** percent of UK respondents answer our survey on "Most common modes of transportation for commuting" with *********************. The survey was conducted in 2025, among 4,517 consumers.

This statistic features the distribution of transport use among tourists in the United Kingdom (UK) in 2014. More than half of tourists used transportation via bus, tube or inner-city train. ** percent used taxis. Trains were used by ** percent to travel between cities. In contrast, * percent used public buses for this purpose.

Transport for London's (TFL) Public Transport Accessibility Levels (PTALs) PTALS are a detailed and accurate measure of the accessibility of a point to the public transport network, taking into account walk access time and service availability. The method is essentially a way of measuring the density of the public transport network at any location within Greater London. Each area is graded between 0 and 6b, where a score of 0 is very poor access to public transport, and 6b is excellent access to public transport. The current methodology was developed in 1992, by the London Borough of Hammersmith and Fulham. The model has been thoroughly reviewed and tested, and has been agreed by the London Borough-led PTAL development group as the most appropriate for use across London. The measure therefore reflects: Walking time from the point-of interest to the public transport access points; The reliability of the service modes available; The number of services available within the catchment; and The level of service at the public transport access points - i.e. average waiting time. It does not consider: The speed or utility of accessible services; Crowding, including the ability to board services; or, Ease of interchange. The PTAL methodology was developed for London where a dense integrated public transport network means that nearly all destinations can be reached within a reasonable amount of time. Research using the ATOS (Access to Opportunities and Services) methodology shows that there is a strong correlation between PTALs and the time taken to reach key services – i.e. high PTAL areas generally have good access to services and low PTAL areas have poor access to services. Notes 6-digit references identify 100m grid squares. The 2015 files are available to download below. This includes the GIS contour files. Current PTAL values can be viewed on TfL’s web site. We also invite you to visit WebCAT: TfL’s Web-based Connectivity Assessment Toolkit. Through an interactive mapping interface users can view for any location in London, PTAL values as well as travel time plots. Associated with the travel time plots users can also select bar charts showing catchment statistics (for example, population, jobs or services) using the same travel time bands as displayed on the map. Watch the video below for more information.

Experimental public transit transport performance statistics by 200 metre grids for a subset of urban centres in Great Britain, with the following fields (Note: These data are experimental, please see the Methods and Known Limitations/Caveats Sections for more details).AttributeDescriptionidUnique IdentifierpopulationGlobal Human Settlement Layer population estimate downsampled to 200 metre (represents the total population across adjacent 100 metre cells)access_popThe total population that can reach the destination cell within 45 minutes using the public transit network (origins within 11.25 kilometres of the destination cell)proxim_popThe total population within an 11.25 kilometre radius of the destination celltrans_perfThe transport performance of the 200 metre cell. The percentage ratio of accessible to proximal populationcity_nmName of the urban centrecountry_nmName of the country that the urban centre belongs toMethods: For more information please visit: · Python Package: https://github.com/datasciencecampus/transport-network-performance · Docker Image: https://github.com/datasciencecampus/transport-performance-docker Known Limitations/Caveats: These data are experimental – see the ONS guidance on experimental statistics for more details. They are being published at this early stage to involve potential users and stakeholders in assessing their quality and suitability. The known caveats and limitations of these experimental statistics are summarised below. Urban Centre and Population Estimates: · Population estimates are derived from data using a hybrid method of satellite imagery and national censuses. The alignment of national census boundaries to gridded estimates introduce measurement errors, particularly in newer housing and built-up developments. See section 2.5 of the GHSL technical report release 2023A for more details. Public Transit Schedule Data (GTFS): · Does not include effects due to delays (such as congestion and diversions). · Common GTFS issues are resolved during preprocessing where possible, including removing trips with unrealistic fast travel between stops, cleaning IDs, cleaning arrival/departure times, route name deduplication, dropping stops with no stop times, removing undefined parent stations, and dropping trips, shapes, and routes with no stops. Certain GTFS cleaning steps were not possible in all instances, and in those cases the impacted steps were skipped. Additional work is required to further support GTFS validation and cleaning. Transport Network Routing: · “Trapped” centroids: the centroid of destination cells on very rare occasions falls on a private road/pathway. Routing to these cells cannot be performed. This greatly decreases the transport performance in comparison with the neighbouring cells. Potential solutions include interpolation based on neighbouring cells or snapping to the nearest public OSM node (and adjusting the travel time accordingly). Further development to adapt the method for this consideration is necessary. Please also visit the Python package and Docker Image GitHub issues pages for more details. How to Contribute: We hope that the public, other public sector organisations, and National Statistics Institutions can collaborate and build on these data, to help improve the international comparability of statistics and enable higher frequency and more timely comparisons. We welcome feedback and contribution either through GitHub or by contacting datacampus@ons.gov.uk.

The National Travel Survey (NTS) is a series of household surveys designed to provide regular, up-to-date data on personal travel and monitor changes in travel behaviour over time. The first NTS was commissioned by the Ministry of Transport in 1965. Further periodic surveys were carried out in 1972/73, 1975/76, 1978/79 and 1985/86 (the UK Data Service holds data from 1972 onwards). Since July 1988 the NTS has been carried out as a continuous survey with field work being carried out in every month of the year, and an annual set sample of over 5,000 addresses. From 2002, the NTS sample was increased approximately threefold, to approximately 15,000 per year. The advantage of the continuous study is that users will be able to discern seasonal and cyclical movements as well as trend changes over time. The NTS is carried out primarily for the purposes of government. The most fundamental use of the National Travel Survey within the Department for Transport (DfT) is as core base data for key transport models. These are critical to the assessment and appraisal of transport scheme proposals (national and local), transport policy proposals, and contribute to the development of our long-term strategy. The NTS data is used to develop consistent sets of transport policies. Because it relates travel to travellers, it makes it possible to relate policies to people and to predict their impact. The survey provides detailed information on different types of travel: where people travel from and to, distance, purpose and mode. The NTS records personal and socio-economic information to distinguish between different types of people, and the differences in the way they travel and how often they do so. The NTS is the only source of national information on subjects such as walking which provide a context for the results of more local studies.

Further information may be found on the gov.uk National Travel Survey webpage.

End-User Licence, Special Licence and Secure Access NTS data The UK Data Archive holds three versions of the NTS:the End User Licence (EUL) versions (SNs 5340 and 6108) contain a comprehensive range of NTS data at Government Office Region geographic level and should be sufficient for most research needs. EUL data are available to registered users of the UK Data Service (see the Administrative and Access section below for details).The Special Licence versions (SNs 7553 and 7804) contain more detailed travel (including accidents), demographic and socio-economic data, and the geographic level is Local Authority/Unitary Authority. Special Licence data are subject to more restricted access conditions.The Secure Access version (SN 7559) contains more detailed information and postcode sector geographies. Secure Access data are subject to further restricted access conditions, including the completion of a training course.Full information about the variables contained at each level are available in the NTS Table Structures spreadsheet, available in the documentation.

This study comprises NTS data for 1995-2001.

For the third edition (October 2015), the data files were replaced with new versions; the variable names have been changed to match the newest NTS data, the contents of the files reflect reprocessing done by the NTS team. The documentation has been updated accordingly.

The 1995-2001 NTS includes:household variables: address type information, accessibility of public transport, access to amenities, household vehicle access, household composition and household socio-economic informationindividual information: age, gender and marital status, social and economic information, frequency of use of various methods of transport, driving licences and type of vehicle driven, employment, occupation and industry details, income, place of work and travel to work, travel benefits connected with work, season ticket details, travel difficulties, playing in the street (for children)vehicle information: vehicle type, registration details, parking, vehicle subsidies, mileage, fuel used and purchased, non-eligible traveltrips: day, date and time, main mode, purpose, origin and destination informationstage: mode, number in party, distance, costslong-distance trips (over 50 miles): mode, purpose, origin and destination

The National Travel Survey (NTS) is a series of household surveys designed to provide regular, up-to-date data on personal travel and monitor changes in travel behaviour over time. The first NTS was commissioned by the Ministry of Transport in 1965. Further periodic surveys were carried out in 1972/73, 1975/76, 1978/79 and 1985/86 (the UK Data Service holds End-User Licence data from 1972 onwards and Special Licence and Secure Access data from 2002 onwards). Since July 1988 the NTS has been carried out as a continuous survey with field work being carried out in every month of the year, and an annual set sample of over 5,000 addresses. From 2002, the NTS sample was increased approximately threefold, to approximately 15,000 per year. The advantage of the continuous study is that users will be able to discern seasonal and cyclical movements as well as trend changes over time. The NTS is carried out primarily for the purposes of government. The most fundamental use of the National Travel Survey within the Department for Transport (DfT) is as core base data for key transport models. These are critical to the assessment and appraisal of transport scheme proposals (national and local), transport policy proposals, and contribute to the development of our long-term strategy. The NTS data is used to develop consistent sets of transport policies. Because it relates travel to travellers, it makes it possible to relate policies to people and to predict their impact. The survey provides detailed information on different types of travel: where people travel from and to, distance, purpose and mode. The NTS records personal and socio-economic information to distinguish between different types of people, and the differences in the way they travel and how often they do so. The NTS is the only source of national information on subjects such as walking which provide a context for the results of more local studies.

Further information may be found on the gov.uk National Travel Survey webpage.

End-User Licence, Special Licence and Secure Access NTS data The UK Data Archive holds three versions of the NTS:the End User Licence (EUL) versions (SNs 5340 and 6108) contain a comprehensive range of NTS data at Government Office Region geographic level and should be sufficient for most research needs. EUL data are available to registered users of the UK Data Service (see the Administrative and Access section below for details).The Special Licence versions (SNs 7553 and 7804) contain more detailed travel (including accidents), demographic and socio-economic data, and the geographic level is Local Authority/Unitary Authority. Special Licence data are subject to more restricted access conditions.The Secure Access version (SN 7559) contains more detailed information and postcode sector geographies. Secure Access data are subject to further restricted access conditions, including the completion of a training course.Full information about the variables contained at each level are available in the NTS Table Structures spreadsheet, available in the documentation.

This study comprises Special Licence Access NTS data for 1995-2001, which was first deposited in 2015. The variable names have been edited to match the newest NTS data and the contents of the files reflect reprocessing of older data done by the NTS team. The standard access End User Licence version of the NTS 1995-2001 is held under SN 6108.

The 2002-2012 NTS includes:household variables: address type information, accessibility of public transport, access to amenities, household vehicle access, household composition and household socio-economic information;individual information: age, gender and marital status, social and economic information, frequency of use of various methods of transport, driving licences and type of vehicle driven, employment, occupation and industry details, income, place of work and travel to work, season ticket details, travel difficulties;vehicle information: vehicle type, registration details, parking, fuel type, mileage, engine capacity;trips: day, date and time, main mode, purpose, origin and destination information;stage: mode, number in party, distance, duration, costs;long-distance trips (over 50 miles): stage: mode, purpose, origin and destination;Please see the Table Structures document available in the table below for the full list of variables.

The National Travel Survey (NTS) is a series of household surveys designed to provide regular, up-to-date data on personal travel and monitor changes in travel behaviour over time. The first NTS was commissioned by the Ministry of Transport in 1965. Further periodic surveys were carried out in 1972/73, 1975/76, 1978/79 and 1985/86 (the UK Data Service holds End-User Licence data from 1972 onwards and Special Licence and Secure Access data from 2002 onwards). Since July 1988 the NTS has been carried out as a continuous survey with field work being carried out in every month of the year, and an annual set sample of over 5,000 addresses. From 2002, the NTS sample was increased approximately threefold, to approximately 15,000 per year. The advantage of the continuous study is that users will be able to discern seasonal and cyclical movements as well as trend changes over time. The NTS is carried out primarily for the purposes of government. The most fundamental use of the National Travel Survey within the Department for Transport (DfT) is as core base data for key transport models. These are critical to the assessment and appraisal of transport scheme proposals (national and local), transport policy proposals, and contribute to the development of our long-term strategy. The NTS data is used to develop consistent sets of transport policies. Because it relates travel to travellers, it makes it possible to relate policies to people and to predict their impact. The survey provides detailed information on different types of travel: where people travel from and to, distance, purpose and mode. The NTS records personal and socio-economic information to distinguish between different types of people, and the differences in the way they travel and how often they do so. The NTS is the only source of national information on subjects such as walking which provide a context for the results of more local studies.

Further information may be found on the gov.uk National Travel Survey webpage.

End-User Licence, Special Licence and Secure Access NTS data The UK Data Archive holds three versions of the NTS:the End User Licence (EUL) versions (SNs 5340 and 6108) contain a comprehensive range of NTS data at Government Office Region geographic level and should be sufficient for most research needs. EUL data are available to registered users of the UK Data Service (see the Administrative and Access section below for details).The Special Licence versions (SNs 7553 and 7804) contain more detailed travel (including accidents), demographic and socio-economic data, and the geographic level is Local Authority/Unitary Authority. Special Licence data are subject to more restricted access conditions.The Secure Access version (SN 7559) contains more detailed information and postcode sector geographies. Secure Access data are subject to further restricted access conditions, including the completion of a training course.Full information about the variables contained at each level are available in the NTS Table Structures spreadsheet, available in the documentation.

2020 and 2021 Disclaimer: Due to changes in the methodology of data collection, changes in travel behaviour and a reduction of data collected during 2020 and 2021, as a result of the coronavirus (COVID-19) pandemic, care should be taken when interpreting this data and comparing to other years, due to the small sample sizes. Please see the background documentation for further details of these changes.Latest edition information:For the thirteenth edition (September 2024), data and documentation for 2023 have been added to the study.Data labelsUsers should note that the SPSS and Stata files for 2023 have been converted from CSV format and do not currently contain variable or value labels. Complete metadata information can be found in the Excel Lookup table files and the NTS Data Extract User Guide within the documentation. The NTS data includes:

attitudinal variables: in 2016 a split-sample experiment was conducted to explore the feasibility of moving attitudinal questions from the household level questionnaire to the individual level questionnaire. In one half of the sample, the attitudinal questions were asked as part of the household questionnaire (as has been in the case in previous years) and in the other half one randomly selected adult per household was asked the attitudinal questions;household variables: address type information, accessibility of public transport, access to amenities, household vehicle access, household composition and household socio-economic information;individual information: age, gender and marital status, social and economic information, frequency of use of various methods of transport, driving licences and type of vehicle driven, employment, occupation and industry details, income, place of work and travel to work, season ticket details, travel difficulties;vehicle information: vehicle type, registration details, parking, fuel type, mileage, engine capacity;trips: day, date and time, main mode, purpose, origin and destination information;stage: mode, number in party, distance, duration, costs;long-distance trips (over 50 miles): stage: mode, purpose, origin and destination; Please see the Lookup Tables documentation for the full list of variables.

This product leverages first-party and third party data sources. It provides anonymized statistics helpful to: - measure the overall transport & logistics activity in Europe and in the UK (depending on the source), potentially potentially by type of goods transported (works, goods transportation, refrigerated...) - provide insights about origins and destinations of vehicles & trucks across Europe, in the UK between cities, communities... - identify stops and standstill areas of trucks, vans and all types of vehicles

All these statistics can feed various use cases: - marketing study for mobility advisory firms (Origins, Destinations, Stops / Standstill) that try to understand how the EV trucks are going to develop - help with marketing and geomarketing use cases to identify where to build / open a new branch or site - help explain and / or predict performance of businesses across geographies

Use cases: ==> Transport & logistics analytics: Traffic consultants, road operators, municipalities and SaaS analytics platforms use our data for understanding road safety, road usage ==> Supply chain: managers trying to look after new transport & logistics solutions ==> Site-selection : Our data help companies looking to open EV charging stations, new shops and stores where the traffic is adapted to their business ==> Dynamic pricing / geomarketing : Our data help companies adjust prices across geographies

Overview

This dataset provides ready-to-use door-to-door public transport travel time estimates for each of the 2011 Census at the lower super output area (LSOA) and data zone (DZ) units (42,000 LSOA/DZ units in total) in Great Britain (GB) to every other reachable within 150 minutes during the morning peak for the year 2023 using. This information comprises an all-to-all travel time matrix (TTM) at the national level. The TTM are estimated for public transport, bicycle, and walking. Public transport estimates are estimated for two times of departure, specifically during the morning peak and at night. Altogether, these TTMs present a range of opportunities for researchers and practitioners, such as the development of accessibility measures, spatial connectivity, and the evaluation of public transport service changes throughout the day.

A full data descriptor is available in 'technical_note.html' file as part of the records of this repository.

Data records

The TTM structure follows a row matrix format, where each row represents a unique origin-destination pair. The TTMs are offered in a set of sequentially named .parquet files (more information about Parquet format at: https://parquet.apache.org/). The structure contains one directory for each mode, where ‘bike’, ‘pt’, and ‘walk’, correspond to bicycle, public transport, and walking, respectively.

Walking

The walking TTM contains 13.3 million rows and three columns. The table below offers a description of the columns.

Bicycle

The bicycle TTM includes 40 million rows and four columns which are described in the table below.

Public transport

The LSOA/DZ TTM consists of six columns and 265 million rows. The internal structure of the records is displayed in the table below:

Office for National Statistics' national and subnational Census 2021.This dataset provides Census 2021 estimates that classify usual residents in England and Wales by their method used to travel to work (2001 specification). The estimates are as at Census Day, 21 March 2021.Census 2021 took place during a period of rapid change. We gave extra guidance to help people on furlough answer the census questions about work. However, we are unable to determine how furloughed people followed the guidance. Take care when using this data for planning purposes. Read more about specific quality considerations in our Labour market quality information for Census 2021 methodology Method of travel to workplace definition: A person's place of work and their method of travel to work. This is the 2001 method of producing travel to work variables.'Work mainly from home' applies to someone who indicated their place of work as their home address and travelled to work by driving a car or van, for example visiting clients.Quality information: As Census 2021 was during a unique period of rapid change, take care when using this data for planning purposes.Comparability with 2011: Not comparable. It is difficult to compare this variable with the 2011 Census because Census 2021 took place during a national lockdown. The government advice at the time was for people to work from home (if they can) and avoid public transport.People who were furloughed (about 5.6 million) were advised to answer the transport to work question based on their previous travel patterns before or during the pandemic. This means that the data does not accurately represent what they were doing on Census Day. This variable cannot be directly compared with the 2011 Census Travel to Work data as it does not include people who were travelling to work on that day. It may however, be partially compared with bespoke tables from 2011. This data is issued at (BGC) Generalised (20m) boundary type for:Country - England and WalesRegion - EnglandUTLA - England and WalesLTLA - England and WalesWard - England and WalesMSOA - England and WalesLSOA - England and WalesOA - England and WalesIf you require the data at full resolution boundaries, or if you are interested in the range of statistical data that Esri UK make available in ArcGIS Online please enquire at content@esriuk.com.The data services available from this page are derived from the National Data Service. The NDS delivers thousands of open national statistical indicators for the UK as data-as-a-service. Data are sourced from major providers such as the Office for National Statistics, Public Health England and Police UK and made available for your area at standard geographies such as counties, districts and wards and census output areas. This premium service can be consumed as online web services or on-premise for use throughout the ArcGIS system.Read more about the NDS.

The National Travel Survey (NTS) is a series of household surveys designed to provide regular, up-to-date data on personal travel and monitor changes in travel behaviour over time. The first NTS was commissioned by the Ministry of Transport in 1965. Further periodic surveys were carried out in 1972/73, 1975/76, 1978/79 and 1985/86 (the UK Data Service holds End User Licence data from 1972 onwards and Special Licence and Secure Access data from 2002). Since July 1988 the NTS has been carried out as a continuous survey with field work being carried out in every month of the year, and an annual set sample of over 5,000 addresses. From 2002, the NTS sample was increased approximately threefold, to approximately 15,000 per year. The advantage of the continuous study is that users will be able to discern seasonal and cyclical movements as well as trend changes over time. The NTS is carried out primarily for the purposes of government. The most fundamental use of the National Travel Survey within the Department for Transport (DfT) is as core base data for key transport models. These are critical to the assessment and appraisal of transport scheme proposals (national and local), transport policy proposals, and contribute to the development of our long-term strategy. The NTS data is used to develop consistent sets of transport policies. Because it relates travel to travellers, it makes it possible to relate policies to people and to predict their impact. The survey provides detailed information on different types of travel: where people travel from and to, distance, purpose and mode. The NTS records personal and socio-economic information to distinguish between different types of people, and the differences in the way they travel and how often they do so. The NTS is the only source of national information on subjects such as walking which provide a context for the results of more local studies.

Further information may be found on the gov.uk National Travel Survey web page.

End-User Licence, Special Licence and Secure Access NTS data The UK Data Archive also holds End User Licence (EUL) NTS data from 1972 onwards (see SNs 2852, 2853, 2855, 3288, 4108, 4583-4585, 6108 and 5340) and Special Licence (SL) NTS data from 1995 onwards (SNs 7804 and 7553). The EUL versions contain a comprehensive range of NTS data at Government Office Region geographic level and should be sufficient for most research needs. The SL versions contain more detailed travel (including accidents), demographic and socio-economic data, and the geographic level is Local Authority/Unitary Authority. These data are subject to more restricted access conditions than EUL. The Secure Access version contains more detailed information and postcode sector geographies. Secure Access data are subject to further restricted access conditions, including the completion of a training course. For full details of the differences between the EUL, SL and Secure Access NTS, see the document '7559_nts_table_structures.xls' in the documentation. Users should always check whether the EUL and SL versions are suitable for their research needs before considering making an application for the Secure Access version.

2020 and 2021 Disclaimer: Due to changes in the methodology of data collection, changes in travel behaviour and a reduction of data collected during 2020 and 2021, as a result of the coronavirus (COVID-19) pandemic, care should be taken when interpreting this data and comparing to other years, due to the small sample sizes. Please see the background documentation for further details of these changes.Latest edition information:For the eleventh edition (September 2024), data and documentation for 2023 have been added to the study.Data labelsUsers should note that the SPSS and Stata files for 2023 have been converted from CSV format and do not currently contain variable or value labels. Complete metadata information can be found in the Excel Lookup table files and the NTS Data Extract User Guide within the documentation. The 2002-2022 NTS includes:

attitudinal variables: in 2016 a split-sample experiment was conducted to explore the feasibility of moving attitudinal questions from the household level questionnaire to the individual level questionnaire. From 2017, a CASI module for transport satisfaction questions was added, where one adult from those present during the household interview is randomly selected to complete the satisfaction questions;household variables: address type information, accessibility of public transport, access to amenities, household vehicle access, household composition and household socio-economic information;individual information: age, gender and marital status, social and economic information, frequency of use of various methods of transport, driving licences and type of vehicle driven, employment, occupation and industry details, income, place of work and travel to work, season ticket details, travel difficulties;vehicle information: vehicle type, registration details, parking, fuel type, mileage, engine capacity;trips: day, date and time, main mode, purpose, origin and destination information;stage: mode, number in party, distance, duration, costs;long-distance trips (over 50 miles): stage: mode, purpose, origin and destination Please see the Lookup Tables All Variables by Level and Licence document available from the Documentation tab for the full list of variables.

This dataset contains a set of semi-structured interviews with (motor) insurers, insurance stakeholders, and stakeholders in transport or law working closely with insurers. The interviews, across multiple countries, were based on semi-structured questions around how current and future mobility developments and innovations - Electric Vehicles, Autonomous Vehicles, Mobility Data, Micro-Mobility and Shared Mobility - affect insures, and how insurers in turn affect these shifts in our mobility. Questions were asked about the most important mobility challenges that insurers witnessed; how these mobility developments affect them from a underwriting, busines, legal, claims and pricing perspective; how insurers are adapting to these development (in terms of collaborations, lobby, learning, etc.); and whether insurers should have an explicit role to play in the mobility transition.

Interviewees (N=52) either consented writtenly through Oxfords consent form (stored) or verbally (on record) to be quoted anonymously. Those who did not agree explicitly to anonymous quotation have been excluded from data archiving (n=13). Data thus comprises of 39 transcripts in Word format (totalling less than 3MB) with insurers or stakeholders in transport or law working closely with (motor) insurers in the United Kingdom, Netherlands or Germany. We've further included 1 semi-structured questionairre in Word format to reference the semi-structured questions asked to stakeholders; a data table with an anonymized overview of the interviewees in Excel format; and an blank consent form shared with interviewees.

All transcripts have been through a round of anonymisation: removal of any direct (names, companies, age, profesional history) and indirect indentifiers (references to people/meetings, etc), with stronger anonymisation the more unique the organisation (as more identifiable). At any time, use of quotes should be anonymously attributed to general branch/sector!

This project studies the role of insurance in the transition of road transport: a systemic transformation of our mobility following changes and innovations in technology, user practices, policy, knowledge and business models.

From an insurance perspective four developments stand out. (1) The growing availability of mobility data from increasingly real-time sensor data from vehicles, infrastructure, cameras, etc. (2) The automation and increasing connectivity of vehicles that shift liability from drivers and operators to automated and connected driving systems. (3) The electrification of vehicles, which adds new (fire) risks and cost calculations. And (4) the new (shared) forms of mobility that emerge around e-scooters, (private) car-sharing and ride-hailing services.

There is a need to govern these trends as they come with new opportunities and risks to transport climate emission targets, road-safety, urban planning and accessibility. There is also a need to understand how insurers respond to these innovations that affect their whole business: from their risk analyses, product development and liability all the way to a potential shrinking of the overall market.

These two needs are closely interrelated. The disciplines that study the insurance aspects of these developments, like law, transport and underwriting, typically do so with the goal to improve and optimize insurance assessments, products and premiums. Missing from this work is the insight from critical security studies (CSS) that insurance acts as a form of governance. For example, people who cannot afford or are rejected for insurance are legally excluded from driving a car in most developed countries. Similarly, insurers actively use premiums to guide 'risky' people, often young drivers, to drive more carefully. Insurance thus helps direct mobility, which implies that there is a direct link between how insurance is organized internally and how these aforementioned trends are governed socially.

CSS offers this insight based on its study of the role that security plays in society. A core argument in CSS is that how people do security or insurance, not just whether people are secure or insured, is important because such acts and decisions inherently have discriminatory effects: they differentiate between those with and without protection and distribute resources accordingly. CSS thus asks why certain technologies, activities or behaviours are considered risky at certain times and places. This is an important question, because the four developments offer an opportunity to understand how insurers observe, learn and adapt their old routines to these new developments. Developments on which they have no or little data, statistical models, terms and conditions or claims handling processes.

The aim of PRINCE is therefore to understand to what extent and how road transport insurance practices are affected by and affecting the above four developments in road transport. The first objective is to collect, classify, analyse and share recent information on the way road transport insurance practices are affected by and affecting the above listed trends. Over the course of 18 months, the project will conduct an academic literature review, a systematic empirical and legal document analysis and up to 75 stakeholder interviews across three carefully selected cases (the United Kingdom, Germany and the Netherlands), organize workshops and distribute the findings. This will be accomplished with the help of a research associate, as the second aim and objective are for the investigator to gain the skills and experience to lead his own future research teams.

In short, this project generates a deeper understanding of the role that insurance plays as a form of governance of a more sustainable road transport system while offering an updated empirical overview of how insurance deals with fast-changing transport innovations.

Air travel was the most common form of transport used to travel overseas by United Kingdom (UK) residents over the period from 2008 and 2019. The number of visits using air travel increased significantly over the years, reaching more than **** million overseas trips in 2019. In contrast, sea travel has slowly decreased over the same time period.

Snapshot img

Rail Logistics Market Size 2024-2028

The rail logistics market size is forecast to increase by USD 56.9 billion at a CAGR of 4.5% between 2023 and 2028. The market is experiencing significant growth due to the increasing demand for efficient and eco-friendly goods carriage. The use of wheeled vehicles on fixed routes offers numerous advantages, including reduced fuel consumption and lower carbon emissions compared to truckload transport. Market trends include the increasing adoption of cloud technology for optimizing rail intermodal operations and enhancing safety. However, challenges persist, such as weather turbulences disrupting schedules and the risks associated with public-private partnerships (PPPs) in rail infrastructure development. Despite these challenges, the rail logistics sector continues to evolve, offering opportunities for stakeholders to innovate and improve the efficiency and sustainability of freight transportation.

Furthermore, the use of hybrid trains, which consume less energy than traditional trains, is another trend in the market. Additionally, cross-border freight transport is becoming easier and more cost-effective with the advancements in rail logistics. However, stakeholders must be aware of the risks associated with Public-Private Partnerships (PPPs) in rail logistics projects. Overall, the market is an essential component of the global logistics industry, providing a sustainable and efficient alternative to road transport.

What will be the Size of the Market During the Forecast Period?

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Rail logistics plays a vital role in the transportation of goods, especially in the context of cross-border trade activities and trade contracts. This sector encompasses the use of wheeled vehicles on tracks to move cargo from one place to another, ensuring a secure and efficient transport system. Safety is a top priority in rail logistics, with weather turbulences and other unforeseen circumstances posing potential challenges. To mitigate these risks, rail logistics relies on fixed routes and schedules to minimize disruptions. Rail intermodal, a critical component of rail logistics, enables the seamless transfer of cargo between different modes of transport, such as trucks and trains.

Additionally, mining activities, in particular, benefit from rail logistics due to the large volumes of raw materials transported over long distances. Infrastructure development plays a pivotal role in the growth of the rail logistics market. Advanced technologies, such as IoT (Internet of Things), AI (Artificial Intelligence), and autonomous train development, are revolutionizing the industry, enabling real-time monitoring, predictive maintenance, and optimized scheduling. Autonomous train development is another area of focus in rail logistics, with the potential to revolutionize the industry by reducing human error and increasing operational efficiency. Mining activities, in particular, can greatly benefit from the use of autonomous trains due to their heavy cargo requirements and remote locations.

In addition, infrastructure development is crucial to the growth of the market. Governments and private organizations are investing in the expansion and modernization of transportation infrastructure to accommodate the increasing demand for secure and reliable transportation solutions. E-commerce and last-mile rail transportation are emerging areas of interest in rail logistics. As e-commerce continues to grow, the need for efficient and cost-effective last-mile transportation solutions becomes increasingly important. Rail logistics offers a viable alternative to traditional truckload transport, providing a more sustainable and cost-effective solution for the final leg of the delivery journey. In conclusion, rail logistics plays a crucial role in the transportation of goods, offering a secure, efficient, and sustainable alternative to traditional truckload transport. With the adoption of advanced technologies and infrastructure development, the market is poised for continued growth and innovation.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Type

  Intermodals
  Tank wagons
  Freight cars


Geography

  Europe

    Germany
    UK


  APAC

    China
    India


  North America

    US


  Middle East and Africa



  South America

By Type Insights

The intermodals segment is estimated to witness significant growth during the forecast period. Intermodal transportation, which involves the use of multiple freight modes to move goods from origin to destination, is gaining popularity among shippers due to its flexibility and efficiency. This mode of transport is particularly beneficial for those sending mu

The National Travel Survey (NTS) is a series of household surveys designed to provide regular, up-to-date data on personal travel and monitor changes in travel behaviour over time. The first NTS was commissioned by the Ministry of Transport in 1965. Further periodic surveys were carried out in 1972/73, 1975/76, 1978/79 and 1985/86 (the UK Data Service holds data from 1972 onwards). Since July 1988 the NTS has been carried out as a continuous survey with field work being carried out in every month of the year, and an annual set sample of over 5,000 addresses. From 2002, the NTS sample was increased approximately threefold, to approximately 15,000 per year. The advantage of the continuous study is that users will be able to discern seasonal and cyclical movements as well as trend changes over time. The NTS is carried out primarily for the purposes of government. The most fundamental use of the National Travel Survey within the Department for Transport (DfT) is as core base data for key transport models. These are critical to the assessment and appraisal of transport scheme proposals (national and local), transport policy proposals, and contribute to the development of our long-term strategy. The NTS data is used to develop consistent sets of transport policies. Because it relates travel to travellers, it makes it possible to relate policies to people and to predict their impact. The survey provides detailed information on different types of travel: where people travel from and to, distance, purpose and mode. The NTS records personal and socio-economic information to distinguish between different types of people, and the differences in the way they travel and how often they do so. The NTS is the only source of national information on subjects such as walking which provide a context for the results of more local studies.

Further information may be found on the gov.uk National Travel Survey webpage.

End-User Licence, Special Licence and Secure Access NTS data The UK Data Archive holds three versions of the NTS:the End User Licence (EUL) versions (SNs 5340 and 6108) contain a comprehensive range of NTS data at Government Office Region geographic level and should be sufficient for most research needs. EUL data are available to registered users of the UK Data Service (see the Administrative and Access section below for details).The Special Licence versions (SNs 7553 and 7804) contain more detailed travel (including accidents), demographic and socio-economic data, and the geographic level is Local Authority/Unitary Authority. Special Licence data are subject to more restricted access conditions.The Secure Access version (SN 7559) contains more detailed information and postcode sector geographies. Secure Access data are subject to further restricted access conditions, including the completion of a training course.Full information about the variables contained at each level are available in the NTS Table Structures spreadsheet, available in the documentation.

Changes to the methodology in 2002 mean that there are some inconsistencies with data for previous years. Most notably, an under-recording of short walks in 2002 and 2003 affects trends over this period, particularly in the number of trips per person.2020 and 2021 Disclaimer:Due to changes in the methodology of data collection, changes in travel behaviour, and a reduction of data collected during 2020 and 2021 as a result of the coronavirus (COVID-19) pandemic, care should be taken when interpreting this data and comparing it to other years due to the small sample sizes. Please see the background documentation for further details of these changes.Latest edition information:For the eighteenth edition (September 2024), data and documentation for 2023 have been added to the study.Data labelsUsers should note that the SPSS and Stata files for 2023 have been converted from CSV format and do not currently contain variable or value labels. Complete metadata information can be found in the Excel Lookup table files and the NTS Data Extract User Guide within the documentation. The NTS data includes:

attitudinal variables: in 2016 a split-sample experiment was conducted to explore the feasibility of moving attitudinal questions from the household level questionnaire to the individual level questionnaire. In one half of the sample, the attitudinal questions were asked as part of the household questionnaire (as has been in the case in previous years) and in the other half one randomly selected adult per household was asked the attitudinal questions;household variables: address type information, accessibility of public transport, access to amenities, household vehicle access, household composition and household socio-economic information;individual information: age, gender and marital status, social and economic information, frequency of use of various methods of transport, driving licences and type of vehicle driven, employment, occupation and industry details, income, place of work and travel to work, season ticket details, travel difficulties;vehicle information: vehicle type, registration details, parking, fuel type, mileage, engine capacity;trips: day, date and time, main mode, purpose, origin and destination information;stage: mode, number in party, distance, duration, costs;long-distance trips (over 50 miles): stage: mode, purpose, origin and destination; Please see the Lookup Tables documentation for the full list of variables.