A simulation study indicated that integrating traveler information with traffic and incident management systems in Seattle, Washington could reduce emissions by 1 to 3 percent, lower fuel consumption by 0.8 percent, and improve fuel economy by 1.3 percent.
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ITS Impacts Assessment for Seattle MMDI Evaluation: Modeling Methodology and Results

Summary Information

This study examined the impacts of integrated advanced traveler information services (ATIS), advanced traffic management systems (ATMS), and incident management systems (IMS) on a mixed freeway/arterial corridor in north downtown Seattle. The regional and corridor level impacts of ITS were captured using the Mitretek Systems Process for Regional Understanding and EValuation of Integrated ITS Networks (PRUEVIIN). EMME/2 was used as the transportation planning model, and INTEGRATION-1.5 was used to generate simulations. The performance measures analyzed included the following: near-term peak period delay reduction, travel time reliability, changes in regional mode choice, corridor travel throughput, fuel consumption, and emission rates.

Measuring ITS impacts over a range of conditions was the key element to an accurate representation of annualized impacts. Accordingly, the simulation was exercised through a series of 30 scenarios. Each scenario represented a particular combination of weather impacts, travel demand variation, as well as variations in incident patterns and accidents. The scenarios were calculated from cluster analyses of traffic flow data (for variations in day-to-day travel demand) and weather/incident impacts (taken from historical archives). Each scenario had a probability of occurrence. The scenarios taken together comprised a representative year of operation.

The evaluation of ITS was broken down into a collection of four groupings: ATMS, ATIS, IMS, and Integration.

  • ATMS included projects that archived and consolidated arterial traffic data from a number of sources and compiled the information at a central location.
  • ATIS was comprised of a collection of pre-trip and en route information services that provided current congestion information based on real-time Washington State Department of Transportation (WSDOT) freeway detector data.
  • IMS attempted to improve detection, response time, and freeway system efficiency under incident conditions.
  • Integration contained only one project (ITS Backbone), which collect data from arterial sensors and integrated ATIS with traffic signal control. Both regional and corridor level simulations were used to evaluate the potential "big-picture" impact of integration on regional travel.

Each experiment was compared to a uniform baseline case representing long-standing traveler information services and traffic management systems in the Seattle corridor. For example, commercial traffic reporting and ramp metering control on I-5 were considered as elements of the baseline case.

Network efficiency impacts included data collected for all vehicles with trips starting in the north corridor between 6:15 AM and 9:00 AM.

Average delay was calculated as the difference between the average travel time in each scenario and free-flow travel times (50 percent of average demand, no accidents in the system, good weather).

Delay reduction was calculated by expressing the difference in average delay between the baseline case and the experimental case as a percentage of baseline average delay.

Throughput was determined by measuring the number trips started between 6:15-9:00 AM and ended before the end of the 9:30 AM peak period. Delay reduction and throughput measures were calculated for each scenario. An annualized figure was then calculated by computing a weighted average of across all scenarios.

System coefficient of variation was calculated by examining the variability of travel time for similar trips in the system taken across all scenarios. This statistic was considered an indicator of travel time reliability in the corridor.

Energy estimates were calculated as total liters of fuel consumed, total hydrocarbons (HC), Carbon monoxide (CO), and Nitrogen oxides (NOx) emissions. Virginia Tech used Link-level speed and stop data derived from simulations to calculate energy consumption and emissions during peak network activity.

FINDINGS (Energy and Environment)

Arterial Data for ATIS Integration Experiment

This experiment modeled the integration of data from arterial loop detectors (along routes SR99 and SR522) with data provided by the WSDOT freeway-based ATIS internet web site and other media. The baseline case assumptions were the same as with the ATIS and ATMS experiments. No changes to existing traffic signal controls along the two arterials were modeled. The only change was that users of ATIS were given the ability to consider real-time estimates of congestion on the two arterial routes (SR99 and SR522) as well as Interstate I-5 when making travel decisions. It was assumed that arterial data was updated every 15 minutes and provided a combined estimate of both link travel time and intersection delay.

A decrease in high-speed stops reduced total CO and NOx emissions by 3.0 percent. A smaller reduction was indicated for HC, while overall fuel consumption dropped by 0.8 percent.

Enhanced ITS Alternative Analysis

The Enhanced ITS Alternative was a prospective integrated deployment of ATIS and ATMS. The experiment featured an improved signal coordination system along routes SR99 and SR522, and a 6 percent user base for estimating Interstate I-5 freeway congestion, and SR99 and SR522 travel times. It was noted that this experiment was different that the other experiments in this study since both the regional and sub-area models (PRUEVIIN) were analyzed to estimate travel demand changes in response to system capacity improvements.

The combination of increased sub-area travel and reduced stops resulted in a mixed bag of energy and emissions impacts. NOx emissions decreased by 1.3 percent. Total fuel consumption increased with an increase in total travel. Average vehicle fuel economy improved by 1.3 percent to 23.5 miles per gallon (mpg) from 23.2 mpg in the Baseline case.

ITS Impacts Assessment for Seattle MMDI Evaluation: Modeling Methodology and Results

ITS Impacts Assessment for Seattle MMDI Evaluation: Modeling Methodology and Results
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Wunderlich, Karl, James Bunch, and James Larkin
Federal Highway Administration, U.S. DOT

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