Tolling and Pricing Program - Links to Tolling and Pricing Program Home

3.0 Synthesis of the State of the Practice

This chapter summarizes the state of the practice for evaluating the travel and environmental impacts of congestion pricing projects. The findings presented here are drawn primarily from a review of published literature associated with the eight study projects described in Chapter 2.0. This chapter is divided into three sections. The first highlights some of the fundamental differences and similarities in the evaluation approaches among the eight study projects. The second section focuses on travel impacts. The final section focuses on environmental impacts.

3.1 Basic Similarities and Differences between Study Projects

All eight of the study projects include before-after (or “with/without”) evaluations of deployed congestion pricing schemes: either short-term demonstrations with simulated congestion charges or long-term deployments with real charges. The Oregon, Puget Sound and Commute Atlanta mileage-based value pricing investigation are examples of the former (short-term, simulated) and the other five projects are of the latter type (long-term, non-simulated).

Two of the projects—London and Singapore—have been evaluated in an on-going manner over many years, encompassing a number of expansions and other changes in the congestion pricing programs. Those projects, to varying extents, have produced a series of results reports which include comparisons and which draw conclusions based on many years of post-deployment data. The six other projects generally focus on shorter, finite, post-deployment evaluation periods of between a few months and, in the case of I-15, up to three years. Although the Minnesota I-394 HOT lanes project is subject to on-going monitoring, the comprehensive evaluation effort was, essentially, a one-time effort.

London demonstrates the most extensive evaluation effort among the study projects in terms of evaluation period (over five years) and the breadth of impacts, performance measures and data sources. The next most extensively studied projects are Stockholm and Singapore.

The simulated pricing field deployment projects—Oregon, Puget Sound and Commute Atlanta—focused more narrowly on individual traveler behavior than the other projects, that is, they did not consider the traffic or environmental ramifications of changes in travel behavior. Further, to varying degrees (Commute Atlanta less so), those three projects focused more narrowly on driving behavior and associated measures and less on other modes than did the other study projects.

3.2 Assessing Travel Impacts

This section contains two parts. The first summarizes travel impacts, performance measures and data collection methods and the second part summarizes travel findings and study limitations.

3.2.1 Impacts, Performance Measures and Data Collection Methods

Table 3‑1 compiles the travel impacts, performance measures and associated data collection methods for each of the eight study projects. Among those projects, three broad types of travel impacts were found most commonly:

  • Traffic impacts – the impact on roadway usage, e.g., traffic volumes, and/or roadway performance, e.g., speeds and collisions.
  • Transit impacts – the impact on transit system usage, e.g., ridership and/or transit performance, e.g., schedule adherence.
  • Traveler behavior impacts – the impact on the behavior of travelers, e.g., routes, modes, time of trip.

The travel impact most commonly considered in the eight study projects is traffic. Transit and traveler behavior impacts are less common, but far from unusual. Few study projects examined safety impacts.

 

Table 3-1. Travel Impacts, Measures and Data Collection Methods

Study/Project

Impacts Analyzed

Associated Performance Measures

Data Collection Method

Simulated Pricing Field Demonstrations

Oregon Mileage Fee Concept and Road User Fee Pilot Program

Traveler behavior (individual)

  • Vehicle miles traveled
  • Vehicle miles traveled by time-of-day (peak vs. off-peak)
  • Route choice
  • Number of trips
  • Demographics
  • Instrumented vehicles (GPS) driven by volunteer, general public travelers
  • Mode choice – stated inclination to use alternate modes (transit, bicycle)
  • Traveler surveys (attitudinal)

Puget Sound Traffic Choices Study

Traveler behavior (individual)

  • Vehicle miles traveled by road type
  • Vehicle hours of travel
  • Number of trips
  • Route choice
  • Trip purpose
  • Demographics
  • Instrumented vehicles (GPS) driven by volunteer, general public travelers

Commute Atlanta Mileage Based Value Pricing Demonstration

Traveler behavior (household)

  • Number of trips
  • Trip lengths
  • Trip duration
  • Intra- vs. extra-regional trip making
  • Demographics
  • Instrumented vehicles driven by general public travelers
  • Panel survey of the participating travelers

Traffic

  • Total vehicle miles traveled
  • Instrumented vehicles driven by volunteer general public travelers

Before-After Project Evaluations – U.S.

Minnesota I-394 MnPASS HOT Lanes

Traffic

  • Hourly traffic volumes by lane
  • Average vehicle speed by lane group (general purpose and HOT)
  • Travel time
  • Permanent roadway detectors

Traveler behavior (individual)

  • Vehicle occupancy
  • Number of carpools
  • Mode choice (transit use)
  • Demographics
  • Traveler surveys (attitudinal) – panel – 800-900 per wave
  • Travel diaries (1-day) with general travel behavior questions – 800-900 participants

San Diego I-15 HOT Lanes

Traffic

  • Traffic volumes
  • Time-of-peak distribution
  • Average speed
  • Vehicle classification
  • Roadway detectors
  • Vehicle occupancy
  • Unknown

Safety

  • Incidents
  • Unknown

Traveler behavior (individual)

  • Mode split
  • Traveler surveys (attitudinal) – panel – 1,500 respondents per wave

Before-After Project Evaluations – International

The Stockholm Trial

Traffic

  • Traffic volumes
  • Objective data collection in the field (various methods used)
  • Vehicle kilometers traveled
  • Estimated using traffic models
  • Journey (travel) times
  • Instrumented vehicles (GPS)
  • Roadside detectors (traffic cameras used to match vehicle images at various points)
  • Vehicle queue lengths at intersections
  • Instrumented vehicle field data collection

Traveler behavior (individual)

  • Demographics
  • Number of trips
  • Origin-destination
  • Mode
  • Time of travel
  • Travel time
  • (Stockholm County residents) Travel diaries (1 day) – panel study in 3 waves (2 pre- and 1 post) – 30,000+ participants
  • (Commuters into pricing zone) – subset of County residents survey – 2,200 participants
  • Demographics
  • Number of trips
  • Mode choice
  • Potentially other measures unspecified in literature
  • (Other County Residents) 875 participants – study methodology unknown

Transit

  • Ridership
  • Travel times
  • Detectors
  • Rider perceptions
  • On-board surveys

Central London Congestion Pricing

Traffic

  • Congestion (the “excess delay” or “lost travel time” as defined as the difference between average network travel rates in uncongested versus congested conditions)
  • Calculated from other data
  • Average network speeds (vehicle kilometers divided by vehicle hours)
  • Average network travel rate (vehicle minutes divided by vehicle kilometers)
  • Speed distributions (the proportion of time spent driving within various speed bands)
  • Instrumented floating car runs
  • Automated license plate matching (cameras)
  • Commercially purchased satellite vehicle tracking
  • Traffic density (number of vehicles per kilometer)
  • Derived from traffic volume data
  • Number of vehicle trips
  • Derived from traffic volume data (may also have been measured through traveler surveys)
  • Journey (travel) times
  • Volunteer, general public driver panels (manual recording of times)
  • Traffic volumes
  • Various manual and automated (roadway detectors) methods
  • Vehicle kilometers driven
  • Derived from traffic volumes
  • Vehicle minutes driven
  • Derived from traffic volume and congestion data
  • Average vehicle occupancy
  • Visual observation
  • Parking and pedestrian activity
  • Manual counts

Transit

  • Transit ridership
  • Manual counts
  • Fare collection data
  • Average bus journey speeds
  • Automatic vehicle location systems
  • Bus reliability – passengers excess waiting time (difference between scheduled and actual bus arrival)
  • Manual schedule adherence monitoring at bus stops/stations
  • Bus reliability – operated mileage versus scheduled mileage (congestion can result in a bus covering less miles, completing fewer runs than scheduled)
  • Transit agency databases

Traveler behavior (household)

  • General travel behavior (modes, routes, times, etc.)
  • Panel surveys of up to 2,300 households
  • Detailed travel behavior (modes, routes, times, etc.)
  • Panel surveys of up to 2,300 households with 1-day travel diaries

Traveler behavior (individual)

  • General travel behavior (modes, routes, times, etc.)
  • Panel surveys of up to 2,400 individuals
  • En-route transit rider surveys
  • Roadside interviews with up to 15,000 travelers (driving, cycling, walking)
  • Focus groups (generally targeting subpopulations like disabled people or shift workers)
  • Detailed travel behavior (modes, routes, times, etc.)
  • Panel surveys of up to 2,400 individuals with
    1-day travel diaries

Safety

  • Number and type of roadway crashes
  • Accident reports

Singapore Area Pricing

Traffic

  • Traffic volumes
  • Trip departure time
  • Average speed
  • Unknown, but likely that most data were objective data, e.g., traffic counts

Traveler behavior

  • Routes
  • Modes
  • Unknown

Safety

  • Number of crashes
  • Unknown, likely accident reports

 

 

Tables 3-2 through 3-4 summarize the state-of-the-practice for travel impacts in terms of the relative prevalence of various performance measures and associated data collection methods. This information is based primarily on the eight study projects. Greater variety was found in the area of traffic impacts than in transit and traveler behavior and therefore Table 3‑2 uses three categories to assess prevalence and Tables 3-3 and 3-4 use only two. Assessments of the relative rarity of various measures are subjective and there are a few close calls. For example, categorizing travel times and speeds as “very common” versus “common.” Assessments for the performance measures are absolute in the sense that they reflect how commonly each measure appears in the evaluation reports that were reviewed. Assessments of methods are relative in the sense that they do not describe how often a particular data collection method appears in the literature overall, but rather, how frequently that method is used among those projects that include the associated performance measure. For example, a “very common” data collection method for an “uncommon” measure means that, in the literature overall, that data collection method is not common.

 

Table 3-2. Traffic Impact Performance Measures and Data Collection Methods

Performance Measures

Associated Data Collection Methods (by prevalence)

Very Common

Common

Less Common

Very Common

Traffic volumes

  • Permanent vehicle detectors (loops, radar, cameras, etc.)
  • Temporary vehicle detectors (radar, cameras, etc.)
  • Manual counts (visual observation)
  • Probe vehicles driven by travelers (e.g., toll tag-equipped)

Vehicle miles traveled

  • Derived from traffic volumes and roadway segment lengths

Empty cell.

  • Probe vehicles driven by travelers

Average speeds

  • Permanent vehicle detectors (loops, radar, cameras, etc.)
  • Temporary vehicle detectors (radar, cameras, etc.)
  • Probe vehicles driven by travelers
  • Probe vehicles driven by evaluators (i.e., “floating car”)

Common

Travel time

  • Probe vehicles driven by evaluators (i.e., “floating car”)
  • Vehicle detectors (loops, radar, cameras, etc.)
  • Probe vehicles driven by travelers

Vehicle classification 1

  • Permanent vehicle detectors (loops, radar, cameras, etc.)
  • Temporary vehicle detectors (radar, cameras, etc.)
  • Manual counts (visual observation)

Less Common

Vehicle occupancy

  • Manual counts (visual observation)

Empty cell.

Empty cell.

Mode split2

  • Derived from traffic counts coupled with average vehicle occupancy; transit passenger counts & other mode (bike, walk)

Empty cell.

Empty cell.

Travel rate (distance per hr./min.)

  • Derived from other data, namely volumes, speeds, roadway link lengths

Empty cell.

Empty cell.

Traffic density

  • Derived from traffic volume data

Empty cell.

Empty cell.

Vehicle minutes or hours driven

  • Derived from vehicle miles driven and speeds

Empty cell.

Empty cell.

Number of accidents or accident rates

  • Accident reports/databases

Empty cell.

Empty cell.

Traveler and/or system operator perceptions of safety

  • Surveys, interviews or focus groups

Empty cell.

Empty cell.

Notes:

1 Rare as an end measure, commonly collected to support environmental analysis (an input to emission rates)

2 This measure appears both as a traffic impact measure as well as a traveler behavior impact measure (Table 3-4) to reflect the two different approaches to collecting these data (counts for traffic vs. surveys for travel behavior) and the different focus in traffic analysis (impact, e.g., person throughput, on roadways) versus travel behavior (impact on people).

 

 

 

Table 3-3. Transit Performance Measures and Data Collection Methods

Performance Measures

Associated Data Collection Methods (by prevalence)

More Common

Less Common

Common

Ridership

  • Automated passenger counters
  • Manual passenger counters

Empty cell.

Less Common

Travel time

  • Automatic vehicle location systems

Empty cell.

Rider attitudes & perceptions

  • En-route surveys

Empty cell.

Bus speeds

  • Automatic vehicle location systems

Empty cell.

Bus schedule adherence:  on-time performance

  • Automatic vehicle location systems
  • Visual observation in the field

Bus schedule adherence – operated mileage

  • Transit agency vehicle operations database

Empty cell.

 

 

 

Table 3-4. Traveler Behavior Impact Performance Measures and Data Collection Methods

Performance Measures

Associated Data Collection Methods (by prevalence)

More Common

Less Common

Common

Individual, general travel behavior (typical routes, modes, time of trip, etc.)

  • Panel surveys (same sample before-after)
  • Cross-sectional surveys (different samples before-after)

Individual traveler attitudes & perceptions

  • Panel surveys (same sample before-after)
  • Cross-sectional surveys (different samples before-after)

Less Common

Individual, specific travel behavior (routes, modes, etc. on specific sample day[s])

  • Panel travel diary (same people before-after)
  • Cross-sectional travel diary (different people before-after)

Household, specific travel behavior (routes, modes, etc. on specific sample day[s])

  • Panel travel diary (same people before-after)
  • Cross-sectional travel diary (different people before-after)

Household, general traveler behavior (typical routes, modes, time of trip, etc.)

  • Panel surveys (same people before-after)
  • Cross-sectional surveys (different people before-after)

Mode choice/mode split

  • Derived from household or individual travel diary data
  • Derived from household or individual general travel survey data

 

 

As indicated in Tables 3-2 through 3-4, and as one would expect with evaluations of deployed projects, all of the traffic and transit measures utilize objective data rather than modeling, simulation or other estimated or derived sources. In contrast, travel behavior measures always rely on travelers’ self reports of their behavior, although in the case of travel diaries—where travelers are asked to very accurately and specifically record the details of individual trips using paper or electronic forms—reported behavior is expected to be a fairly accurate reflection of actual behavior.

Overall, explicit consideration of mode choice changes and the change in mode splits or shares—the percentage of person trips made by various modes—is not extremely rare, but is far from standard. Although some evaluations do not consider mode changes at all, most of them do, with the smaller, less comprehensive studies often inferring some mode change effects based on changes in traffic volumes and transit ridership. The most comprehensive evaluations, such as London, do explicitly consider mode changes and attempt a complete accounting, e.g., of the X number of car trips that were eliminated, Y percent went to transit, etc. However, even among the more robust evaluations, comprehensive treatments which capture both the total change in person trips and trace all of the mode-to-mode changes are very uncommon. In London, the disposition of the car trips that were eliminated was examined, but a complete accounting of total person trips before and after by mode was not performed.

Within the area of traveler behavior surveys, variations can be observed based on the following major variables:

  • Pre- and post-deployment surveying (before and after) versus pre-only or after-only.
  • Panel (longitudinal) samples (the same people participate in the before and after survey) versus cross-sectional samples (different people participate in the before and after survey).
  • Household surveys, in which each adult in the household is surveyed, versus surveys of only a single traveler.
  • General travel behavior information, where travelers describe their typical behavior, versus detailed and specific travel behavior information in which travelers record travel details for one or more specific days in a diary.
  • Travel diaries alone, versus travel diaries in conjunction with in-vehicle data collection.

The state-of-the-practice in regard to these parameters can be summarized from the eight study projects as follows:

  • Pre- and post-deployment surveying is standard.
  • Panel (longitudinal) studies are typical.
  • The largest, most comprehensive and robust evaluations of large-scale pricing schemes (e.g., London and Stockholm) use both household and individual surveys. The less comprehensive evaluations and evaluations of smaller-scale pricing projects tend to use one or the other, with individual traveler surveys being somewhat more common.
  • Large, comprehensive evaluations utilize surveys gauging both general traveler behavior as well as specific travel behavior collected through diaries.
  • Use of instrumented vehicles in general is common, but instrumentation of vehicles driven by volunteers who are also maintaining travel diaries is uncommon.

Considering the full range of travel impacts (traffic, transit, traveler behavior, safety) the state of the practice in regard to instrumented vehicles, including traffic probes, can be summarized as follows:

  • Overall, instrumented vehicle data collection is common.
  • Collection of speed and/or travel time data from GPS-equipped buses and cars is common and transit ridership is often collected using on-board passenger counters.
  • For average roadway speeds and travel times, in the cases where instrumented vehicles are used, the most common approach is for the evaluators to perform a set number of data collection runs themselves. It is less common to recruit public volunteers or to purchase commercial data derived from various inputs, including traffic probes.
  • Instrumented vehicles for collecting detailed and specific traveler behavior are common in simulated congestion pricing field demonstrations. All three study projects of that type used instrumented vehicles driven by public volunteers. This is probably because data were collected over much longer periods than is possible using a travel diary and/or because a pricing changes were dynamic and a high degree of accuracy was needed, for example in the Puget Sound study.

3.2.2 Reported Findings and Study Limitations

Appendix A includes a table summarizing the main travel related findings of the eight study projects. Results indicate that a variety of congestion pricing projects have been shown to be effective in reducing traffic congestion. Highlights of reported findings include the following:

  • Significant reductions in vehicle travel (VMT or VKT) on the order of 10 percent are common.
  • Vehicle trips have been reduced by between 7 and 20 percent.
  • Travel reductions between 3 and 14 percent.
  • Speed increases between 6 and 21 percent.
  • Mode shift from driving to transit, with transit ridership increases between 6 and 37 percent.

In reviewing the findings of congestion pricing evaluations, the emphasis in this study was to understand how the evaluation methodologies and challenges impacted the ability of researchers to draw definitive conclusions. The table in Appendix A includes a column that summarizes major caveats or limitations relevant to specific findings reported for the eight study projects. These caveats and limitations can be summarized as follows:

  • Although some references to study limitations are common, detailed discussions of how various aspects of the evaluation methodology or context may impact conclusions are not common in the published literature.
  • Many evaluators acknowledge that exogenous factors have impacted their findings but it is very rare to quantitatively adjust results to eliminate variance related to exogenous factors.
  • Commonly cited exogenous factors include fuel price changes, other transportation projects, seasonal variations, survey samples that do not accurately represent the population, and background traffic growth related to land development.
  • Few concerns are noted about the fundamental accuracy of objective data such as traffic volumes, speeds and travel times.
  • Some evaluations utilize control groups, but many do not.
  • Challenges in reconciling travel diary data with objective transit and traffic data as well challenges in collecting comprehensive carpooling, telecommuting, bicycle and pedestrian data make it very difficult to perform a comprehensive accounting of mode choice changes.

3.3 Assessing Environmental Impacts

This section contains two parts. The first part summarizes environmental impacts, performance measures and data collection methods and the second part summarizes environmental findings and study limitations.

3.3.1 Impacts, Performance Measures and Data Collection Methods

A 2008 FHWA lessons-learned report on their Value Pricing Pilot Program noted that congestion pricing evaluations have paid less attention to equity and environmental impacts than traffic impacts, project operations, and public and customer satisfaction.51 That is also generally the case among the eight study projects.

Table 3‑5 identifies the environmental impacts, performance measures and associated data collection methods for each of the eight study projects. Among those projects, three broad types of environmental impacts were common:

  • Air Quality – the impact on pollutant levels.
  • Noise – the impact on traffic noise levels.
  • Environmental Justice – the extent to which positive and negative impacts are disproportionate among people of different races and income levels, including the delay of benefits and/or a disproportionate share of adverse impact accruing to minority and low-income populations.

 

Table 3-5. Environmental Impacts, Measures and Data Collection Methods

Study/Project

Impacts Analyzed

Associated Performance Measures

Data Collection Method

Simulated Pricing Field Demonstrations

Oregon Mileage Fee Concept and Road User Fee Pilot Program

No Environmental Analysis Performed

Puget Sound Traffic Choices Study

No Environmental Analysis Performed

Commute Atlanta Mileage Based Value Pricing Demonstration

Air Quality (cumulative vehicle emissions) – methodology identified but analysis was not performed

  • CO, PM2.5, PM10 and ozone emissions
  • Calculated (estimated) based on objective traffic data and regional emission rates

Air Quality (CO hotspot microscale) – methodology identified but analysis was not performed

  • Localized CO levels
  • Calculated (estimated) using EPA CALINE4 microscale model

Before-After Project Evaluations – U.S.

Minnesota I-394 MnPASS HOT Lanes

Air quality (cumulative vehicle emissions)

  • CO levels
  • Roadside monitors

Noise

  • Sound levels
  • Roadside monitors
  • MINNOISE model (based on FHWA Traffic Noise Model)

Environmental justice

  • Surveyed perceptions among different socio-demographics
  • Traveler surveys (attitudinal) – panel – 800-900 per wave
  • Travel diaries (1-day) with general travel behavior questions – 800-900 participants

San Diego I-15 HOT Lanes

Air quality (cumulative vehicle emissions)

  • VOC, NOx, PM10 and CO emissions.
  • Calculated (estimated) based on objective traffic data and regional emission rates

Noise

  • Monitored sound levels
  • Roadside monitors

Before-After Project Evaluations – International

The Stockholm Trial

Air quality (cumulative vehicle emissions)

  • Emissions of PM10, NOx, CO2, CO, and VOC
  • Estimated using the European Union “ARTEMIS” model, utilizing both monitored and estimated traffic inputs
  • Pollutant levels
  • Roadside monitors
  • Vehicle emission exposure levels
  • Dispersion models utilizing objective traffic data coupled with emission rate and meteorological inputs

Noise

  • Sound levels
  • Roadside monitors

Environmental justice (equity)

  • Origins and destinations
  • Travel times
  • Congestion charges paid
  • Travel adaptation costs
  • Pricing revenue redistribution impacts
  • Travel diaries (1 day) – two waves – 24,000 participants
  • Regional travel demand modeling of with and without pricing conditions using travel-diary derived specific travel behavior

London Congestion Pricing

Air quality (cumulative vehicle emissions)

  • Emissions of PM10, NOx, and CO2
  • Roadside monitors to collect total concentrations (for reference; not expected to be conclusive)
  • Calculated (estimated) emissions to better understand project contributions to changes in emissions; used observed traffic impacts and regional emissions rates

Noise

  • Sound levels
  • Roadside monitors

Environmental justice

  • Perceptions of various types of travelers (varying by income, mode use, residence location, vehicle ownership, physical ability/disability, shift workers, etc.)
  • Individual traveler and household panel surveys
  • Roadside interviews
  • Focus groups

Singapore Area Pricing

Air quality (cumulative vehicle emissions)

  • Emissions of CO, NOx and smoke/haze
  • Calculated (estimated) based on objective traffic data and regional emission rates
  • Roadside monitors

Pedestrian safety (perceived)

  • Pedestrian perceptions of safety
  • Uncertain, but seemingly via interviews

Environmental justice

  • Geographic distribution of traffic impacts
  • Travel modeling analysis (comparing travel impacts to geographic distributions of various income and other socio-economic variables)
  • Attitudes and perceptions of various types of travelers
  • Surveys

 

 

Pre-deployment estimation of the environmental impacts of congestion pricing projects—such as environmental assessments (EAs) and environmental impact statements (EISs) developed in the U.S. in compliance with the National Environmental Policy Act of 1969 (NEPA)—do consider a much wider range of potential impacts, including land use. However, the present study focuses on measurement of the actual, post-deployment impacts of congestion pricing and, with one exception, there were no examples of those other types of environmental impacts among the study projects. The one exception—the one other environmental impact that was found in the study projects (London and Stockholm)—is the impact of congestion pricing on business and the economy. However, in neither London nor Stockholm were these impacts included within the “environmental” impact category. This state of the practice summary focuses on the more common impacts: air quality, noise and environmental justice.

Some of the eight congestion pricing study projects—the simulated pricing field demonstrations in Oregon, the Puget Sound region, and Atlanta—did not consider any environmental impacts whatsoever. However, with the exception of the seeming omission of noise and the addition of pedestrians perceptions of safety in Singapore, all of the other study projects considered air quality, noise and—although under various names and varying emphases—environmental justice. In several cases, “environmental justice” impacts were termed “equity” impacts or were part of broader investigations such as “social impacts.”


In regard to performance measures and data collection methods, air quality and noise environmental impact analyses show less variation from study to study and fewer measures and methods overall than do travel impact analyses. The most common air quality performance measure—used in each air quality analysis that was reviewed—is the volume of pollutant emissions from roadway traffic. The only other performance measure that was found, and which is much less commonly considered, is ambient pollutant concentrations. Only one data collection method was found for vehicular pollutant emissions: calculating emissions by multiplying project-attributable changes in vehicle miles traveled on roadway links by model-derived emission factors (e.g., grams emitted per mile) corresponding to the observed speeds on the roadway links. Likewise, there was only one data collection method found for ambient pollutant concentrations: roadside pollution sensors. None of the study projects included in-depth considerations of greenhouse gases. Rather, when greenhouse gases are addressed at all, it is typically done by including the precursor CO2 among the calculated vehicle emissions. No examples of the sort of atmospheric modeling necessary to actually estimate green house gases were found.

There is even less variation in evaluation practices related to noise impacts. Noise impacts in all of the study projects were measured in terms of ambient sound levels, in decibels, and in all cases roadside noise monitors were used to collect the data.

Pedestrian safety was only considered as an environmental impact and evaluated in Singapore. Pedestrian safety was measured in terms of pedestrians’ perceptions which were gathered through surveys, interviews or focus groups.

In contrast to air quality and noise, analyses of environmental justice or equity impacts vary more from project to project. This may be because environmental justice analyses typically include more qualitative or subjective elements and, therefore, or more of an art than a science, or it may be that as a relatively new area of interest, fewer standardized approaches have thus far emerged. Although specific approaches vary, environmental justice analyses generally focus on two main areas. The first is to examine how travel impacts distribute geographically and, using geographic socioeconomic databases, infer whether projects differentially impact (both benefits and disbenefits) areas with concentrations of low income and/or minority populations. The second element employs public surveys and or focus groups to gauge the perceptions of low income and/or minority populations.

3.3.2 Reported Findings and Study Limitations

Appendix B includes a table summarizing the main environmental impact findings of the eight study projects. Highlights of reported findings include the following:

  • Reductions in calculated vehicle emissions of various pollutants of up to 16 percent.
  • No project-attributable changes in monitored air quality levels.
  • No significant project-attributable changes in modeled noise levels.
  • No project-attributable changes in monitored noise levels.
  • Benefits and costs of congestion pricing do impact different types of people differently, but negative impacts to lower income earners have generally been less than anticipated and generally not disproportional.
  • Horizontal equity considerations such as residential and work locations and access to travel alternatives play as much or more of a role than vertical equity considerations (namely ability to pay the pricing charge) in explaining differential impacts among various types of people.
  • How pricing revenues are spent can have a significant impact on the net costs and benefits for different people.

Caveats and limitations associated with environmental impacts focus largely on the inability to differentiate project contributions from exogenous factor influences on monitored air quality impacts. Although some state-of-the-practice reviews have identified the failure to test for air quality implications of possible changes in traffic flow (e.g., more cruising and less stop and go) as a weakness, this issue is rarely noted by the evaluators of specific projects. There are limited discussions of caveats and study limitations in regard to environmental justice. When discussed, limitations sometimes focus on measurement issues such as survey sample limitations and the inherently subjective and “less scientific” nature of perception data.

3.4 Knowledge Gaps

This section identifies gaps in the understanding of the travel and environmental impacts of congestion pricing.

3.4.1 Travel Impacts

Table 3‑6 summarizes the aspects of travel impacts that have generally been well established through before-after evaluations as well as those areas where understanding is less complete. Each of these gaps is discussed following Table 3‑6.

 

Table 3-6. Travel Impact Knowledge Gaps

Better Understood Impacts
(Knowledge)

Less Understood Impacts
(Knowledge Gaps)

Short-term impacts (from a few months up to a year after deployment)

Long term impacts

Localized impacts

Regional impacts

Cumulative impacts (projects plus exogenous factors)

Project-attributable impacts

Individual travel behavior changes

Household travel behavior changes

Vehicle trips

Person trips

Average speeds

Vehicle speed fluctuations (driving cycle)

Average performance

Variability in performance (reliability)

Transit ridership changes

Transit crowding implications

 

Long Term Impacts

Long-term impacts are not well understood because there have been relatively few long-term evaluations. Although the Singapore and London evaluations have continued over many years, the ability to draw conclusions is still limited by the fact that not all analyses have been continued and because over these longer periods the masking influence of exogenous factors has made isolation of project impacts especially challenging. In particular, there is not a good understanding of the long-term traveler behavior associated with congestion pricing, e.g., will travelers who, in the short term, are willing to continue driving and pay charges switch to other modes or make other changes, such as moving, switching jobs or telecommuting, over the long term? Conversely, will travelers who initially shift to transit or make other changes to avoid the charge eventually drift back to driving—and if so, how many of them and why? Even when long-term traffic and transit ridership data have been studied, only the aggregate change is usually considered and the components of the change are unclear. For example it is unclear whether static mode shares over time mean that travel behavior changes in response to pricing have ended or whether changes continue but off-set one another, e.g., drivers continue to switch to transit but as transit becomes crowded other travelers switch to driving. This area of uncertainty includes a lack of information on long-term impacts on auto ownership.

Regional Impacts

Another gap concerns the regional impacts of congestion pricing. Although some evaluations have considered travel impacts over a fairly large area (e.g., London and Stockholm), many evaluations have focused on the priced facilities/zone and the immediately adjacent portions of the transportation system. Of course, it is resource intensive to study larger areas, and most evaluations have focused their attention on the areas where the most significant impacts are expected, which is a logical strategy given constrained resources. Unfortunately, this has created something of a self-perpetuating cycle: evaluators do not examine regional impacts in part because there is not strong evidence that there will be regional impacts but the reason there is no strong evidence is not because evaluations have found few impacts but simply because those impacts are very seldom assessed.

Project-Attributable Impacts

Another significant gap in the understanding of travel impacts concerns the specific influence of the pricing project in observed changes. The combined or cumulative impact of pricing projects—the impact of the project coupled with the influence of a wide range of exogenous factors—has been relatively well established in many evaluations. However, most evaluations are not able to accurately isolate the discrete impact of the project. Most evaluations acknowledge one or two exogenous factors that may be relevant, e.g., gas prices or other transportation system changes, and some go as far as to describe the changes in those exogenous factors and qualitatively consider the potential general impact of those factors as they draw conclusions about the project impacts. But rarely are the impacts of the exogenous factors addressed quantitatively. The 2008 FHWA Value Pricing Pilot Program Lessons Learned Final Report came to similar conclusions, noting that:

  • Attempts to distinguish the effects of pricing from outside influences such as gas prices or economic swings have been modest at best.
  • Use of controls is limited.
  • Opportunities for improvement include attention to controls and statistical tests to insure valid results and to rule out influences of ongoing swings in gasoline prices and economic conditions.

Even in the few cases where evaluators have estimated the quantitative impact of certain exogenous factors, results have sometimes been challenged. For example, different researchers disagree about the how much of the traffic reductions were due to fuel increases.52

Use of household travel diaries in conjunction with conventional surveys which focus on the influences on travel behavior can be a powerful means for improved understanding of exogenous factors. However, as the evaluators of the Commute Atlanta mileage-based value pricing program evaluation found, use of household travel diaries is not effective unless changes in households between the before and after periods are carefully considered and unless sample sizes are large enough to balance significant demographic variation among households. The Commute Atlanta researchers concluded that the several well known and frequently cited U.S. congestion pricing evaluations have not sufficiently controlled for such factors.

Household Travel Behavior Changes

Use of household traveler surveys and travel diaries is not rare, but it is far from commonplace. Also, when household travel behavior has been investigated, there is often insufficient focus on shifts and trade-offs within the households, such as how a vehicle is used by family member X which was formerly driven by family member Y who has shifted to transit in response to pricing. Likewise, there has been little to no significant investigation of how total household travel budgets—in terms of time and money—factor into these intra-household changes and the connection to specific pricing strategies. As a result, the understanding of household impacts of congestion pricing projects is not complete.

Person Trips

The understanding of the impact on congestion pricing projects on person trips, especially person throughput and overall mode shares is incomplete. Many evaluations focus foremost on traffic impacts, or vehicle trips. Many of those evaluations that have included consideration of other modes have focused on transit ridership. Some of the largest and most robust evaluations, like London, have tracked person trip shifts to some degree—e.g., tracing X number of curtailed driving trips into the pricing zone—but those analyses have not included a truly comprehensive accounting of all person trips and their shifts. There has also been very limited consideration of person throughput. Challenges in cost-effectively collecting average vehicle occupancy data and data from travelers who telecommute, forego trips, bicycle, or walk likely contribute to the under consideration of person trip impacts.

Vehicle Speed Fluctuations (Driving Cycle)

Most evaluations focus on the before-after changes in the average speeds on specific roadway links or across roadway networks and these changes are relatively well understood. However, there is not yet a sufficient understanding of impacts on vehicle driving cycle (proportion of vehicle operation in acceleration, deceleration, idle and cruising modes) that are related to changes in traffic flow characteristics and which may not be accompanied by significant changes in average speeds. A roadway that shows little to no change in average speed may in fact demonstrate improved flow and a significantly different driving cycle profile. For example, pre-pricing, a road may have an average speed of 30 miles per hour (mph) that reflects brief bursts of 45 mph travel punctuated by frequent stops (idle). After pricing, the average speed may still be 30 mph but it may reflect a steady, 30 mph flow with no stops and starts. To date, evaluations have neither clearly identified the significance of traffic flow improvements on driving cycle and emissions nor eliminated the possibility that they may be as or more important than reductions in traffic volumes.53

Variability in Performance (Reliability)

Most evaluations focus on average or typical transportation system performance. There has been far less attention paid to the variability in transportation performance, that is, reliability. Gaps in this area include both objective quantification of reliability as well as thorough understanding of traveler attitudes and responses to varying levels of reliability.

Transit Crowding Implications

In so much as one objective of many pricing strategies is to shift some travel from driving to public transportation, transit services and capacity play a key role in congestion pricing success. Although evaluations often document the net changes to transit ridership, the implications of transit capacity and crowding are not well understood. As many transit agencies across the U.S. are implementing or considering service cut-backs, understanding these issues is especially important now.

3.4.2 Environmental Impacts

This section discusses gaps in the understanding of the air quality, noise, and environmental justice (equity) impacts of congestion pricing projects.

Air Quality

What is currently understood regarding the impact of congestion pricing projects on air quality is based on the analyses that have calculated vehicle emissions. As noted in Section 3.2, roadside monitoring of before and after air quality has not enhanced that understanding.

Overall, the calculated vehicle emissions analyses that have been done have provided an understanding of the approximate or partial air quality impacts of congestion pricing projects. Inaccuracies and incompleteness stem from a failure to accurately specify all of the key input parameters—both traffic and emission rate-related.

Table 3‑7 identifies those determinants of congestion pricing project vehicle emission impact that are usually well represented in analyses as well as those that are not. It is the latter that underlie the gaps or uncertainties in the current understanding of the air quality impacts of congestion pricing projects.

 

Table 3-7. Air Quality Knowledge Gaps

Better Understood Components of
Congestion Pricing Vehicle Emission Changes

Less Understood Components of
Congestion Pricing Vehicle Emission Change

Cumulative impacts (VMT, speed)

Project-attributable impacts (VMT, speed)

Localized impacts

Regional impacts

Average daily impacts

Hourly variation

Empty cell.

Driving cycle changes (traffic flow change)

Empty cell.

Vehicle mix

 

 

Project-Attributable VMT. As discussed in Section 3.3.1, most analyses acknowledge a number of exogenous factors impacting observed, before-after changes in traffic volumes and average speeds, but very rarely are the traffic inputs to emissions volume quantitatively adjusted to eliminate the portion of variation attributable to exogenous factors. Indeed, analysts very seldom understand (or agree on) exactly how much of the observed variation is due to factors such as fuel price changes or employment changes.

Regional VMT and Speed Changes. Also as noted in Section 3.3.1, the analysis of the traffic impacts of congestion pricing projects typically focus on the priced facility/area and, somewhat less typically, may also include the immediately adjacent roadways. Examinations of potential impacts farther away, throughout the region, are rare. Correspondingly, emissions analyses have been unable to capture any of more distant impacts that may be created.

Hourly Variation. Many congestion pricing projects can be expected to have significantly different impacts during different times of day. It does not appear that hourly variations in VMT and or link speeds are standard considerations in congestion pricing project air quality analysis, and thus the understanding of these impacts is incomplete.

Driving Cycle Changes (Traffic Flow Change). The “grams per mile” emission rates (“factors”) used in emission calculations are almost always derived from emission factor models such as the U.S. Environmental Protection Agency (EPA) MOBILE6 model or the California Air Resources Board EMFAC model. Those models utilize assumptions—which, to varying degrees, can be manipulated by users—regarding the “driving cycles” of vehicles. Driving cycle refers to the proportion of a vehicle’s travel (VMT) under acceleration, deceleration, idle, and cruise (constant speed). Evaluations of congestion pricing project emissions impacts seldom include adjustments to reflect driving cycle assumptions in the emission factor models based on project-attributable impacts to driving cycles. The likely reasons for omitting such potentially important probably changes include: 1) It is usually unclear whether a project has impacted vehicle driving cycles (most traffic analysis look only at net changes in average roadway link speeds), and 2) Some analysts do not understand the significance of driving cycle in the emission factor estimation process, which is opaque in so much as it occurs internal to the emission factor model.

Vehicle Mix. Vehicle emission rates—grams per mile or grams per hour (idle)—vary significantly by vehicle type. The traffic analyses within most evaluations of congestion pricing projects do not appear to explicitly study project impacts on vehicle mix and, as a result, these changes are usually not included in the air quality analysis. That is, the breakdown of total observed link VMT for the before and after scenarios utilize the same vehicle mix. To the extent that congestion pricing projects do not differentially impact different vehicle types, it is of course appropriate to use the same mix, but to the extent that projects do impact this area, these impacts are seldom reflected in the emissions analyses.

Noise

There are no gaps, per se, in the understanding of the noise impacts of congestion pricing. This is because, even allowing for the likely inaccuracies in calculated (modeled) noise levels and, in the case of monitored noise levels, the inability to differentiate project impacts from exogenous impacts, congestion pricing projects have not been shown to, and are unlikely to, generate the magnitude of traffic changes needed to produce a change in noise levels that is perceptible to most people. Hearing sensitivity among humans is non-linear in that very large increases in the noise-generating activity are necessary to produce even the smallest perceptible changes in noise levels. In the case of traffic, at least a doubling (or halving) of traffic volume will be necessary to produce a change in noise levels that is noticeable to most people.54 Unless a congestion pricing project is expected to increase or decrease traffic levels by 50 percent or more, and/or noise is an extremely significant concern in a community, there is little value in even attempting to gauge the noise impacts of congestion pricing projects.

Environmental Justice

Overall, review of the published literature associated with the eight study projects as well as other documents assessing congestion pricing project effects and evaluation methods yielded fewer insights into knowledge gaps in the area of environmental justice than in the areas of travel impacts and air quality. That may be due in part to the fact that, although there is considerable information on environmental justice (or “equity” as it is often termed), there has been somewhat less focus on this topic than on travel impacts, a conclusion shared by a recent report on lessons learned from the FHWA Value Pricing Pilot Program.55 It may also be a function of the fact that much of the focus on environmental justice topics has been on predicting the impacts as part the design of the scheme so as to maximize public acceptance rather than solely on measuring the impacts of deployed projects. Predicting impacts has been a particular focus among European congestion pricing researchers.

Although there is not extensive information identifying gaps in the environmental justice knowledgebase, four specific areas can be identified:

  • The need for more results, overall, and the importance of considering environmental justice issues in all evaluations.
  • The need for particular focus on “horizontal equity” issues, which pertain to those aspects of equity or environmental justice that pertain to issues other than income (vertical equity), including geographic locations and auto access.
  • Greater investigation into the long-term equity implications of congestion pricing projects, including on land use and population.
  • Greater emphasis on how the uses of congestion charging revenues can impact both the perceived and actual equity of pricing projects.

The importance of continued examination of horizontal equity issues is based in part on evaluation findings that have shown that the vertical equity issues associated with congestion pricing projects may not be as great, or at least not as singular of a focus as had been expected.56 The need to gather more information on the long-term environmental justice impacts of congestion pricing reflects a concern common to essentially all impact areas. The importance of an improved understanding of the impacts of pricing revenue redistribution has been cited by several researchers, including the 2008 FHWA Value Pricing Pilot Program lessons learned report, the CURACAO study and a 2009 study by the RAND Corporation.57

 


 

51 K.T. Analytics and Cambridge Systematics, Inc., 2008.

52 Coordination of Urban Road User Charging Organisational Issues (CURACAO), “Deliverable D2: State of the Art Review (FINAL). Prepared by the Institute for Transport Studies, University of Leeds. May 2009.

53 Institute for Transport Studies, University of Leeds, 2009.

54 Institute for Transport Studies, University of Leeds, 2009.

55 K.T. Analytics and Cambridge Systematics, Inc., 2008.

56 K.T. Analytics and Cambridge Systematics, Inc., 2008.

57 Ecola, Lissa and Light, Thomas, “Technical Report – Equity and Congestion Pricing, A Review of the Evidence,” RAND Corporation, 2009.

Office of Operations