Real travel times in Dublin are ** percent longer than travel under free-flow conditions, making it the most congested urban sprawl in the world as of 2023. This figure refers to additional average travel time throughout the week. Tackling trust in transport With trust in public transport globally taking a knock following the outbreak of Covid-19, switching to public transport modes for commuting trips to save time, emissions, and traffic may prove difficult. Activities at transit stations declined in many cities around the world as a result of ebbing demand amid the coronavirus pandemic. Consequently, transport services in highly populated cities have suffered devastating financial losses. While public transport transit had started to pick up in the beginning of 2021, it could not offset the drop recorded as a result of the pandemic. India: a climate for new policies? Among the twelve cities displayed, India is represented by *****. To tackle high levels of congestion, a congestion pricing policy was recently proposed in India, which would serve to introduce parking fees and thus push commuters to take public transport rather than drive their cars to work. Surveys collecting public opinion on this proposal have indicated that this would be a popular policy, should it be implemented. The motive behind curbing congestion in the nation’s largest cities is more than just to reduce pollution levels and time spent in traffic; India has some of the highest levels of traffic-related fatalities globally: some ******* people died in traffic accidents in 2019 – this is the highest number on record since 2005.

Travel times in Mexico City are ** percent longer during heavy traffic times, making it the most traffic jam-prone large city in North America. While this figure refers to additional average travel time throughout the day, on average, weekday evening peak times make traveling by car even longer. In the United States, Los Angeles overtook New York as the most congested city in 2023. Effects of traffic Although working from home became the new normal amid the pandemic, workers in the U.S. can spend, on average, some ** to ** minutes commuting to work, depending on the region. This can be longer in large cities, adding a significant time to people’s working days without increasing their income. Increased traffic also leads to a higher number of fatal crashes, all else being equal. Pollution concerns Transportation is also one of the leading contributors to carbon dioxide emissions, and traffic jams contribute to this by extending the time that cars are running. Local air pollution is also a concern, particularly in Mexico where almost ****** people died from air pollution exposure in 2019.

Traffic Congestion Index: Average: United States: New York City data was reported at 22.820 Index in 24 Nov 2023. This records a decrease from the previous number of 28.110 Index for 23 Nov 2023. Traffic Congestion Index: Average: United States: New York City data is updated daily, averaging 23.835 Index from Jan 2019 (Median) to 24 Nov 2023, with 1682 observations. The data reached an all-time high of 66.530 Index in 06 Oct 2022 and a record low of 0.840 Index in 31 Mar 2020. Traffic Congestion Index: Average: United States: New York City data remains active status in CEIC and is reported by CEIC Data. The data is categorized under Global Database’s United States – Table TI.TCI: Traffic Congestion Index: Average: by Cities (Discontinued). [COVID-19-IMPACT]

Traffic Congestion Index: Average: Japan: Tokyo data was reported at 46.710 Index in 24 Nov 2023. This records an increase from the previous number of 22.510 Index for 23 Nov 2023. Traffic Congestion Index: Average: Japan: Tokyo data is updated daily, averaging 9.650 Index from Jan 2019 (Median) to 24 Nov 2023, with 1682 observations. The data reached an all-time high of 68.560 Index in 18 Mar 2019 and a record low of 0.730 Index in 01 Jan 2021. Traffic Congestion Index: Average: Japan: Tokyo data remains active status in CEIC and is reported by CEIC Data. The data is categorized under Global Database’s Japan – Table TI.TCI: Traffic Congestion Index: Average: by Cities (Discontinued). [COVID-19-IMPACT]

In 2023, the congestion level of Bengaluru amounted to ** percent each, meaning that it took ** percent more time to get from one point to another compared to a free flow situation. Comparatively, the congestion level in Sydney and Hong Kong amounted to ** and ** percent respectively during the same year.

In terms of the amount of time lost during rush hours in traffic, Dublin was the most congested city center in the world in 2023. In Dublin city center, about *** hours in a year were lost in traffic during rush hours. Furthermore, The city centers of Lima and Mexico City were the second and third most congested in the world that year, with *** and *** hours lost to traffic, respectively.

The Inrix Inc. study reveals that the United States (U.S.) is ranked as the most congested developed country in the world, with drivers spending an average of 42 hours a year in traffic during peak hours. According to this study, the direct and indirect costs of congestion to all U.S. drivers amounts to nearly $300 billion in 2016, an average of $1,400 per driver.U.S. cities dominated the top 10 most congested cities globally, with Los Angeles (first), New York (third), San Francisco (fourth), Atlanta (eighth), and Miami (10th) each dealing with an economic drain on the city upwards of $2.5 billion caused by traffic congestion. Los Angeles commuters spent an average of 104 hours last year in traffic jams during peak congestion hours more than any other city in the world. This contributed to congestion costing drivers in Los Angeles $2,408 each and the city as a whole $9.6 billion from direct and indirect costs. Direct costs relate to the value of fuel and time wasted, and indirect costs refer to freight and business fees from company vehicles idling in traffic, which are passed on to households through higher prices.

This dataset contains the current estimated speed for about 1250 segments covering 300 miles of arterial roads. For a more detailed description, please go to https://tas.chicago.gov, click the About button at the bottom of the page, and then the MAP LAYERS tab. The Chicago Traffic Tracker estimates traffic congestion on Chicago’s arterial streets (nonfreeway streets) in real-time by continuously monitoring and analyzing GPS traces received from Chicago Transit Authority (CTA) buses. Two types of congestion estimates are produced every ten minutes: 1) by Traffic Segments and 2) by Traffic Regions or Zones. Congestion estimate by traffic segments gives the observed speed typically for one-half mile of a street in one direction of traffic. Traffic Segment level congestion is available for about 300 miles of principal arterials. Congestion by Traffic Region gives the average traffic condition for all arterial street segments within a region. A traffic region is comprised of two or three community areas with comparable traffic patterns. 29 regions are created to cover the entire city (except O’Hare airport area). This dataset contains the current estimated speed for about 1250 segments covering 300 miles of arterial roads. There is much volatility in traffic segment speed. However, the congestion estimates for the traffic regions remain consistent for relatively longer period. Most volatility in arterial speed comes from the very nature of the arterials themselves. Due to a myriad of factors, including but not limited to frequent intersections, traffic signals, transit movements, availability of alternative routes, crashes, short length of the segments, etc. speed on individual arterial segments can fluctuate from heavily congested to no congestion and back in a few minutes. The segment speed and traffic region congestion estimates together may give a better understanding of the actual traffic conditions.

Provide the congested road sections and time periods of this city for public reference.

Traffic Congestion Index: Average: Czech Republic: Prague data was reported at 6.720 Index in 24 Nov 2023. This records a decrease from the previous number of 26.630 Index for 23 Nov 2023. Traffic Congestion Index: Average: Czech Republic: Prague data is updated daily, averaging 14.565 Index from Jan 2019 (Median) to 24 Nov 2023, with 1680 observations. The data reached an all-time high of 80.580 Index in 15 Dec 2022 and a record low of 0.160 Index in 28 Jun 2020. Traffic Congestion Index: Average: Czech Republic: Prague data remains active status in CEIC and is reported by CEIC Data. The data is categorized under Global Database’s Czech Republic – Table TI.TCI: Traffic Congestion Index: Average: by Cities (Discontinued). [COVID-19-IMPACT]

Modeled probability of congestion for selected cities based on Twitter and OpenStreetMap data on a grid cell basis with a resolution of 100 meters. The data set includes the cities of Barcelona, ​​Berlin, Cincinnati, Kiev, London, Madrid, Nairobi, New York City, San Francisco, Sao Paulo and Seattle. The range of values ​​is from 0 (probably normal traffic flow) to 1 (high probability of traffic flow delay). Methodology: Based on Twitter and OpenStreetMap (OSM) data, a model was trained with the help of machine learning, which predicts the probability of traffic jams within the cities. Publicly provided data from UBER was used as reference data (https://movement.uber.com). The number of tweets and the number of points of interest from OSM near roads were used as indicators in the model. In addition, car journeys were simulated with the help of the openrouteservice based on the spatial distribution of the population and relevant POIs and taken into account in the model.

These files provide more detailed outputs from BITRE's 'Freight vehicle congestion in Australia’s five major cities - 2019' publication (see: https://www.bitre.gov.au/publications/2021/freight-vehicle-congestion-australias-five-major-cities-2019), which reported freight vehicle telematics based measures of traffic congestion for freight vehicles on 53 selected routes across Australia’s five mainland state capital cities—Sydney, Melbourne, Brisbane, Adelaide and Perth. The selected routes comprise the major motorways, highways and arterial roads within each city that service both passenger and freight vehicles.

Disclaimers: https://www.infrastructure.gov.au/disclaimers.

The main aim of this dataset is to enable detection of traffic congestion from surveillance cameras using one-stage object detectors. The dataset contains congested and uncongested traffic scenes with their respective labels. This dataset is collected from different surveillance cameras video footage. To prepare the dataset frames are extracted from video sources and resized to a dimension of 500 x 500 with .jpg image format. To Annotate, the image LabelImg software has used. The format of the label is .txt with the same name as the image. The dataset is mainly prepared for YOLO Models but it can be converted to other models format.

This dataset contains the current estimated congestion for the 29 traffic regions. For a detailed description, please go to https://tas.chicago.gov, click the About button at the bottom of the page, and then the MAP LAYERS tab. The Chicago Traffic Tracker estimates traffic congestion on Chicago’s arterial streets (non-freeway streets) in real-time by continuously monitoring and analyzing GPS traces received from Chicago Transit Authority (CTA) buses. Two types of congestion estimates are produced every 10 minutes: 1) by Traffic Segments and 2) by Traffic Regions or Zones. Congestion estimates by traffic segments gives observed speed typically for one-half mile of a street in one direction of traffic. Traffic Segment level congestion is available for about 300 miles of principal arterials. Congestion by Traffic Region gives the average traffic condition for all arterial street segments within a region. A traffic region is comprised of two or three community areas with comparable traffic patterns. 29 regions are created to cover the entire city (except O’Hare airport area). There is much volatility in traffic segment speed. However, the congestion estimates for the traffic regions remain consistent for a relatively longer period. Most volatility in arterial speed comes from the very nature of the arterials themselves. Due to a myriad of factors, including but not limited to frequent intersections, traffic signals, transit movements, availability of alternative routes, crashes, short length of the segments, etc. Speed on individual arterial segments can fluctuate from heavily congested to no congestion and back in a few minutes. The segment speed and traffic region congestion estimates together may give a better understanding of the actual traffic conditions.

Worldwide Traffic Congestion Ranking [between: 19Jun2022 & 26Jun2022]

TCI, calculated only for the center of the tracked location (the city image is split in 9 equal rectangles, forming a 3x3 grid. The central rectangle is taken into consideration when calculating TCI).

Every 20 minutes, the web app saves an image for each tracked location, containing the traffic data reported by Google Maps. After a couple of minutes, the images are analyzed, and the percentages of the 4 traffic colors are calculated.

Let's call these percentages: green → P0 orange → P1 red → P2 dark red → P3

Obviously , the sum of all these percentages is 100: P0 + P1 + P2 + P3 = 100 Based on these percentages, the TCI (Traffic Congestion Index) is calculated:

TCI = (0 * P0) + (1 * P1) + (2 * P2) + (3 * P3)

So the minimum value of TCI is 0, and the maximum value of TCI is 300 (highly improbable to happen). Examples:

Traffic Congestion Index: Average: Austria: Vienna data was reported at 10.870 Index in 24 Nov 2023. This records a decrease from the previous number of 28.150 Index for 23 Nov 2023. Traffic Congestion Index: Average: Austria: Vienna data is updated daily, averaging 11.820 Index from Jan 2019 (Median) to 24 Nov 2023, with 1681 observations. The data reached an all-time high of 52.970 Index in 30 Apr 2019 and a record low of 0.580 Index in 29 Mar 2020. Traffic Congestion Index: Average: Austria: Vienna data remains active status in CEIC and is reported by CEIC Data. The data is categorized under Global Database’s Austria – Table TI.TCI: Traffic Congestion Index: Average: by Cities (Discontinued). [COVID-19-IMPACT]

This dataset contains the historical estimated congestion for over 1,000 traffic segments, starting 6/11/2024 (except for a single time slice on 3/8/2024). Older records are in https://data.cityofchicago.org/d/sxs8-h27x. The most recent estimates for each segment are in https://data.cityofchicago.org/d/n4j6-wkkf. The Chicago Traffic Tracker estimates traffic congestion on Chicago’s arterial streets (non-freeway streets) in real-time by continuously monitoring and analyzing GPS traces received from Chicago Transit Authority (CTA) buses. Two types of congestion estimates are produced every 10 minutes: 1) by Traffic Segments and 2) by Traffic Regions or Zones. Congestion estimates by traffic segments gives observed speed typically for one-half mile of a street in one direction of traffic. Traffic Segment level congestion is available for about 300 miles of principal arterials. Congestion by Traffic Region gives the average traffic condition for all arterial street segments within a region. A traffic region is comprised of two or three community areas with comparable traffic patterns. 29 regions are created to cover the entire city (except O’Hare airport area). There is much volatility in traffic segment speed. However, the congestion estimates for the traffic regions remain consistent for a relatively longer period. Most volatility in arterial speed comes from the very nature of the arterials themselves. Due to a myriad of factors, including but not limited to frequent intersections, traffic signals, transit movements, availability of alternative routes, crashes, short length of the segments, etc. Speed on individual arterial segments can fluctuate from heavily congested to no congestion and back in a few minutes. The segment speed and traffic region congestion estimates together may give a better understanding of the actual traffic conditions.

The city of Bogotá, Colombia, ranked first as the Latin American metropolis most prone to traffic jams in 2020. According to the index, the Colombian capital experienced that year an average traffic increase of 53 percent during rush hours, in comparison to the city's level of traffic in uncongested times. Peru's capital, Lima, recorded the second worst congestion level that year, meaning that a road trip during peak hours took around 42 percent longer than under low traffic conditions. Out of the top 12 Latin American cities evaluated in 2020, seven are located in Brazil.

This dataset contains the historical estimated congestion for the 29 traffic regions, starting in approximately March 2018. Older records are in https://data.cityofchicago.org/d/emtn-qqdi. The most recent estimates for each segment are in https://data.cityofchicago.org/d/t2qc-9pjd.

The Chicago Traffic Tracker estimates traffic congestion on Chicago’s arterial streets (non-freeway streets) in real-time by continuously monitoring and analyzing GPS traces received from Chicago Transit Authority (CTA) buses. Two types of congestion estimates are produced every 10 minutes: 1) by Traffic Segments and 2) by Traffic Regions or Zones. Congestion estimates by traffic segments gives observed speed typically for one-half mile of a street in one direction of traffic. Traffic Segment level congestion is available for about 300 miles of principal arterials. Congestion by Traffic Region gives the average traffic condition for all arterial street segments within a region. A traffic region is comprised of two or three community areas with comparable traffic patterns. 29 regions are created to cover the entire city (except O’Hare airport area). There is much volatility in traffic segment speed. However, the congestion estimates for the traffic regions remain consistent for a relatively longer period. Most volatility in arterial speed comes from the very nature of the arterials themselves. Due to a myriad of factors, including but not limited to frequent intersections, traffic signals, transit movements, availability of alternative routes, crashes, short length of the segments, etc. Speed on individual arterial segments can fluctuate from heavily congested to no congestion and back in a few minutes. The segment speed and traffic region congestion estimates together may give a better understanding of the actual traffic conditions. Current estimates of traffic congestion by region are available at http://bit.ly/103beCf.

🇬🇧 English:

This synthetic dataset provides location-based traffic congestion levels on an hourly basis over the last 30 days. It can be used to train time series models like LSTM and XGBoost to forecast traffic intensity.

Use this dataset to:

• Train time series models to predict congestion levels
• Analyze traffic patterns based on location and time
• Develop AI-powered traffic management systems

Features:

• location: Name of the location or neighborhood
• date: Date in YYYY-MM-DD format
• time: Hour of the day (e.g., 08:00)
• congestion_level: Congestion score between 0 (low) and 10 (high)

🇹🇷 Türkçe:

Bu sentetik veri seti, son 30 güne ait saatlik trafik yoğunluğu bilgilerini lokasyon bazlı olarak sunar. Trafik yoğunluğunu tahmin etmeye yönelik zaman serisi modellerinin eğitimi için uygundur.

Bu veri seti ile:

• LSTM ve XGBoost gibi modellerle trafik tahmini yapılabilir
• Lokasyon ve saate göre trafik analizi yapılabilir
• Trafik yönetim sistemleri geliştirilebilir

Özellikler:

• location: Lokasyon adı
• date: Tarih bilgisi (YYYY-MM-DD)
• time: Günün saati (örn. 08:00)
• congestion_level: 0 (düşük) ile 10 (yüksek) arasında trafik yoğunluğu skoru