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New Mobility Options in Travel Demand Forecasting and Modeling: A Guide (2024)

Chapter: Chapter 2 - Travel Demand Metrics

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Suggested Citation:"Chapter 2 - Travel Demand Metrics." National Academies of Sciences, Engineering, and Medicine. 2024. New Mobility Options in Travel Demand Forecasting and Modeling: A Guide. Washington, DC: The National Academies Press. doi: 10.17226/27827.
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Suggested Citation:"Chapter 2 - Travel Demand Metrics." National Academies of Sciences, Engineering, and Medicine. 2024. New Mobility Options in Travel Demand Forecasting and Modeling: A Guide. Washington, DC: The National Academies Press. doi: 10.17226/27827.
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Suggested Citation:"Chapter 2 - Travel Demand Metrics." National Academies of Sciences, Engineering, and Medicine. 2024. New Mobility Options in Travel Demand Forecasting and Modeling: A Guide. Washington, DC: The National Academies Press. doi: 10.17226/27827.
×
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Suggested Citation:"Chapter 2 - Travel Demand Metrics." National Academies of Sciences, Engineering, and Medicine. 2024. New Mobility Options in Travel Demand Forecasting and Modeling: A Guide. Washington, DC: The National Academies Press. doi: 10.17226/27827.
×
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Suggested Citation:"Chapter 2 - Travel Demand Metrics." National Academies of Sciences, Engineering, and Medicine. 2024. New Mobility Options in Travel Demand Forecasting and Modeling: A Guide. Washington, DC: The National Academies Press. doi: 10.17226/27827.
×
Page 10
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Suggested Citation:"Chapter 2 - Travel Demand Metrics." National Academies of Sciences, Engineering, and Medicine. 2024. New Mobility Options in Travel Demand Forecasting and Modeling: A Guide. Washington, DC: The National Academies Press. doi: 10.17226/27827.
×
Page 11
Page 12
Suggested Citation:"Chapter 2 - Travel Demand Metrics." National Academies of Sciences, Engineering, and Medicine. 2024. New Mobility Options in Travel Demand Forecasting and Modeling: A Guide. Washington, DC: The National Academies Press. doi: 10.17226/27827.
×
Page 12
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Suggested Citation:"Chapter 2 - Travel Demand Metrics." National Academies of Sciences, Engineering, and Medicine. 2024. New Mobility Options in Travel Demand Forecasting and Modeling: A Guide. Washington, DC: The National Academies Press. doi: 10.17226/27827.
×
Page 13

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6 Travel Demand Metrics The emergence of NMOs has contributed to significant changes in mobility and travel demand patterns across the nation in recent years (as discussed in Section 1.1). These impacts are not uniformly distributed across the nation. Depending on the jurisdiction, the impact of NMOs can vary significantly due to existing transportation patterns and variation in NMO adoption and usage rates. Thus, it is not surprising that a large share of TDFMs do not explicitly consider the impact of NMOs in their frameworks. Given the substantial resources required to modify TDFMs, it is important that agencies examine whether and how NMOs influence mobility and travel demand in their jurisdictions prior to updating TDFMs. The first step in addressing these questions will be to examine how mobility and travel demand in the jurisdiction have altered since the latest TDFM implementation. Initially, the research team was under the impression that there might be some guidance for determining when to adapt the TDFM components. However, the team did not find any literature on travel demand metrics for TDFM updates. Hence, the research team developed custom measures that identify mobility and travel behavior modifications while drawing on existing practices in travel demand modeling to facilitate easy adoption. This chapter provides direction to practitioners on evaluating the impact of NMOs on various travel demand components that are currently modeled within TDFMs. Specifically, the authors analyze the travel behavior changes in urban regions that can be associated with NMO emer- gence and adoption. The travel demand metrics take into account the scale and scope of each NMO. The analysis is also organized by type of TDFM, categorized as (a) the trip-based model or (b) the tour-based or activity-based model. The metrics focus on various travel behavior compo- nents of the TDFMs to allow for a comprehensive examination of NMO impacts. 2.1 Trip-Based Model For the trip-based model, the research team suggests metrics relevant for the following components: (a) sociodemographic and socioeconomic variables, (b) trip generation, (c) trip distribution, (d) mode choice, and (e) traffic assignment. 2.1.1 Sociodemographic and Socioeconomic Variables Mobility and travel patterns are significantly influenced by sociodemographic and socio- economic variables. Hence, any potential changes to these variables due to the emergence of NMOs need to be represented in the TDFM. The authors suggest two specific measures in this category to evaluate changes: (a) vehicle ownership and (b) job accessibility. In communities with access to NMOs, because of the higher access to shared micromobility, TNCs, and CAVs, it is possible to reduce car ownership levels. The increased access to shared C H A P T E R   2

Travel Demand Metrics 7   micromobility and TNCs could encourage multimodal trips that include a combination of shared micromobility, TNCs, and public transit modes. With increased market penetration of CAVs, individuals could alter travel behavior in terms of owning vehicles and considering MaaS. Thus, various NMOs can contribute to a reduction in levels of car ownership. Using data from travel surveys conducted in the study region [such as a jurisdiction-specific household travel survey or the National Household Travel Survey (NHTS)], or U.S. Census data, agency personnel can examine changes in automobile ownership levels. The specific comparison metric can examine changes in two forms: (1) average household vehicle ownership and (2) proportion of vehicle ownership across different vehicle ownership levels (0, 1, 2, and 3 or more). The metrics should be computed across two cross-sections of data from before NMO introduction (or low NMO adoption) to the period when NMO adoption is being examined. For vehicle ownership trends, it is suggested that the impact be measured over subregions (such as dense zones with shared mobility installations). A significant change in vehicle ownership levels will warrant an updated vehicle ownership model for the TDFM. For example, using NHTS data from 2009 and 2017, it can be observed that vehicle ownership levels remained stable: 1.86 in 2009 and 1.88 in 2017 (NHTS 2023). A similar relationship can be observed based on the share across vehicle ownership levels; Table 2.1 presents an example. Agency personnel can examine changes in vehicle ownership for their region by evaluating data from their jurisdiction to consider TDFM update decisions. NMOs can also contribute to improved job accessibility in the jurisdiction and influence the mobility patterns of individuals. The job accessibility measure can examine the number of jobs accessible within a 15-minute interval of each traffic analysis zone. A comparison of job acces- sibility prior to and after NMO implementation would be a useful metric to represent mobility inputs for TDFMs. 2.1.2 Trip Generation The adoption of NMOs can influence mobility behavior, resulting in higher trip rates across a jurisdiction. An examination of changes to trip rates prior to and after NMO implementa- tion will be useful to evaluate whether trip-generation modules need to be updated. Given the variation in NMO adoption across trip purpose, sociodemographics, and location, it is benefi- cial to compare these rates in the jurisdiction, by demographic segments, trip purpose, and location (such as central business district). The data for such analysis will need to be compiled from jurisdiction-specific survey data or NHTS survey data available for the study region. The data will need to be processed to examine trip rates by trip purpose, demographic segment, and location. If the comparison exercise yields meaningful differences across the pre- and post- implementation scenarios, then it may be beneficial to consider a TDFM update. For trip-based models, a useful measure can be the share of work trips present among all trips. From 2009 to 2017, the percentage of work trips has increased marginally from 15.6% to 17.4% (NHTS 2023). Such jurisdiction-specific comparisons across all trip purposes can offer insights into potential changes to trip-generation patterns. Number of Vehicles 2009 NHTS 2017 NHTS 0 8.7 8.9 1 32.3 33.5 2 36.3 33.1 3 or more 22.7 24.5 Table 2.1. Vehicle ownership shares obtained from NHTS for 2009 and 2017 (NHTS 2023).

8 New Mobility Options in Travel Demand Forecasting and Modeling: A Guide 2.1.3 Trip Distribution An important input for the trip-distribution module is the generalized cost function. The introduction of NMOs in the jurisdiction could influence travel times and costs due to the changes in the availability of travel modes. The generalized cost variable after NMO adoption can be compared to the generalized cost variable adopted in the TDFM model. If the analysis finds changes in the generalized cost variable across the jurisdiction, then it would be useful to update the TDFM with newer generalized cost functions. These newer cost functions could significantly alter the overall distribution patterns generated by TDFMs. Typically, travel time, travel cost, tolls, vehicle operating cost, parking cost, and vehicle ownership cost (if there is no separate vehicle ownership model) are used to generate the generalized cost function for each origin–destination pair. Different weights are assigned to these variables based on the origin– destination type (e.g., low-income zone, urban area). With NMOs under consideration, it would be important to see if there is a significant change in the cost function. For instance, parking cost might be reduced significantly due to the presence of shared micromobility, TNC, and CAV, and could increase the attractiveness of a destination. Another dimension of trip distribution that is likely to be affected is the overall trip length distribution in the region. Using data from prior to and after NMO emergence, analysts can examine changes in trip length distribution across the jurisdiction. With the emergence of shared micromobility, it is likely that trip length will be lower. However, the emergence of CAVs could contribute to an increase in trip lengths across the jurisdiction. 2.1.4 Mode Choice The emergence of NMOs is most likely to influence the mode choice component of TDFMs. Typically, mode choice models are estimated and calibrated by trip purpose. Note that there are no guidelines on a threshold for mode inclusion in TDFMs. The research team compiled the various mode shares from 15 TDFMs across the country to identify a potential threshold value to consider using (see Table 2.2). This review shows that mode inclusion across several TDFMs is guided by mode availability and not mode share. For example, several TDFMs include modes with a very small share (e.g., the transit share for San Diego is 1.60%, the park-and-ride share for Metroplan Orlando is 0.03%, and the kiss-and-ride share for Washington, DC, is 0.45%). The researchers recognize that the reasons for including public transit alternatives might be driven by sustainability and minimum accessibility considerations for vulnerable households. Notes: TPO = transportation planning organization; N/A = data not available for these categories. Agency/ Region Metroplan Orlando Southeast Florida Volusia TPO Space Coast TPO Ocala TPO Lake- Sumter TPO New York Washington,DC Drive alone 51.55 37.00 52.61 52.07 53.16 51.60 55.50 48.42 Shared ride 2 32.01 24.50 29.69 30.27 30.94 31.31 5.50 27.77 Shared ride 3+ 15.61 19.40 17.36 17.46 15.79 17.09 1.00 17.31 Walk/transit 0.74 1.90 0.32 0.19 0.11 0.00 23.25 4.70 Park & ride/transit 0.03 0.20 0.00 0.00 0.00 0.00 5.60 1.35Kiss & ride/transit 0.06 0.20 0.02 0.01 0.00 0.00 0.45 Walk — 13.05 — — — — 5.45 — Bike — 1.50 — — — — 0.65 — School bus — 2.25 — — — — 1.55 — Taxi — — — — — — 1.50 — Micromobility N/A N/A N/A N/A N/A N/A N/A N/A Taxi and TNC N/A N/A N/A N/A N/A N/A N/A N/A Other N/A N/A N/A N/A N/A N/A N/A N/A Table 2.2. Mode shares in a sample of TDFMs employed in practice.

Travel Demand Metrics 9   The inclusion of NMOs in TDFMs might not meet the same standards. Hence, it is suggested that if the share of an alternative mode across the jurisdiction is higher than 1%, then it might be useful to update the mode choice component with the newer alternative. At the same time, for shared micromobility, it is likely that jurisdiction mode share will be smaller than 1%, while the mode share in a set of zones in the jurisdiction might be quite a bit higher. Hence, the researchers recommend including a travel mode in a subset of locations if the mode share in the subset of locations is higher than 2% (even if the jurisdiction share does not amount to 1%). The data for the comparison exercise can be compiled using jurisdiction-specific household travel survey or NHTS data for the jurisdiction. The mode share for commute travel can also be retrieved from U.S. Census data for comparison. The emergence of NMOs (in particular CAVs) is likely to influence value of travel time mea- sures for mode choice. With the advent of CAVs, it is possible that individuals’ valuation of travel time may undergo significant changes. Potentially, with autonomous vehicles, individuals may not consider travel time as burdensome as it is now considered with manual driving. It might be useful to examine whether the adoption of NMOs introduces significant changes to value of travel time measures. If so, the TDFM mode-choice module will need to be adapted. 2.1.5 Traffic Assignment The main changes in traffic assignment inputs are typically associated with roadway and transit capacity. The emergence of CAVs (automobiles and transit vehicles) is most likely to affect traffic assignment modules. Agency personnel should examine changes to capacity mea- sures across roadways in response to CAV adoption rates in the jurisdiction. In the presence of significant increases to capacity (relative to when the TDFM was implemented), it might be beneficial to update the TDFM with the updated capacity values. 2.2 Tour-Based or Activity-Based Models It is important to note that many of the metrics suggested for trip-based models will also be relevant for tour-based or activity-based TDFMs. However, the measures need to be adapted for these systems. For the activity-based model, the team will examine changes relevant for (a) sociodemographic and socioeconomic variables, (b) activity generation and scheduling, (c) tour- and trip-level mode choice, (d) destination choice, and (e) traffic assignment. Agency/ Region Chicago San Diego Knoxville Chattanooga- Hamilton County Puget Sound Region Mid- American Regional Council Anchorage (Alaska) Drive alone 43.56 44.70 72.00 41.00 43.60 88.60 45.22 Shared ride 2 20.63 24.55 23.90 27.90 22.90 22.26Shared ride 3+ 17.37 19.70 24.00 20.80 19.71 Walk/transit 4.83 1.60 0.10 0.70 2.60 1.62 0.75 Park & ride/transit 0.10 0.50 0.01Kiss & ride/transit 0.02 Walk 10.17 7.00 1.80 1.90 8.70 6.24 8.39 Bike 0.85 0.80 0.20 0.10 0.90 0.40 1.92 School bus 2.37 1.00 2.00 4.30 — — 1.72 Taxi 0.22 — — — — — — Taxi and TNC — 0.55 — — — — — Micromobility — 0.10 — — — — — Other — — — — — 3.14 — Table 2.2. (Continued).

10 New Mobility Options in Travel Demand Forecasting and Modeling: A Guide 2.2.1 Sociodemographic and Socioeconomic Variables The same measures suggested for trip-based TDFMs are applicable for tour-based or activity- based TDFMs (see Section 2.1.1). 2.2.2 Activity Generation and Scheduling The adoption of NMOs can increase opportunities for individuals and result in higher activity participation and modified travel schedules. Agencies should evaluate how activity generation and scheduling behavior are altered with the emergence of NMOs. Toward this end, data on activity generation and scheduling prior to and after NMO introduction can be compared. The comparison, similar to the trip-generation comparison, should take into account variation by activity purpose (such as work or leisure), sociodemographics, and location. The data for such analysis will need to be compiled from jurisdiction-specific survey data or NHTS survey data available for the study region. The data will need to be processed to examine activity participa- tion and schedules by activity purpose, demographic segments, and location. If the comparison exercise yields meaningful differences across the pre- and post-implementation scenarios, then it might be beneficial to consider a TDFM update. An example measure for the comparison is share of work tours among all tours. From 2009 to 2017, the percentage of work trips has increased significantly, from 20.9% to 26.9% (NHTS 2023). The increase can be attributed to more trip chaining during work tours. It is important to parse other possible factors associated with these travel behavior changes. For example, due to changes in travel behavior post-COVID, it is likely that work tour behavior would be different compared to the pre-COVID time period. Thus, agencies need to verify whether the changes in travel behavior are connected to NMO adoption. 2.2.3 Tour- and Trip-Level Mode Choice As outlined in Section 2.1.4, the emergence of NMOs is most likely to influence the mode choice component of the TDFM. In tour-based and activity-based models, the mode choice decision is analyzed at the tour-resolution level (a series of trips in a trip chain). The emergence of NMOs can result in an increased incidence of tours, with multiple transportation modes being chosen for mobility. For instance, individuals can use shared micromobility to travel from home to a coffee shop, then take transit to arrive at a restaurant, and then use a TNC service to return home. The consideration of such tour modes in the TDFM will require a significant modification to current tour-level mode choice models through adding newer alternatives. Prior to modifying the tour-level mode choice model, it is important that agencies evaluate the impact of NMOs on tour mode share. The comparison of tour mode share prior to and after NMO adoption will allow agencies to examine NMO impact on the jurisdiction. The agency will need to examine mode choice behavior at the tour level. Agencies with tour-based and activity-based TDFMs will already have resources in place to examine tour mode choice (as opposed to simple trip mode choice). These resources will need to be leveraged to generate changes in tour mode choice by tour purpose, sociodemographics, and location. The comparison will allow agencies to determine the need for a TDFM mode choice update. 2.2.4 Destination Choice The emergence of NMOs can offer enhanced accessibility to destinations. Thus, their emer- gence could alter destination choice behavior across the jurisdiction. Agencies can examine how destination choice behavior has altered with the introduction of NMOs by examining observed destination choice behavior components from detailed household survey data. The

Travel Demand Metrics 11   comparison can focus on metrics such as the share of different destination activity types and destination distance by activity type. These metrics can be computed separately by sociodemo- graphics, travel mode, and location. For some parts of a jurisdiction, the emergence of shared micromobility is likely to increase activity participation at destinations closer to trip origins. On the other hand, adoption of CAVs might contribute to increased distances between destina- tions. Thus, every jurisdiction will need to carefully review jurisdiction-specific data to identify travel behavior changes. 2.2.5 Traffic Assignment The same measures suggested for trip-based TDFMs are applicable for tour-based or activity- based TDFMs (see Section 2.1.5). 2.3 Summary This chapter presented a series of travel demand measures useful in examining how NMOs have contributed to altering travel behavior in a region. Based on the discussion, the authors have compiled in Table 2.3 a summary of travel demand metrics that can be considered by practitioners to examine the impact of NMOs on travel demand in the study region. The measures generated have been incorporated into quick reference charts (Figures 2.1 and 2.2 for trip-based and tour- or activity-based models, respectively) with data sources for examining these metrics. These charts are designed to assist practitioners in examining the overall process for TDFM update consideration. The authors recognize that the metrics proposed might not necessarily capture causal direc- tionality specific to an NMO. For example, if the travel demand metric identifies a reduction in vehicle ownership levels in the jurisdiction (or in part of a jurisdiction), it is not possible to associate the reduction with a specific NMO. Metric Potential Impact of NMOs Average household vehicle ownership Reduced private vehicle ownership due to increased NMO usage. Proportion of vehicle ownership Higher share of zero-vehicle households, fewer multicar households. Trip-generation rates/ activity generation Increased trip rates for discretionary activities combined with work activities. Jobs/destinations accessible within 15 minutes NMOs may alter accessibility by different modes and overall; NMOs may alter friction factors or destination choice parameters. Trip length and generalized costs The emergence of NMOs can reduce out-of-vehicle time, reduce cost of vehicle ownership, and contribute to reduced generalized cost. Improvements in routing due to CAVs can contribute to reduced congestion and lower travel times. Mode choice/trip and tour level NMOs could alter how non-automobile modes are used by providing first- mile/last-mile connectivity in denser urban areas (in particular, shared micromobility and TNCs). The emergence of NMOs can increase multimodal trips and tours due to ease of connectivity between shared micromobility and TNCs. Value of travel time The emergence of NMOs (in particular CAVs) is likely to reduce the value of travel time. (Travelers can perform other activities while traveling.) Roadway capacity (for assignment) CAV may alter roadway capacity, depending on fleet mix and roadway type (may increase or decrease susceptibility to congestion). Table 2.3. TDFM metrics that represent the impact of NMOs on travel demand.

Socio- demographic and socioeconomic variables Trip generation Trip distribution Mode choice Traffic assignment Trip-Based Model Average household vehicle ownership Proportion of vehicle ownership across different vehicle ownership levels Trip rate by trip purpose Trip rate by demographic segment Trip rate by spatial location (such as central business district) Generalized cost function Trip length distribution and its variation across the jurisdiction Mode share of NMO in jurisdiction Mode share of NMO in specific zones Capacity measure Jurisdiction-specific household survey data, NHTS data for the jurisdiction, U.S. Census data for the jurisdiction Jurisdiction-specific household survey data, NHTS data for the jurisdiction Jurisdiction-specific household survey data, jurisdiction-specific origin- destination survey data Jurisdiction-specific household survey data, NHTS data for the jurisdiction, U.S. Census data for the jurisdiction Jurisdiction-specific roadway/transit capacity analysis Components Metrics Data Source Figure 2.1. Travel demand metrics for trip-based model.

Socio- demographic and socioeconomic variables Activity generation and Scheduling Tour- and trip- level mode choice Destination choice Traffic assignment Activity-Based Model Average household vehicle ownership Proportion of vehicle ownership across different vehicle ownership levels Activity participation and schedules by activity type Activity participation and schedules by demographic segment Activity participation and schedules by location (such as central business district) Tour mode share of NMO in jurisdiction Tour mode share of NMO in demographic segments Tour mode share of NMO in specific zones Share of different destination activity types Destination distance by activity type Capacity measure Jurisdiction-specific household survey data, NHTS data, U.S. Census data Jurisdiction-specific household survey data, NHTS data Jurisdiction-specific household survey data, NHTS data, U.S. Census data Jurisdiction-specific household survey data, jurisdiction- specific origin-destination survey data Jurisdiction-specific roadway/transit capacity analysis Components Metrics Data Source Figure 2.2. Travel demand metrics for activity-based model.

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Emerging transportation technologies and shared mobility services, or new mobility options (NMOs), are affecting travel behavior and demand. NMOs may include shared micromobility, transportation networking companies (TNCs), and connected and autonomous vehicles (CAVs). As NMOs grow in availability and use, transportation planners and decision-makers need to be able to understand how to harness positive and mitigate negative impacts.

NCHRP Research Report 1113: New Mobility Options in Travel Demand Forecasting and Modeling: A Guide, from TRB's National Cooperative Highway Research Program, provides travel demand modeling practitioners with ways to consider NMOs in travel demand forecasting models (TDFMs) - one of the primary tools available to understand potential impacts and future uncertainties.

Supplemental to the report are NCHRP Web-Only Document 399: Developing a Guide for New Mobility Options in Travel Demand Forecasting and Modeling; datasets of Use Case 1: Data, Code, and Tutorials for Household Vehicle Ownership Use Case; Use Case 2: Data, Code, and Tutorials for Household Trip Rates Use Case; Use Case 3: Data, Code, and Tutorials for Mode Choice Use Case; an Implementation of Research Findings and Products document; and a PowerPoint presentation of the research.

Any software included is offered as is, without warranty or promise of support of any kind either expressed or implied. Under no circumstance will the National Academy of Sciences or the Transportation Research Board (collectively “TRB”) be liable for any loss or damage caused by the installation or operation of this product. TRB makes no representation or warranty of any kind, expressed or implied, in fact or in law, including without limitation, the warranty of merchantability or the warranty of fitness for a particular purpose, and shall not in any case be liable for any consequential or special damages.

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