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Page 126
Suggested Citation:"APPENDIX D: DATA TYPES." National Academies of Sciences, Engineering, and Medicine. 2022. Mobility on Demand and Automated Driving Systems: A Framework for Public-Sector Assessment. Washington, DC: The National Academies Press. doi: 10.17226/26820.
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Suggested Citation:"APPENDIX D: DATA TYPES." National Academies of Sciences, Engineering, and Medicine. 2022. Mobility on Demand and Automated Driving Systems: A Framework for Public-Sector Assessment. Washington, DC: The National Academies Press. doi: 10.17226/26820.
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Page 128
Suggested Citation:"APPENDIX D: DATA TYPES." National Academies of Sciences, Engineering, and Medicine. 2022. Mobility on Demand and Automated Driving Systems: A Framework for Public-Sector Assessment. Washington, DC: The National Academies Press. doi: 10.17226/26820.
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Page 129
Suggested Citation:"APPENDIX D: DATA TYPES." National Academies of Sciences, Engineering, and Medicine. 2022. Mobility on Demand and Automated Driving Systems: A Framework for Public-Sector Assessment. Washington, DC: The National Academies Press. doi: 10.17226/26820.
×
Page 129
Page 130
Suggested Citation:"APPENDIX D: DATA TYPES." National Academies of Sciences, Engineering, and Medicine. 2022. Mobility on Demand and Automated Driving Systems: A Framework for Public-Sector Assessment. Washington, DC: The National Academies Press. doi: 10.17226/26820.
×
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Page 131
Suggested Citation:"APPENDIX D: DATA TYPES." National Academies of Sciences, Engineering, and Medicine. 2022. Mobility on Demand and Automated Driving Systems: A Framework for Public-Sector Assessment. Washington, DC: The National Academies Press. doi: 10.17226/26820.
×
Page 131
Page 132
Suggested Citation:"APPENDIX D: DATA TYPES." National Academies of Sciences, Engineering, and Medicine. 2022. Mobility on Demand and Automated Driving Systems: A Framework for Public-Sector Assessment. Washington, DC: The National Academies Press. doi: 10.17226/26820.
×
Page 132
Page 133
Suggested Citation:"APPENDIX D: DATA TYPES." National Academies of Sciences, Engineering, and Medicine. 2022. Mobility on Demand and Automated Driving Systems: A Framework for Public-Sector Assessment. Washington, DC: The National Academies Press. doi: 10.17226/26820.
×
Page 133
Page 134
Suggested Citation:"APPENDIX D: DATA TYPES." National Academies of Sciences, Engineering, and Medicine. 2022. Mobility on Demand and Automated Driving Systems: A Framework for Public-Sector Assessment. Washington, DC: The National Academies Press. doi: 10.17226/26820.
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Page 134

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126 REPORT APPENDIX D: DATA TYPES The following section contains potential questions, performance metrics, and data sources to assess different areas of the transportation network. These areas include: • Behavioral shifts: Traveler behavior including modes taken, origins and destinations, and VMT; • Economics and labor: How the transportation network is paid for by public agencies, private providers, and travelers; • Environment and energy: Impacts of transportation modes on GHG emissions and the energy grid; • Equity and inclusion: Accessibility of the transportation network by a variety of users including individuals with disabilities, low-income households, and rural communities; • Goods movement: Efficiency and impacts of goods moving and being delivered; • Infrastructure and land use: Development, redevelopment, and use of land for transportation purposes, such as parking and roadways; • Operational efficiency: Productivity of the transportation network and how innovations can change this; • Public perception: Views on characteristics of the transportation network including accessibility, mobility, congestion, and service quality; • Public transit ridership: Demographic makeup of riders of a variety of transportation modes; • Technical accomplishments: Technological elements that can support improved transportation networks; • Traffic congestion: Changes to congestion caused by transportation innovations and system changes; and • Traveler safety: Perceived or actual changes in safety on or around different transportation modes.

127 REPORT Table 23. Behavioral Shift Data Types Questions Performance Metrics Data Sources How has the driving of personal owned vehicles changed? Has there been less dependency on personal vehicles? Annual mileage on personal vehicles Local, regional, and national estimates of VMT and personal miles traveled Number of vehicles shed, purchased, or suppressed Annual mileage on personal vehicles Estimates of local and regional VMT How has the use of travel modes changed, specifically when traveling to and from transit stations? Average number of modes used over time Bikesharing trips divided by total trips Mode shares, specifically to and from transit stops Surveys User activity data Vehicle activity data (including mobility on demand [MOD] and AV modes) How has vehicle ownership changed? Have users shed, purchased, or suppressed a vehicle purchase? Multi-occupant vehicle trips divided by total trips Multi-occupant trips divided by total trips Number of vehicles shed, purchased, or suppressed Estimates of local and regional VMT Vehicle activity data (including MOD and AV modes) How has overall VMT changed? Can these changes be observed on a local, regional, and/or national scale? Annual mileage on personal vehicles Multi-occupant vehicle trips divided by total trips Multi-passenger TNC/taxi trips divided by total trips Local, regional, and national estimates of VMT Estimates of local and regional VMT Estimates of national VMT Vehicle activity data (including MOD and AV modes)

128 REPORT Table 24. Economics and Labor Data Types Questions Performance Metrics Data Sources Have agency costs and revenue changed? Will the system be financially sustainable? Average cost per mile Public transit farebox recovery ratio Public transit operational cost per passenger mile or trip Transit agency revenue data Transit agency cost data Transit agency payment data Transit agency ridership data Has automation changed traveler productivity? Are passengers able to accomplish tasks they were unable to before? Percentage of traveler minutes spent with cell phone service or Wi-fi Monetary value of perceived productivity gained from automation over time Surveys User activity data App usage data Has the unemployment rate changed? How could this change be attributed to the system? Unemployment rate Number of jobs accessible within 30 minutes of travel time Percent of population with access to 100,000+ jobs within 45 minutes of travel Bureau of Labor Statistics Census data User data Are public agencies subsidizing the transportation network? If so, what is the level of public subsidy? Public transit operational subsidy per passenger mile Driving subsidy per passenger mile per trip Average cost per trip by mode Transit agency cost data App usage data Transit agency payment data How much are transportation users paying for the use of the system? Average cost per mile percent of annual household income spent on transportation MOD and AV operational subsidy per passenger mile/trip Surveys Transit agency cost data App usage data

129 REPORT Table 25. Environment and Energy Data Questions Performance Metrics Data Sources What percentage of MOD devices/vehicles or AVs are electric? How does this impact GHG emissions? Emission impact from change in behavior Total transportation emissions per household Energy consumption per passenger mile traveled MOD and AV fleet specifications MOD and AV activity data EV charging data User activity data How have overall GHG emissions been impacted by automation? How has overall energy consumption been impacted? Fuel efficiency of MOD devices and vehicles and AVs Emissions per mile of MOD devices/vehicles and AVs Energy consumption of MOD devices and vehicles and AVs CO2 emissions per passenger trip Environmental Protection Agency vehicle fuel economy MOD and AV vehicle activity data VMT estimates Are there observed reductions from decreased VMT and/or use of more efficient vehicles? Total transportation energy consumption per capita Energy consumed by vehicles idling Number of SOV trips avoided Passenger transportation energy consumption per capita Transit agency curb data Traffic sensor data VMT estimates Vehicle activity data What has been the effect of EV charging on the electric grid? Are there temporal charging trends emerging? Energy delivered to EVs over time Cost of energy delivered to EVs Energy consumption per passenger trip Efficiency of MOD devices and vehicles and AVs EV charging data User activity data

130 REPORT Table 26. Equity and Inclusion Questions Performance Metrics Data Sources Does the transportation system comply with ADA standards? Is the level of service for the general population comparable to people with disabilities? Average per-mile and per-trip costs for people with disabilities Number of wheelchair accessible vehicles in taxis/TNC fleets Comparison of wait and travel times by people with disabilities and the general population Demographic profile of MOD and AV users Transit agency statistics Surveys Are there any discrepancies among service for different demographic groups? Average travel time to employment centers by income group Existing service options for different demographics (e.g., way to book trips without smartphones) Transit agency statistics Vehicle activity data Are a variety of individuals able to reach public transportation? Average distance to nearest public transit stop by income group Average taxi/TNC wait time by gender Percentage of canceled taxi/TNC rides by race/ethnicity Surveys User activity data Has the system expanded to reach vulnerable populations? Frequency of service Percentage of work and education trips accessible within 30-minute public transit travel time Census data Vehicle activity data Surveys Table 27. Goods Movement Questions Performance Metrics Data Sources Has goods movement changed? VMT per goods delivery vehicle Number of goods delivered Goods delivery fleet sizes Surveys Goods delivery provider data Local and regional VMT estimates What impacts does good delivery result in? Travel times for vehicles Comparison of roadway repair rates CO2 emissions per goods delivery vehicle Number of goods delivered per household Surveys Public agency data Roadway repair data How is goods delivery impacting the transportation network? Number of goods delivery services at transportation facilities Number of goods delivery services Public transit data Surveys

131 REPORT Table 28. Infrastructure and Land Use Questions Performance Metrics Data Sources Has parking utilization changed? Are the types of vehicles within parking spaces changed? Number of parking spaces by location Parking lot utilization by type of vehicle Parking utilization over time Space reservation (e.g., for carsharing vehicles) Transit agency parking data Transit agency parking enforcement data Parking enforcement data Has curbspace utilization changed? How has this utilization changed? Curbspace utilization over time Curbspace utilization by type of vehicle Number of citations for double parked vehicles Transit agency parking data Surveys Local and regional crash records MOD and AV crash records Is there EV infrastructure that allows for charging? How is this infrastructure being used? Number of EV parking stations by location Percentage of available EV charging stations over time Energy delivered to EVs over time EV charging data for nervy charging stations Surveys Electricity usage Is land being used by new transportation infrastructure and/or new developments served by new transportation infrastructure? Acreage of land taken for new transportation infrastructure Number of roadways constructed Acres of land converted to redevelopment Number of residential units built on land Census data Developer data Transit agency data Table 29. Operational Efficiency Questions Performance Metrics Data Sources Is the transportation network performing at an acceptable level of service? Revenue per passenger mile Roadway/intersection level of service grade Frequency of public transit service Transit agency revenue data Local transit agency ridership Rider surveys Can public transit agencies sustain new pilots and integrate them into existing services? Cost of existing services Cost of pilot programs Level of service of existing services Average trip time Transit agency revenue data Level of service grades User data Can the transportation system be changed to provide more service? Can these changes be expanded to other regions? Average travel time by distance or origin/destination Average capacity usage rate per vehicle Ability to make scalability changes to system Level of service grades User activity data Vehicle activity data Traffic sensor data Is public transit service improving? Have trip planning and wait times decreased? Average trip planning time Average wait time for shared mode Average wait time between transfers Distances of origin and destinations Surveys Transit agency data Census data

132 REPORT Table 30. Public Perception Questions Performance Metrics Data Sources Are travelers experiencing improved mobility and accessibility? Perceived mobility and accessibility Surveys Has connectivity to transit stations improved? Perceived connectivity to transit stations Surveys Have travelers’ experiences translated into a better quality of life and more overall happiness? Overall customer satisfaction (e.g., specific ratings) Quality of life and happiness ratings Surveys How do travelers view MOD and AV services? Do they express concern for safety with automation? Comfort level with automation (e.g., ranging from feeling safe to feeling) Surveys Table 31. Public Transit Ridership Questions Performance Metrics Data Sources How has public ridership changed? Has the ridership demographic of public transit ridership changed? Demographic makeup of public transit riders Demographic makeup of new riders User activity data Transit agency ridership Are there changes in the spatial or temporal distribution of trips? Are there routes or lines that have experienced different frequencies of use? Modal split by location and time of day Ratio of average public transportation journey time at different times of the day Transit agency ridership Service hours for transit agencies Service hours for alternative modes How has vehicle occupancy changed? Are there certain vehicles that are more crowded than before? Average vehicle occupancy by route and time of the day Estimated percentage of traveler trips taken for which seats are not available Surveys User activity data Transit agency ridership

133 REPORT Table 32. Technical Accomplishments Questions Performance Metrics Data Sources Has hardware or software been developed to support the system? How are the travelers using this technology? Percentage of population with access to a smartphone Percentage of non-auto trips booked through an app Surveys App usage data Transit agency statistics Have systems been integrated? Can travelers access a single platform for trip planning, booking, and payment? Payment collected via apps Availability of comparison of modes by cost and time via an integrated trip planning platform Transit agency statistics User activity data Surveys Do travelers have access to up- to-date, accurate information regarding their mobility options? Existence of opt-in alert systems Number of customer inquiries regarding route and travel information Surveys Transit agency call logs System testing results Is there access to new data? Are there consistent and reliable methods of data generation, storage, and access? Existence and size of data bases that are shared and accessible Number of data sources by type (e.g., private sector) Database metadata App usage data Table 33. Traffic Congestion Questions Performance Metrics Data Sources Is congestion improving? Roadway/intersection level of service Commute times Number of telecommute days per year Time MOD vehicles and AVs spend circling Level of service ratings Traffic sensor data VMT estimates User data Are people driving less in general? VMT per employee Average daily number of trips Number of vehicles registered per capita Census data DMV data User activity data Are more people using shared modes? Average vehicle occupancy Number of SOV trips avoided by origin/destination Hours per use of MOD or AV MOD and AV vehicle activity data Activity data

134 REPORT Table 34. Traveler Safety Questions Performance Metrics Data Sources Have vehicle, bicycle, and/or pedestrian crashes declined? Crashes per million VMT Crashes per 1,000 cyclists Crashes per 1,000 pedestrians Local and regional crash records VMT estimates How do the collision rates of MOD and AV vehicles compare to more traditional transportation modes? Number of MOD and AV vehicle collisions Vehicle miles driven by MOD and AV vehicles MOD and AV crash records MOD and AV vehicle activity data Have crime rates changed? Has crime changed within vehicles and at stations? Percentage of travelers who feel safe from crime while waiting for public transportation Number of criminal incidents in transit agency domain Transit agency crime data Local and regional crime data Surveys Has there been a decrease in lower-level offense (e.g., fraudulent use of parking spaces)? Number of parking tickets given Parking enforcement costs over time Transit agency parking enforcement data

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Innovative and emerging mobility services offer travelers more options to increase mobility and access goods and services. In addition, various technological developments have the potential to alter the automotive industry and traveler experience, as well as mobility and goods access.

The TRB National Cooperative Highway Research Program's NCHRP Web-Only Document 331: Mobility on Demand and Automated Driving Systems: A Framework for Public-Sector Assessment provides resources that identify key stakeholders and partnerships, offers emerging lessons learned, and provides sample regulations that can be used to help plan for and integrate emerging modes.

The document is supplemental to NCHRP Research Report 1009: Shared Automated Vehicle Toolkit: Policies and Planning Considerations for Implementation.

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