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Applying Archived Operations Data in Transportation Planning: A Primer

1. INTRODUCTION

Making the Case - Using Archived Operations Data for Transportation Planning

Effective transportation planning and investment decision making depends on timely, comprehensive, and accurate data. Traditionally, data for planning has come from manual collection techniques and approximations from model output. While these sources are still used, transportation planners at the State, metropolitan, and local level are beginning to leverage an increasingly prevalent kind of transportation data—archived operations data.

Archived operations data is information collected and stored to support the monitoring and management of the transportation system. Enormous amounts of data on the performance of the transportation system are generated daily. For example, there are over 4 billion probe-based road segment speeds generated in the United States each day. California alone produces nearly 30 million sensor measurements for speed and volume daily.1 Archived operations data can include traffic, transit, bike, pedestrian, construction, and weather information that is usually collected in real-time by intelligent transportation system (ITS) infrastructure, such as in pavement inductive loop detectors, radar detectors, remote traffic microwave sensors (RTMS), Bluetooth, and E-ZPass or other unique identifier tag readers. It also includes incident or event information entered into electronic logs by transportation or public safety personnel. Although not exhaustive, Table 1 contains many of the common types of archived operations data that are collected.

Transportation planners at the State, metropolitan, and local level are finding they are able to rely less on assumptions and modeled data, solve a broader range of problems, and make more effective decisions with archived operations data.

Historically, operations data was used almost exclusively "in the moment" to help manage the system in real-time, and much of the data generated from these systems was either not archived or was archived in ways that made it extremely difficult to access and work with. Most operations data archives were only used for auditing purposes and were only accessible by one or two individuals within the agency's operations or information technology (IT) group. However, with shrinking budgets; a focus on performance-based planning; a greater commitment to data quality; and advances in sensors, collection techniques, and archiving technologies, agencies are realizing the value of these data collection efforts beyond day-to-day operations and are tapping into opportunities to make better use of existing operations data. This has led them to enhance operations data archiving in ways that make it more readily available to larger and more diverse user groups, such as planners. Transportation planners at the State, metropolitan, and local level are finding they are able to rely less on assumptions and modeled data, solve a broader range of problems, and make more effective decisions.

However, with shrinking budgets; a focus on performance-based planning; a greater commitment to data quality; and advances in sensors, collection techniques, and archiving technologies, agencies are realizing the value of these data collection efforts beyond day-to-day operations and are tapping into opportunities to make better use of existing operations data. This has led them to enhance operations data archiving in ways that make it more readily available to larger and more diverse user groups, such as planners. Transportation planners at the State, metropolitan, and local level are finding they are able to rely less on assumptions and modeled data, solve a broader range of problems, and make more effective decisions.

Table 1. Types of archived operations data.2
Archived Operations Data Type Description Description
Traffic volume, speed, class, and occupancy from point and probe data sources Information collected by agencies and third parties from roadway sensors that could include inductive loops, side-fired sensors (acoustic, microwave, etc.), radar, and video. Also includes data from probe-based systems— either agency-owned or third-party supplied.
Event, work zone, and incident information Information entered by each agency into its own incident management system. Data typically includes incident location, type, severity, information about the vehicles involved and their status, to whom are notifications made and which responders are on-scene, lane closures, response plans or detours, and messages on dynamic message signs (DMS) or highway advisory radio (HAR).
Weather data Weather alerts, temperature, precipitation types and rates, wind speeds, radar data, and other information from the National Weather Service, third parties, the media, etc. Also includes weather and pavement surface conditions that agencies gather from their roadway weather information systems.
Device operational status Data on the operational status of roadway devices from each agency, including traffic detectors, DMS, traffic signals, HAR, roadway weather information systems, and closed circuit television (CCTV) cameras, where available.
Managed lane status Data on when high-occupancy vehicle (HOV) restrictions are in effect, direction of reversible lanes by time of day, and price of high-occupancy toll lanes by time.
Surveillance video Live CCTV feeds focused on roadways, assets, or pedestrians.
Transit alerts Transit alerts, service disruptions, and other information transmitted by transit providers—both public and private.
Automated vehicle locations (AVLs) The locations and status of freeway service patrols, transit vehicles, or other assets equipped with AVL hardware.
Signal status Operational status of signals at intersections or ramp meters, such as operational, maintenance mode, flashing, or offline.
Signal timing plans Signal timing plans, current or future timing schemes.
Computer-aided dispatch (CAD) information Data from public safety computer-aided dispatch (CAD) systems, such as fire, emergency medical services, and law enforcement. Can include dispatch requests, incident types, severity, responder requests, or even lane status.
Static, descriptive information Data from public safety CAD systems, such as fire, emergency medical services, and law enforcement. Can include dispatch requests, incident types, severity, responder requests, or even lane status.
Decision-support response plans The various actions that the departments of transportation (DOTs) are likely to take to help minimize congestion impacts and clear roads more quickly. Could include pre-programmed DMS messages, signal timing plans, traveler information strategies, detours, etc., that are grouped together into a single, cohesive "plan of action" ready to implement. The sharing of these response plans can help agencies to better coordinate so that one agency's response plan is not in conflict with another's.
Parking data Location of parking facilities, number of spaces occupied and available, time and duration of parking space utilization, current fees, restrictions, and data on how to reserve a space
Travel time Often a derivative of speed data, travel time data represents the number of minutes it takes a person to travel from one location to another. Travel times are often divided into road segments where the start and end points of the segments are intersections or key features, such as bridges or tunnels. Vehicle travel time data can be derived from point sensor speed data. It also can be directly measured by probes, such as license plate recognition, toll tag transponders, Global Positioning Systems, and cell phone tracking. Alternatively, it can be estimated and predicted from other data sources.
Freight movements Mixture of data related to the origin-destination (O-D) of various shipments or types of shipments, statistics on the type of goods being shipped, the mode by which the goods are shipped, value of the goods, quantity of goods, type of shipping container, and safety records.
O-D data Tells operations personnel and planners where trips begin; where they end; and, sometimes, the routes that are taken. This data can be valuable for planning purposes and is useful for real-time operations when trying to measure the impact of various traveler information strategies and the impact of incidents on arterials and other secondary roads. Private data aggregator services routinely make O-D data anonymous.
Routing data Data that can be used by both emergency first responders and the traveling public to determine the fastest route, shortest path, etc., from one point to another. Comprised of road network data, turning restrictions, speed limits, and other information related to route types and distances.

Benefits of Archived Data to Transportation Planners

Archived operations data provides numerous benefits to planners. Archived operations datais more representative of existing conditions than modeled data, can serve multiple purposes within a transportation agency, and enables new types of analyses to support better planning and investment decisions. Key benefits of using archived operations data in planning are discussed below.

Fosters Performance-based Planning and Programming

The use of archived operations data enables performance-based planning and programming at metropolitan planning organizations (MPOs) and State departments of transportation (DOTs) through the use of system performance data to guide decision making. As explained further in Chapter 5. Planning Opportunities for Archived Operations Data – Basic to Innovative, archived operations data are a critical part of many performance-based planning and programming activities, including:

Definitions of Reliability

Travel time reliability is typically defined in one of two valid ways.

  • The variability in travel times that occur on a facility or for a trip over the course of time.
  • The number of times (trips) that either "fail" or "succeed" in accordance with a predetermined performance standard or schedule.

In both cases, reliability (more appropriately, unreliability) is caused by the interaction of the factors that influence travel times:

  • Fluctuations in demand (which may be due to daily or seasonal variation or by special events).
  • Traffic control devices.
  • Traffic incidents.
  • Inclement weather.
  • Work zones.
  • Physical capacity (based on prevailing geometrics and traffic patterns).

These factors produce travel times that are different from day to day for the same trip.

  • Setting outcome-based objectives.
  • Developing and tracking performance measures.
  • Identifying system performance needs and problems.
  • Analyzing and evaluating scenarios, programs, projects, or strategies.
  • Prioritizing and selecting programs and projects.
  • Monitoring and evaluating the impacts of implemented programs and projects.

Provides a More Complete Picture of System Performance

Comprehensive real-world data can supplant synthetic data generated by a model and improve decision making in some applications. Because archived data is continuously collected, it overcomes the sampling error inherent in using small samples of data collected manually, which provides planners with a better reflection of reality. Large sets of operations data that are archived across long time periods are valuable in assessing the true variability and range of values. They can be used to evaluate more useful performance measures and provide verification that models are not unintentionally emulating unusual conditions.

Visualization of archived operations data inspires important insights into the performance characteristics of the transportation system, which supports more effective decisions on where to spend limited transportation dollars.

Opens Up New Types of Analyses to Support the Planning Process

The highly detailed nature of archived operations data allows for many types of analyses previously unavailable to planners, such that the planning process is able to address a wider variety of issues. For example, archived data facilitates the inclusion of reliability into long-range transportation plans (LRTPs). Travel time reliability could not be adequately measured or modeled without archived data, because it requires continuous data collection to see patterns over time.

Enables More Sophisticated Modeling

Archived data can improve existing analysis tools and enable new ones by providing more complete input data than have been traditionally used, as well as more extensive data for calibration and validation. Planners also are able to calibrate more detailed models, such as activity-based and dynamic traffic assignment models, that benefit from archived operations data. The use of archived operations data at agencies around the United States enables the use of tools developed under various Strategic Highway Research Program 2 (SHRP 2) projects that present new reliability and capacity modeling tools and analysis methods.

Examples of Planning Use of Archived Operations Data

Washington State Department of Transportation

The Washington State DOT (WSDOT) has multiple applications for archived operations data to support their planning and overall performance management processes. WSDOT conducts before and after studies of congestion mitigation projects or programs as funds allow, typically with loop detector data in the Puget Sound region or automatic license plate recognition technology if funded by the project, and then provides the results as part of the Gray Notebook or in the Annual Congestion Reports. The agency also uses data from the NPMRDS. WSDOT recently reported on the effects of tolling operations and its traffic incident management program.

Houston-Galveston Area Council

The Houston-Galveston Area Council used archived operations data to investigate how the timing of evacuation decisions affects traffic patterns. Archived operations data was used to identify potential improvements during the Hurricane Katrina and Hurricane Rita evacuations and to advance future plans for elements, such as the timing of government evacuation announcements and employee dismissals.

New Jersey Department of Transportation

The New Jersey DOT (NJDOT) uses archived operations and safety data to visualize and identify candidate project areas (Figure 1).

Map of New Jersey showing five areas that are candidates for using archived operations and safety data to visualize and identify candidate project areas.
Figure 1. Map. New Jersey Department of Transportation management system integration of candidate project areas.
Source: New Jersey Department of Transportation.

Maryland DOT State Highway Administration

Archived operations data is used to support travel demand forecasting and simulation model calibration (estimating model parameters) and validation (confirming model performance). The Maryland DOT State Highway Administration (SHA) uses probe-based speed data from a third party data vendor and the I-95 Corridor Coalition's Vehicle Probe Project (VPP) Suite to calibrate its traffic simulation models, which serve as the basis for analyzing future scenarios for long-term planning. SHA also uses speed and traffic count data to calibrate existing conditions for travel demand and mesoscopic models, including the Maryland Statewide Transportation Model (MSTM).

Primer Purpose and Overview

The purpose of this primer is to assist transportation planners in effectively using archived operations data for developing, analyzing, and evaluating transportation plans and programs. This primer addresses the needs of planners from organizations with very little archived operations data, as well as organizations with an overwhelming amount of data. It raises planners' awareness of the opportunities afforded through archived operations data and provides guidance on how to take advantage of that data to expand and improve planning practices. It also identifies new and innovative applications for archived operations data in planning. This primer helps planners and their operations data partners overcome the barriers to obtaining and using data, regardless of whether they are just getting started with using the data or have substantive experience.

This primer helps address the numerous reasons why archived operations data has not been fully adopted for transportation planning and programming. For example, planners may not be aware that the data exists or may have difficulty accessing it. They also may not know how to mine, manipulate, manage, or use the data to support their work. In addition, data collected for real-time system monitoring can be extremely large. While distilling the data to inform investment decisions is achievable, it may require new expertise or tools.

The remainder of this document is organized as follows. Readers are encouraged to jump to the section of this primer that speaks to their specific need at any given time.

The purpose of this primer is to assist transportation planners in effectively using archived operations data for developing, analyzing, and evaluating transportation plans and programs.

Chapter 1: Introduction – introduces the reader to the application of archived operations data for planning, including the benefits, a range of uses in planning, and real-world examples.

Chapter 2: Meeting a Range of Planning Needs with Archived Operations Data – provides readers with an overview of a spectrum of planning activities that are enabled or enhanced with archived operations data. The case studies in Chapter 5 give an in-depth look at how archived operations data can be used for individual planning activities.

Chapter 3: Conquering the Challenges of Using Archived Operations Data – provides concrete guidance on handling institutional and technical challenges associated with obtaining, archiving, analyzing, and incorporating operations data into transportation planning activities.

Chapter 4: Obtaining Archived Data that Planners Need – informs readers about data sources, collection techniques, format, quality, and archiving.

Chapter 5: Planning Opportunities for Archived Operations Data – Basic to Innovative – offers rich case studies of archived operations data used to support planning and programming, which are often drawn from real-world experiences of planning organizations across the United States.

Chapter 6: Getting Started – presents readers with tips and a checklist on how to get started using archived operations data, whether they have a little or a lot of data.

1 Pack, Michael, University of Maryland Center for Advanced Transportation Technology Laboratory, Personal Communication, 2015. [ Return to note 1. ]

2 Pack, Michael and Ivanov, Nikola, National Cooperative Highway Research Program Synthesis 460, Sharing Operations Data Among Agencies (Washington, DC: 2014). Available at: http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_syn_460.pdf. [ Return to note 2. ]

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