Traffic Surveillance

 
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Archived Data User Services (ADUS)

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TYPES OF DATA COLLECTED BY THE TMC

Traffic management centers collect traffic and incident information about the transportation network; this data is then combined with other operational and control data in order to manage the transportation network and to produce traveler information. Archiving this data is an effective way of making it available for operators who want to make improvements to their particular transportation system.

The data TMCs collect typically includes:

  • Vehicle traffic volumes
  • Traffic incidents (time sequence of events, location, cause, number of lanes blocked, etc)

It may also include:

  • Vehicle speeds
  • Travel time and delay distribution
  • Current and scheduled work zones (location, number of lanes closed, scheduled duration, etc)
  • Vehicle emissions
  • Vehicle classification (percent trucks)

RATIONALE FOR ARCHIVING DATA

Archiving these data provides a cost-effective way to obtain data that can be used by transportation planners, designers, and researchers alike for many purposes. Data can be used for facility planning and design, developing control strategies, congestion tracking, emissions modeling, deployment of response personnel, and schedule of maintenance and construction. Traffic management centers in particular can use archived data to analyze performance measures, determine the optimum ways of managing crises, and ultimately develop other means of operating the transportation system at its maximum efficiency.


POTENTIAL BENEFITS

  • Huge volumes of data are available at relatively low costs.
  • Allows for better long-term transportation management and planning.
  • The detailed nature of ITS-generated data allows for more accurate analysis and makes possible those applications that were previously too costly to undertake.
  • Removes temporal sampling bias from estimates.
  • Allows for the study of continuous variability in system performance and response.
  • Improves the evaluation of ITS deployment.
  • Promotes integration of ITS with traditional information systems.
  • Encourages the use of data for multiple purposes.
  • Mutual interest in data generated by ITS will stimulate cooperation among stakeholders, which could lead to the "main streaming" of ITS into standard transportation practice, particularly among transportation planners.

HISTORY

The need for archived data was initially expressed in the mid-90s at the Highway Performance Monitoring System Steering Committee on August 7, 1996 and later at a conference in Irvine, CA in March 1997. In January 1998, the FHWA sponsored a meeting to discuss the uses of archived data, the end result of which was a decision to revise the National Architecture to include a new user service, namely the Archived Data User Service (ADUS). In September 1999, ADUS was officially added to the Architecture. In May 2000, the FHWA developed an ITS Data Archiving Five-Year Program Description, which explains the need for a Federal program that addresses the archiving and multi-agency use of data generated from ITS applications.


ASSESSMENT

Development of Archived Data Services

Archived data services is relatively new and only a few state DOTs and associated transportation organizations have begun to implement this service as part of their daily operations. Those DOTs and TMCs that regard archived data as valuable for future planning and operations improvements have implemented their archiving systems to varying extents. The Metro District Transportation Management Center in Minneapolis, MN has been continually archiving traffic data since 1984 and has used it extensively for traveler information purposes, whereas the Dallas, TX DOT has archived only a few types of data somewhat intermittently. Another approach to archiving data was taken by the TransPort program in Oregon, where a specific type of data (i.e. loop data) was archived — in an experiment independent of the ODOT’s normal operations— in order to prove the utility of archiving data for future applications. TransPort installed 10 mainline inductive loop detector stations and associated on-ramp detectors at 10 eastbound on-ramps along an 11-mile corridor along eastbound US 26 in Portland. Vehicle counts, occupancy and speeds were archived, and it was determined that such simple, directly measurable variables would be useful in setting standards and determining widely-applicable performance measures. Still other DOTs nationwide are archiving several types of data simultaneously, but most have not gathered a substantial amount of data over a long enough period of time to use for noteworthy planning and operations improvements. However, the value inherent in having such archived data is widely recognized and its planning and operational benefits highly anticipated. Those DOTs and transportation agencies that are archiving data have determined that most data users prefer to access the data in an online format or on CD-ROMs.

The State of ADUS Today

Although archived data user services are potentially a rich resource for improving many transportation decisions, most notably for enhancing transportation planning and operations, not many regions are employing ADUS, mainly because this service requires a significant amount of institutional evolution, particularly in terms of infrastructure investments. Although ADUS is new to the ITS Architecture, it is expected that more and more TMCs will recognize the value of implementing a performance measurement system based on archived traffic data.

Implementation and Operational Barriers

  • The reluctance to share data among jurisdictions will continue to hinder full ADUS implementation.The lack of ADUS standardization for both data
  • Data quality and accessibility slows the integration of different information from multiple sources.
  • More efficient data checking methods are needed to ensure accuracy, and reliability, absence of bias, and validity of estimation methods.
  • Organizations like the TMC generally do not have the staff needed for both incident management and data archiving.
  • Data ownership, maintenance, or control issues may become problematic.
  • The planning community is not progressing as rapidly as the transit community on ADUS. This is because the entities that produce the desirable planning data (e.g. the TMCs) are organizationally separate from the data users (e.g. the planners).
  • TMCs are not collecting data explicitly on ITS Safety issues; those who are interested in safety have to find their own ways to relate ITS-generated data to the topic of highway safety.
  • Many agencies that collect data on incidents withhold safety data in order to honor privacy and liability issues.
  • It is unclear which entity should bear the cost of the various ADUS applications; whether it should be borne by the data user or provider (TMC) is still a major barrier to implementation. Currently, there are some data users who obtain data by paying the data producers directly; alternatively, there are some data brokers who make the data available to interested users at a cost.

Costs

  • Costs for implementing ADUS are needed to:
  • Articulate and communicate the needs for archived data
  • Save the data
  •   Re-format the archived data into a user-friendly format
  •   Re-vamp the existing software to accommodate archived data
  •   Address data quality issues
  • Make archived data accessible in a timely manner
  • Integrate the archived data with non-ITS data to meet broader data needs
  • Reconcile data incompatibilities among different data sources
  • Forge mutually beneficial partnerships among data-producing agencies and data users

ADUS STANDARDS

The purposes of having standards for ADUS are to:

  • Define and promote the interfaces necessary to implement ADUS in the field.
  • Ensure consistent deployment of ADUS nationwide.
  • Eliminate duplicate data collection and storage.
  • Coordinate with other ITS data dictionary efforts that are relevant to ADUS.
  • Reconcile differences between the data definitions in pre-existing ITS data dictionaries and those of ADUS stakeholders.

Standards for ADUS are still under development, but it is important to note that any standards developed should take into account that the standards for each ITS entity will effect ADUS. Close coordination between ADUS and each ITS user service is thus essential. Aimed at creating “open” protocol standards that will serve the transportation information network and be adaptable to different communications architectures, the National Transportation Communications for ITS Protocol (NTCIP) is currently developing standards that will facilitate the use of ITS devices for data collection.

Below is a diagram of the various ITS Sources that interact with an archived data management system:

“Standards Will Promote the Development of Interfaces for Archived Data Management Systems” From the Strategic Plan for the Development of ADUS Standards, May 2000.

 

The diagram below illustrates the flow of data from its collection stage to its archiving stage:

"ADUS Standards Take Many Forms and Support the Various Process of the Data Stream" From the Strategic Plan for the Development of ADUS Standards, May 2000.


CASE STUDIES

Collected from studies by the Texas Transportation Institute, Cambridge Systematics, and PATH.

Portland, Oregon

Dallas, Texas
Daltrans is a component of the Dallas office of the Texas DOT that collects traffic information from intelligent transportation system. The Dallas district office currently operates closed circuit TVs (incident detection and management), changeable message signs (traffic control), online and media outlets (en-route and pre-trip traveler information), and lane control signs (traffic management). Daltrans currently collect video from CCTVs, incident information, current and scheduled work zones, and point data (volume, occupancy, and speed) from video.

The following types of data are currently archived:

  • Incident detection
  • Current and scheduled work zones

There is no significant data being archived at this time.

Fort Worth, Texas TxDOT-Fort Worth
The TXDOT – Fort Worth is not archiving data as of October 2002, but it has in past years under the TransVISION project. TransVISION is a component of the Fort Worth office of the Texas DOT that collects traffic information from intelligent transportation systems. TransVISION operates detector surveillance and closed circuit TVs (incident detection and management), ramp meters/changeable message signs/lane control signals (traffic control and management), and online and media outlets (en-route and pre-trip traveler information). TxDOT – Ft. Worth has developed procedures to track system performance that rely on archived data but have not regularly implemented these procedures. TransVISION currently collects point data (volume, occupancy, speed, percent trucks) from double loops and radar detectors, video from CCTV, metering rates and ramp queues, incident information, and current and scheduled work zones.

The following types of data are currently archived:

  • Video snapshots associated with incidents
  • Incident information
  • Current and scheduled work zones

Archived data are currently used largely by universities for research purposes, ITS evaluation, and traffic planning purposes. TransVISION is planning to operate a local data warehouse for their purposes, as well as share data with other centers (i.e. The North Central Texas Council of Governments – NCTOG). Thus far, archived data have been used to evaluate ramp metering and other research evaluation projects.

Houston, Texas TxDOT-Houston
The Houston district office of the TxDOT gathers traffic data largely from two different sources: 1) TranStar, a multi-governmental facility that is responsible for the planning, design, operations and maintenance of transportation operations and traffic emergency management operations within the Greater Houston Area 2) TTI (Texas Transportation Institute), an official research agency for the Texas Department of Transportation and the Texas Railroad Commission. The TxDOT-Houston currently operates detector and probe vehicle surveillance/closed circuit TV (incident detection and management), and ramp meters/changeable message signs/lane control signals/HOV lanes (traffic control and management). TxDOT-Houston tracks system performance on an annual basis using probe vehicle data from the AVI traffic monitoring system. It currently collects point data (volume, occupancy, speed, percent trucks) from double loops and some VIDs, probe vehicle travel times from the AVI traffic monitoring system, video from CCTV, metering rates and ramp queues, incident information, and current and scheduled work zones.

The following types of data are currently archived:

  • Anonymous vehicle travel times
  • Volume, occupancy, and speed from loops and VIDS since mid-2000 by TTI

Archived data has been used to evaluate ramp metering as well as overall system performance at TranStar. TTI-Houston office currently maintains travel time data from the AVI traffic monitoring system, The TTI-Houston office has also been archiving loop detector data from the TranStar database since mid-2000 under agreements with the TxDOT. The TranStar warehouse will eventually archive numerous data being generated or collected at TranStar. The data warehouse will be managed through a relational database, with access through internal networks or the Internet.

San Antonio, Texas TxDOT-Austin
TransGuide is an Intelligent Transportation System designed by the San Antonio District of the Texas Department of Transportation (TxDOT). TransGuide currently operates detector surveillance/closed circuit TV (incident detection and management), changeable message signs/lane control signals (traffic control and management), online and media outlets (en-route and pre-trip traveler information), highway-rail intersection monitoring. It currently collects point data (volume, occupancy, speed, percent trucks) from double loops and some VIDs, travel times from an AVI traffic monitoring system, video from CCTV, incident information, and current and scheduled work zones.

The following types of data are currently archived:

  • Traffic volume, occupancy and speed
  • Probe vehicle travel time
  • Vehicle classification
  • Incidents
  • Current and scheduled work zones
  • Road and weather conditions

Archived data is largely used by agencies other than TransGuide, such as state universities and other research groups. The data is used primarily for research purposes, ITS evaluation, and traffic planning purposes. This data is also used in FHWA Mobility Monitoring Study conducted by TTI/Cambridge Systematics. TransGuide currently maintains a publicly-accessible FTP site that has the most recent month of data files in ASCII-text format.

Atlanta, Georgia Georgia DOT
The Georgia DOT currently operates detector surveillance/closed circuit TV/service patrol (incident detection and management), changeable message signs/lane control signals (traffic control and management), online and media outlets (en-route and pre-trip traveler information), and the NAVIGATOR

system. The NAVIGATOR system currently collects point data (volume, occupancy, speed) from VID, video from CCTV, incident information, and current and scheduled work zones.

The following types of data are currently archived:

  • Traffic volume, occupancy, and speed

Archived data is used mostly by university researchers (Georgia Tech) for research on operational strategies, ITS evaluation, and traffic monitoring and planning purposes. Data is also being used in a FHWA Mobility Monitoring Study conducted by TTI/Cambridge Systematics. The GDOT maintains archived data in ASCII-text files at the 15-minute level; GDOT also provides this data on CD upon request, while researchers at Georgia Tech maintain the archived data in a relational database.

Detroit, Michigan Michigan DOT
The Michigan DOT currently operates detector surveillance/closed circuit TV/service patrol (incident detection and management), changeable message signs/lane control signals (traffic control and management), and online and media outlets (en-route and pre-trip traveler information). The Michigan DOT collects point data (volume, occupancy, speed) from double loop detectors, video from CCTV, incident information, and current and scheduled work zones.

The following types of data are currently archived:

  • Traffic volume, occupancy, and speed

Archived data is shared with Detroit MPO for traffic planning and analysis purposes; data is also used in a FHWA Mobility Monitoring Study conducted by TTI/Cambridge Systematics. The MDOT maintains the most recent data “online” for a short period (about one week), then permanently archives them to magnetic trap cartridges.

Hampton Roads, Virginia Virginia DOT
The Virginia DOT currently operates detector surveillance/closed circuit TV (incident detection and management), changeable message signs/lane control signals (traffic control and management), and online and media outlets (en-route and pre-trip traveler information). The Virginia DOT collects point data (volume, occupancy, speed) from double loop/microwave radar/acoustic detectors, video from CCTV, incident information, road and weather conditions, and current and scheduled work zones.

The following types of data are currently archived:

  • Traffic volume, occupancy, and speed
  • Incident information

Archived data is used largely by Virginia Transportation Research Council (VTRC) for research on operational strategies, ITS evaluation, and traffic monitoring and planning purposes. Data is also used in a FHWA Mobility Monitoring Study conducted by TTI/Cambridge Systematics. VTRC operates and maintains the ITS data archives for the VDOT.

Montgomery County, Maryland Maryland DOT
The Montgomery County DOT currently operates detector surveillance/closed circuit TV/fixed-wing aircraft (incident detection and management), closed-loop signal system/emergency vehicle preemption (arterial traffic control and management), changeable message signs/lane control signals (traffic control and management), and online and media outlets (en-route and pre-trip traveler information). The Montgomery County DOT collects data from sampling and presence loop detectors, video from CCTV, incident information, and current and scheduled work zones.
  • The following types of data are currently archived:
  • Traffic volumes are transferred to and archived by M-NCPPC
  • Probe vehicle travel time
  • Incidents
  • Current and scheduled work zones

Archived data is used mostly by M-NCPPC (Maryland National Capital Park
and Planning Commission) for traffic planning and analysis purposes (i.e. network volume maps).  M-NCPPC uses Internet protocols (TCP/IP) and a custom software product to query the DOT database in the early morning and retrieve the most recent 24 hours of 5-minute volume data. Once the data has been retrieved, M-NCPPC loads the data into an Oracle server, does error checking, and then computes peak hour intersection volumes and other volume quantities used in planning applications.

Phoenix, AZ ADOT
The ADOT currently operates detector surveillance, closed circuit TVs, and an incident response team (incident detection and management); changeable message signs/lane control signals (traffic control and management); and online and media outlets (en-route and pre-trip traveler information). The ADOT collects point data (volume, occupancy, speed, vehicle classification) from double loop/passive acoustic detectors, video from CCTV, incident information, and current and scheduled work zones.
  • The following types of data are currently archived:
  • Traffic volume, occupancy, and speed

Archived data is used primarily for research on operational strategies, ITS evaluation, and traffic monitoring and planning purposes. Data is also used in a FHWA Mobility Monitoring Study conducted by TTI/Cambridge Systematics. The ADOT maintains the original 20-second data as collected from the detectors; they store recent data on-line in compressed text formats, and keep old data on CDs. They have also developed software to provide archived data upon request in a number of different aggregation levels and formats.

Orange County, CA Caltrans UCI TMC Orange County TMC
UC Irvine collects incident, congestion and ramp metering information. The system support staff analyzes the data to develop operational strategies and the planning department uses the data to determine alternative plans for capital improvements. Data is stored on the server for a year and is backed-up on tape; this data is available to the public at no cost on the web.

Minneapolis, MN Metro District Transportation Management Center
This center uses loop detectors and closed circuit TVs for incident detection and congestion tracking. They provide an online map that shows congestion levels every minute and icons indicating various types of incidents. They also post the data on a server that can be accessed by organizations providing traffic information; this is used to provide traffic reports on the radio and television, SmarTraveler’s telephone traveler information system, and other traffic web sites. This center has been archiving traffic data since 1984, and they now store it on the server for one year, after which time it is transferred to CDs. Anyone in MnDOT can access the data anytime.

Seattle, Washington WSDOT
The WSDOT collects freeway detector data and has developed a CD-based data archive for Seattle freeways. The data is used for testing and evaluating operational improvements (i.e. ramp metering and HOV lanes), freeway performance monitoring, pavement design, and freight performance analysis. The CD is used to hold three months of 5-minute summary data and the CDs are made available upon request.

Berkeley, CA University of California PeMS
PeMS is a freeway performance measurement system that collects detector data from several Caltrans’ districts including Los Angeles, Orange County, and Sacramento. The data is archived online (http://transacct.eecs.berkeley.edu) for anyone who has a password. The data is used for freeway performance using speeds, estimated travel times, and vehicle volumes.

Portland, Oregon TransPort
The ODOT, the City of Portland, Tri-Met (Portland’s transit agency), Metro (regional government), and other regional jurisdictions have developed the TransPort (Transportation Portland) program. This program brings together the various agencies to develop, operate and evaluate traffic management, incident response, and traveler information systems as part of the region’s ATMS. In this experiment, 10 mainline inductive loop detector stations (with detectors located in each lane) and associated on-ramp detectors at 10 eastbound on-ramps were installed along an 11-mile corridor along eastbound US 26 between Helvetia Road and Skyline Road in Portland, Oregon.

The data recorded by these sensors included:

  • vehicle count
  • occupancy and speed as measured by the detectors in each lane and on each on-ramp and are aggregated locally every 20 seconds

These 20-sec data were transmitted to the traffic management center (TMC) via the regional fiber optics network. Typically ODOT archives their data at a 15-minute level, but for this experiment data was requested in its most raw form available. The time period chosen for this experiment was the week of Monday, October 30 to Friday, November 3, 2000. This experiment showed it is possible to obtain information assessing the functionality of the facility using archived data from loop detectors. The information will assist in determining the best performance measures for obtaining simple, quick and accurate ideas about the functionality of the corridor, enhancing decision-making. With simple, directly measurable variables such as vehicle count, speed, occupancy and incident information, it is possible to set some standards and determine some performance measures that can be generated for the transportation systems around Portland and in that way keep track of the general performance of the network. The archived data may be available on the Internet in the future.


INFORMATION ON TRAFFIC DATA COLLECTION

Types of ITS Traffic Sensors

Most of the traffic data sent to the TMC is created by various types of traffic sensors that include:

Vehicle-based Detectors

Road-based Detectors

 In-Road

Loop Detectors
These are the most commonly used detectors. They sense when a car enters the pavement over the loop and how long it covers the loop, thus providing a count of vehicles crossing the loop and a measure of the time the loop is occupied from which the vehicle density (vehicles per mile) can be estimated.

 Roadside

Active infrared
Active infrared (or laser range finder technology) provide presence, speed, volume, occupancy, and classification information in day and night conditions. The detector senses a portion of the reflected energy in its field of view; the distance of an object from the detector is found by measuring the two-way travel time of the infrared pulse, from the detector to the target and back.

Passive Infrared
Passive infrared provide volume, occupancy, and presence information. Passive IR detectors do not transmit energy, but rather they measure energy emitted by objects in their field of view. They detect vehicle presence by measuring the difference in emitted energy (i.e. temperature) from the road and vehicles.

Radar
Radar detection can provide vehicle counts, densities, and speeds. Vehicle speeds are measured using the Doppler effect (measuring frequency shifts between the transmitted and received beam caused by the vehicle in motion); vehicle counts are determined by accumulating information on each vehicle detected. A second type of radar detector transmits a signal that is swept over a range of frequencies; this technique measures the range to the vehicle and thus can detect presence.

Doppler Microwave
Doppler Microwaves sensors are able to count vehicles, measure speed and detect vehicle presence. Microwave detectors fall into two categories: Doppler or radar devices. Radar devices, also known as pulse microwave, measure the time it takes for a portion of the microwave radiation to be reflected from the target area to a receiver. Microwave radar vehicle detectors transmit electromagnetic energy at the speed of light in frequency bands between 2.5 to 24.0 GHz.

Pulse Ultrasonic
Pulse Ultrasonic can measure speed, occupancy, presence, and, in some configurations, queue length. These sensors operate on the same principles as Doppler radar, except that they emit sound waves with frequencies between 20 and 200 KHz, which are above the human audible range. They are pressure waves that travel through the air at about 740 mph at sea level.

Video Image Detection (VID)
Video image detection systems (VIDS) employ machine vision technology to automatically analyze traffic data collected with Closed Circuit Television (CCTV) systems. VID is used for actuated intersection detection, automated traffic counts, ramp metering, freeway management and automatic incident detection. VID can count vehicles, detect vehicle presence, and read license plates.

Vehicle-based Detectors

GPS
A GPS (Global Positioning System) receiver collects the real-time latitude and longitude information of a GPS-equipped vehicle and sends this information to a central computer for processing. Over the past several years, GPS has become the preferred AVL system for fleet and travel behavior studies because it collects accurate and detailed travel times as well as captures variability in travel behavior across multiple days.

Cellular Phones
Under manual surveillance, wireless phone users can call into a Transportation Management Center (TMC) or Traveler Information Center and report incidents or traffic conditions. Under automatic surveillance, wireless service providers automatically collect geo-location data of wireless phones and forward it to TMCs.

In-vehicle tags
Probe vehicles are equipped with electronic tags that can track the vehicle’s speed, location, and travel time along roadways that have a tag reader infrastructure.

For more information, see the ITS Decision Traffic Surveillance Section

Communications

The various components of ITS user services, such as roadside transponders, CCTV, and infrared sensors, rely upon communication linkages in order to transmit and receive data. These components communicate with each other through various mediums, including wireless networks, fiber optics, radio signals, and wireline connections. These modes of communication in turn comprise the telecommunications infrastructure which allows ITS user services to function efficiently, not only as independent systems, but also as parts of the larger network of intelligent transportation systems. The best communications technologies to use for communicating traffic data will depend on the nature and volume of the data, distances between where the data is collected and processed, communication services available in the area, and their cost. The preferred communication medium for ITS is fiber optic because of its high capacity and reliability; however, it is too expensive for many locations. Leased telephone lines are more commonly used but are less reliable. Most agencies use a combination of communications media.

There are two categories of communications technologies: wireline and wireless.

Wireline Technologies
  • Twisted pair copper
  • Coaxial cable
  • Fiber optic (multimode and single mode)
Wireless Technologies
  • Microwave
  • Cellular (digital and analog)
  • Cellular digital packet data
  • Spread spectrum
  • Digital and trunked radio systems

The table below compares the various characteristics of communications technologies. This chart was created in 1995, and there have since been many advances, especially in wireless communications.

Comparison of Communications Technologies

For more information see the ITS Decision Telecommunications Report

Data Quality

Quality control of data is important for ADUS for a number of reasons. Firstly, it is difficult to detect errors (using traditional manual techniques) in the large volume of operations data that ADUS produces. Secondly, archived data users may have very different data requirements (i.e. accuracy requirements) than real-time users of that same data. Thirdly, when only minimal error detection is performed as data are being collected in real-time, an accumulation of erroneous or missing data can result.

Data quality has several aspects to consider:

Accuracy – Different levels of accuracy are needed for different operations

Reliability – Maintenance practices greatly affect reliability, although different levels of maintenance are necessary for different data collection systems to remain useful.

Absence of bias  - This refers to the extent to which the sensor data reflects the entire facility being monitored.

Validity of estimation methods – Missing data is often replaced by estimated data, a practice that may cause some unknowing users to draw erroneous conclusions.


REFERENCES

Albert, Luke. An Evaluation of the Potential of Public/Private Partnerships for the Management of Archived ITS Data, Texas A & M University, August 1999. Link to Report

Archived Data User Service Self-Evaluation Report, FHWA Nevada DOT, November 2000. Link to Report

Chen, Chaos. Petty, Karl. Skabardonis, Alexander. Varaiya, Pravin. Jia, Zhanfeng. Freeway Performance Measurement System: Mining Loop Detector Data, UC Berkeley Institute of Transportation Studies, January 2001. Link to Report

Definging and Measuring Traffic Data Quality
Link to Report

Dahlgren, Joy; Garcia, Reinaldo; Turner, Shawn. Completing the Circle: Using Archived Operations Data to Better Link Decisions to Performance, UC Berkeley PATH, 2001. Link to Report

ITS Data Archiving Five-Year Program Description, US DOT, March 2000. Link to Report

Strategic Plan for the Development of ADUS Standards, Cambridge Systematics, Inc., May 2000. Link to Report

Turner, Shawn. Guidelines for Developing ITS Data Archiving Systems, TTI, September 2001. Link to Report


Author: Lauren Smith

Hosted by the Institute of Transportation Studies at
the University of California at Berkeley and Caltrans