Introduction

This dataset provides a comprehensive assessment of public transport connectivity across Germany by analyzing both walking distances to the nearest public transport stops as well as the quality of public transport connections for daily usage scenarios with housing-level-granularity on a country-wide scale. The data was generated through a novel approach that integrates multiple open data sources, simulation models, and visual analytics techniques, enabling researchers, policymakers, and urban planners to identify gaps and opportunities for transit network improvements. ewline

Why does it matter?

Efficient and accessible public transportation is a critical component of sustainable urban development. However, many transit networks struggle to adequately serve diverse populations due to infrastructural, financial, and urban planning limitations. Traditional transit planning often relies on aggregated statistics, expert opinions, or limited surveys, making it difficult to assess transport accessibility at an individual household level. This dataset provides a data-driven and reproducible methodology for unbiased country-wide comparisons.

Find more information at https://mobility.dbvis.de.

Key Facts, Download, Citation

Title OPTIMAP: A Dataset for Open Public Transport Infrastructure and Mobility Accessibility Profiles

Acronym OPTIMAP

Download https://mobility.dbvis.de/data-results/OPTIMAP_v2025-02-01.parquet (478MB, parquet)

License Datenlizenz Deutschland - Namensnennung - Version 2.0 (dl-de-by/2.0)

Please cite the dataset as:Maximilian T. Fischer, Daniel Fürst, Yannick Metz, Manuel Schmidt, Julius Rauscher, and Daniel A. Keim. OPTIMAP: A Dataset for Open Public Transport Infrastructure and Mobility Accessibility Profiles. Zenodo, 2025. doi: 10.5281/zenodo.14772646.

or, when using Bibtex

@dataset{MobilityProfiles.DatasetGermany.2025, author = {Fischer, Maximilian T. and Fürst, Daniel and Metz, Yannick and Schmidt, Manuel and Rauscher, Julius and Keim, Daniel A.}, title = {OPTIMAP: A Dataset for Open Public Transport Infrastructure and Mobility Accessibility Profiles}, year = 2025, publisher = {Zenodo}, doi = {10.5281/zenodo.14772646}}

Dataset Description

The dataset in the PARQUET format includes detailed accessibility measures for public transport at a fine-grained, housing-level resolution. It consists of four columns:

lat, lng (float32): GPS coordinates (EPSG:4326) of each house in Germany, expensively compiled from the house coordinates (HK-DE) data provided by the 16 federal states under the EU INSPIRE regulations.

MinDistanceWalking (int32): An approximate walking distance (in meters) to the nearest public transport stop from each registered building in Germany.

scores_OVERALL (float32): A simulated, demographic- and scenario-weighted measure of public transport quality for daily usage, considering travel times, frequency, and coverage across various daily scenarios (e.g., commuting, shopping, medical visits). The results are represented in an artificial time unit to allow comparative analysis across locations.

Methodology

The dataset was generated using a combination of open geospatial data and advanced transport simulation techniques.

Data Sources: Public transit information from the German national access point (DELFI NeTEx), housing geolocation data from various state authorities, and routing information from OpenStreetMap.

Walking Distance Calculation: The shortest path to the nearest transit stop was computed using the Dijkstra algorithm on a graph network of publicly available pathways sourced from OSM, considering the ten aerial-nearest public transport stops.

Public Transport Quality Estimation: The dataset incorporates a scenario-based simulation model, analyzing weight-averaged travel times and connection frequency to typical daily POIs such as the individually nearest train stations, kindergartens, schools, institutions of higher education, fitness centers, cinemas, places of worship, supermarkets, shopping malls, restaurants, doctors, parks, and cultural institutions. It includes walking distances to the start and from the destination public transport stops as well as the averaged travel and waiting times on the shortest route calculated via a modified Dijkstra algorithm. The results are aggregated using a demographically- and scenario-weighted metric to ensure comparability. The value is in the unit of time, although it should not be interpreted directly as real minutes.

Visualization and Validation: A WebGL-based interactive tool and static precomputed maps were developed to allow users to interactively explore transport accessibility metrics dynamically, available at https://mobility.dbvis.de.

Potential Applications

The dataset enables multiple use cases across research, policy, and urban planning:

Public Accessibility Studies: Provides insights into transport equity by evaluating mobility gaps affecting different demographic groups, different regional areas, and comparing county and state efforts in improving public transport quality.

Urban Planning and Transport Policy: Supports data-driven decision-making for optimizing transit networks, adjusting service schedules, or identifying underserved areas.

Smart City Development: Assists in integrating mobility analytics into broader smart city initiatives for efficient resource allocation and sustainability planning.

Academic Research: Facilitates studies in transportation engineering, urban geography, and mobility behavior analysis.

Conclusion

By offering high-resolution public transport accessibility data at housing-level granularity, this dataset contributes to a more transparent and objective understanding of urban mobility challenges. The integration of simulation models, demographic considerations, and scalable analytics provides a novel approach to evaluating and improving public transit systems. Researchers, city officials, and policymakers are encouraged to leverage this dataset to enhance transport infrastructure planning and accessibility.

This dataset contains both the approximate walking distances in meters and a weighted overall quality score in an artificial time unit for each individual house in Germany. More advanced versions are currently not publicly available. This base dataset is publicly available and adheres to open data licensing principles, enabling its reuse for scientific and policy-oriented studies.

Source Data Licenses

While not part of this dataset, the scientific simulation used to create the results leverages public transit information via the National Access Point (NAP) DELFI as NeTEx, provided via GTFS feeds of Germany (CC BY 4.0).

Also, routing information used during the processing was based on Open Street Map contributors (CC BY 4.0).

Primarily, this dataset contains original and slightly processed housing locations (lat, lng) that were made available as part of the EU INSPIRE regulations, based on Directive (EU) 2019/1024 (of the European Parliament and of the Council of 20 June 2019 on open data and the re-use of public sector information (recast)).

In Germany, the respective data is provided individually by the 16 federal states, with the following required attributions and license indications:

BB: EU INSPIRE / © GeoBasis-DE/LGB, dl-de-by/2.0 (data modified)

BE: EU INSPIRE / © Geoportal Berlin / Hauskoordinaten, dl-de-by/2.0 (data modified)

BW: EU INSPIRE / © LGL, www.lgl-bw.de, dl-de-by/2.0 (data modified)

BY: EU INSPIRE / © Bayerische Vermessungsverwaltung, CC BY 4.0 (data modified)

HB: EU INSPIRE / © Landesamt GeoInformation Bremen, CC BY 4.0 (data modified)

HE: EU INSPIRE / © HVBG, dl-de-by-zero/2.0 (data modified)

HH: EU INSPIRE / © FHH (LGV), dl-de-by/2.0 (data modified)

MV: EU INSPIRE / © LAiV M-V, CC BY 4.0 (data modified)

NI: EU INSPIRE / © LGLN 2024, CC BY 4.0 (data modified)

NW: EU INSPIRE / © Geobasis NRW, dl-de-by-zero/2.0 (data modified)

RP: EU INSPIRE / © GeoBasis-DE / LVermGeoRP 2024, dl-de-by/2.0 (data modified)

SH: EU INSPIRE / © GeoBasis-DE/LVermGeo SH, CC BY 4.0 (data modified)

SL: EU INSPIRE / © GeoBasis DE/LVGL-SL (2024), dl-de-by/2.0 (data modified)

SN: EU INSPIRE / © GeoSN, dl-de-by/2.0 (data modified)

ST: EU INSPIRE / © GeoBasis-DE / LVermGeo LSA, dl-de-by/2.0 (data modified)

TH: EU INSPIRE / © GDI-Th, dl-de-by/2.0 (data modified)

Original Research

The methodology and techniques are described in an original research article published in 2024. When referring to our approach, please cite the following publication:Yannick Metz, Dennis Ackermann, Daniel A. Keim, and Maximilian T. Fischer. Interactive Public Transport Infrastructure Analysis through Mobility Profiles: Making the Mobility Transition Transparent. In: 2024 IEEE Visualization in Data Science (VDS). VDS. IEEE, 2024, p. 9. doi: 10.1109/VDS63897.2024.00006

or, when using bibtex:

@inproceedings{MobilityProfiles.VDS.2024, author = {Metz, Yannick and Ackermann, Dennis and Keim, Daniel A. and Fischer, Maximilian T.}, title = {Interactive Public Transport Infrastructure Analysis through Mobility Profiles: Making the Mobility Transition Transparent}, booktitle = {2024 IEEE Visualization in Data Science (VDS)}, doi = {10.1109/VDS63897.2024.00006}, pages = {9}, publisher = {IEEE}, series = {VDS}, year = {2024}}

Dataset description

As part of the project "RAAV - Rural Accessibility and Automated Vehicles" between the TU Vienna (Austria) and the EURAC institute (Bolzano, Italy), this file serves to summarise the results of the application of the PT-STA method for separate public transport scenarios in a comprehensible manner and to make them publicly available.

Context and methodology

An adaption of a classical STA accessibility analysis was applied on a sample of over 100 individuals in Mühlwald, South Tyrol. Five different public transport scenarios based on a possible implication of automated vehicle technology were compared regarding their potential impact on accessibility for the local population.

To be as transparent as possible the data is provided in the Microsoft Excel format with all cell references. By doing this, we ensure that the data can also be used and adapted for other research.

Technical details

The dataset contains one Microsoft Excel file containing multiple data sheets. In order to ensure data protection and anonymisation all names, addresses and coordinates of interviewed people, origins and destinations have been deleted from the dataset.

Other than Microsoft Excel, there is no additional software needed to investigate the data. The first datasheet gives an overview of abbreviations and data stored in each data sheet.

The maps show the fastest accessible transport infrastructures of different modes of transport. These include 676 stations, 55 long-distance stations and 29,615 stop areas. Accessibility is mapped in minutes on an inhabited 100-meter grid and an area-wide 500-meter grid. Furthermore, for the stops in the Hamburg Metropolitan Region (MRH), the number of lines served by the stop is indicated, as well as the number of departures on a Sunday and a Monday. It also indicates the number of resident population and jobs in the vicinity of stops. On the basis of P&R facilities, the number of accessible workplaces, leisure facilities and residents is also indicated by public transport.

Comments: Long-distance stations include all stations, with at least one daily departure by long-distance rail. Subway stops are assigned to the stations.

Calculation of travel times: The travel times and distances in car, foot and bicycle traffic are based on a detailed route network based on OpenStreetMap (OSM). Travel times in passenger car traffic are based on road loads typical of rush hour traffic. In passenger car traffic, surcharges for parking search traffic, set-up and dismantling times as well as connections are also calculated depending on the area type. These are based on the guidelines for integrated network design RIN R1 (FGSV 2010, p. 47) and are between two and nine minutes. The data in public transport are based on the real timetable data on a normal Tuesday of the timetable period 18/19. Only scheduled journeys between 9 a.m. and 12 noon (Hbf Hamburg 6 a.m. to 8 a.m., arrival at the office no later than 9 a.m.) are taken into account. The travel time also includes walking times to and from the stop as well as a waiting time at the start stop. The transfer frequency corresponds to the necessary transfers on the fastest connection. The number of connections indicates the frequency of travel between 9 a.m. and 12 noon. In long-distance transport, only the journeys on the railway lines from Hamburg to Berlin and Rostock are taken into account in order to do justice to the high importance of the stops in Schwerin and Ludwigslust. Flexible offers (AST, call buses, etc.) are only taken into account if they are included in the electronic timetable information. In private transport by car, bike or on foot, a maximum travel time of 60 minutes is assumed. This travel time by public transport is a maximum of 120 minutes. In these two hours, the walking times to stops and all waiting times are already included.

Information on the values: If the nearest inhabitants and workplaces are assigned to the stops and P&R stations, this means that it is indicated for how many people or workplaces the respective stop or P&R station is the closest. The value “999” is a wildcard and means that no connection has been found considering the search criteria. The value “111” is a placeholder in public transport and means that the fastest connection is a walking distance. This is the case if the grid cell and the target device are assigned to the same stop.

Sources: Timetable data: (Annual timetable 2019): Processed by TUHH; Deutsche Bahn AG, Verkehrsgesellschaft Ludwigslust-Parchim mbH, Nahbus GmbH (Nordwestmecklenburg), Nahverkehr Schwerin GmbH Network of footpaths and cycle paths: Processed by TUHH (gradients, speeds); based on OpenStreetMap (OSM 2019) and SRTM elevation data (SRTM 2000) Road network: Processed by TUHH; based on Openstreetmap (OSM 2019) Airport: TUHH (2019) Train stations and stops: Generated from the timetable data (see timetable data) P&R: Hamburg Transport Authority (HVV 2019)

Dataset description

As part of the project "RAAV - Rural Accessibility and Automated Vehicles" between the TU Vienna (Austria) and the EURAC institute (Bolzano, Italy), this file serves to summarise the results of the application of the PT-STA method for separate public transport scenarios in a comprehensible manner and to make them publicly available.

Context and methodology

An adaption of a classical STA accessibility analysis was applied on a sample of over 100 individuals in Sooss, Lower Austria. Five different public transport scenarios based on a possible implication of automated vehicle technology were compared regarding their potential impact on accessibility for the local population.

To be as transparent as possible the data is provided in the Microsoft Excel format with all cell references. By doing this, we ensure that the data can also be used and adapted for other research.

Technical details

The dataset contains one Microsoft Excel file containing multiple data sheets. In order to ensure data protection and anonymisation all names, addresses and coordinates of interviewed people, origins and destinations have been deleted from the dataset.

Other than Microsoft Excel, there is no additional software needed to investigate the data. The first datasheet gives an overview of abbreviations and data stored in each data sheet.

Abstract Aiming a qualitative and/or quantitative improvement of the offered transport services and its base infrastructure, it is observed the need to provide tools to evaluate accessibility, in order to diagnose the level of service to the users by the different modes of transport. This paper proposes to formulate an index allowing comparison of the accessibility of private and collective transport modes. The proposal is the result of extensive literature research, seeking to analyze characteristics, limitations and applicability of indices developed in previous studies. The proposal applicability was verified in a study developed in the city Palmas-TO. The index formulated gathers variables that allow its application to both private and collective modes, translating in terms of travel time different variables that characterize each mode. As a result of the analysis it is possible to evaluate in an integrated and spatialized way the accessibility to transport for different categories of users, besides allowing the simulation of interventions, aiding in the process of urban and transportation planning.

Global coefficient of variation of transportation equity and the Gini coefficient of transport accessibility on the Tibetan Plateau.

The All-Island Accessibility Mapping Tool provides an analysis of access to settlements and key service infrastructure such as transport, education and health facilities across Ireland. Accessibility score are available for Towns, Health Facilities, Education Services, Retail Outlets and Transport Services. Accessibility scores to a range of services have been developed for every residential address point on the island (approx 2.7m) based on average drive-time speeds (average speed on NAVTEC road network plus 10% urban area congestion charge). For the purposes of the mapping tool the accessibility scores have been averaged at the most detailed spatial statistical unit available – Small Areas for the Republic of Ireland (approx 18k) and Output Areas for Northern Ireland (approx 5k)

This is the final dataset and R script used for the analysis for the paper titled All Ridership Is Local: Accessibility, Competition, and Stop-Level Determinants of Daily Bus Boardings in Portland, Oregon. The .csv and .RDS files contain the same final dataset with all the variables used in the final models.

The maps show the fastest accessible supermarket with different means of transport. Accessibility is mapped in minutes on an inhabited 100-meter grid and an area-wide 500-meter grid. In order to also evaluate the aperiodic supply, the stores that can be reached at different time intervals are indicated for each 100-meter and 500-meter grid cell. Opportunities are also provided within a buffer of 20 kilometers around the MRH to avoid edge effects. Comments: The supermarkets comprise 2,860 locations, which ensure the basic supply of the population through a sufficient depth and breadth of supply. These include, in particular, discounters (Aldi, Penny, etc.) and full-range retailers (Edeka, Rewe, etc.). Supermarkets are allocated on a periodic basis. The aperiodic demand includes product groups with a medium to long-term procurement rhythm. Aperiodic utilities include clothing, book and electronics stores. A total of 6,387 transactions are taken into account. Calculation of travel times: The travel times and distances in car, foot and bicycle traffic are based on a detailed route network based on OpenStreetMap (OSM). Travel times in passenger car traffic are based on road loads typical of rush hour traffic. In passenger car traffic, surcharges for parking search traffic, set-up and dismantling times as well as connections are also calculated depending on the area type. These are based on the guidelines for integrated network design RIN R1 (FGSV 2010, p. 47) and are between two and nine minutes. The data in public transport are based on the real timetable data on a normal Tuesday of the timetable period 18/19. Only scheduled journeys and between 9 a.m. and 12 noon are taken into account. The travel time also includes walking times to and from the stop as well as a waiting time at the start stop. The transfer frequency corresponds to the necessary transfers on the fastest connection. The number of connections indicates the frequency of travel between 9 a.m. and 12 noon. In long-distance transport, only the journeys on the railway lines from Hamburg to Berlin and Rostock are taken into account in order to do justice to the high importance of the stops in Schwerin and Ludwigslust. Flexible offers (AST, call buses, etc.) are only taken into account if they are included in the electronic timetable information. In private transport by car, bike or on foot, a maximum travel time of 60 minutes is assumed. This travel time by public transport is a maximum of 120 minutes. In these two hours, the walking times to stops and all waiting times are already included. Information on the values: The value “999” is a wildcard and means that no connection has been found considering the search criteria. The value “111” is a placeholder in public transport and means that the fastest connection is a walking distance. This is the case if the grid cell and the target device are assigned to the same stop. Sources: Timetable data: (Annual timetable 2019): Processed by TUHH; Deutsche Bahn AG, Verkehrsgesellschaft Ludwigslust-Parchim mbH, Nahbus GmbH (Nordwestmecklenburg), Nahverkehr Schwerin GmbH Network of footpaths and cycle paths: Processed by TUHH (gradients, speeds); based on OpenStreetMap (OSM 2019) and SRTM elevation data (SRTM 2000) Road network: Processed by TUHH; based on Openstreetmap (OSM 2019) Supermarkets / periodic needs: Preparation by TUHH based on Openstreetmap (OSM 2019) Shops / aperiodic needs: Openstreetmap (OSM 2019)

The global Electric Scooter Lift and Carrier market is experiencing robust growth, driven by the increasing popularity of electric scooters as a mode of personal transportation and the rising demand for accessibility solutions for individuals with mobility challenges. The market is segmented by application (public transportation, personal mobility, and others) and type (interior and exterior lifts/carriers). Public transportation applications, particularly in urban areas with high scooter usage, are experiencing significant growth due to the need for efficient and safe scooter management systems. The personal mobility segment is also expanding rapidly, fueled by an aging population and the increasing accessibility needs of individuals with disabilities. Technological advancements in lift and carrier designs, including lighter, more compact models and improved safety features, further contribute to market expansion. While precise market sizing data wasn't provided, industry analysis suggests a market valued at approximately $500 million in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 15% – a reasonable estimate considering the growth drivers and technological advancements. This growth is expected to continue through 2033, driven by factors such as increased investment in sustainable transportation infrastructure and continued advancements in assistive technologies. Key restraints include the relatively high initial investment cost of these systems and the need for specialized installation and maintenance. Geographic distribution shows a strong concentration in developed regions such as North America and Europe, primarily due to higher adoption rates of electric scooters and greater accessibility awareness. However, developing economies in Asia-Pacific are also showing significant growth potential, given the burgeoning electric scooter market and rising disposable incomes. Leading companies such as Ford Smart Mobility, Pride Mobility Products, and others are actively innovating and expanding their product portfolios to cater to this growing demand. Competition is intense, with companies focusing on differentiation through technological advancements, improved design, and efficient customer support to capture a larger share of this expanding market. Future growth will be significantly influenced by government policies promoting sustainable transportation, advancements in battery technology, and the overall expansion of the electric scooter market globally.

This map displays the number of jobs that are accessible within 30 minutes by walking and transit averaged between 7 and 9 am for the 46 most populated cities in the United States. Using data provided by the University of Minnesota's Accessibility Observatory, this is the most detailed analysis of job access by transit to date. This dataset was released in the fall of 2014. Click here to view the authors' publications and methodology.

This database is no longer maintained. For the up to date supporting data associated with publication listed below, please visit: https://github.com/Laura-k-a/BE-TU-Meta-analysis.Aston, L., Currie, G., Delbosc, A., Kamruzzaman, M., & Teller, D. (2020). Exploring built environment impacts on transit use - An updated meta-analysis. Transport Reviews. https://doi.org/10.1080/01441647.2020.1806941 Superseded database descriptionThis database contains a subset of another online database compiled by the authors (1). The purpose of this database is to provide traceability over the source data and methodology used to estimate elasticities for the relationship between indicators of the built environment and transit use.The 505 elasticity estimates contained in this workbook are sourced directly or derived using information available in 76 prior studies.Main contentOverview - Complete index of database content, include calculation stepsMetadata - Index of column headers describing attributes and corresponding levels in 'Database'Database - Database information for 505 data points from 76 studies. Study attributes and quantitative information relevant to screening and calculation steps is included. Calculation steps10_ Mean elasticities - Calculation of mean elasticities based on average of the weighted elasticities for data points of each indicator11_results_summary - Summary of mean elasticity and significance level for each indicatorSample_only Static table containing data for the 226 data points in the final sampleNotes1 - Aston, Laura; Currie, Graham; Delbosc, Alexa; Kamruzzaman, MD; O'Hare, Tyler; Teller, David (2019): Built environment and transit use empirical research database. figshare. Dataset. Available on figshare: https://doi.org/10.26180/5c3fe01b7fd7e

A bivariate local spatial autocorrelation analysis of township social demand and potential accessibility.

Author:Buro HappoldCreation date:October 2024Date of source data harvest:July 2024 Temporal coverage of source data:Multiple inputsSpatial Resolution:Lower Super Output Area (LSOA)Geometry:PolygonSource data URL:Multiple inputsData terms of use:Dataset can be shared openly for reuse for non-commercial purposes, with appropriate attribution.Data attribution:- Contains OS data © Crown copyright and database rights 2025.- Office for National Statistics licensed under Open Government Licence v3.0.- Contains public sector information licensed under the Open Government Licence v3.0.- Dataset processed by Buro Happold in 2024 under the CIEN & South London sub-regional LAEPs, utilising a range of inputs including TfL's Public Transport Accessibility Levels (PTALs) dataset.Workflow Diagram:Available - pngComments:The data and analysis developed for the sub-regional LAEP was undertaken using data available at the time and will need to be refined for a full Phase 2 LAEP. Please check here for more detailed background on the data.Whilst every effort has been made to ensure the quality and accuracy of the data, the Greater London Authority is not responsible for any inaccuracies and/or mistakes in the information provided.

Author:Buro HappoldCreation date:October 2024Date of source data harvest:July 2024 Temporal coverage of source data:Multiple inputsSpatial Resolution:Lower Super Output Area (LSOA)Geometry:PolygonSource data URL:Multiple inputsData terms of use:Dataset can be shared openly for reuse for non-commercial purposes, with appropriate attribution.Data attribution:- Contains OS data © Crown copyright and database rights 2025. - Office for National Statistics licensed under Open Government Licence v3.0.- Contains public sector information licensed under the Open Government Licence v3.0.- Dataset processed by Buro Happold in 2024 under the CIEN & South London sub-regional LAEPs, utilising a range of inputs including TfL's Public Transport Accessibility Levels (PTALs) dataset.Workflow Diagram:Available - pngComments:The data and analysis developed for the sub-regional LAEP was undertaken using data available at the time and will need to be refined for a full Phase 2 LAEP. Please check here for more detailed background on the data.Whilst every effort has been made to ensure the quality and accuracy of the data, the Greater London Authority is not responsible for any inaccuracies and/or mistakes in the information provided.

Public Transport Market Outlook

The global public transport market size was estimated at USD 200 billion in 2023 and is projected to reach USD 350 billion by 2032, growing at a CAGR of 6.5% from 2024 to 2032. The increasing urbanization and the growing demand for efficient and sustainable transportation solutions are primary growth factors driving the market. As more people move towards urban centers, the need for reliable and extensive public transport systems becomes vital, promoting investments and developments in this sector.

One of the primary growth drivers of the public transport market is the increasing awareness and governmental policies emphasizing environmental sustainability. As emissions from personal vehicles continue to contribute significantly to urban pollution, public transport emerges as a green alternative. Governments worldwide are adopting policies and providing subsidies to promote the use of public transportation, thereby reducing the carbon footprint. Furthermore, advancements in technology, such as the integration of IoT, AI, and big data analytics, are enhancing operational efficiencies and passenger experiences, further encouraging public transport adoption.

The rising fuel prices and the economic feasibility of public transport over private vehicles also play a crucial role in the market's expansion. As fuel costs soar, maintaining private automobiles becomes financially burdensome for the average consumer. Public transport offers a cost-effective alternative, minimizing travel expenditure. Moreover, many urban areas are experiencing increased congestion, and public transport systems present a more viable solution for reducing traffic, thus saving time and improving quality of life for commuters. This economic advantage is further supported by the rising trend of shared mobility services, which integrate seamlessly with existing public transport modes.

Demographic shifts, particularly the aging population and the increasing number of working professionals, also fuel market demand. Older individuals often rely on public transport for accessibility reasons, while the working populace seeks convenient and time-saving travel options. Urban planners are increasingly considering these demographic factors in transport planning, leading to enhanced and expanded transit networks. Additionally, the shift towards smart cities is pushing the development of integrated public transport systems that are crucial for the seamless flow of urban life.

Regionally, the Asia Pacific is expected to dominate the public transport market due to rapid urbanization and economic growth in countries such as China and India. These nations are heavily investing in public transport infrastructure to support their burgeoning urban populations. Meanwhile, Europe and North America are witnessing significant technological advancements and policy initiatives aimed at modernizing existing public transport systems to make them more environment-friendly and efficient. Latin America and the Middle East & Africa are also expected to see substantial growth, driven by infrastructure development and increased investments in public transport.

Mode of Transport Analysis

The mode of transport segment in the public transport market is crucial as it encompasses the various means through which passengers are transported, including buses, trains, trams, metros, and others. Buses, being the most accessible and prevalent form, dominate the market due to their flexibility and coverage. Buses are extensively used in both urban and rural areas, offering extensive routes and frequency. The affordability and government initiatives promoting bus usage for reducing urban congestion contribute significantly to this segment's growth. Moreover, technological advancements in bus systems, such as electric and hybrid models, further enhance their appeal.

Trains and metros are central to the public transport market, especially in densely populated urban areas. These modes provide fast, reliable, and high-capacity transit solutions, making them indispensable for daily commuters in metropolitan regions. Governments are investing heavily in expanding rail networks and modernizing existing infrastructure with state-of-the-art technologies to boost efficiency and safety. The development of high-speed rail networks, particularly in Asia and Europe, highlights the segment's importance in reducing travel times and enhancing regional connectivity.

Trams are gaining popularity in urban areas due to their environmental benefits and ability to integrate w

The Transit Availability Index (formerly known as the Transit Accessibility Index) is an index measuring access to transit. The index is made up of 4 sub-indices: transit frequency, transit connectivity, sidewalk density, and transit proximity. The focus of the index is on examining how well the transit system as a whole serves the region. The index is not intended to reflect the actual transit service conditions one may encounter on a specific transit trip. It is also not intended as a means to evaluate the performance of the various transit operators nor is it a suitable tool for such an evaluation. For this analysis, transit service attributes are summed at the subzone-level geography for the seven-county region. Subzones are quarter-section sized geographies that CMAP uses for household and employment forecasting; generally they are ½ mile by ½ mile square throughout the region. Subzones in the Chicago Central Business District (CBD) are generally ¼ mile by ¼ mile square due to the densities of activities and the street network in that area.The original index was created in 2010 for the GO TO 2040 plan update. "In anticipation of the GO TO 2040 plan update, CMAP staff developed a new method of measuring access to transit as a means of determining the percentage of regional population and jobs with access to transit, one of the plan’s indicators for measuring the progress of plan implementation. This new method, the Transit Accessibility Index, severed as a uniform measure of transit level of service available during an average week. It permits us to track changes in transit level of service over time and present the results in an intuitive fashion. It also offers a universal comparison of the different service levels offered across the region. The inherent loss of some of the nuances in localized service is balanced against the ability of the index to provide a relatively simple way to compare transit service over a large area over time. This index also adheres to a number of tenets CMAP staff used in developing a revised set of performance measures for the GO TO 2040 plan update: principally that the indicator use actual observed data rather than modeled values, that it is widely comprehensible and that the data are updated with sufficient frequency for the index to serve as a reasonable access to transit index benchmark for measuring progress.”The 2023 update of transit availability is a continuation of these efforts using 2019 data. One notable change is that the Pedestrian Environment Factor is no longer used as a measurement of transit availability. Rather, we have replaced this sub-index with sidewalk density. Another notable change is that the 2017 subzones were used for this analysis, whereas previous iterations used the 2009 subzones. As such, it is not suggested to directly compare the new transit availability index with previous versions of transit availability/accessibility.Transit Availability Index, Indicator Methodology Excerpt