need to consult a schedule—throughout the day and every single day of the week. Continual investment in improved transit frequency in Seattle is essential for many reasons: Post-pandemic transit is likely to remain less commuter-focused and oriented specifically to Downtown Seattle and must adapt to new travel behaviors and patterns. To support everyday trips by transit (not just commutes), people need reliable mobility at all times, such as early mornings, midday, evenings and at night all days of the week, not just at peak times on weekdays. Transit needs to accommodate work schedules of non-traditional and low-income workers including the times noted above. Transit should be attractive for all types of trips throughout the week, including education, shopping, and recreational trips, as well as cultural gatherings. An excellent transit network is necessary to accommodate the mode shift required to respond to the impacts of climate change in the next decade. Frequent transit reduces wait time, increases reliability, and values the time for existing and future riders. Frequent transit makes transfers more feasible and allows a network of routes to function as a system. A connected network of frequent transit services is also critical to achieve STP climate goals, which require dramatic increases in transit ridership and VMT reduction to support broader efforts to reduce greenhouse gas (GHG) emissions from transportation. High transit frequencies as part of a reliable, all-day service network can create a more equitable transportation system, making it possible for people of all ages, incomes, and abilities to get where they want to go regardless of when or where they need to travel. The Transit Element presents a vision for frequent transit service in Seattle that goes beyond the original Frequent Transit Network (FTN) presented in the 2016 Transit Master Plan. Refresh Cycle: None, Static. Manually as required.Original Publish: 5/23/2024Update Publish: 7/11/2024 per Policy and Planning teamContact: Policy and Planning team.

A large body of research has demonstrated that land use and urban form can have a significant effect on transportation outcomes. People who live and/or work in compact neighborhoods with a walkable street grid and easy access to public transit, jobs, stores, and services are more likely to have several transportation options to meet their everyday needs. As a result, they can choose to drive less, which reduces their emissions of greenhouse gases and other pollutants compared to people who live and work in places that are not location efficient. Walking, biking, and taking public transit can also save people money and improve their health by encouraging physical activity. The Smart Location Database summarizes several demographic, employment, and built environment variables for every census block group (CBG) in the United States. The database includes indicators of the commonly cited “D” variables shown in the transportation research literature to be related to travel behavior. The Ds include residential and employment density, land use diversity, design of the built environment, access to destinations, and distance to transit. SLD variables can be used as inputs to travel demand models, baseline data for scenario planning studies, and combined into composite indicators characterizing the relative location efficiency of CBG within U.S. metropolitan regions. This update features the most recent geographic boundaries (2019 Census Block Groups) and new and expanded sources of data used to calculate variables. Entirely new variables have been added and the methods used to calculate some of the SLD variables have changed. More information on the National Walkability index: https://www.epa.gov/smartgrowth/smart-location-mapping More information on the Smart Location Calculator: https://www.slc.gsa.gov/slc/

This line feature class represents the Miami-Dade County Bus Routes within the City of Miami. The data has been obtained from Miami-Dade County Open Data Hub and clipped to contain only the routes that fall within the city limits. For a countywide layer, please refer to the Miami-Dade County Open Data Hub at https://gis-mdc.opendata.arcgis.com/.The Transportation dataset enhances transportation equity and access by mapping transit routes, hubs, and service reliability across Miami. It supports inclusive mobility for children, older adults, and people with disabilities, while offering low-cost or free travel options and on-demand services prioritizing senior centers, government buildings, and medical facilities. Data Refresh Frequency: This dataset is refreshed on a weekly basis, regardless of whether any updates have occurred in the source data. Users should note that the data is reprocessed and reloaded each week to ensure availability and consistency, even in the absence of changes.

Ridership is the number of people who board public transportation vehicles. Passengers are counted each time they board vehicles no matter how many vehicles they use to travel between their origin and destination. Ridership data is sourced from the Virginia Department of Rail and Public Transportation (DRPT) data portal.

Intermodal Passenger Connectivity DatabaseThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the Bureau of Transportation Statistics (BTS), displays the Intermodal Passenger Connectivity Database (IPCD). According to BTS, IPCD is a "nationwide database of passenger transportation terminals, with data on the availability of connections among the various scheduled public transportation modes at each facility." The types of passenger transportation terminals include:Scheduled airline service airportsIntercity bus stationsIntercity and transit ferry terminalsLight-rail transit stationsHeavy-rail transit stationsPassenger-rail stationsBike-share stationsThe data describes the availability and locations of the above types of passenger transportation terminals. Note, transit bus service locations are not specifically included.Collins Avenue 5300 Block (Miami Beach, FL)Data currency: This cached Esri federal service is checked weekly for updates from its enterprise federal source (Intermodal Passenger Connectivity Database IPCD) and will support mapping, analysis, data exports and OGC API – Feature access.Data.gov: Intermodal Passenger Connectivity Database (IPCD) (National) - National Geospatial Data Asset (NGDA) Intermodal (Passenger)Geoplatform: Intermodal Passenger Connectivity Database (IPCD) (National) - National Geospatial Data Asset (NGDA) Intermodal (Passenger)OGC API Features Link: (Intermodal Passenger Connectivity Database - OGC Features) copy this link to embed it in OGC Compliant viewersFor more information: Intermodal Passenger Connectivity Database IPCDFor feedback please contact: Esri_US_Federal_Data@esri.comThumbnail image courtesy of: Metropolitan Transportation Authority of the State of New YorkNGDA Data SetThis data set is part of the NGDA Transportation Theme Community. Per the Federal Geospatial Data Committee (FGDC), Transportation is defined as the "means and aids for conveying persons and/or goods. The transportation system includes both physical and non-physical components related to all modes of travel that allow the movement of goods and people between locations".For other NGDA Content: Esri Federal Datasets

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.

Dataset shows an individual’s statistical area 3 (SA3) of usual residence and the SA3 of their workplace address, for the employed census usually resident population count aged 15 years and over, by main means of travel to work from the 2018 and 2023 Censuses.

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The main means of travel to work categories are:

• Work at home
• Drive a private car, truck, or van
• Drive a company car, truck, or van
• Passenger in a car, truck, van, or company bus
• Public bus
• Train
• Bicycle
• Walk or jog
• Ferry
• Other.

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Main means of travel to work is the usual method which an employed person aged 15 years and over used to travel the longest distance to their place of work.

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Workplace address refers to where someone usually works in their main job, that is the job in which they worked the most hours. For people who work at home, this is the same address as their usual residence address. For people who do not work at home, this could be the address of the business they work for or another address, such as a building site.

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Workplace address is coded to the most detailed geography possible from the available information. This dataset only includes travel to work information for individuals whose workplace address is available at SA3 level. The sum of the counts for each region in this dataset may not equal the total employed census usually resident population count aged 15 years and over for that region. Workplace address – 2023 Census: Information by concept has more information.

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This dataset can be used in conjunction with the following spatial files by joining on the SA3 code values:

• Statistical area 3 2023 (generalised)

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Download data table using the instructions in the Koordinates help guide.

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Footnotes

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Geographical boundaries

Statistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023. Address data from 2013 and 2018 Censuses was updated to be consistent with the 2023 areas. Due to the changes in area boundaries and coding methodologies, 2013 and 2018 counts published in 2023 may be slightly different to those published in 2013 or 2018.

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Subnational census usually resident population

The census usually resident population count of an area (subnational count) is a count of all people who usually live in that area and were present in New Zealand on census night. It excludes visitors from overseas, visitors from elsewhere in New Zealand, and residents temporarily overseas on census night. For example, a person who usually lives in Christchurch city and is visiting Wellington city on census night will be included in the census usually resident population count of Christchurch city. 

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Population counts

Stats NZ publishes a number of different population counts, each using a different definition and methodology. Population statistics – user guide has more information about different counts. 

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Caution using time series

Time series data should be interpreted with care due to changes in census methodology and differences in response rates between censuses. The 2023 and 2018 Censuses used a combined census methodology (using census responses and administrative data).

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Workplace address time series

Workplace address time series data should be interpreted with care at lower geographic levels, such as statistical area 2 (SA2). Methodological improvements in 2023 Census resulted in greater data accuracy, including a greater proportion of people being counted at lower geographic areas compared to the 2018 Census. Workplace address – 2023 Census: Information by concept has more information.

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Working at home

In the census, working at home captures both remote work, and people whose business is at their home address (e.g. farmers or small business owners operating from their home). The census asks respondents whether they ‘mostly’ work at home or away from home. It does not capture whether someone does both, or how frequently they do one or the other.

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Rows excluded from the dataset

Rows show SA3 of usual residence by SA3 of workplace address. Rows with a total population count of less than six have been removed to reduce the size of the dataset, given only a small proportion of SA3-SA3 combinations have commuter flows.

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About the 2023 Census dataset

For information on the 2023 dataset see Using a combined census model for the 2023 Census. We combined data from the census forms with administrative data to create the 2023 Census dataset, which meets Stats NZ's quality criteria for population structure information. We added real data about real people to the dataset where we were confident the people who hadn’t completed a census form (which is known as admin enumeration) will be counted. We also used data from the 2018 and 2013 Censuses, administrative data sources, and statistical imputation methods to fill in some missing characteristics of people and dwellings.

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Data quality

The quality of data in the 2023 Census is assessed using the quality rating scale and the quality assurance framework to determine whether data is fit for purpose and suitable for release. Data quality assurance in the 2023 Census has more information.

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Quality rating of a variable

The quality rating of a variable provides an overall evaluation of data quality for that variable, usually at the highest levels of classification. The quality ratings shown are for the 2023 Census unless stated. There is variability in the quality of data at smaller geographies. Data quality may also vary between censuses, for subpopulations, or when cross tabulated with other variables or at lower levels of the classification. Data quality ratings for 2023 Census variables has more information on quality ratings by variable.

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Main means of travel to work quality rating

Main means of travel to work is rated as moderate quality.

Main means of travel to work – 2023 Census: Information by concept has more information, for example, definitions and data quality.

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Workplace address quality rating

Workplace address is rated as moderate quality.

Workplace address – 2023 Census: Information by concept has more information, for example, definitions and data quality.

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Using data for good

Stats NZ expects that, when working with census data, it is done so with a positive purpose, as outlined in the Māori Data Governance Model (Data Iwi Leaders Group, 2023). This model states that "data should support transformative outcomes and should uplift and strengthen our relationships with each other and with our environments. The avoidance of harm is the minimum expectation for data use. Māori data should also contribute to iwi and hapū tino rangatiratanga”.

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Confidentiality

The 2023 Census confidentiality rules have been applied to 2013, 2018, and 2023 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2023 Census data. Counts are calculated using fixed random rounding to base 3 (FRR3) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.

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Percentages

To calculate percentages, divide the figure for the category of interest by the figure for ‘Total stated’ where this applies.

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Symbol

-999 Confidential

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Inconsistencies in definitions

Please note that there may be differences in definitions between census classifications and those used for other data collections.

Frequent, reliable transit service is the foundation of a transportation system that empowers all travelers and makes Seattle a truly transit-friendly city. A robust transit network is essential if Seattle is to meet its climate goals and address transportation-related inequities. At its most fundamental level, a transit network is made up of transit infrastructure such as bus lanes, transit signals, and bus stops, often arranged in corridors.

Introduction

This travel time matrix records travel times and travel distances for routes between all centroids (N = 13231) of a 250 × 250 m grid over the populated areas in the Helsinki metropolitan area by walking, cycling, public transportation, and private car. If applicable, the routes have been calculated for different times of the day (rush hour, midday, off-peak), and assuming different physical abilities (such as walking and cycling speeds), see details below.

The grid follows the geometric properties and enumeration of the versatile Yhdyskuntarakenteen seurantajärjestelmä (YKR) grid used in applications across many domains in Finland, and covers the municipalities of Helsinki, Espoo, Kauniainen, and Vantaa in the Finnish capital region.

Data formats

The data is available in multiple different formats that cater to different requirements, such as different software environments. All data formats share a common set of columns (see below), and can be used interchangeably.

• `Helsinki_Travel_Time_Matrix_2023.csv.zst`: comma-separated values (CSV) of all data columns, without geometries. This data set contains all routes in one file, and can be filtered by origin or destination according to the analysis at hand. The data records can also be joined to the geometries as available below. The file is compressed using the Zstandard algorithm, that many data science libraries, for instance, pandas, support transparently, directly, and automatically.
• `Helsinki_Travel_Time_Matrix_2023_travel_times.gpkg.zip`: an OGC GeoPackage standard file containing all data columns and the geometries that relate to the destination grid cell. The data set is delivered as a ZIP archive, which many GIS systems and libraries, e.g., GDAL/OGR, QGIS, or geopandas, support natively.
• `Helsinki_Travel_Matrix_2023_travel_times.csv.zip`: a set of 13231 comma-separated value files containing the routes to one destination grid cell each. The files contain all data columns, no geometry, and can be joined to the geometries as available below. Filenames of the individual files within the ZIP archive follow the pattern `Helsinki_Travel_Time_Matrix_2023_travel_times_to_5787545.csv` where `5787545` is replaced by the `to_id` by which the rows in the file are grouped. Use the `from_id` column to join with the geometries from one of the files below.

Geometry, only:

• `Helsinki_Travel_Time_Matrix_2023_grid.gpkg.zip`: an OGC GeoPackage standard file containing the geometries and IDs of the grid used in the analysis. This file can be joined both to the `from_id` and `to_id` columns of the data files. The data set is delivered as a ZIP archive, which many GIS systems and libraries, e.g., GDAL/OGR, QGIS, or geopandas, support natively.
• `Helsinki_Travel_Time_Matrix_2023_grid.shp.zip`: an ESRI Shapefile archive containing the geometries and IDs of the grid used in the analysis. This file can be joined both to the `from_id` and `to_id` columns of the data files.

Table structure

from_id

to_id

walk_avg

walk_slo

bike_avg

bike_fst

bike_slo

pt_r_avg

pt_r_slo

pt_m_avg

pt_m_slo

pt_n_avg

pt_n_slo

car_r

car_m

car_n

walk_d

Data for 2013, 2015, and 2018

At the Digital Geography Lab, we started computing travel time matrices in 2013. Our methodology has changed in between the iterations, and naturally, there are systematic differences between the iterations’ results. Not all input data sets are available to recompute the historical matrices with new methods, however, we were able to repeat the 2018 calculation using the same methods as the 2023 data set, please find the results below, in the same format.

For the travel time matrices for 2013 and 2015, as well as for 2018 using an older methodology, please refer to DOI:10.5281/zenodo.3247563.

Methodology

Computations were carried out for Wednesday, 15 February, 2023, and Monday, 29 January, 2018, respectively. ‘Rush hour’ refers to an 1-hour window between 8 and 9 am, ‘midday’ to 12 noon to 1 pm, and ‘nighttime’ to 2-3 am.

All routes have been calculated using r5py, a Python library making use of the R⁵ engine by Conveyal, with modifications to consider local characteristics of the Helsinki use case and to inform the computation models from local real-world data sets. In particular, we made the following modifications:

Walking

Walking speeds, and in turn walking times, are based on the findings of Willberg et al., 2023, in which we measured walking speeds of people of different age groups in varying road surface conditions in Helsinki. Specifically, we chose to use the average measured walking speed in summer conditions for `walk_avg` (as well as the respective `pt_*_walk_avg`), and the slowest quintile of all measured walker across all conditions for `walk_slo` (and the respective `pt_*_walk_slo`).

Cycling

Cycling speeds are derived from two input data sets. First, we averaged cycling speeds per network segment from Strava data, and computed a ratio between the speed ridden in each segment and the overall average speed. We then use these ratios to compute fast, slow, and average cycling speeds for each segment, based on the mean overall Strava speed, the mean speeds cycled in the Helsinki City Bike bike-share system, and the mean between the two.

Further, in line with the values observed by Jäppinen (2012), we add a flat 30 seconds each for unlocking and locking the bicycle at the origin and destination.

Public Transport

We used public transport schedules in General Transit Feed Specification (GTFS) format published by

Frequent, reliable transit service is the foundation of a transportation system that empowers all travelers and makes Seattle a truly transit-friendly city. A robust transit network is essential if Seattle is to meet its climate goals and address transportation-related inequities. At its most fundamental level, a transit network is made up of transit infrastructure such as bus lanes, transit signals, and bus stops, often arranged in corridors. The transit service that travels on this infrastructure can be described as a series of routes that connect different parts of a community for a number of hours per day at a certain frequency (the number of trips at a bus stop per hour). SDOT’s vision for the service aspect of the transit network is followed by a vision for transit infrastructure in the sections below. Public input and surveys consistently point to transit frequency as the most critical factor that influences ridership behavior. This fundamental concept directly informs SDOT’s shared vision for a “Frequent Transit Network” (FTN), which builds from the 2016 Transit Master Plan (TMP) and establishes aspirational frequency targets for transit corridors throughout the city. A high-frequency transit network enables people to move through the city with confidence in a timely arrival—and without the need to consult a schedule—throughout the day and every single day of the week. Continual investment in improved transit frequency in Seattle is essential for many reasons: Post-pandemic transit is likely to remain less commuter-focused and oriented specifically to Downtown Seattle and must adapt to new travel behaviors and patterns. To support everyday trips by transit (not just commutes), people need reliable mobility at all times, such as early mornings, midday, evenings and at night all days of the week, not just at peak times on weekdays. Transit needs to accommodate work schedules of non-traditional and low-income workers including the times noted above. Transit should be attractive for all types of trips throughout the week, including education, shopping, and recreational trips, as well as cultural gatherings. An excellent transit network is necessary to accommodate the mode shift required to respond to the impacts of climate change in the next decade. Frequent transit reduces wait time, increases reliability, and values the time for existing and future riders. Frequent transit makes transfers more feasible and allows a network of routes to function as a system. A connected network of frequent transit services is also critical to achieve STP climate goals, which require dramatic increases in transit ridership and VMT reduction to support broader efforts to reduce greenhouse gas (GHG) emissions from transportation. High transit frequencies as part of a reliable, all-day service network can create a more equitable transportation system, making it possible for people of all ages, incomes, and abilities to get where they want to go regardless of when or where they need to travel. The Transit Element presents a vision for frequent transit service in Seattle that goes beyond the original Frequent Transit Network (FTN) presented in the 2016 Transit Master Plan. Refresh Cycle: None, Static. Manually as required.Original Publish: 5/23/2024Update Publish: 7/11/2024 per Policy and Planning teamContact: Policy and Planning team.

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