This graph shows the number of Memorial Day travelers in the United States from 2019 to 2023, with a forecast for 2024. There were over 38 million Memorial Day travelers forecast to be traveling by automobile in 2024, showing growth over the previous year.

When asked during a 2024 survey what the maximum distance they would be willing to drive on a road trip was, ** percent of respondents in the United States said between six and 10 hours. Comparatively, ** percent of respondents said 11 to 15 hours.

The leading state for summer road trips in the United States in 2024 was Florida, with ** percent of respondents planning to take a road trip to or through the state. California and Nevada followed in the ranking, with ** and ** percent of respondents, respectively, saying they would travel to or through these states.

Experience the attraction 'Cars ROAD TRIP' anew! Perfectly plan with statistics from November 2021 on waiting times or queue times and weather data.

According to a 2024 study, ** percent of respondents surveyed in the United States planned to take road trips lasting ***** to **** hours, while ** percent opted for trips between **** and ***** hours. Meanwhile, only ** percent of respondents expected to take trips less than ***** hours long.

Travel time data is collected in real-time from commercial vehicles and has been provided in this dataset for four separate weeks in 2016 and for two months in 2017.

2016 TTDS Data:

* Monday 25 July 2016 - Sunday 31 July 2016

* Monday 8 August to Sunday 14th August 2016

* Monday 21 November 2016 – Sunday 27 November 2016

* Monday 26 December 2016 – Sunday 1 January 2017 (school holidays and New Year’s Eve)

2017 TTDS Data:

* September 2017

* October 2017

Please refer to the Roads Realtime data which provides the same underlying data as the Road Travel Time data presented here.

The fields for this data set include the Position Time, the GPS location, the Bearing in degrees, the speed in KPH and the Speed Limit for that section.

The Federal Highway Administration estimates vehicle miles traveled on all roads and streets in each month.

A survey of travelers in the United States revealed that ** percent were likely to go on a summer road trip in 2025. This showed significant growth over the previous year's figure of ** percent.

In order to collect data on the state of traffic, the City of Montreal is deploying a network of sensors using Bluetooth technology on certain strategic road segments and making it possible to calculate the travel time on these segments. This data set provides information on the road segments for which travel times are generated; travel times are available in the data set Travel times on road segments (historical)This third party metadata element was translated using an automated translation tool (Amazon Translate).

Helsinki Region Travel Time Matrix contains travel time and distance information for routes between all 250 m x 250 m grid cell centroids (n = 13231) in the Helsinki Region, Finland by walking, cycling, public transportation and car. The grid cells are compatible with the statistical grid cells used by Statistics Finland and the YKR (yhdyskuntarakenteen seurantajärjestelmä) data set. The Helsinki Region Travel Time Matrix is available for three different years:

2018

2015

2013

The data consists of travel time and distance information of the routes that have been calculated between all statistical grid cell centroids (n = 13231) by walking, cycling, public transportation and car.

The data have been calculated for two different times of the day: 1) midday and 2) rush hour.

The data may be used freely (under Creative Commons 4.0 licence). We do not take any responsibility for any mistakes, errors or other deficiencies in the data.

Organization of data

The data have been divided into 13231 text files according to destinations of the routes. The data files have been organized into sub-folders that contain multiple (approx. 4-150) Travel Time Matrix result files. Individual folders consist of all the Travel Time Matrices that have same first four digits in their filename (e.g. 5785xxx).

In order to visualize the data on a map, the result tables can be joined with the MetropAccess YKR-grid shapefile (attached here). The data can be joined by using the field ‘from_id’ in the text files and the field ‘YKR_ID’ in MetropAccess-YKR-grid shapefile as a common key.

Data structure

The data have been divided into 13231 text files according to destinations of the routes. One file includes the routes from all statistical grid cells to a particular destination grid cell. All files have been named according to the destination grid cell code and each file includes 13231 rows.

NODATA values have been stored as value -1.

Each file consists of 17 attribute fields: 1) from_id, 2) to_id, 3) walk_t, 4) walk_d, 5) bike_f_t, 6) bike_s_t, 7) bike_d, 8) pt_r_tt, 9) pt_r_t, 10) pt_r_d, 11) pt_m_tt, 12) pt_m_t, 13) pt_m_d, 14) car_r_t, 15) car_r_d, 16) car_m_t, 17) car_m_d, 18) car_sl_t

The fields are separated by semicolon in the text files.

Attributes

from_id: ID number of the origin grid cell

to_id: ID number of the destination grid cell

walk_t: Travel time in minutes from origin to destination by walking

walk_d: Distance in meters of the walking route

bike_f_t: Total travel time in minutes from origin to destination by fast cycling; Includes extra time (1 min) that it takes to take/return bike

bike_s_t: Total travel time in minutes from origin to destination by slow cycling; Includes extra time (1 min) that it takes to take/return bike

bike_d:Distance in meters of the cycling route

pt_r_tt: Travel time in minutes from origin to destination by public transportation in rush hour traffic; whole travel chain has been taken into account including the waiting time at home

pt_r_t: Travel time in minutes from origin to destination by public transportation in rush hour traffic; whole travel chain has been taken into account excluding the waiting time at home

pt_r_d: Distance in meters of the public transportation route in rush hour traffic

pt_m_tt: Travel time in minutes from origin to destination by public transportation in midday traffic; whole travel chain has been taken into account including the waiting time at home

pt_m_t: Travel time in minutes from origin to destination by public transportation in midday traffic; whole travel chain has been taken into account excluding the waiting time at home

pt_m_d: Distance in meters of the public transportation route in midday traffic

car_r_t: Travel time in minutes from origin to destination by private car in rush hour traffic; the whole travel chain has been taken into account

car_r_d: Distance in meters of the private car route in rush hour traffic

car_m_t: Travel time in minutes from origin to destination by private car in midday traffic; the whole travel chain has been taken into account

car_m_d: Distance in meters of the private car route in midday traffic

car_sl_t: Travel time from origin to destination by private car following speed limits without any additional impedances; the whole travel chain has been taken into account

METHODS

For detailed documentation and how to reproduce the data, see HelsinkiRegionTravelTimeMatrix2018 GitHub repository.

THE ROUTE BY CAR have been calculated with a dedicated open source tool called DORA (DOor-to-door Routing Analyst) developed for this project. DORA uses PostgreSQL database with PostGIS extension and is based on the pgRouting toolkit. MetropAccess-Digiroad (modified from the original Digiroad data provided by Finnish Transport Agency) has been used as a street network in which the travel times of the road segments are made more realistic by adding crossroad impedances for different road classes.

The calculations have been repeated for two times of the day using 1) the “midday impedance” (i.e. travel times outside rush hour) and 2) the “rush hour impendance” as impedance in the calculations. Moreover, there is 3) the “speed limit impedance” calculated in the matrix (i.e. using speed limit without any additional impedances).

The whole travel chain (“door-to-door approach”) is taken into account in the calculations: 1) walking time from the real origin to the nearest network location (based on Euclidean distance), 2) average walking time from the origin to the parking lot, 3) travel time from parking lot to destination, 4) average time for searching a parking lot, 5) walking time from parking lot to nearest network location of the destination and 6) walking time from network location to the real destination (based on Euclidean distance).

THE ROUTES BY PUBLIC TRANSPORTATION have been calculated by using the MetropAccess-Reititin tool which also takes into account the whole travel chains from the origin to the destination: 1) possible waiting at home before leaving, 2) walking from home to the transit stop, 3) waiting at the transit stop, 4) travel time to next transit stop, 5) transport mode change, 6) travel time to next transit stop and 7) walking to the destination.

Travel times by public transportation have been optimized using 10 different departure times within the calculation hour using so called Golomb ruler. The fastest route from these calculations are selected for the final travel time matrix.

THE ROUTES BY CYCLING are also calculated using the DORA tool. The network dataset underneath is MetropAccess-CyclingNetwork, which is a modified version from the original Digiroad data provided by Finnish Transport Agency. In the dataset the travel times for the road segments have been modified to be more realistic based on Strava sports application data from the Helsinki region from 2016 and the bike sharing system data from Helsinki from 2017.

For each road segment a separate speed value was calculated for slow and fast cycling. The value for fast cycling is based on a percentual difference between segment specific Strava speed value and the average speed value for the whole Strava data. This same percentual difference has been applied to calculate the slower speed value for each road segment. The speed value is then the average speed value of bike sharing system users multiplied by the percentual difference value.

The reference value for faster cycling has been 19km/h, which is based on the average speed of Strava sports application users in the Helsinki region. The reference value for slower cycling has been 12km/, which has been the average travel speed of bike sharing system users in Helsinki. Additional 1 minute have been added to the travel time to consider the time for taking (30s) and returning (30s) bike on the origin/destination.

More information of the Strava dataset that was used can be found from the Cycling routes and fluency report, which was published by us and the city of Helsinki.

THE ROUTES BY WALKING were also calculated using the MetropAccess-Reititin by disabling all motorized transport modesin the calculation. Thus, all routes are based on the Open Street Map geometry.

The walking speed has been adjusted to 70 meters per minute, which is the default speed in the HSL Journey Planner (also in the calculations by public transportation).

All calculations were done using the computing resources of CSC-IT Center for Science (https://www.csc.fi/home).

Background: Transport of critically ill patients for diagnostic and/or therapeutic management involves significant consumption of resources. In an effort to improve the delivery of care to these patients and decrease resource utilization, Hill-Rom (Batesville, IN, USA) have developed a self-contained device (CarePorterTM) designed to provide both intensive care unit (ICU) support and transport capability. We hypothesized that the use of the CarePorter when compared with a standard or specialty bed (with transfer to a stretcher) would decrease the number of personnel and time required for transport without altering the current ICU standards of care. Results: Over a 3 month period, 35 ventilated patient transports were divided into the following groups: specialty bed to stretcher (n = 13), standard bed (n = 9) and CarePorter (n = 13). The APACHE II score at the time of transport was not different between the groups, nor was the ongoing care being delivered. The CarePorter group had a statistically greater fractional inspiration of oxygen and positive end expiratory pressure, when compared with the other two groups (P < 0.05). The use of the CarePorter device decreased the number of personnel required to transport a patient (2.1 ± 0.3 vs 3.6 ± 0.5 for the standard bed and and 3.2 ± 0.7 for the specialty bed; P = 0.0001). The CarePorter also decreased the number of resources utilized for the preparation of a patient for transport (P = 0.001) when compared to the other groups. This was primarily due to the transfer of patients from specialty beds to a stretcher. Overall respiratory therapy time was also much less with the CarePorter (5.9 ± 5.7 min), when compared with the standard (26 ± 10 min) or specialty bed (22 ± 11 min) (P = 0.0008). In addition, the CarePorter group also had a higher nursing satisfaction score with the overall transport (P = 0.008). Conclusions: Use of the CarePorter device resulted in maximization of the delivery of patient care, time savings, significantly improved utilization of escort personnel

The dataset is outcome of a paper "Floating Car Data Map-matching Utilizing the Dijkstra Algorithm" accepted for 3rd International Conference on Data Management, Analytics & Innovation held in Kuala Lumpur, Malaysia in 2019. The floating car data (FCD representing movement of cars with their position in time) is produced by the traffic simulator software (further referred to as Simulator) published in [1] and can be used as an input for data processing and benchmarking. The dataset contains FCD of various quality levels based on the routing graph of the Czech Republic derived from Open Street Map openstreetmap.org. Should the dataset be exploited in scientific or other way, any acknowledgement or references to our paper [1] and dataset are welcomed and highly appreciated. Archive contents The archive contains following folders. city_oneway and city_roadtrip - FCD from the city of Brno, Czech Republic where FCD is based on Origin-Destination in case of oneway and Origin-Destination-Origin in case of a road trip intercity_oneway and intercity_roadtrip - FCD from cities of Brno, Ostrava, Olomouc and Zlin, all Czech Republic where FCD is based on Origin-Destination in case of oneway and Origin-Destination-Origin in case of a road trip Content explanation All four of mentioned folders contain raw FCD as they come from our Simulator, post-processed FCD enriching Simulator FCD, and obfuscated raw FCD (of both low and high obfuscation level). In the both obfuscated data sets, each measured point was moved in a random direction a number of meters given by drawing a number from a Gaussian distribution. We utilized two Gaussian distributions, one for the roads outside the city (N(0,10) for the lower and N(0,20) for the higher obfuscation level) and one for the roads inside the city (N(0,15) and N(0,30) respectively). Then some predefined number of randomly chosen points were removed (3% in our case). This approach should roughly represent real conditions encountered by FCD data as described by El Abbous and Samanta [2]. In case of post-processed road trip data, there is one extra dataset with "cache" suffix representing the very same dataset limited to a 5-minute session memoization. This folder also contains a picture of processed FCD represented on a map. Data format Standard UTF-8 encoded CSV files, separated by a semicolon with the following columns: RAW Header session_id;timestamp;lat;lon;speed;bearing;segment_id Data session_id: (Type: unsigned INT) - session (car) identifier timestamp: (Type: datetime) - timestamp in UTC lat: (Type: unsigned long) - latitude as used in Google maps lon: (Type: unsigned long) - longitude as used in Google maps speed: (Type: unsigned INT) - actual speed in kmh bearing: (Type: unsigned INT) - actual bearing in angles 0-360 segment_id: (Type: unsigned long) - unique edge identifier POST-PROCESSED Header gid;car_id;point_time;lat;lon;segment_id;speed_kmh;speed_avg_kmh;distance_delta_m;distance_total_m;speedup_ratio;duration;segment_changed;duration_segment;moved;duration_move;good;duration_good;bearing;interpolated Data gid: (Type: unsigned long) - global identifier of a record car_id: (Type: unsigned INT) - session (car) identifier point_time: (Type: datetime) - timestamp with timezone lat: (Type: unsigned long) - latitude as used in Google maps lon: (Type: unsigned long) - longitude as used in Google maps segment_id: (Type: unsigned long) - unique edge identifier speed: (Type: unsigned INT) - actual speed in kmh speed_avg_kmh: (Type: unsigned long) - actual average speed of a car in kmh distance_delta_m: (Type: unsigned long) - actual distance delta in metres distance_total_m: (Type: unsigned long) - actual total distance of a car in metres speedup_ratio: (Type: unsigned long) - actual speed-up ratio of a car duration: (Type: time) - actual duration of a car segment_changed: (Type: boolean) - signals if actual segment of a car differs from the previous one duration_segment: (Type: time) - actual duration on a segment of a car moved: (Type: boolean) - signals if actual position of a car differs from the previous one duration_move:(Type: time) - actual duration of a car since moving good: signals if actual record values satisfies all data constraints (all true as derived from Simulator) duration_good: actual duration of a car since when all constraints conditions satisfied bearing: (Type: unsigned INT) - actual bearing in angles 0-360 interpolated: (Type: boolean) - signals if actual segment identifier is calculated (all false as derived from Simulator) References [1] V. Ptošek, J. Ševčík, J. Martinovič, K. Slaninová, L. Rapant, and R. Cmar, Real-time traffic simulator for self-adaptive navigation system validation, Proceedings of EMSS-HMS: Modeling & Simulation in Logistics, Traffic & Transportation, 2018. [2] A. El Abbous and N. Samanta. A modeling of GPS error distri-butions, In proceedings of 2017 European Navigation Conference (ENC), 2017.

The Highway Statistics Series consists of annual reports containing analyzed statistical information on motor fuel, motor vehicle registrations, driver licenses, highway user taxation, highway mileage, travel, and highway finance. These information are presented in tables as well as selected charts. It has been published annually since 1945.

The overall objective of the i-DREAMS project is to setup a framework for the definition, development, testing and validation of a context-aware safety envelope for driving (‘Safety Tolerance Zone’), within a smart Driver, Vehicle & Environment Assessment and Monitoring System (i-DREAMS). Taking into account driver background factors and real-time risk indicators associated with the driving performance as well as the driver state and driving task complexity indicators, a continuous real-time assessment is made to monitor and determine if a driver is within acceptable boundaries of safe operation. Moreover, safety-oriented interventions were developed to inform or warn the driver real-time in an effective way as well as on an aggregated level after driving through an app- and web-based gamified coaching platform. The conceptual framework, which was tested in a simulator study and three stages of on-road trials in Belgium, Germany, Greece, Portugal and the United Kingdom on a total of 600 participants representing car, bus, and truck drivers, respectively. Specifically, the Safety Tolerance Zone (STZ) is subdivided into three phases, i.e. ‘Normal driving phase’, the ‘Danger phase’, and the ‘Avoidable accident phase’. For the real-time determination of this STZ, the monitoring module in the i-DREAMS platform continuously register and process data for all the variables related to the context and to the vehicle. Regarding the operator, however, continuous data registration and processing are limited to mental state and behavior. Finally, it is worth mentioning that data related to operator competence, personality, socio-demographic background, and health status, are collected via survey questionnaires. More information of the project can be seen from project website: https://idreamsproject.eu/wp/

This dataset contains naturalistic driving data of various trips of participants recruited in i-Dreams project. Various different types of events are recorded for different intensity levels such as headway, speed, acceleration, braking, cornering, fatigue and illegal overtaking. Running headway, speed, distance, wipers use, handheld phone use, high beam use and other data is also recorded. Driver characteristics are also available but not part of this sample data. In the i-Dreams project, raw data for a particular trip was collected via CardioID gateway, Mobileye, wristband or CardioWheel. These trip data are fused using a feature-based data fusion technique, namely geolocation through synchronization and support vector machines. The system provided by CardioID integrates several data streams, generated by the different sensors that make up the inputs of the i-Dreams system. The sample dataset is fused, processed as well as aggregated to produce consistent time series data of trips for a particular time interval such as 30 secs/ 60 secs or 2- minutes intervals. More datasets can be acquired for analysis purposes by following the data acquisition process given in the data description file.