In the fiscal year 2023, the congestion rate of main railway sections in Japan's Nagoya Metropolitan Area increased from *** to *** percent. Congestion rates in Nagoya were higher than in Osaka, but lower than in Tokyo.

the data of Gaussian Process Regression Based Traffic Prediction and Rate Coordination for Service Chain Congestion Optimization

One measure used to analyze roadway reliability is the Planning Time Index (PTI). It is the ratio of the 95th percentile travel time relative to the free-flow (uncongested) travel time. PTI helps in understanding the impacts of nonrecurring congestion from crashes, weather, and special events. It approximates the extent to which a traveler should add extra time to their trip to ensure on-time arrival at their destination. A value of 1.0 indicates a person can expect free-flow speeds along their route. A 2.0 index value indicates a traveler should expect that the trip could be twice as long as free-flow conditions. PTI values from 2.0 to 3.0 indicate moderate unreliability, and ones greater than 3.0 are highly unreliable.

The data comes from aggregated Global Positioning System probe data—anonymized data from mobile apps, connected vehicles, and commercial fleets—provided to the Probe Data Analytics (PDA) Suite by INRIX, a travel data technology company. The PDA Suite was created by a consortium of sponsors, including the Eastern Transportation Coalition and the University of Maryland.

PTI values by region, subregion, and county are grouped either as highway facilities or local roads. Highways are roadway segments classified as interstates, turnpikes, and expressways in the PDA Suite. Local roads are segments classified as U.S. routes, state routes, parkways, frontages, and others. The PDA Suite reports weekday hourly averages by facility type and direction. Average weekday values are reported by facility type and direction, within the following time periods:

• Morning (AM): 6:00 AM–9:59 AM;
• Midday (MD): 10:00 AM–2:59 PM;
• Evening (PM): 3:00 PM–6:59 PM;
• Nighttime (NT): 7:00 PM–5:59 AM; and
• Daily: 12:00 AM–11:59 PM.

Although INRIX data collection precedes years reported in Tracking Progress, early years of reporting are highly variable based on a lack of facility coverage. The years from 2011 onward show higher stability for highway facilities for most counties and for the region. For local facilities, 2014 and beyond is where values seem most stable due to more widespread facility coverage.

Historic data for the federal Transportation Performance Management (TPM) system performance reporting requirements is shown. These are Level of Travel Time Reliability (LOTTR), Level of Truck Travel Time Reliability (TTTR), and Annual Hours of Peak-Hour Excessive Delay (AHPHED). The entire states of Pennsylvania and New Jersey are included for LOTTR and TTTR, so the region’s figures can be compared with statewide data.

LOTTR is used to calculate the percentage of roadway segments that are considered reliable. A road segment with an LOTTR of less than 1.5 is considered reliable. Reliable segment lengths in miles are multiplied by their Annual average daily traffic (AADT) values times the average number of people in a vehicle. Then, this sum is then divided by the exact same product for all road segments, to get the resulting percentage of roadway that is reliable for the geography.

TTTR measures how consistent travel times are for trucks on interstates. This can be helpful with analyzing goods movement along the region’s interstates. TTTR is calculated by dividing the 95th percentile of travel times by the 50th percentile of travel times, using the highest value over the Morning (AM), Midday (MD), Evening (PM), Nighttime (NT), and weekend. Each interstate segment multiplies its length by the travel time ratio, the results are summed and then divided by total Interstate length in the geography to determine the area’s TTTR value.

AHPHED is the average number of hours per year spent by motorists driving in congestion during peak periods. This can be useful for analyzing the impact of congestion from an individual’s perspective, since it analyzes how many hours the average person spends stuck in congestion. The figures used are based on the 2010 urbanized area boundaries in the Census. In 2020, they were renamed to urban areas. There are only Mercer County PHED values from 2021 onward, because they only apply to the second four-year TPM performance period, when the Trenton, NJ Urban Area was required to track metrics and set performance targets. AHPHED per capita is that figure divided by the urban area’s population during that year.

Congestion is susceptible to external forces like the economy. A downturn can reduce congestion, but this reflects fewer and shorter trips for households and businesses during lean times and may not represent an improvement. Therefore, it may be useful to correlate these results with the Miles Driven indicator.

SDCC Traffic Congestion Saturation Flow Data for January to June 2023. Traffic volumes, traffic saturation, and congestion data for sites across South Dublin County. Used by traffic management to control stage timings on junctions. It is recommended that this dataset is read in conjunction with the ‘Traffic Data Site Names SDCC’ dataset.A detailed description of each column heading can be referenced below;scn: Site Serial numberregion: A group of Nodes that are operated under SCOOT control at the same common cycle time. Normally these will be nodes between which co-ordination is desirable. Some of the nodes may be double cycling at half of the region cycle time.system: SCOOT STC UTC (UTC-MX)locn: Locationssite: Site numbersday: Days of the week Monday to Sunday. Abbreviations; MO,TU,WE,TH,FR,SA,SU.date: Reflects correct actual Date of when data was collected.start_time: NOTE - Please ignore the date displayed in this column. The actual data collection date is correctly displayed in the 'date' column. The date displayed here is the date of when report was run and extracted from the system, but correctly reflects start time of 15 minute intervals. end_time: End time of 15 minute intervals.flow: A representation of demand (flow) for each link built up over several minutes by the SCOOT model. SCOOT has two profiles:(1) Short – Raw data representing the actual values over the previous few minutes(2) Long – A smoothed average of values over a longer periodSCOOT will choose to use the appropriate profile depending on a number of factors.flow_pc: Same as above ref PC SCOOTcong: Congestion is directly measured from the detector. If the detector is placed beyond the normal end of queue in the street it is rarely covered by stationary traffic, except of course when congestion occurs. If any detector shows standing traffic for the whole of an interval this is recorded. The number of intervals of congestion in any cycle is also recorded.The percentage congestion is calculated from:No of congested intervals x 4 x 100 cycle time in seconds.This percentage of congestion is available to view and more importantly for the optimisers to take into account.cong_pc: Same as above ref PC SCOOTdsat: The ratio of the demand flow to the maximum possible discharge flow, i.e. it is the ratio of the demand to the discharge rate (Saturation Occupancy) multiplied by the duration of the effective green time. The Split optimiser will try to minimise the maximum degree of saturation on links approaching the node.

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Asia-Pacific Tolling and City Congestion Market Size 2024-2028

The Asia-Pacific (APAC) - Tolling and City Congestion Market size is forecast to increase by USD 3.57 billion, at a CAGR of 16.07% between 2023 and 2028. Electronic tolling systems offer significant benefits for both users and operators. For motorists, the convenience of contactless payments and faster travel through toll plazas is a major advantage. The increasing number of vehicles on the road necessitates efficient traffic management solutions, making electronic tolling an essential component of modern transportation infrastructure. Moreover, electronic tolling systems enable real-time data collection and analysis, providing valuable insights for transportation authorities to optimize traffic flow and improve overall road network efficiency. The rise in demand for seamless and hassle-free travel experiences further underscores the importance of electronic tolling systems in today's fast-paced world.

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

APAC Tolling and City Congestion Market Forecast 2024-2028

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Market Dynamic

The evolution of tolling systems has seen significant advancements with the introduction of electronic toll collection methods such as radio-frequency identification (RFID), global navigation satellite system (GNSS), and global positioning system (GPS). These technologies enable seamless transactions on toll bridges, toll roads, and toll tunnels by automatically deducting fees without requiring vehicles to stop. Countries like Guatemala face challenges such as raw material shortages and shipping delays impacting infrastructure projects, including toll road operations. Innovations in video analytics and infrared technology enhance traffic monitoring and toll enforcement, ensuring efficient toll collection and traffic management. Electronic Toll Collection (ETC) systems deployed by toll road operators improve user convenience and road network efficiency. Dedicated short-range communication (DSRC) further enhances the reliability of tolling infrastructure, providing real-time data for transportation planning and revenue management.

Driver - Convenience of electronic tolling for users and operators

Electronic tolling makes toll and parking payments convenient and swift for drivers. Automated collection of payments with minimal physical interactions could also be made possible by operators, thereby improving operational efficiency. In APAC, the adoption of RFID-based electronic tolling is rapidly growing, owing to the low cost of RFID technology.

Moreover, electronic tolling is also used for vehicle identification, eliminating the inconvenience of carrying physical copies of vehicle papers. Electronic tolling systems for new vehicles in these countries will also be needed, with the aim of improving road infrastructure. Therefore, the rising convenience of electronic tolling for users and operators will drive the growth of the APAC - Tolling and City Congestion Market during the forecast period.

Trends - Increasing investment in smart cities by governments

Initiatives to make cities more efficient, sustainable, and livable frequently involve the use of advanced technology. A major component of smart city initiatives is the use of intelligent traffic management technologies that monitor, control, and manage traffic flow, alleviate congestion, and improve safety. A variety of urban challenges, including traffic congestion, air pollution, and energy efficiency, are being addressed by governments in numerous nations through the implementation of smart city initiatives.

Moreover, the increasing government investment in smart city initiatives is hastening the adoption of intelligent traffic management technologies in cities all over the world. This contributes to improving traffic flow, reducing congestion, and increasing security in cities. Therefore, the Asia Pacific - Tolling and City Congestion Market is anticipated to be positively impacted by governments' increasing investments in smart cities during the forecasted period.

Challenge - Lack of proper transportation infrastructure

A lack of adequate transportation infrastructure will negatively impact intelligent traffic management. If a transport system does not have sufficient roads, bridges, tunnels, and other vital elements, it will be very difficult to guarantee an efficient flow of traffic. It will be difficult to control traffic signals, lane usage, and other aspects of traffic management without a sufficient transportation infrastructure.

Additionally, it will be more difficult to maintain clear roads and minimal congestion if transportation infrastructure is lacking. Consequently, productivity will be severely affected and emergency services may require more time to reach the scene. Along with lowering

The global tolling and city congestion market, valued at $3.23 billion in 2025, is projected to experience robust growth, driven by increasing urbanization, traffic congestion in major cities worldwide, and the growing need for efficient transportation management systems. The market's Compound Annual Growth Rate (CAGR) of 16.07% from 2025 to 2033 indicates significant expansion potential. Key growth drivers include the rising adoption of advanced technologies like Urban Traffic Management (UTM) systems, electronic tolling solutions, and Automated Traffic Management Systems (ATMS). These technologies offer improved traffic flow, reduced congestion, and enhanced revenue collection for municipalities. Furthermore, government initiatives promoting sustainable transportation and smart city development are fueling market growth. While data privacy concerns and the initial high capital investment required for implementing these systems pose challenges, the long-term benefits of reduced congestion and improved traffic efficiency outweigh these restraints. The market is segmented by product type (UTM, electronic tolling, ATMS) and geography, with North America and Europe currently dominating the market share due to advanced infrastructure and high technology adoption rates. However, rapid urbanization and infrastructure development in Asia-Pacific are expected to drive substantial growth in this region over the forecast period. Major players like Conduent Inc., Cubic Corp., and Kapsch TrafficCom are leading the market with innovative solutions and strategic partnerships. Competitive strategies focus on technological advancements, geographic expansion, and mergers and acquisitions to consolidate market share. The market's success hinges on effective partnerships between public and private sectors, fostering collaborative efforts in planning, implementing, and maintaining efficient tolling and traffic management systems. Technological advancements, particularly in artificial intelligence and machine learning, are expected to further enhance the capabilities of these systems, leading to improved accuracy, efficiency, and real-time traffic optimization. The integration of these systems with other smart city initiatives will further contribute to the overall growth of the market. While challenges remain, the long-term outlook for the tolling and city congestion market remains positive, driven by the persistent need for sustainable and efficient urban transportation solutions.

In the fiscal year 2023, the congestion rate of main railway sections in Japan's Osaka Metropolitan Area increased from *** to *** percent. Accordingly, trains were occupied only slightly above their designated passenger number.

SDCC Traffic Congestion Saturation Flow Data 2019. Traffic volumes, traffic saturation, and congestion data for sites across South Dublin County. Used by traffic management to control stage timings on junctions. It is recommended that this dataset is read in conjunction with the ‘Traffic Data Site Names SDCC’ dataset.A detailed description of each column heading can be referenced below;scn: Site Serial numberregion: A group of Nodes that are operated under SCOOT control at the same common cycle time. Normally these will be nodes between which co-ordination is desirable. Some of the nodes may be double cycling at half of the region cycle time.system: SCOOT STC UTC (UTC-MX)locn: Locationssite: Site numbersday: Days of the week Monday to Sunday. Abbreviations; MO,TU,WE,TH,FR,SA,SU.date: Reflects correct actual Date of when data was collected.start_time: NOTE - Please ignore the date displayed in this column. The actual data collection date is correctly displayed in the 'date' column. The date displayed here is the date of when report was run and extracted from the system, but correctly reflects start time of 15 minute intervals. end_time: End time of 15 minute intervals.flow: A representation of demand (flow) for each link built up over several minutes by the SCOOT model. SCOOT has two profiles:(1) Short – Raw data representing the actual values over the previous few minutes(2) Long – A smoothed average of values over a longer periodSCOOT will choose to use the appropriate profile depending on a number of factors.flow_pc: Same as above ref PC SCOOTcong: Congestion is directly measured from the detector. If the detector is placed beyond the normal end of queue in the street it is rarely covered by stationary traffic, except of course when congestion occurs. If any detector shows standing traffic for the whole of an interval this is recorded. The number of intervals of congestion in any cycle is also recorded.The percentage congestion is calculated from:No of congested intervals x 4 x 100 cycle time in seconds.This percentage of congestion is available to view and more importantly for the optimisers to take into account.cong_pc: Same as above ref PC SCOOTdsat: The ratio of the demand flow to the maximum possible discharge flow, i.e. it is the ratio of the demand to the discharge rate (Saturation Occupancy) multiplied by the duration of the effective green time. The Split optimiser will try to minimise the maximum degree of saturation on links approaching the node.

Lyon recorded the lowest congestion level in April 2020, in the midst of a national lockdown in response to the COVID-19 pandemic. This congestion rate fluctuated during the rest of the year, ending at 26 percent in December. The yearly average congestion rate of 25 percent was an overall decrease compared to 2019.

SDCC Traffic Congestion Saturation Flow Data 2019. Traffic volumes, traffic saturation, and congestion data for sites across South Dublin County. Used by traffic management to control stage timings on junctions. It is recommended that this dataset is read in conjunction with the ‘Traffic Data Site Names SDCC’ dataset.A detailed description of each column heading can be referenced below;scn: Site Serial numberregion: A group of Nodes that are operated under SCOOT control at the same common cycle time. Normally these will be nodes between which co-ordination is desirable. Some of the nodes may be double cycling at half of the region cycle time.system: SCOOT STC UTC (UTC-MX)locn: Locationssite: Site numbersday: Days of the week Monday to Sunday. Abbreviations; MO,TU,WE,TH,FR,SA,SU.date: Reflects correct actual Date of when data was collected.start_time: NOTE - Please ignore the date displayed in this column. The actual data collection date is correctly displayed in the 'date' column. The date displayed here is the date of when report was run and extracted from the system, but correctly reflects start time of 15 minute intervals. end_time: End time of 15 minute intervals.flow: A representation of demand (flow) for each link built up over several minutes by the SCOOT model. SCOOT has two profiles:(1) Short – Raw data representing the actual values over the previous few minutes(2) Long – A smoothed average of values over a longer periodSCOOT will choose to use the appropriate profile depending on a number of factors.flow_pc: Same as above ref PC SCOOTcong: Congestion is directly measured from the detector. If the detector is placed beyond the normal end of queue in the street it is rarely covered by stationary traffic, except of course when congestion occurs. If any detector shows standing traffic for the whole of an interval this is recorded. The number of intervals of congestion in any cycle is also recorded.The percentage congestion is calculated from:No of congested intervals x 4 x 100 cycle time in seconds.This percentage of congestion is available to view and more importantly for the optimisers to take into account.cong_pc: Same as above ref PC SCOOTdsat: The ratio of the demand flow to the maximum possible discharge flow, i.e. it is the ratio of the demand to the discharge rate (Saturation Occupancy) multiplied by the duration of the effective green time. The Split optimiser will try to minimise the maximum degree of saturation on links approaching the node.

Congestion levy rates for 2023

cong: Congestion is directly measured from the detector. If the detector is placed beyond the normal end of queue in the street it is rarely covered by stationary traffic, except of course when congestion occurs. If any detector shows standing traffic for the whole of an interval this is recorded. The number of intervals of congestion in any cycle is also recorded.The percentage congestion is calculated from:No of congested intervals x 4 x 100 cycle time in seconds.This percentage of congestion is available to view and more importantly for the optimisers to take into account.cong_pc: Same as above ref PC SCOOTdsat: The ratio of the demand flow to the maximum possible discharge flow, i.e. it is the ratio of the demand to the discharge rate (Saturation Occupancy) multiplied by the duration of the effective green time. The Split optimiser will try to minimise the maximum degree of saturation on links approaching the node.

These data document historical toll gates, including locations, rates and other regulations, in the period 01.01.2005-31.12.2021. The data cover all known toll gates within Norwegian borders. The data reveals that both the number of toll gates and the toll level has increased over time. In recent years, the discount for electric vehicles and other zero-emission vehicles has been reduced. Detailed historical data of this kind can be used to study a number of transport economic issues, for instance related to travel demand, Pigouvian taxation, electric vehicle incentives and distributional effects. We encourage those who are interested to use the data to contact us if you notice any errors or irregularities.

The Congestion Levy is administered by the State Revenue Office pursuant to the Congestion Levy Act 2005 (the Act). The Act came into operation on 1 January 2006 and its purpose is to impose a levy on parking spaces in the central business district and inner Melbourne to reduce traffic congestion.\r \r This file contains the Congestion Levy rates for 2020 and includes the following: Area and Amount.

As of January 2025, the Davao Prison & Penal Farm was the most congested prison facility in the Philippines. The prison facility had a 608 percent congestion rate as of this period. It also had the highest occupancy rate among other prison cells in the Philippines.

cong: Congestion is directly measured from the detector. If the detector is placed beyond the normal end of queue in the street it is rarely covered by stationary traffic, except of course when congestion occurs. If any detector shows standing traffic for the whole of an interval this is recorded. The number of intervals of congestion in any cycle is also recorded.The percentage congestion is calculated from:No of congested intervals x 4 x 100 cycle time in seconds.This percentage of congestion is available to view and more importantly for the optimisers to take into account.cong_pc: Same as above ref PC SCOOTdsat: The ratio of the demand flow to the maximum possible discharge flow, i.e. it is the ratio of the demand to the discharge rate (Saturation Occupancy) multiplied by the duration of the effective green time. The Split optimiser will try to minimise the maximum degree of saturation on links approaching the node.

Congestion Levy Rates - 2012\r \r

Congestion Levy Rates - 2013

This paper presents a propagation dynamics model for congestion propagation in complex networks of airspace. It investigates the application of an epidemiology model to complex networks by comparing the similarities and differences between congestion propagation and epidemic transmission. The model developed satisfies the constraints of actual motion in airspace, based on the epidemiology model. Exploiting the constraint that the evolution of congestion cluster in the airspace is always dynamic and heterogeneous, the SIR epidemiology model (one of the classical models in epidemic spreading) with logistic increase is applied to congestion propagation and shown to be more accurate in predicting the evolution of congestion peak than the model based on probability, which is common to predict the congestion propagation. Results from sample data show that the model not only predicts accurately the value and time of congestion peak, but also describes accurately the characteristics of congestion propagation. Then, a numerical study is performed in which it is demonstrated that the structure of the networks have different effects on congestion propagation in airspace. It is shown that in regions with severe congestion, the adjustment of dissipation rate is more significant than propagation rate in controlling the propagation of congestion.

This project focused specifically on design treatments that can be used to improve travel time reliability. The objectives of this research were to (1) identify the full range of possible roadway design features used by transportation agencies to improve travel time reliability and reduce delays from key causes of nonrecurrent congestion, (2) assess their costs and operational and safety effectiveness, and (3) provide recommendations for their use and eventual incorporation into appropriate design guides. This research generated two companion products that allow transportation agencies and professionals to apply these research findings effectively in daily practice. These products are the Design Guide for Addressing Nonrecurrent Congestion, which is a catalogue of the design elements and their associated use information, and the Analysis Tool for Design Treatments to Address Nonrecurring Congestion, which is a tool to execute the various analysis procedures and models to measure the effectiveness of a design element on travel time reliability. This zip file contains comma separated value (.csv) files of data to support SHRP 2 Report S2-L07-RR-1, Identification and Evaluation of the Cost-Effectiveness of Highway Design Features to Reduce Nonrecurrent Congestion, https://rosap.ntl.bts.gov/view/dot/4040 The compressed zip file is 12 MB. These files can be unzipped using any zip compression/decompression software. The .csv files can be read with any basic text editor.