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3.0 DATA ANALYSIS

Experience from the I-394 MnPASS projects indicates a high level of accuracy with data available from the MnPASS database and the Minnesota State Patrol. To ensure this continued high quality, members of the Battelle team will conduct a visual inspection of the data and will use automated range checks to identify any outliers or suspect data. Any data concerns identified will be checked with representatives from the system operator, Mn/DOT, and the Minnesota State Patrol.

The data obtained from the MnPASS database and from the Minnesota State Patrol will be used to examine measures of effectiveness contained in the tolling, environmental, equity, goods movement, and cost benefit analyses. Standard statistical techniques will be applied to the measure of effectiveness calculations using the tolling data. Examples of the data analysis conducted using the tolling data are discussed below.

  • MnPASS transponder purchases in the I-35W travel shed. The number of MnPASS transponders purchased by individuals in the I-35W corridor travel shed will be examined over time. Monitoring transponder purchases is important, as a valid transponder is needed to use the I-35W HOT lanes and PDSL. The number of transponders purchased provides an indication of the potential pool of users for the HOT lanes and the PDSL. This analysis will track purchases and the various marketing and outreach efforts (documented in the content analysis test plan) to identify the impact of different marketing and outreach activities on actual transponder sales. Transponder sales by different methods – including over the Internet, by telephone, and at MnPASS Service Centers – will be tracked and analyzed. The number of transponder sales over time will also be compared to the experience on I-394 to provide a benchmark. As noted later, examining the zip code of the address of record of MnPASS transponder purchases provides one method to identify possible equity concerns.
  • Toll transactions on the I-35W HOT lanes and the PDSL. Examining toll transactions in the I-35W HOT lanes and the PDSL provides a basic indication of use levels. The combination of toll transaction data and level of service (LOS) from the traffic data test plan will be used to examine demand elasticities in the HOT lanes and the PDSL. It will also be used to identify changes in the composition of vehicles, including carpools and tolled vehicles, using the HOT lanes and the PDSL. Examples of how the toll transaction data will be analyzed are highlighted below.
    • Toll transaction by segment will be analyzed to identify those segments with high levels of use and those with lower levels of use. The traffic conditions in the adjacent general-purpose freeway lanes will be examined based on data from the traffic data test plan to explore possible correlations between traffic congestion in the general-purpose freeway lanes and the use of HOT lanes and PDSL.
    • Toll transactions by time-of-day will be analyzed to assess use of the HOT lanes and the PDSL during different times of the day and different directions of travel. This information will also be compared to data from the traffic data test plan to assess traffic congestion in the general-purpose freeway lanes during the time periods of high use.
    • Toll transaction data will be used in combination with data from the traffic data test plan to assess the influence of changes in toll rates on the operation of the HOT lanes and the PDSL, including managing toward LOS and travel speed targets.
    • Toll transaction data will be used in combination with the sensor data from the traffic data test plan and the number of buses from the transit data test plan to estimate the vehicle mix (tolled vehicles, carpools, and buses) using the HOT lanes and the PDSL. This analysis will compare historical and pre-deployment data on the number of carpools from the quarterly Mn/DOT I-35W HOV/HOT reports with current estimates of carpool use from the toll transaction analysis.
  • Toll revenue data will be examined and analyzed. Data on the average tolls; the average toll by time period, segment, and direction; and the highest toll by time period, segment, and direction will be examined.
  • Total toll revenues. Total MnPASS revenues will be used as input to the cost benefit analysis.
  • Potential equity concerns. MnPASS data will be used to examine potential equity concerns related to the HOT lanes and the PDSL. Data on the zip code of record for MnPASS transponder holders will be used in this analysis. The MnPASS system can provide the zip code of record without compromising any privacy concerns. The number of transponder holders for zip codes in the I-35W catchment area will be obtained from Mn/DOT and the system operator. The zip codes will be aggregated as closely as possible to census tracts in the corridor. Census data on income, automobiles per household, households without an automobile available, ethnicity, and age will be examined to help identify characteristics of MnPASS users and potential equity concerns. Frequency of use by zip code zone of transponder holders will also be examined to the extent possible. This analysis will explore potential differences in frequency of use by individuals residing in areas with different socio-economic characteristics.
  • Change in violation rates in the I-35W HOV and HOT lanes. Information from the Mn/DOT I-35W HOV/HOT lane Quarterly Reports and the citations issued by the Minnesota State Patrol will be used to assess changes in vehicle-occupancy violation rates on the I-35W HOT lanes pre- and post-deployment. Based on the experience with expansion of the I-394 HOV lanes to HOT lanes, it is anticipated that vehicle-occupancy violations will decline with the expansion of the existing I-35W HOT lanes to HOT lanes as part of the Minnesota UPA. Information on the historical and pre-deployment violation rates will be compared to those post-deployment.