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21st Century Operations Using 21st Century Technologies
Table of Contents

Analysis of Travel Choices and Scenarios for Sharing Rides
Final Report

Chapter 1. Project Overview

Governments and road users have a direct interest in ensuring efficient operations and use of highway investments and resources. Updated information regarding factors affecting growth of vehicle miles traveled (VMT) and remedies to curtail such growth—including both those within and those beyond government control and influence—would help guide improvements to highway efficiency.

With the proliferation of shared mobility options, it is becoming more important to extract and understand traveler behavior and decision choices (such as vehicle/mode, vehicle occupancy, service types, and times) and how various travel cost and performance factors can affect those choices. The goal is to grow the capability of service providers and governments to assess how changing the choice set provided to travelers can support shifts toward more efficient road use and travel patterns. It will also support the analysis of alternative future scenarios and their effects on travel choices and aggregate outcomes, such as congestion.

The central question posed in this project is: What influences a user's decision between riding or driving alone versus a more transportation-system-efficient choice, and what specific financial- and travel-time-related levers can be brought to bear to influence this choice? A complicating factor around the central question is the complex public-private interplay between available "drive-alone" versus "shared-ride" mobility options.

Project Purpose

In light of the larger central question, the purpose of this study is to gain a deeper understanding of the factors influencing traveler decisions about driving or taking a transportation network company (TNC) trip alone versus with others. The study focuses on learning about the tradeoffs among desired features of different travel options and trip price, and the potential effects of mode-shifting incentives and disincentives.

Approach and Methodology

The study developed and/or used three major data source types—TNC surveys, app-based ridesharing data, and studies in the literature about behavioral responses to changes to the price of driving and parking. After general reporting of findings, the study developed, explained, and made available an analytic model that enables users to test their own scenarios nationally or in different U.S. locations. This report highlights a few sample case studies enabled by the assessment analytic model.

Survey of Transportation Network Company Users

In fall 2018, the research team was able to review data gathered by a large TNC, which administered a survey of its riders and provided related administrative data about trips. The TNC survey used a raffle-based incentive and targeted users who had scheduled a ride via the company's app in the previous 24 hours. The survey targeted users of the company's traditional private ridehailing product and its ridesharing product.

Using the respondents' most recent trip as an anchor, the survey asked users questions about their choice of shared (ridesharing) or private ridehailing products and how that choice might have changed had they been presented with shared and private options at different prices and trip durations. The survey offered respondents a series of choices between their observed trip cost and travel time, and alternatives. Unlike a typical stated preference survey, trip alternative questions asked respondents to make decisions as if these options had been presented for their recent trip, which provided the respondents with realistic context anchored in a recent experience. The survey also asked respondents to provide information about their trip purpose, personal characteristics (e.g., annual income, age, gender), and travel behavior (e.g., car ownership, frequency of transit use) not available from the administrative data acquired.

The TNC administered the survey between November 26 and December 10, 2018, in 15 markets that have access to its ridesharing product: Atlanta, Austin, Boston, Chicago, Denver, Las Vegas, Los Angeles, Miami, Nashville, New York City, Philadelphia, Portland (Oregon), San Francisco, Seattle, and Washington, DC. The sample size in each market ranged from 154 (Nashville) to 761 (New York City). The TNC oversampled smaller markets to capture multiple population segments in these markets that could later be weighted to the true market size.

Analysis of App-Based Carpooling Use

While many app-based carpooling solutions are emerging, data on incentives and their impact on carpooling rates are hard to obtain. This study used data and analyses provided by two app developers (Metropia™ and Hytch™) to evaluate the impact of incentives on carpooling behavior. Findings from both apps are not representative of the other app-based carpooling systems in the marketplace. The significant variations in how each app-based carpooling solution interfaces with its users and in the incentive structures prevent the findings from these datasets from being generalizable. More importantly, the data gathered from both apps preceded this study, so the ability to conduct deliberate tests in line with research questions for this study was limited.1 One important limitation was that travel choice data gathered via surveys prior to app use was not sufficiently specific to allow discernment of the degree of behavior change caused by incentives offered in the apps.

Literature Review on Driving/Parking Price and Behavioral Response

A series of studies was reviewed to arrive at an elasticity value to use to estimate the effect on mode share of increasing the relative cost of driving alone over carpooling in a set of metropolitan areas (the same metropolitan areas studied in the TNC survey).2,3,4,5 Both the effects of parking and non-parking costs were reviewed. In addition to these studies, the literature review analysis referred to the Trip Reduction Impacts of Mobility Management Strategies (TRIMMS) model from the Center for Urban Transportation Research at the University of South Florida. This provides another method for estimating the impacts of various transportation demand management (TDM) strategies, which itself cited Hymel, Small, and Van Dender, and Concas and Nayak for their demand elasticity values for non-parking and parking costs, respectively.6 Considering all these studies and reviews together, this paper proposes a benchmark travel price elasticity of -0.30 for drive-alone trips. This assumed elasticity affects all travel costs, which this paper calculates for each of the metropolitan areas included in the TNC survey using sources such as the American Automobile Association and City Observatory's Price of Parking tool.

Development of an Analytical Model for City-Level Scenario Assessments

The study formulated various scenarios seeking to understand the potential use of higher-occupancy modes. The scenarios were informed by research findings on the use of shared rides in the context of TNCs, app tools providing carpooling incentives, and price changes for personal vehicle travel. To evaluate the effect of these scenarios on ridesharing behavior, an analytic model was created to provide an understanding of how changes in trip cost and travel time affect mode choice for various market segmentations in different cities.

The analytic model was constructed in R (statistical open-source software) and R Shiny (a package for interactive web-based applications in R). The analytic model code, user instructions to run the model, and an example of model outputs are available at Intelligent Transportation System (ITS) CodeHub,7 the U.S. Department of Transportation's (USDOT) portal for open-source ITS code.

Readers can download the code from the ITS CodeHub and run the analytic model to further explore the findings of the research in this report. Readers can conduct tests of a preferred policy scenario at a particular price point for one of the fifteen cities included in the model.

Document Organization

The rest of the document is organized as follows:

  • Chapter 2 provides an overview of research findings related to sharing rides in an environment of on-demand ridehailing.
  • Chapter 3 provides an overview of research findings related to sharing rides encouraged by app tools providing carpooling incentives.
  • Chapter 4 describes various scenarios for time and cost differentials and their associated impacts on the likelihood of sharing rides.
  • Chapter 5 describes the implications to cities based on the findings of this project.
  • Chapter 6 identifies potential areas for future research beyond the scope of this study.

1 FHWA requested that Metropia™ and Hytch™ each analyze data they had already gathered prior to this study in response to questions developed by FHWA for this study. FHWA describes the results of this analysis in Chapter 3 of this report. FHWA also used the analysis from Metropia™ as inputs in experimental scenario 4, as described in Chapter 4. Because such data were not initially gathered to answer FHWA's questions, however, FHWA did not consider it adequate for its use in the analytical model it subsequently developed that enables city-level policy assessments, which is described later in this document and is made available to the public as part of this research. [ Return to Note 1 ]

2 Shoup, D. (2005). Planning Advisory Services Report Number 532: Parking Cash-out, American Planning Association, Chicago, IL. [ Return to Note 2 ]

3 Concas, S. and Nayak, N. (2012). "A Meta-analysis of Parking Pricing Elasticity." Presented at the Transportation Research Board Annual Meeting. [ Return to Note 3 ]

4 Farber, M. and Weld, E. (2013). Econometric Analysis of Public Parking Price Elasticity in Eugene, Oregon, Thesis, University of Oregon, Eugene, OR. [ Return to Note 4 ]

Litman, T. (2013). Understanding Transport Demands and Elasticities: How Prices and Other Factors Affect Travel Behavior, Victoria Transport Policy Institute. [ Return to Note 5 ]

6 Hymel, K.M., Small, K.A., and Van Dender, K. (2010). "Induced Demand and Rebound Effects in Road Transport." Transportation Research Part B: Methodological, 44, pp.1220–1241. [ Return to Note 6 ]

7 The direct link to the project information on ITS CodeHub is https://doi.org/10.21949/1520429 [ Return to Note 7 ]