An Interim Guidebook on the Congestion Management Process
in Metropolitan Transportation Planning

Appendix B. Performance Measures and Analysis Tools

Measures for Performance-Based Planning

Accessibility

• Average travel time from origin to destination
• Average trip length
• Percentage of employment sites within x miles of major highway
• Number of bridges with vertical clearance less than x feet

Mobility

• Origin-destination travel times
• Average speed or travel time
• Vehicle miles traveled (VMT) by congestion level
• Lost time or delay due to congestion
• Level of service or volume-to-capacity ratios
• Vehicle hours traveled or VMT per capita
• Person miles traveled (PMT) per VMT
• Customer perceptions on travel times
• Delay per ton-mile
• PMT per capita or worker
• Person hours traveled
• Passenger trips per household

Economic Development

• Economic cost of crashes
• Economic cost of lost time
• Percentage of wholesale, retail, and commercial centers served with unrestricted (vehicle) weight roads

Quality of Life

• Lost time due to congestion
• Accidents per VMT or PMT
• Tons of pollution generated
• Customer perception of safety and urban quality
• Average number of hours spent traveling
• Percentage of population exposed to noise above certain threshold

Environmental and Resource Consumption

• Tons of pollution
• Number of days in air quality noncompliance
• Fuel consumption per VMT or PMT
• Number of accidents involving hazardous waste

Safety

• Number of accidents per VMT, year, trip, ton mile, and capita
• Number of high accident locations
• Response time to accidents
• Accident risk index
• Customer perception of safety
• Percentage of roadway pavement rated good or better
• Construction-related fatalities

Operating Efficiency (System and Organizational)

• Cost for transportation system services
• Cost-benefit measures
• Average cost per lane-mile constructed
• Origin-destination travel times
• Average speed
• Percentage of projects rated good to excellent
• Volume-to-capacity ratios
• Cost per ton-mile
• Customer satisfaction

System Preservation

• Percentage of VMT on roads with deficient ride quality
• Percentage of roads and bridges below standard condition
• Remaining service life
• Maintenance costs
• Roughness index for pavement

Outcomes (Operational) Performance Measures

• Quantity of travel (users’ perspectives)
  • Person-miles traveled
  • Truck-miles traveled
  • VMT
  • Persons moved
  • Trucks moved
  • Vehicles moved
• Quality of travel (users’ perspectives)
  • Average speed weighted by person-miles traveled
  • Average door-to-door travel time
  • Travel time predictability
  • Travel time reliability (% of trips that arrive in acceptable time)
  • Average delay (total, recurring, & incident-based)
  • Level of Service (LOS)
• Utilization of the system (agency’s perspective)
  • Percent of system heavily congested (LOS E or f)
  • Density (passenger cars per hour per lane)
  • Percentage of travel heavily congested
  • V/C ratio
  • Queuing (frequency and length)
  • Percent of miles operating in desired speed range
  • Vehicle occupancy (persons per vehicle)
  • Duration of congestion (lane-mile-hours at LOS E or f)
• Safety
  • Incident rate by severity (e.g., fatal, injury) and type (e.g., crash, weather)
• Incidents
  • Incident-induced delay
  • Evacuation clearance time

Outputs (agency performance)

• Incident response time by type of incident
• Toll revenue
• Bridge condition
• Pavement condition
• Percent of ITS equipment operational

The Puget Sound Regional Council has produced a set of performance measures of particular interest because of its multimodal character. A summary of these measures is presented in the table below:

Measure	Highway	HOV	Transit
Travel Time and Delay	Point-to-Point Peak Travel Time Point-to-Point Peak Congestion Delay Congestion Duration	Point-to-Point Peak Travel Time Point-to-Point Peak Congestion Delay Congestion Duration	Point-to-Point Peak Travel Time Point-to-Point Peak Congestion Delay Congestion Duration
Travel Time Reliability	Standard Deviation of Peak Travel Time "Buffer Index" (Buffer Index is a measure of system reliability related to the additional travel time (compared to the average) necessary to complete a trips based on the 95th percentile)	Standard Deviation of Peak Travel Time	On-time Performance
System Access	Not Applicable	Percent of Park and Ride Capacity Used	Percent of Park and Ride Capacity Used Percent of Population within x Distance of Transit Percent of Ridership with 2 or more Transfers
Throughput	Peak Hour Person Movement	Peak Hour Person Movement	Peak Hour Person Movement
Crowding	Lane Density or Occupancy	Lane Density or Occupancy	Peak Hour Load Factor Lane Density (HOV or Bus Lanes) Percent of Terminal Capacity Used
Safety	Accident Rate	Accident Rate	Transit Accidents and Crimes
Travel Time and Delay	Point-to-Point Peak Travel Time Point-to-Point Congestion Delay	Point-to-Point Midday (?) Travel Time Point-to-Point Congestion Delay	Not Applicable

Measure	Ferries	Freight	Nonmotorized
Travel Time Reliability	Schedule Reliability (Percent on-time Departures and Percent on-time Arrivals	Standard Deviation of peak Travel Time	Not Applicable
System Access	Percent of Park and Ride Capacity used Percent of Peak-Period Transit Access Capacity Used Percent of Trips Require a Ferry-to-Ferry Transfer	Not Applicable	Sidewalk Completeness Bicycle Route Completeness
Throughput	Peak Hour Person Movement	Not Applicable	Regional Trail Segments At or Over Capacity
Crowding	Boat Wait Time Percent of Terminal Capacity Used	Lane Occupancy or Occupancy Percent of Terminal Capacity Used	Not Applicable
Safety	Accident Rate	Accident Rate	Pedestrian or Bicycle Accidents or Crimes

Analytical Methods

Current methods of data collection in widespread use which could support development of multimodal performance measures include:

• Manual Traffic and Transit Surveillance. On the highway side, this category includes traffic volume counts, spot speed observations, classification counts, aerial photography, videography, and license plate matching. For transit, this category includes boarding and alighting counts, peak load counts, and Section 15 reporting.
• Manual Vehicle Surveillance. This category includes floating car studies and the use of instrumented vehicles.
• Manual Freight and Goods Movement Surveillance. This category includes weight measurements, shipment records, average fuel consumption rate reports, travel logs, vehicle registration data and inspection records, Census of Transportation, Commodity Flow Survey, National Transportation Statistics Annual Report, Truck Inventory and Use Survey, and shipper logs.
• User Surveys. This category includes home travel surveys, roadside interviews and origin-destination surveys, onboard transit surveys, panel surveys, travel diaries, focus groups, and customer surveys.

The following data collection methods are emerging and will be increasingly available in the future:

• Advanced Traffic Management Systems (ATMS)/Traffic Surveillance Technologies. These ITS technologies collect information about the status of the traffic stream. Technologies in this category include loop detectors, infrared sensors, radar and microwave sensors, machine vision, aerial surveillance, closed circuit television, and acoustic, in-pavement magnetic, and vehicle probes.
• Advanced Traveler Information Systems (ATIS)/Vehicle Navigation and Surveillance Technologies. These ITS technologies include vehicle navigation technologies, which determine the vehicle position in real time (GPS, LORAN, dead reckoning, localized beacons, map database matching and cellular triangulation); and vehicle surveillance technologies, which collect a variety of information about specified vehicles (weigh-in-motion devices, vehicle identification, vehicle classification, and vehicle location).
• Payment Systems Technologies. These ITS technologies not only allow electronic fund transfer between the traveler and the service provider, but also enable vehicle recognition. They include Automatic Vehicle Identification (AVI), smart cards, and electronic funds management systems.

Current data storage, manipulation, and dissemination methods include:

• Highway Performance Monitoring System (HPMS) and Highway Economic Requirements System (HERS). These methods are statewide and urban area databases of a stratified sample of roadways. They are used to summarize highway conditions; select a set of needed improvements to highways based on minimum tolerable conditions specified by the program user (HPMS) or economic criteria based on benefit-cost analysis (HERS); and estimate the costs and consequences of these improvements.
• Computerized Databases. These databases could include information relative to highway, transit, freight, or other transportation system information. They are developed by Federal, state, and local agencies for the purpose of planning, budgeting, monitoring, and evaluating the transportation system.
• Geographic Information Systems (GIS) and Computerized Mapping. These methods are used to store, organize, display, and analyze geographically-referenced transportation-related data.
  The following data storage and manipulation methods are emerging and will be increasingly available in the future:
• Advanced Traveler Information Systems (ATIS)/Communications Technologies. ITS communications technologies transmit and receive information from mobile and stationary sources (highway advisory radio, FM subcarrier, spread spectrum, microwave, infrared, commercial broadcasts, infrared or microwave beacons, cellular phones, two-way radio, and two-way satellites).
• Interagency Coordination Technologies. These ITS technologies connect traveler-related facilities to other agencies such as police, emergency service providers, weather forecasters and observers, traffic management centers (TMS), transit operators, etc.
• Database Processing Technologies. These ITS technologies manipulate, configure, or format transportation-related data for sharing among various platforms. General purpose database software is currently being adapted to transportation needs such as data fusion, maps, and travel services.
• Work Scheduling, Reporting, and Inspection Technologies. With these technologies, can combine the data collection and data storage processes into one. These technologies include palm-sized and notebook computers, hand-held portable data entry terminals, bar-code scanners, electronic clipboards, and voice recognition systems.

Current data analysis and forecasting methods include:

• Sketch Planning Techniques. These techniques include sketch planning demand models, systematic analysis and transfer of empirical data, quick-response travel estimation techniques, level of service (LOS), V/C ratio, and vehicle volume and speed estimation procedures. Some examples include the ITS Deployment Analysis System (IDAS) for determining the potential impacts of ITS applications, and EPA’s COMMUTER Model, based on the FHWA TDM

Evaluation Model, which can be used to estimate emission reduction potential for travel demand management strategies.

• Macroscopic Simulation Models. These traffic models are based on deterministic relationships developed through research on highway capacity and traffic flow. The simulation for a macroscopic model takes place on a highway section-by-section basis rather than on an individual vehicle basis. Typical software packages include TRANSYT.7F, CORFLO, and FREQ.
• Mesoscopic Simulation Models. Mesoscopic simulation models combine the properties of both microscopic (discussed below) and macroscopic simulation models. As in microscopic models, the mesoscopic models’ unit of traffic flow is the individual vehicle. Their movement, however, follows the approach of the macroscopic models and is governed by the average speed on the travel link. Mesoscopic model travel simulation takes place on an aggregate level and does not consider dynamic speed/volume relationships. As such, mesoscopic models provide less fidelity than the microsimulation tools, but are superior to the typical planning analysis techniques. Examples of mesoscopic simulation models include CONTRAM, DynaMIT and DYNASMART.
• Microscopic Simulation Models. These traffic models simulate the movement of individual vehicles, based on theories of car-following and lane-changing. Typically, the model simulates a statistical distribution of vehicles that enter the transportation network and then tracks them through the network on a second-by-second basis. Typical software packages include NETSIM, FRESIM, and INTEGRATION.
• Land Use Allocation Models. These models reflect the effects of the transportation system (i.e., effects on accessibility, economic development potential, etc.) on the type spatial distribution of future development.
• Travel Demand Models. Traditional travel demand models follow a four-step process, including trip generation, trip distribution, mode choice, trip assignment, and activity based models. A number of software packages can be used to implement this process, including TRANPLAN, MINUTP, and EMME/2.
• Freight and Goods Movement Models. These methods include trend analysis, freight network models, and freight transportation demand models. Trend analysis uses historical growth rates for certain key markets, and projects these growth rates into the future, modified by correction factors reflecting competitive conditions, macroeconomic environments, and projections of technological efficiency improvements. Freight network models can handle a large number of freight modes, network links, and nodes, and can contain explicit mode choice algorithms based on minimization of cost and time by mode and route. Freight transportation demand models are similar to network models, although they differ in that demand models explicitly estimate behavioral relationships such as mode and route choice.
• Impact Models. These models are used to estimate emissions, fuel consumption, and safety impacts of transportation improvements. Typical software packages include MOBILE and EMFAC.

Decision Support Methodology for Selecting Traffic Analysis Tools (http://ops.fhwa.dot.gov/trafficanalysistools/tat_vol2/Vol2_Methodology.pdf) presents step-by-step guidance for the tool selection process, along with a list of recommended readings. An automated tool that implements the guidance can be found at the FHWA Traffic Analysis Tools Web site at: http://ops.fhwa.dot.gov/trafficanalysistools/toolbox.htm.

The following data analysis and forecasting methods are emerging and will be increasingly available in the future:

• Traffic Prediction Models. These ITS technologies can be used to predict future traffic characteristics based on real-time information. Algorithms under development include real-time traffic prediction and traffic assignment.
• Traffic Control Models. These ITS-related models relate to the real-time control of traffic. Algorithms under development include optimal control and incident detection, and the mutual effects of these processes on one another.
• Routing Models. These ITS-related models relate to the routing of vehicles, including the generating of step-by-step driving instructions to a specified destination. Algorithms under development include the scheduling of drivers, vehicles, and cargo; route selection; commercial vehicle scheduling; and route guidance (NCHRP Web Document 26: Multimodal Transportation: Development of a Performance-Based Transportation Planning Process; http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_w26-a.pdf).