California Department of Transportation

Other Traffic Data Topics:

Department Reports:

The Mile Marker, January 2014
The Mile Marker, August 2014
Caltrans Program Review Final Report January 2014

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Mobility Performance Reporting and Analysis Program

The Mobility Performance Reporting and Analysis Program manages the quarterly and annual reporting of transportation system performance information. The annual report, the Mobility Performance Report (MPR), is legislatively mandated by Government Code section 14032.6:

The department shall, within existing resources, collect, analyze, and summarize highway congestion data and make it available upon request to California regional transportation planning agencies, congestion management agencies, and transit agencies.

Prior to 2009, Caltrans' annual congestion report was known as the State Highway Congestion Monitoring Program (HICOMP) Annual Data Compilation.  The name was changed in recognition of the large methodological changes that were implemented starting in 2009.  Because of these changes, comparison of the data between the two reports is not recommended.

Quarterly reports are prepared by each Caltrans district.

Click on the tabs above to be taken to documents available on this website:

  • At a Glance: Dashboard values of mobility indicators
  • Historical: Statewide and District charts depicting time series data over 2001-2013
  • Analytics: Statewide charts depicting the interaction of two or more mobility indictors over 2001-2013
  • Bottlenecks: Statewide Bottleneck Maps, District Bottleneck Maps
  • Data Source: Performance Measurement System explanation and discussion
  • Statistics: Statewide Annual and District Quarterly Mobility Performance Statistics
  • Official Reports: Mobility Performance Reports (MPR) and HICOMP

Mobility Performance Reporting and Analysis Program Staff

Districts

District Contact List

Headquarters

Manager/Coordinator
Ms. Dru Dunton
Webmaster, Subject Matter Expert, content provider
916-653-4310
dru.dunton@dot.ca.gov

Bottleneck Mapping Project
Vacant

Ad hoc:

GIS Staff, Assets Management Branch
Ms. Shiriedel Acayan
Transportation Planner (GIS)
916-651-9377
shiriedel.acayan@dot.ca.gov


If you would like to be informed when content has been added or updated, please click here to send an email request.



Updated: October 8, 2014

At a Glance

Percentage Statewide Freeway Vehicle Detection Coverage in 2013

Population
Employment
Mainline Miles


Measure

2013 Value

Percentage Change over 2012

Direction of Change

Five-Year Trend

Population 38.3 million .9%

Employment 16.9 million 2.07%

Detection 38 thousand 7%

Vehicle Miles of Travel

126.3 million 8%

Vehicle Hours of Delay 248 million at 60 miles per hour
-------------
105.7 million at 35 miles per hour

12%
------------

13%

Gasoline $3.93 per gallon -3.6%

Historical Statistics

Statewide  District  Data Sources

Because permanent vehicle detectors have been installed on California's State highways, we are able to obtain reliable data with which to characterize the use of the State highways in terms of miles of travel, hours of delay, and other measures of mobility. The data not only describe performance, but also enable managers and stakeholders to identify locations where corrections should be made to ensure the proper allocation of public resources.

Most of California's population (97%) live in areas that have detection. Detection is not deployed in the North Coast, Shasta Cascade, or High Sierra regions of California. These areas are enclosed within Caltrans' districts 1, 2, and 9, and include the following counties: Del Norte, Humboldt, Lake, and Mendocino (North Coast, D1); Lassen, Modoc, Plumas, Shasta, Siskiyou, Tehama, and Trinity (Shasta Cascade, D2); Inyo and Imperial (High Sierra, D9).

Some counties within other districts do not have detection: Colusa and Glenn (Sacramento Area, D3); San Benito (Central Coast, D5); Kings (south Central Valley, D6); Alpine (north Central Valley, D10); and Imperial (San Diego-Imperial, D11).

Other counties did not have detection over the full survey period, 2001-2013. They include Sierra (detection deployed in 2010), Butte (detection deployed in 2011), Mariposa (detection deployed in 2012), and Central Coast counties Monterey, San Luis Obispo, Santa Barbara, and Santa Cruz (detection deployed in 2012/2013).

The source of the data for the survey period 2001-2013 was Performance Measurement System (PeMS) version 12.3, not the current version 14.x. The extant official reports, HICOMP and MPR, are all based on the PeMS database that used the same speed algorithms over that period. PeMS 14 uses a different set; thus, the values reported here more closely match the published reports. More discussion about the differences between PeMS versions 12. 3 and 14 can be found in the Data Source tab.


Statewide

Population  Employment  Detection  Vehicles Miles of Travel  Vehicle Hours of Delay  Fuel Costs  Freeway Congestion


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Population   

California's population grew slowly over the period 2001-2013, a cumulative total of 10.4%, or an average of just under .9% annually. During the economic downtown, the rate of increase dropped steadily from 2007 through 2010, when it gained momentum. Population increased by .9% last year over 2012. The proportion of California's population that lives in areas where PeMS detection is present increased by 5% because detection was deployed in Monterey, San Luis Obispo, Santa Barbara, and Santa Cruz counties beginning in 2012.


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Employment

California's employment was relatively flat through 2007, after which it declined by 4.4% in 2009 from 2008. The rate of employment has ticked up since then, and levelled off the past two years. Employment increased by 2% last year over 2012. The proportion of California's employed that lives in areas where PeMS detection is present increased by 6% because detection was deployed in Monterey, San Luis Obispo, Santa Barbara, and Santa Cruz counties beginning in 2012.


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Detection

The number of detectors grew by 302% over the past 13 years, at an average annual rate of 25%. The rate of deployment has slowed in the past three years. Much of the recent deployment has been in Monterey, San Luis Obispo, Santa Barbara, and Santa Cruz counties beginning in 2012, and District 8. The percentage of detectors reported good and bad has varied over time. In 2013, a daily average of 64% of detectors were reported good and 36% were reported bad. Bad detectors include those categorized on a given day as "Line Down", "Controller Down", "No Data", "Insufficient Data", "Card Off", "High Value", "Intermittent", "Constant", or "Feed Unstable".


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Vehicle Miles of Travel (VMT)

Not all of California's highways have vehile detection stations, largely because historically there has been comparatively little use or delay on large stretches of highway in rural areas. Caltrans' Division of Research, Innovation, and System Information tracks the vehicle miles of travel on all California roadways, including all State highways. The chart below shows how an increasing percentage–a percentage that has more than doubled since 2001–of State highways are monitored with PeMS detection.


With an increase in detection comes an increase in reported use (vehicle miles of travel) and delay. The interplay of the change in detection, VMT, and VHD are evaluated on the Analytics page. VMT has increased steadily since 2001; however, the rate of increase slowed during periods of economic downturn, most notably in 2006, 2008, and 2011. VMT increased 8.3% last year over 2012.


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Vehicle Hours of Delay

Delay recorded on State highways with detection is measured at a number of speeds, but the most common are severe delay–all delay through 35 miles per hour–and all delay up to 60 miles per hour. The chart below shows that severe delay currently accounts for about 43% of all recorded delay, a proportion that has trended slightly upward since 2009.


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Fuel Costs

Retail prices of gasoline in California have nearly tripled in the past 13 years. There was some volatility during the economic downturn, but the trend upward picked up again in 2010. Fuel prices declined 4% in 2013 over 2012. The interplay between fuel prices and travel demand as measured in vehicle miles of travel is evaluated on the Analytics page.


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Freeway Congestion

Congestion–the outcome of delay experienced by California's motorists–is understood most clearly when expressed as the total amount of delay on a particular freeway in a particular county. Using the methodology established in the mobility performance reporting and analysis program (MPRP) initiated in 2009, total delay was parsed into freeways by county, as shown in the chart below. Delay is shown in stacked columns that represent cumulative congestion over the four years by freeway location. This chart is one of several graphical representations of the change in freeway congestion over time. Several others follow.

Delay by freeway at 35 miles per hour (mph) was not calculated in the MPRP until 2012. All charts below measure freeway delay up to 60 mph except the last, which depicts freeway delay at 35 mph for 2012 only.


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The chart below shows freeway delay by route and county for each of the four MPRP years, in rank order based upon delay in 2012, highest to lowest.


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The bump chart below depicts the rank order volativity over the past four years. Rank order between 2009 and 2011 moved around considerably below the third rank, but rank order between 2011 and 2012 was fairly stable between ranks 1 and 13.


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Freeway delay at 35 mph in 2012 largely followed the rank order of freeway delay in the same year at 60 mph. The top four are the same, but other freeways swap positions (one or two ranks) between the two lists. In all, 18 of 20 freeways appear on both lists. I–105 in Los Angeles and SR–91 in Orange County don't make the top twenty at 35 mph; SR–57 and SR–55, both in Orange County, post more significant delay at 35 mph than at 60 mph. Interstate 80 in Alameda County ranks number 17 at 60 mph; at 35 mph, it shoots up to rank 10.



Districts

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Population  Employment  Detection  Vehicles Miles of Travel  Vehicle Hours of Delay at 35 mph  Vehicle Hours of Delay at 60 mph  

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Population

Population growth varied among the districts. District 8, composed of San Bernardino and Riverside counties, grew 27% in the 13 year period surveyed, followed by District 6, south Central Valley, which grew 21%. The largest numerical growth occurred also in District 8, followed by the Bay Area, south Central Valley, and Los Angeles. The Bay Area posted the largest population gain last year over 2012.

 
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Employment

Employment rose and fell among the districts over the survey period. The Bay Area was the biggest employment loser (-8,300); District 8 gained the most jobs (+236,900). The south Central Valley and the San Diego area also posted gains over 100,000. Los Angeles gained the most employment in 2013 over 2012; the Bay Area was a close second. All districts gained in employment in 2013 over 2012.

 
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Detection

Detection increased in all districts over the period 2009-2013. District 4 gained the most detection over the period; District 8 showed the largest percentage increase in 2013 over 2009. Overall, detection in all districts increased 39% in 2013 over 2009.

 

 


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Vehicle Miles of Travel (VMT)

Changes in VMT can be misleading, given that until system buildout is achieved, an increase in detection generally causes an increase in the metric being measured. See the charts on the Analytics page that consider detection and VMT together.

 
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Vehicle Hours of Delay (VHD) at 35 miles per hour

Changes in VHD can be misleading, given that until system buildout is achieved, an increase in detection generally causes an increase in the metric being measured. See the charts on the Analytics page that consider detection and VHD together.

 
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Vehicle Hours of Delay (VHD) at 60 miles per hour

Changes in VHD can be misleading, given that until system buildout is achieved, an increase in detection generally causes an increase in the metric being measured. See the charts on the Analytics page that consider detection and VHD together.

 

Data Sources

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Population Data:

State of California, Department of Finance: E-4 Population Estimates for Cities, Counties, and the State, 2001-2010, with 2000 & 2010 Census Counts. Sacramento, California, November 2012; and E-4 Population Estimates for Cities, Counties, and the State, 2011-2014, with 2010 Benchmark, Sacramento, California, May 1, 2014.
Available at http://www.dof.ca.gov/research/demographic/reports/estimates/e-4/2011-20/view.php

Employment Data:

State of California, Employment Development Department (EDD), Labor Market Information Division. Downloaded using the Data Search Tool available at:http://www.labormarketinfo.edd.ca.gov/cgi/dataanalysis/areaselection.asp?tablename=labforce in June and July 2014.

Performance Measurement System (PeMS) Data:

Accessed from PeMS Legacy downloaded June-July 2014.

The source of the data for the survey period 2001-2013 was PeMS version 12.3, not the current version 14.x. The extant official reports, HICOMP and MPR, are all based on the PeMS database that used the same speed algorithms over that period. PeMS 14 uses a different set of algorithms; thus, the values reported here more closely match the published reports. More discussion about the differences between PeMS versions' 12. 3 and 14 can be found in the Data Source tab.

Caltrans Division of Research, Innovation, and System Information (DRISI) Data:

Division of Research, Innovation, and System Information (DRISI), Public Road Data, 2001-2012, accessed from http://www.dot.ca.gov/hq/tsip/hpms/datalibrary.php, June 2014.

Statewide Tables and Figures Data:

Published on this Web site (under the Statistics tab) as Statewide Mobility Performance Statistics, these data are preliminary and unofficial, with respect to their publication status as a Mobility Performance Report. In practice, the unofficial statistics are identical to the official statistics in the future published reports. Statewide Tables and Figures are provided to meet our public reporting requirements in a timely manner.

Fuel Data:

U.S.Department of Energy, U.S. Energy Information Administration, Weekly Retail Gasoline and Diesel Prices, California. Series history download accessed at http://www.eia.gov/dnav/pet/pet_pri_gnd_dcus_nus_w.htm on July 14, 2014.

Analytical Statistics

Statewide    Data Sources

Statewide

Population and Mobility Performance Change    Population and VMT    Population and VHD

Employment and Mobilty Performance Change    Employment and VMT    Employment and VHD

VMT and VHD    VHD and VMT

Fuel Prices and VMT    Change in Gas Prices and VMT


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Population and Vehicle Miles of Travel (VMT), Vehicle Hours of Delay at 60 miles per hour, and Detection Change

As population increased incrementally over the period, detection deployment spiked in 2006-2007, and again in 2010. Coincidentally, delay also spiked, but plummeted during the economic downturn regardless of the increased detection. VMT change followed the direction of deployment change for most of the period, except three years when the rate of VMT increase slowed while detection increased. The deployment of detection is now moving toward buildout, and the rate of increase is slowing from an annual average of 25% to just above 5%. VMT and delay have been steadily increasing since 2011. Both rates dropped dramatically from historic highs in 2010, but delay actually posted a negative rate, dropping nearly 10% from the year before.

 


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Population and VMT

VMT increased steadily over the survey period, from just over 1,600 miles driven (and detected) per person, to over twice that amount, 3,400 miles driven (and detected) per person in 2013. The extent to which this can be explained by an increase in detection can be teased out by looking at the data published by DRISI, which shows a peak of 185 billion VMT in 2006 from 168 billion in 2001, dropping down to 178 billion in 2012. The methodology used to collect the DRISI data is different from PeMS, and Caltrans is researching the reasons why the data differ, apart from the change in PeMS detection over time.

 
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Population and VHD

The slight steady increase in population did not correlate strongly with the behavior of delay at either 35 or 60 miles per hour. Delay more closely followed employment as a proxy for the economy, as seen below, rather than population.

 
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Employment and Vehicle Miles of Travel (VMT), Vehicle Hours of Delay at 60 miles per hour, and Detection Change

Employment in the areas with detection hovered around 15 million over most of the period. In 2013, detection was added to the Central Coast, accounting for a 6% increase in employment in PeMS-covered areas. Delay values in 2007-2009 reflected the decline in employment. The spike in 2010 might be explained by the substantial increase in detection that year. As detection deployment slowed beginning in 2011, employment, VMT, and VHD moved in the same direction: as employment increased, VMT and VHD grew, and grew at an increasing rate.

 


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Employment and VMT

VMT showed absolute number gains throughout the survey period. Some of the gains may be explained by the increase in detection. Even in the years of economic downturn, when employment dipped, VMT still rose. It may be claimed that motorists still drive despite a bad economy and declining employment, but that claim cannot be proved. What would vehicle miles of travel and vehicle hours of delay have been if we had full detection is a counterfactual question that can't be answered here. One controversial approach is to extrapolate current levels into the past. This approach might be worth consideration in the future after deployment has been built out and detection health has risen into the target zone for a period of several years or more. Nonetheless, we will never be certain what real delay would have been with full detection in place.

 


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Employment and VHD

We would expect to see an increase in severe congestion (that is, delay up to 35 miles per hour) when the economy is on the uptick, assuming that delay has been mitigated to the maximum extent possible within the existing highway system. Severe and total delay moved in the same direction over the survey period, but total delay rose more sharply since 2011. Some of the detection deployed this decade was added to areas that have less severe congestion, such as the detection added to the Central Coast counties.

 


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VMT and VHD at 60 and 35 miles per hour

In the two graphs that follow, we can make inferences about the interaction of the two variables, vehicle miles of travel and vehicles hours of delay, because the detection deployed measures both metrics simultaneously. Although severe and total delay move in the same direction, their movement does not parallel VMT. Delay declined during periods of economic downturn whereas VMT still climbed, though by a lower rate.

 


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VHD and VMT

 


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Gas Prices and VMT

Do motorists drive less when the cost of gas goes up? In economic terms, are they sensitive to changes in price--what is the consumer's price elasticicty of demand for gasoline? Most economic studies suggest some elasticity in the short run but less elasticity over the long run. In California, fuel prices dropped only in 2009 and 2010. Despite a trebling of fuel prices over the survey period, motorists still increased the number of miles they drove, according to PeMS data. DRISI annual vehicle miles of travel showed a flat response. Given the flat movement of population and employment in California over the survey period, the AVMT data suggest that Californians' gasoline demand is inelastic in the face of rising prices.

 


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Change in Gas Prices and VMT

 


   


Data Sources

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Population Data:

State of California, Department of Finance: E-4 Population Estimates for Cities, Counties, and the State, 2001-2010, with 2000 & 2010 Census Counts. Sacramento, California, November 2012; and E-4 Population Estimates for Cities, Counties, and the State, 2011-2014, with 2010 Benchmark, Sacramento, California, May 1, 2014.

Available at http://www.dof.ca.gov/research/demographic/reports/estimates/e-4/2011-20/view.php

Employment Data:

State of California, Employment Development Department (EDD), Labor Market Information Division. Downloaded using the Data Search Tool available at http://www.labormarketinfo.edd.ca.gov/cgi/dataanalysis/areaselection.asp?tablename=labforce in June and July 2014.

Performance Measurement System (PeMS) Data:

Accessed from PeMS Legacy downloaded June-July 2014.

The source of the data for the survey period 2001-2013 was PeMS version 12.3, not the current version 14.x. The extant official reports, HICOMP and MPR, are all based on the PeMS database that used the same speed algorithms over that period. PeMS 14 uses a different set of algorithms; thus, the values reported here more closely match the published reports. More discussion about the differences between PeMS versions' 12. 3 and 14 can be found in the Data Source tab.

Caltrans Division of Research, Innovation, and System Information (DRISI) Data:

Division of Research, Innovation, and System Information (DRISI), Public Road Data, 2001-2012, accessed from http://www.dot.ca.gov/hq/tsip/hpms/datalibrary.php, June 2014.

Statewide Tables and Figures Data:

Published on this Web site (under the Statistics tab) as Statewide Mobility Performance Statistics, these data are preliminary and unofficial, with respect to their publication status as a Mobility Performance Report. In practice, the unofficial statistics are identical to the official statistics in the future published reports. Statewide Tables and Figures are provided to meet our public reporting requirements in a timely manner.

Fuel Data:

U.S.Department of Energy, U.S. Energy Information Administration, Weekly Retail Gasoline and Diesel Prices, California. Series history download accessed at http://www.eia.gov/dnav/pet/pet_pri_gnd_dcus_nus_w.htm on July 14, 2014.

Cost of Delay

The costs of delay include the cost of extra fuel expended, increased operating costs, lost time, and greenhouse gas emissions. The existing methodologies used to calculate these costs are out of date. We are in the process of reviewing, investigating, and updating both the methodologies and the data used to support the methodologies.

If you would like to be informed when the cost of delay calculations are available, please click here to send an email request.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Bottleneck Mapping

Document Types  Statewide Bottleneck Maps  District Bottleneck Maps  Caltrans Earth Map and Layers


Summary. Bottleneck Mapping is a subproject of the Mobility Performance Report, which is one of the products of the Mobility Performance Reporting and Analysis Program (MPRAP). The Mobility Performance Report is prepared by the California Department of Transportation (Caltrans) and District staff to provide detailed data about highway system performance related to congestion and mobility. Caltrans collects vehicle counts and calculates speeds at all hours of the day and all days of the week in major metropolitan areas throughout California via the Caltrans Performance Measurement System (PeMS--see Data Source tab). This information helps identify congestion bottlenecks and results in more cost-effective investments to improve the performance of the State Highway System.

Background. PeMS defines a bottleneck as “a persistent and significant drop in speed between two locations on a freeway.” Bottlenecks are determined by the bottleneck identification algorithm in PeMS. This algorithm looks at speeds along a facility and declares a bottleneck at a location where there has been a drop in speed of at least 20 mph between the current detector and the detector immediately downstream. This speed drop must persist for at least five out of any seven contiguous five-minute data points, and the speed at the detector in question must be below 40 mph. While PeMS identifies the detector locations where these conditions are met, these bottleneck locations are only approximate (based on the locations where detectors are present).

The bottlenecks identified through the PeMS Bottleneck Identification Algorithm are filtered by a number of factors to obtain the bottlenecks mapped in the documents below. This filtering was done to create a consistent bottleneck analysis process for all districts, and to only report bottlenecks that are recurrent and causing large amounts of delay. The bottlenecks reported include bottleneck locations that were active on at least 20 percent of all weekdays during the year, persisted for at least 15 minutes on average, and caused more than 100 vehicle hours of delay (VHD) per weekday. This filtering means that some rural districts had less than 10 bottlenecks to report in the AM Peak and PM Peak periods. These top bottleneck locations are shown on District PDF maps, along with lines depicting the congestion queue length resulting from these bottlenecks. If a District had more than 10 bottlenecks that met the criteria described above, those additional bottlenecks and their congestion queues are shown on the District’s map.

Process. Bottleneck maps are created by the GIS staff in the Assets Management Branch of the Office of Performance, Division of Traffic Operations, using data obtained by each district from PeMS (the Performance Measurement System--see the Data Source tab). The data are processed and filtered in an Excel workbook. MPRAP staff review all district submissions and clear them for mapping. GIS staff use ArcGIS to convert the processed data into KML (Keyhole Markup Language) files that can be viewed at the street level in Google or Caltrans Earth. District technical personnel review their KML files and ground truth (use their engineering judgment and local knowledge to validate) each congestion segment and verify the bottleneck locations and lengths. The revised data are then exported into PDFs and JPEGs that are inserted into the annual Mobility Performance Report. In some cases, districts revise their workbooks, or request changes based on the appearance of the PDFs, and the whole process is repeated until the districts have validated all bottlenecks, and all map versions match.

Development. The Bottleneck Workbook was designed and created by Jordan Chan in District 4 to automate the processing of raw bottleneck data from PeMS and refine it into an ordered list. The workbook saves substantial time, reduces error, and improves accuracy in identifying bottlenecks that meet filtering criteria. The workbook design, however, preserves the ability of the district technical staff to make engineering judgments about their data. Dru Dunton, who manages the Mobility Performance Reporting and Analysis Program and collaterally manages the Bottleneck Mapping Project, modified the Bottleneck Workbook to meet Caltrans-wide requirements and additional GIS requirements. Matt Sidor introduced numerous changes in the 2011 and 2012 bottleneck mapping process. He revamped the design of the 2010 printed map and took a fresh look at the data input process, fixing postmile discrepancies and automating and streamlining the mapping process to create maps faster and more efficiently. His efforts led to more accurate locations of bottlenecks, which is essential in identifying the location of remediation projects and the proper allocation of resources. Matt helped district staff identify the destination of congested segments that began on one freeway and ended on another, and devised a method to map and document the trans-freeway segment. He also raised the issue of overlapping bottlenecks, resulting in an additional step in creating a target list that minimizes redundancy in priorities and ultimately leads to a better allocation of resources.


Bottleneck Maps

Document Types

KML Files

KML files may be viewed in Google Earth or GPS Visualizer, among others.

Bottlenecks are mapped in KML format prior to creating formatted PDFs. The congested segments are shown as red lines on the freeway; the averaged location of the sensor(s) detecting the congestion is depicted by the red car icon.

Clicking on the icon in the active KML file will reveal attribute data showing the California postmile, length, route information, and so forth.

District technical personnel view the mapped bottlenecks at the street level to verify the accuracy of the data used to produce the maps.

Thumbnail of District 7 2012 AM Bottlenecks Map
KML format
   
PDF Files

PDF files may be viewed in Adobe Reader.

After the KML files are approved by District technical personnel, GIS staff in the Assets Management Branch map the congested segments according to a formatted template. The congested segments are shown as red lines on the freeway; an arrow depicts the direction of traffic. Its placement represents the averaged location of the sensor(s) detecting the congestion. The congested segment is offset from the freeway.

The top bottlenecks, up to ten, are identified by the applicable boxed number, with leader line, on the map. More information about the top bottlenecks is included in the bottleneck tables of the district chapters in the published MPR.

Thumbnail of District 7 2012 AM Bottlenecks Map2
PDF format

Statewide Bottleneck Maps

Available KMZs:
2010 AM Statewide Bottlenecks in Google Earth Format
2010 PM Statewide Bottlenecks in Google Earth Format


District Bottleneck Maps

2010

Data were downloaded from the Caltrans Performance Measurement System (PeMS) in August 2011 using the Performance>Bottlenecks>Top Bottlenecks query for the period January 1, 2010 through December 31, 2010. Maps were created from processed data between September and November 2011. The maps were published in the 2010 Mobility Performance Report that was issued in July 2013 by the California Department of Transportation for the California State Transportation Agency.

Available PDFs published in the MPR:  2010 Bottleneck Maps


2011 & 2012

Data were downloaded from the Caltrans Performance Measurement System (PeMS) using the Performance>Bottlenecks>Top Bottlenecks query for the periods January 1, 2011 through December 31, 2012. Maps were created from processed data between July 2013 and February 2014. The maps will be published in the 2011 and 2012 Mobility Performance Reports to be issued this year by the California Department of Transportation for the California State Transportation Agency.


Available PDFs, preliminary and unofficial:  2011 Bottleneck Maps  2012 Bottleneck Maps

For more information about the content of a District map, please contact the relevant District technical personnel listed in the contact list here.



Caltrans Earth Map and Layers

Caltrans GIS Services maintain a Caltrans Earth website with a link to Caltrans Earth, a geospatial tool that provides Caltrans-specific data in map form. Map layers are also available to the public through this website; bottleneck layers will be coming soon.

 

Performance Measurement System (PeMS)

Data are obtained from the Caltrans Performance Measurement System (PeMS). Data are collected in real-time from nearly 40,000 individual detectors spanning the freeway system across all major metropolitan areas of the State of California.

PeMS is also an Archived Data User Service (ADUS) that provides over ten years of data for historical analysis. It integrates a wide variety of information from Caltrans and other local agency systems including:

  • Traffic Detectors
  • Incidents
  • Lane Closures
  • Toll Tags
  • Census Traffic Counts
  • Vehicle Classification
  • Weight-In-Motion
  • Roadway Inventory

To use PeMS, you must apply for an account. Registration is free and takes only a few minutes. Accounts are typically approved within one to two business days. For questions regarding PeMS, please contact Tim Hart.


Map of Vehicle Detector Station (VDS) Deployment:



PeMS 14 versus PeMS 12

With the release of PeMS 14, the algorithms that compute speed were updated to more accurately represent those speeds. Because delay calculations are based on speed, the changes are intended to improve the accuracy of delay calculations. Historical data was reprocessed in PeMS 14 using the new algorithms. Therefore, PeMS 14 portrays mobility data in California over the life of the database. Read the release notes for PeMS 14.0 here.

Reports on this website compare data from each version separately. The 2013 reports are based entirely on data from PeMS 12. The 2014 reports are based entirely on data from PeMS 14. The charts below show how data compare between the two versions over the period 2001-2013, when much of the detection was deployed.

Another change in PeMS 14 was the removal of conventional highways—state routes that never meet free flow conditions—from the calculations performed by the software to create sums of performance measures when queried. Districts 3, 5, 10, and 11 have conventional highways whose data was included in PeMS 12, but not PeMS 14. However, sorting out whether the difference between the two versions is due to the algorithm change, the absence of conventional highway data, or an organic change in mobility, is difficult.

Percentage of PeMS 12.3 Data Reported by PeMS 14.0
District Performance Measure 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
3 VMT 113% 106% 117% 120% 121% 104% 106% 107% 104% 102% 102% 95% 108%
VHD35 87% 106% 78% 65% 53% 63% 72% 64% 69% 69% 61% 66% 74%
VHD60 91% 95% 81% 64% 59% 67% 65% 78% 69% 65% 72% 66% 80%
                             
4 VMT 11% 71% 144% 110% 106% 107% 106% 105% 104% 103% 102% 97% 98%
VHD35 6% 20% 38% 84% 80% 73% 87% 83% 95% 78% 62% 59% 66%
VHD60 7% 29% 56% 90% 87% 79% 89% 88% 98% 85% 69% 65% 69%
                             
5 VMT Not Participating in Performance Reporting/No Detection 100% 105%
VHD35 49% 90%
VHD60 62% 111%
                             
6 VMT Not Participating in Performance Reporting/No Detection 85% 106% 106% 99% 103% 101% 102% 101% 102%
VHD35 44% 2% 83% 27% 19% 32% 62% 37% 32%
VHD60 89% 11% 117% 23% 25% 31% 56% 36% 51%
                             
7 VMT 24% 85% 103% 103% 93% 103% 105% 102% 100% 101% 100% 100% 100%
VHD35 26% 74% 97% 101% 92% 90% 106% 91% 99% 83% 99% 97% 98%
VHD60 24% 75% 96% 101% 97% 94% 114% 95% 103% 87% 98% 97% 99%
                             
8 VMT 89% 101% 64% 73% 104% 111% 113% 108% 104% 100% 99% 102% 104%
VHD35 69% 79% 73% 71% 88% 72% 86% 79% 103% 95% 90% 86% 81%
VHD60 77% 81% 64% 61% 94% 79% 98% 90% 94% 86% 87% 87% 83%
                             
10 VMT Not Participating in Performance Reporting/
No Detection
91% 97% 97% 96% 94% 80% 94% 95%
VHD35 54% 169% 45% 30% 48% 46% 46% 38%
VHD60 47% 77% 64% 51% 65% 62% 51% 43%
                             
11 VMT 49% 63% 105% 107% 111% 104% 107% 108% 102% 100% 100% 101% 100%
VHD35 58% 69% 96% 102% 106% 102% 110% 109% 97% 97% 89% 87% 89%
VHD60 54% 64% 100% 106% 105% 102% 107% 104% 98% 98% 95% 92% 96%
                             
12 VMT 0% 101% 103% 103% 103% 103% 107% 102% 102% 100% 99% 98% 98%
VHD35 0% 76% 104% 104% 97% 95% 68% 66% 98% 90% 90% 68% 76%
VHD60 0% 81% 105% 106% 100% 102% 73% 71% 107% 94% 94% 78% 83%

Vehicle Miles of Travel

Vehicle miles of travel (VMT) in both versions follows roughly the same curve for all districts. Larger gaps between the solid and dashed lines for a district might be related to adjustments to the sensitivity of detectors (fine-tuning) after deployment, a more notable occurrence during the system build out period than in the past five years.



Vehicle Hours of Delay at 35 miles per hour

Vehicle hours of delay at 35 miles per hour (VHD35) varies greatly from PeMS 12 to PeMS 14 for Districts 4, 12, 7, and several others to a lesser extent. The trend is downward for all districts. Three factors are responsible for these disparities: algorithm change, the removal of conventional highways , and organic change in mobility.



Vehicle Hours of Delay at 60 miles per hour

Vehicle hours of delay at 60 miles per hour (VHD60) varies greatly from PeMS 12 to PeMS 14 for all districts, especially Districts 4 and 12. The trend is downward for all districts except District 5 (San Luis Obispo).

Preliminary and Unofficial Statistics

Annual  Quarterly 2012-2013  Quarterly 2014-

"Preliminary and Unofficial Statistics" are data that have not yet been published in a Mobility Performance Report that has been signed by the Department of Transportation. When using these data, please be sure to note that the data are not official.

Preliminary and unofficial data may be replaced as data verification and content finalization proceed to Department and Agency approval. The documents available on this website are the most current and can be cited as follows:

State of California, Department of Transportation, Division of Traffic Operations, Document Title, month, year. Unofficial Statistics. Sacramento, California, footer date.


Annual Statistics

Statewide Annual Mobility Performance Statistics
Available PDFs:
2011 Statewide Mobility Performance Statistics

2012 Statewide Mobility Performance Statistics

District Annual Mobility Performance Statistics

Calendar Year 2011

Available PDFs:

Calendar Year 2012

Available PDFs:



Quarterly Statistics


Quarterly Mobility Performance Statistics, Data Source: PeMS 12.x

PeMS data for the 2013 reporting year and prior years were based on a different speed algorithm from the one used in the current PeMS version, 14.0, released on April 29, 2014. The reports based on the old speed algorithm show marked differences in delay for most districts from the reports prepared for the 2014 reporting year, based on a comparison of 2013 data in both PeMS versions.

The magnitude of the difference in each district is presented in tabular and graphical comparisons in the Data Source tab. The change in speed algorithms was undertaken to provide more accurate speed estimation, more accurate aggregation over lanes and over time, and more accurate long term trends calculations. Read the release notes for PeMS 14.0 here.

Although the data are different for most measures of congestion, VMT is largely the same. The upward trend in delay continues, but the 2014 baseline is lower than the former baseline for most districts except District 7, based in Los Angeles. Smaller adjustments in "g-factors" have been ongoing systemwide since before 2005 to maintain detector accuracy.

Delay trends and levels should be compared within the versions, not between. Therefore, the quarterly reports will be kept separate, as below.


  DISTRICT 3, MARYSVILLE
DISTRICT 8, SAN BERNARDINO
  2012   Compilation of D3 Q1-Q4 Reports   2012   Compilation of D8 Q1-Q4 Reports
  2013   Compilation of D3 Q1-Q4 Reports   2013   Compilation of D8 Q1-Q4 Reports
       
  DISTRICT 4, OAKLAND   DISTRICT 10, STOCKTON
2012   Compilation of D4 Q1-Q4 Reports 2012   Compilation of D10 Q1-Q4 Reports
New! 2013   Compilation of D4 Q1-Q4 Reports     2013  Awaiting PeMS reconciliation.   
     
DISTRICT 5, SAN LUIS OBISPO   DISTRICT 11, SAN DIEGO
2012   Limited detection. No reporting.   2012   Compilation of D11 Q1-Q4 Reports
2013   Compilation of D5 Q1-Q4 Reports   2013   Compilation of D11 Q1-Q4 Reports
       
DISTRICT 6, FRESNO   DISTRICT 12, IRVINE
2012   Compilation of D6 Q1-Q4 Reports   2012   Compilation of D12 Q1-Q4 Reports
  2013   Compilation of D6 Q1-Q4 Reports   2013   Compilation of D12 Q1-Q4 Reports
       
  DISTRICT 7, LOS ANGELES    
  2012  Compilation of D7 Q1-Q4 Reports    

2013  Compilation of D7 Q1-Q4 Reports    


Quarterly Mobility Performance Statistics, Data Source: PeMS 14.x

PeMS data for the 2014 reporting year and future years are based on a different speed algorithm from the one used in former PeMS' versions. The reports based on the new speed algorithm show marked differences in delay for most districts from the reports prepared for the previous reporting years, based on a comparison of 2013 data in both PeMS versions.

The magnitude of the difference in each district is presented in tabular and graphical comparisons in the Data Source tab. The change in speed algorithms was undertaken to provide more accurate speed estimation, more accurate aggregation over lanes and over time, and more accurate long term trends calculations. Read the release notes for PeMS 14.0 here.

Although the data are different for most measures of congestion, VMT is largely the same. The upward trend in delay continues, but the 2014 baseline is lower than the former baseline for most districts except District 7, based in Los Angeles. Smaller adjustments in "g-factors" have been ongoing systemwide since before 2005 to maintain detector accuracy.

Delay trends and levels should be compared within the versions, not between. Therefore, the quarterly reports will be kept separate, as below.

2014 quarterly reports are provisional and subject to future revision. Data are downloaded from PeMS and entered into an Excel workbook that cross-checks the data obtained from PeMS. Any discrepancies between PeMS queries are forwarded to PeMS staff to reconcile. Discrepancies can be caused by PeMS software errors, changes in detector county of assignment, disabled detectors, change of detector type, and detection configuration updates provided by district staff. Reconciliation is the step required to ensure that all delay recorded is associated with the correct freeway and county, and in the correct amount.

Finalized reports are comprised of reconciled data; thus, the reports may differ from data obtained directly from PeMS.


  DISTRICT 3, MARYSVILLE
DISTRICT 8, SAN BERNARDINO
  2014  D3 2014 Q1 Report   2014  D8 2014 Q1 Report
       
  DISTRICT 4, OAKLAND   DISTRICT 10, STOCKTON
2014   2014  
     
DISTRICT 5, SAN LUIS OBISPO   DISTRICT 11, SAN DIEGO
2014  D5 2014 Q1 Report   2014  D11 2014 Q1 Report
       
DISTRICT 6, FRESNO   DISTRICT 12, IRVINE
New! 2014  D6 2014 Q1 Report New!  2014  D12 2014 Q1 Report
       
  DISTRICT 7, LOS ANGELES    
New!  2014  D7 2014 Q1 Report    


For more information about the content of a District report, please contact the relevant District technical personnel listed in the contact list here.


Official Reports

Mobility Performance Reports (MPR)

Sent to the California Legislature in accordance with Government Code section 14032.6.

Highlights from the forthcoming 2011 and 2012
Mobility Performance Reports:
  • 4.2% increase in vehicle miles of travel in 2012 over 2011 (compared with a .4% increase in 2011 over 2010)
  • 8.3% increase in vehicle hours of delay at 35 miles per hour in 2012 over 2011 (compared with a 9.6% decrease in 2011 over 2010)
  • Overall employment increase of 3%, 2010–2012
  • Overall population increase of 1.4%, 2010–2012

Available PDFs:   2009 MPR   2010 MPR

Completion Schedule for Future MPRs

Click here for the most recent completion schedule for the 2011, 2012, 2013, and 2014 MPRs.

Highway Congestion Monitoring Program (HICOMP)

2008 was the last year in which the HICOMP Report was prepared. In 2009, Caltrans began preparing the Mobility Performance Report (MPR), which represents the completion of a transition to an improved way of measuring congestion. Because of the marked differences in methodology between the MPR and the HICOMP Annual Data Compilation, comparison of the congestion information between the two is not recommended. For more details on the differences between the HICOMP Report and the MPR, read the first chapter of the MPR 2009.

Available PDFs   2005 HICOMP    2006 HICOMP   2007 HICOMP   2008 HICOMP

 

 

 

 

 

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