CSIS Metrics Quantitative Metrics Update Hunter Owens, Data and Digital Services Constraints & Metric Design • Focused on SB1 Cycle –projects in earlier phases may not have thislevel of data, a different methodology will be developed• Pre-PID projects for example, are the focus of the prior update • No such thing as a perfect project• CAPTI has nuances, trade-offs • Projects that score well on one metric might score poorly on others • Focused on alignment • Program fit remains the first criteria • 8 total quantitative metrics aligned to the various CAPTI Principles • Opportunity to advance projects that are CAPTI-aligned • Methodology Doc will be sent out for comments • Scoring Rubric allows all projects to be scored based on objective criteria, no stack ranking CSIS Metrics • Safety • Vehicle Miles Traveled • Accessibility • Disadvantaged Communities-Access to Destinations & Jobs • Disadvantaged Communities –Traffic Impacts • Passenger Mode Shift • Land Use & Natural Resources • Multimodal Freight and Freight Efficiency • In order to understand how the CSIS Metrics will be used, we tested the previous SB1 cycle nominations to Caltrans HQ • Since the data collection was not aligned to the metrics, we could not score every project on every metric • Total of 53 Projects • Exercise allows us to refine the scoring process, but not correct scores for projects that will be resubmitted Safety Metric • Draft metric:• Evaluates the following• Proven safety countermeasures' crash reduction factors • Counts of relevant crashes in a 5-year lookback period inproject area from SWITRS/TIMS/TASAS • Data required• Project location for each mode/intervention • Safety countermeasures • Crash reduction factors (CRFs) with references • Count of relevant crashes in a 5-year lookback period inproject area (optional) Safety Metric • Scoring• 0: Project area has crashes and nosafety countermeasuresidentified • 1: Project area has no crashes, nosafety countermeasuresidentified • 2: Project area has no fatal orserious injury crashes, low (lessthan10%) crash reduction factor • 3: Has either fatal or serious injurycrashes, low crashreductionfactor • 4: No fatal or serious injury crashes,high (greater than orequal to 10%)crash reduction factor • 5: Has either fatal or serious injurycrashes, high crashreductionfactor Safety Metric • Project Info• Projects score well by providingthe following• Proven Need (history of crashes) • Proven Countermeasures • Key Notes:• Metric requires District /LocalEngineer to review andprovide countermeasures, crashreduction factors, crash counts • Source safety data covers on andoff system Safety Metric Sample Scores: • A total of 49 Cycle 3projects were evaluated:• 25 scored 5 • 3 scored 4 • 6 scored 1 • 15 scored 0 • However, these scores are extremely unrepresentative due to limited info from project documents.• Most projects had at least 1 safety countermeasure • Draft metric:• Evaluate increase or reduced annualVMT • Score from –5 to 5 based oncategories of increase • Data required• VMT estimate from project proposal • Project description and location INDUCING OR REDUCING TRAFFIC VMT Scoring Guide Score VMT Range (Annual Change) Example Projects -5 Increase > 5 million New Urban Freeway Lane -3 Increase between 1 million and 5 million Urban / Suburban Road Widenings, -1 Increase between 1 and 1 million Widening of a short arterial or overpass 0 No change in VMT EV Charging 1 Decrease between 1 and 1 million Active Transportation Improvements, smaller Transit projects, road diets 3 Decrease between 1 million and 5 million Mid-sized Transit Projects 5 Decrease > 5 million Large Urban Mass transit, Interregional Rail • The assumption is that your environmental documents will contain a VMT number• If project environmental pre-dates SB743 or project hasn’t gotten past environmental, use NCST Calculator• Caveat: Rural Counties (non-MSA counties) widening is presumed no impact • If project is transit / active transportation, use ridership model (FTA STOPS or similar) contact CSIS@dot.ca.gov • If a range is provided, the lowest possible score will be assumed • In the Cycle 3 Test Scoring, 53 projects were scored based on project documents or the NCST calculator Score Count of Score -5 1 -3 5 -1 14 0 13 1 10 3 9 5 1 Isochrone (post project implementation) • Accessibility, in the CSIS context, represents how many destination a person can reach within a 2 hour time thresholds• Destinations farther away are awarded less weight using a decay function • Utilize Conveyal Platform + hundreds millions of trip level calculations to determine the net gain • Gains in auto-accessibility are harder to realize because the auto-network is more built out than the multimodal network in most cases Map Description automatically generated Isochrone (baseline) • Draft metric:• Estimate the percentage increase of jobs + destinations that residents can accesspost project implementation • Score from –5 to 5 based oncategories of increase or decrease • Data required• Project location for each mode/intervention • Project description (mode,type of project component) ACCESS TO DESTINATIONS –JOBS & OTHER • Scoring:• 0: 0% change in population-weighted access • 1: > 0% -.25% increase in population-weighted access • 2: > .25% -.5% increase in population-weighted access • 3: > .5% -.75% increase in population-weighted access • 4: > .75% -1% increase in population-weighted access • 5: > 1% increase in population-weighted access • Negatives scores will be given for inverse access change with the same ranges. Example Scoring • A hypothetical project would increase the total number of jobs accessible from 3,080 to 3,598 (a net gain of 518 jobs) • All 10 travel analysis zones(TAZs) gain access to new jobs due to the project, but this gain is not evenly distributed, ranging all the way from a 1% to 552% increase • The number of workers in each TAZ also varies but is the same in both the baseline and build scenarios • The worker-weighted percent change in access is 10.91%, and the average change in worker-weighted jobs accessible is 33.6 Baseline BUILD TAZ Accessible Destinations People Accessible Destinations People % Change 1 435 54 440 54 1% 2 654 35 660 35 1% 3 345 65 380 65 10% 4 456 123 470 123 3% 5 345 234 350 234 1% 6 342 123 348 123 2% 7 123 243 200 243 63% 8 234 34 300 34 28% 9 123 21 300 21 144% 10 23 3 150 3 552% Total 3,080 935 3,598 935 17% Metric Value % Change (across TAZs) 16.82% Weighted % Change (across TAZs) 10.91% Average Change (across TAZs) 51.8 Weighted Average Change (across TAZs) 33.6 Example Scoring • Project has multiple components along the 101corridor, including a Bus Only Lane, New Bike andPed infrastructure and Auxiliarylanes • Four modes: Bike, Ped, Car,Transit • Percent Population-Weighted Accessibility Change:0.286% • Sample Accessibility Score:2 • Map displays the relative change in accessibilityfor the bike project components within a 30km buffer accessibility_map_bike_30kmbuffer.png Example Scoring • Overall, we were able to run accessibility analysis for 38 projects from Cycle 3 • The average score was .97, representing an average percent change in accessibility of .12% across work and non-work destinations per project. • Multimodal projects generally scored the highest. This is due to auto accessibility baselines being relatively high, while transit / active transportation have larger access gaps • Draft metric:• Evaluate DAC population-weighted percent change inaccessibility withConveyal• Weighted according to EQI demographic overlay definition (in anAB 1550 low-income household and/or non-white) • Work and non/work destinations • Scoring• Same as accessibility, but DAC-weighted accessibility numbers • Data required• Project location for each mode/intervention • Project description • Jobs and Destinations combined into a single metric tocaptureboth points in CAPTI Principle, so metric averages twoscores • Accessmetrics will be similar, overall scoring encourages delivering benefits to disadvantaged communities (DAC). • Rural Projects can score well due to relative access, high proportionof DAC • Aligns to ½ CAPTI Principle:• Strengthening our commitment to social and racial equity byreducing public health andeconomic harms and maximizingcommunity benefits to disproportionately impacteddisadvantagedcommunities,low-incomecommunities, and Black, Indigenous,andPeople of Color (BIPOC) communities, in urbanized and ruralregions, and involvethese communities early in decision-making.Investments should also avoid placing newor exacerbating existingburdens on these communities, even ifunintentional. • (Other half, DAC Traffic Impacts Metric) Example Scoring • Projecthas multiple components along the 101corridor, including a Bus Only Lane, New Bike andPed infrastructure and Auxiliarylanes • Four modes: Bike, Ped, Car,Transit • Percent Disadvantaged Communities Population-Weighted Accessibility Change:0.230% • Sample Disadvantaged Communities Access to Destinations Score:2 Map Description automatically generated Example Scoring • Overall, we were able to run accessibility analysis for 24 projects from Cycle 3 • The average score was 1.33, representing an average percent change in accessibility of .18% across work and non-work destinations per project • Projects generally scored within the same range as the general accessibility metric, with a few exceptions • Draft metric:• Amount of additional projected truck-weighted AADToccurring impacting EQI traffic exposure screened communities.• Truck traffic is weighted at 6x car traffic • EQI Traffic Exposure Screen Definition: Census blocks that are:• low-income (per AB 1550) • >= 64.2% non-white (statewide %) • at or above the 80thpercentile for truck-weighted traffic proximity and volume • Scoring• -5: Project increases truck-weighted AADT by >= 10%, is in an area screened by the EQI Traffic Exposure Screen • -3: Projects increases truck-weighted AADT by between 0% and 10%, is in an area screened by the EQI Traffic Exposure Screen • 0:No change in AADT anticipated / noimpact • 3: Decline in truck-weighted AADT of between 0% and 10%, in an area screened by the EQI Traffic Exposure Screen • 5: Decline in truck-weighted AADT by >= 10% in an area screened by the EQI Traffic Exposure Screen • Data required• Additional AADT in project footprint (500m buffer around project area) • Project Detail• Projects score poorly by increasingtruck weighted AADTinside particularly disadvantaged communities • Score well by reducing AADT insideparticularly disadvantagedcommunities • Rural Context: unlikely to hit population threshold • Key Notes:• Naturally in tension w/ Freight metrics. • CAPTI: "Investments should also avoid placingnewor exacerbating existingburdens on these communities, even ifunintentional. " • For Cycle 3 Projects, we used Cal B/C (where available) to calculate the assumed change in AADT • 46 Projects were in a geographic area that would qualify for the Traffic Impact Score • The total number of projects scored was 11, with an average score of –1.67 • Draft metric:• Evaluatechange in ratio of transit/active transportation accessibility to autoaccessibility. • [maxpopulation-weighted non-auto accessibility] / [population weightedauto accessibility] • Data required• Project location for eachmode/intervention • Project description • Key Notes:• Projects score well by increasing the ratio of destinations that onecanaccess via non-auto modes • Answers: "Howmany destination can I reach w/o a car vs with?" Map Description automatically generated • Scoring:• 0:No average change in population-weighted mode shift ratio • 1: > 0 -.001 average change in population-weighted mode shift ratio • 2: > .001 -.002 average change in population-weighted mode shift ratio • 3: > .002 -.003 average change in population-weighted mode shift ratio • 4: > .003 -.004 average change in population-weighted mode shift ratio • 5: > .004 average change in population-weighted mode shift ratio • Negatives scores will be given for inverse mode-shift changes with the sameranges. Example Project Score • Anewbikelaneisplanned onAlpine BlvdinStockton,CAbetweenWMarch Lnand EHarding W • Thebikelanewillreduce theleveloftrafficstressfrom4(high)to 2(low) • Thislower-stressroutewillcreate amoredirectlinkresulting inshortertraveltimesinsomecases • Accessibility is calculated forboth the baseline and buildbike scenarios • Baseline and build bikeaccessibility outputs aredivided by auto accessibilityto create baseline and buildaccessibility ratios • The difference between thebaseline and build ratios iscalculated and apopulation-weightedaverage of the differences iscalculated for changeswithin a 24 km buffer of theproject alignment LOWER IMPACT PROJECT Example Project Score • Overall,theprojectwouldincreasethemode-shiftratioby0.0006.In thehighestimpactareas,theratiowouldincreaseby.007 • Projectwouldreceive a 1 • Note:Thissameprojectwouldscorerelativelywellonaccessibility,butgenerallylarger-scaleprojectsscorehigher onmodeshift Stockton.PNG LOWER IMPACT PROJECT Example Project Score • The Capitol Corridor Intercity RailService currently operates betweenAuburn, CA and San Jose, CA • Long-term planning documentsidentify a need for higher speedand higher-frequency rail servicealong the corridor • This example assumes 30-minutefrequencies and a 100% increase inspeed along the route • Accessibility is calculated for both thebaseline and build rail scenarios • Baseline and build rail accessibilityoutputs are divided by auto accessibilityto create baseline and build accessibilityratios (assuming auto accessibility isunchanged) • The difference between the baseline andbuild ratios is calculated and apopulation-weighted average of thedifferences is calculated for changeswithin a 50 km buffer of the projectalignment HIGHER IMPACT PROJECT Example Project Score • Overall, the project wouldincrease the mode-shift ratioby0.004. In the highest impactareas, the ratio would increaseby .14 • Project would receive a 4 • Note: This same project wouldalso score highly on accessibility HIGHER IMPACT PROJECT Overall Project Scores • 35 Projects were scored for mode shift, with an average score of .2 • Projects that did well typically had strong Transit or Active Transportation Elements • Projects that did poorly generally lacked multimodal elements altogether and/or significantly increased auto access • Most scores were 1, 0, and -1 Freight Metric • Draft metric:• Evaluate sustainability based on the percentage of the project budget dedicated to CA Sustainable Freight Action Plan typologies • Evaluate efficiency based on throughput, Truck Travel Time Reliability Index • Data required• Project location for each freight mode/intervention • Project description • Scoring: Sustainability Scores• 1: Less than 50% of the project budget is dedicated to sustainable freight action plan typologies. • 2: Between 50 and 90% of project budget is dedicated to sustainable freight action plan typologies. • 2.5: >90% of the project budget is dedicated to sustainable freight action plan typologies. • Efficiency Scores:• 1: Truck Travel Time Reliability index <= 1.5 • 2: Truck Travel Time Reliability index > 1.5 • 2.5: Increase in Modal Freight ORTruck Travel Time Reliability Index >= 3.0 Freight Metric • Projects score well by:• Focusing on sustainable freight elements as a large proportion of project budget • Providing specific metrics on increased freight efficiency or promoting a shift to modal freight • Key Notes:• CAPTI Principle: Developing a zero-emission freight transportation system that avoids and mitigates environmental justice impacts, reduces criteria and toxic air pollutants, improves freight’s economic competitiveness and efficiency, and integrates multimodal design and planning into infrastructure development on freight corridors. • Additional details on freight efficiency measurement are in progress with SMEs Freight Scores • The Freight Metric was unable to be scored for Cycle 3 Projects because we did not collect line-item budget level data, and the sustainability metric requires line-item budget data (% of budget dedicated to sustainable freight action plan typologies) • Additionally, we did not have time to run the efficiency metric for all the proposed projects • However, we identified a few projects that would have scored highly CYCLE 3 TESTING SCORING Freight Projects Detail • Harbor Drive Project• MultipleSustainable Freight Action Plan typologies • The Project will include zero-emission commercial vehicle charging stations (up to three) with electrical conduit infrastructureto assist in the transition of truck fleets to models using sustainable fuels and achieve Portside Community emission reductiontargets • Fresno UPRR Double Track• Entirely Modal Freight • Shift from Road Freight to Rail Land Use and Natural Resources • Draft metric:• Evaluate whether project supports non-SOV travel in an urbanized area eligible for infill developmentaccording to the OPR Sitechecktool. • Projects can have a positive by creating new high-quality transit areas (PRC –21155, 21064.3). HQTAs trigger infill-friendly policies:• No parking minimums • CEQA streamlining • Projects in a rural context can score well by preserving Natural and Working Lands (Sitechecktool) • Data required• Project locations for non-SOV elements • Project description • Projected change in transit schedules Land Use and Natural Resources • Projects score well by:• Urban/suburban context: creating new HQTAs • Rural Context: enhancing natural and working lands • Key Notes:• Definition of urban/suburban: project intersects an incorporated city • Definition of "supporting": existencenon-SOVtravel project element Land Use and Natural Resources • Additional Key Notes:• Metric is a combination of 2 CAPTI Principles, to incorporate urban and natural land uses• Promoting compact infill development while protecting residents and businesses from displacement by funding transportation projects that support housing for low-income residents near job centers, provide walkable communities, and address affordability to reduce the housing-transportation cost burden and auto trips. • Protecting natural and working lands from conversion to more intensified uses and enhance biodiversity by supporting local and regional conservation planning that focuses development where it already exists and align transportation investments with conservation priorities to reduce transportation’s impact on the natural environment. Land Use and Natural Resources SCORING • -1: Project is in infill development area but does not identify a non-SOV element that supports infill development• OR project is within200 meters of natural / working lands and does not identify significant enhancement • 0: Project is not in infill areaOR not within 200 meters of natural working lands • 1: Project is in infill area, has a supporting element, but project has a projected increase in VMT • 2: Project is in infill area, has a supporting element, and does not have a projected decrease in VMT• OR project is within 200m of natural/working lands and only describesmitigations • 3: Between 0 and .5 sq miles of new HQTA • 4: Between .5 and 1 sq miles of new HQTA • 5: Greater than 1 sq mile of new HQTA• OR project has a significant enhancement to natural and working landswhile being located within 200 meters of Site Check Protected Areas Land Use Example Projects • Urban area –Creating HQTA• Watsonville-SC bus-on-shoulder allows Santa Cruz Metro to increase peak hour frequencies from every half-hour to every 15 minutes -> 31 sq mi of new HQTA around corridors for Routes 69A, 69W, 71, and 91X • Note that project still includes mitigation elements for sensitive coastal habitat • Must commit to new transit service, not just enable HYPOTHETICAL EXAMPLES Land Use Natural Resources Scoring • Overall, 37 projects were evaluated:• 3 Urban projects created new HQTA• 2 scored a 5 (increased frequency or new light rail) • 1 scored a 4 (new train stop) • 30 Urban projects did not create new HQTA• 19 scored 2 or 1 • 11 scored –1 (no clear non-SOV infrastructure) • 4 Rural projects (no proximity to incorporated areas)• 3 scored a 5 (had wildlife crossings) • 1 (Desert Rail Infrastructure) did not go through environmental review CYCLE 3 EXAMPLE SCORING Metric Safety 49 Projects Scored, 25 Scored 5 Vehicle Miles Traveled 53 Projects Scored, 37 between -1 & 1 Accessibility 38 Projects Scored, Average score of 0.97 Disadvantaged Communities –Access to Destinations & Jobs 38 Projects Scored, Average score of 1 Disadvantaged Communities –Traffic Impacts 11 Projects Scored, Average Score of -1.64 Passenger Mode Shift 35 Projects Scored, Average Score of 0.2 Freight N/A Land Use & Natural Resources 37 Projects Scored, Plurality scored 2 or below, but 3 out of 4 Rural Projects scored 5 Total Score Thanks! • CSIS Metric team is on hand to help you get preliminary scores foryour project• Have your Caltrans District Partners email csis@dot.ca.govto setup a time • Caltrans Districts can invite external stakeholders to review meeting at their discretion • This presentation will be sent out for comments, as will theunderlying methodology document.• Email comments to csis@dot.ca.gov • During the SB1 Nomination process, there will data collection, scoring and validation phases • Questions? Sample Project • Example Project: Sacramento 15-min bus network enhancement • DAC-weighted • Worker-Weighted DAC.PNG Workers.PNG