The Evolution, Usage And Trip Patterns Of Taxis & Ridesourcing Services .
The Evolution, Usage and Trip Patterns of Taxis & Ridesourcing Services -- Evidence from 2001, 2009 & 2017 US National Household Travel Survey Xiatian Wu, M.S. Institute of Transportation Studies University of California, Davis 1715 Tilia Street, Davis, CA, USA, 95616 Tel: 313-888-4037 Email: xtwu@ucdavis.edu ORCID: https://orcid.org/0000-0002-4851-1323 Don MacKenzie, Ph.D. Civil & Environmental Engineering, University of Washington University of Washington, MOR 121B, Seattle, WA, USA, 98195 Tel: 206-685-7198 Email: dwhm@uw.edu ORCID: https://orcid.org/0000-0002-0344-2344 This document is the accepted manuscript of the following publication: Wu, X., MacKenzie, D. The evolution, usage and trip patterns of taxis & ridesourcing services: evidence from 2001, 2009 & 2017 US National Household Travel Survey. Transportation (2021). https://doi.org/10.1007/s11116-021-10177-5
Abstract Given the rapid adoption of ridesourcing services (RS), it is critical for transportation planners and policymakers to understand their impacts and keep policies up to date. This study contributes to the literature by using representative samples captured in the 2001, 2009 and 2017 National Household Travel Surveys to explore how taxis and ridesourcing (T/R) services have evolved and shaped people’s travel behavior pre- and post-disruption at the US national level. It characterizes and visualizes the asymmetries in demand spatially and temporally for T/R trips, showing that ridesourcing has greatly increased T/R trips from flexible and optional activity locations to home, which vary by times of day. It also characterizes tours involving T/R services, showing that while simple optional tours (such as home – recreation – home) represent the largest share of tours involving T/R, the fastest growth has been in simple mandatory tours (such as home – work – home). Tours involving T/R grew from 0.4% of all tours in 2009 to 1% of all tours in 2017, mostly within densely populated and transit-oriented regions. Although less than 1% of T/R trips involved a direct transfer to or from transit, one-third of all tours containing T/R also included transit. However, at the same time, 40% of T/R-containing tours also involved auto trip(s). Overall, this study reveals the complex relationships among their underlying sociodemographic characteristics, RS adoption and usage behavior, and daily tour patterns. Key Words: Ridesourcing, Taxi, Household Travel Survey, Travel Behavior, Tour Pattern
1 Introduction App-based on-demand ride services provided by transportation network companies (TNCs), also called ridehailing or ridesourcing (RS from now on), have exploded in popularity around the world. Though similar to taxis in providing on-demand transport from door to door, they are distinguished by their advanced information technology, which enables efficient matching of drivers and passengers, dynamic pricing, and easy payment (Rayle, Dai, Chan, Cervero, & Shaheen, 2016). This convenience, along with lower prices, has allowed them to attract customers who previously may have used other modes or not traveled at all (Schaller, 2018; Shaheen, 2016). In 2018, Uber and Lyft, the two largest service providers in the US, were estimated to hold nearly 90% of the for-hire ride market (including taxis and RS) with 4.2 billion annual trips (Schaller, 2018). The rapid growth of TNCs has posed significant challenges for researchers and policymakers, as the deliberative policy making process does not always align with the speed at which these services have been adopted (Riggs, 2018). A critical set of questions exists relating to how TNCs integrate with the incumbent transportation system, and what behavioral changes have they instigated. One line of existing research has focused on the characteristics of RS users and trips, mainly by analyzing survey data (e.g. who, when, where & why). The services were found to be used by people across the socioeconomic spectrum, yet a disproportionate number of users were younger, better educated, more affluent and more likely to be employed than the average (Rayle et al., 2016; Young & Farber, 2019). A national survey by Pew Research Center indicated that up until 2016, 15% of Americans and 21% of urban Americans had used RS Apps (Smith, 2016). Despite the breadth of their nominal service areas, utilization had been somewhat limited to niche markets in dense urban areas, and the adoption rate among urban residents (29%) was double that of suburbanites (13%) (Clewlow & Mishra, 2017b). Ridesourcing trips tend to be short, with a median value ranging from two to three miles depending on the region (Feigon & Murphy, 2018). Different groups of users had cited different motivations for using RS services over an alternative mode. The primary reasons cited by urban car users were avoiding driving under the influence (DUI) and avoiding difficulties and price of parking; the most common reasons for transit users were high speed and short wait time; whereas the top reasons for taxi users were short wait time and convenience of hailing and payment (Clewlow & Mishra, 2017b; Dills & Mulholland, 2018; Feigon & Murphy, 2018; Greenwood & Wattal, 2017; Peck, 2017; Rayle et al., 2016). Avoiding DUI had been widely cited, suggesting that those services are very likely to be associated with social activities. Indeed, multiple studies have revealed that an appreciable fraction of all RS trips were served for nighttime social/recreational trip purposes. Hampshire et al.’s online survey found that these comprised two-thirds of total Uber/Lyft trips in Austin, USA (Hampshire, Simek, Fabusuyi, Di, & Chen, 2017). Clewlow & Mishra’s study suggested that 75% of RS adopters regularly used the services for recreational trips to bars and parties (Clewlow & Mishra, 2017a). Similarly, APTA’s study showed that those services were most frequently used for social trips between 10 pm and 4 am (Murphy, 2016). Another strand of literature has looked into the impact of RS services on other modes of transportation. As a major competitor to taxis, the rise of TNCs has evidently dealt a swift blow to the whole taxi industry in many cities (New York City Department of Transportation, 2018; Schaller, 2018; SFMTA, 2014). Compared with taxis in several US cities, RS vehicles have higher percentages of both productive operating hours and miles with passengers in the vehicle 2
(Cramer & Krueger, 2016; Henao & Marshall, 2018; Komanduri, Wafa, Proussaloglou, & Jacobs, 2018). The interaction between RS and transit can be classified into 3 categories: (a) RS trips used to get to or from public transit stations, (b) RS trips taken in lieu of public transit, and (c) RS trips taken when/where transit was not an option (Feigon & Murphy, 2018), which represent the relationship of connection, substitution and complementation, respectively. Scenarios (a) and (c) may constitute a positive effect on transit ridership, though the former is direct and the latter is indirect; whereas scenario (b) would reduce transit ridership. The APTA’s study found that RS is most popular during off-peak hours when public transit is sparse or unavailable, indicating that RS may be complementary to transit (Murphy, 2016). Hall et al. estimated the effect of Uber on public transit ridership with a difference-in-differences design, which concluded that Uber was complementary to transit and the effect was more salient in larger cities and for smaller transit agencies (Hall, Palsson, & Price, 2018). On the other hand, both Rayle et al. (Rayle et al., 2016) and Shaheen (Shaheen, 2016) provided evidence that RS both substituted and complemented public transit, while 8% of extra trips were new (induced). In summary, the interactions between RS with other modes carry strong geographic and temporal components. Despite these early findings, several critical gaps remain. First, most previous studies were conducted at the local or at best regional level, so there is limited knowledge on TNC’s overall impact nationally. Some divergent trends witnessed in different contexts through different research methods make it harder for policymakers to infer impacts and regulate accordingly. Nevertheless, two recent studies have shed some light on national trends by analyzing US National Household Travel Survey (NHTS) data. Tracking back from 2017 to 1995, Taylor & Gattett examined the national trends in carpooling, transit, taxi and RS use for travelers with varying level of income and personal vehicle access (Taylor & Garrett, 2019). Based solely on 2017 NHTS data, Grahn et al. explored socioeconomic, frequency of use, and spatial characteristics associated with TNC users (Grahn, Harper, Hendrickson, Qian, & Matthews, 2019). However, another issue is that most of those studies only focused on individual RS trips, which account for only a small fraction of the information in a person’s daily travel log. Looking at individual trips in isolation does not fully characterize how people link their complex daily travels between locations with a range of travel modes, and how RS services are integrated into the system. To bridge the aforementioned research gaps, this study has two goals based upon the analysis of the 2001, 2009 and 2017 US NHTS data: (1) to make a multi-year comparison exploring how daily travels served by taxis and ridesourcing have evolved over time at US national level; and (2) to make a profile and travel behavior comparison among RS non-users, occasional users and frequent-users to better understand the impacts of RS services. Instead of looking into individual trips, the analysis is conducted at the tour level which provides more insights into travel demand based on location, purpose, and mode. This has been recognized as a better approach to understand travel behavior by considering daily travel as a series of interdependent decisions (Shiftan, 1998; Ye, Pendyala, & Gottardi, 2007). 2 Data Since 1969, the NHTS has been conducted periodically to understand Americans’ travel behavior. The three most recent surveys – from 2001, 2009 and 2017 – were used for this study. Each survey wave contains Household, Person, Vehicle and Trip tables providing information 3
related to household socio-demographic attributes, personal characteristics of each family member aged five or older, household vehicle attributes and daily travel information, respectively. In particular, the Trip table documents origins and destination (OD), departure and arrival time, and travel mode used for each trip segment during an assigned travel day. Therefore, based on the travel log, individual trips with trip ODs, directed routes and travel modes can be chronologically chained into tours, beginning and ending at home (Hensher & Reyes, 2000; McGuckin, Zmud, & Nakamoto, 2005; Ye et al., 2007). The survey method and definition of certain terms in the NHTS questionnaires had been adjusted over time, although most of the travel information is compatible among these three surveys. More details about the survey methodology are available in the user guide of each survey (Federal Highway Administration, 2020). In terms of on-demand transportation, taxicab and limousine were treated as separate travel modes in 2001 survey, then combined into one category as taxicab in 2009. Ridesourcing services including Uber, Lyft and Sidecar were added to the taxicab category in the 2017 survey. For consistency’s sake, taxicab and limousine in 2001 survey data were fused into taxicab category in this paper. One deficiency of this data is that we cannot distinguish RS trips from traditional taxi trips in the 2017 survey, therefore, the trip-level analyses in this study represent the trends of taxis and ridesourcing services (T/R) collectively. Nevertheless, as previous studies suggested, TNCs have posed dramatic disruptions on the taxi industry (Li, Wu, Ban, & Wang, 2020; Schaller, 2018) and their rapid growth has been propelled in part by pulling riders away from taxis (Alemi, Circella, Handy, & Mokhtarian, 2018; Clewlow & Mishra, 2017a; New York City Department of Transportation, 2018). Therefore, the growth of T/R trips since 2009 that is observed in this study is due almost certainly to the growth of ridesourcing. All statistics presented in this paper are weighted using the NHTS-supplied expansion weight of each observation, to generate unbiased population-level estimates. TABLE 1 presents summary statistics for T/R users at both the household and person levels, as well as daily total T/R trips nationwide from these three surveys. The growth was modest from 2001 to 2009, yet a significant boost is evident between 2009 and 2017, doubling the T/R users and nearly tripling the daily total trips. 0.9% of total population had taken T/R trips based on the 2017 survey which accounted for 0.5% of all trips. TABLE 1 Weighted summary statistics of T/R users and trips Households (million) Persons (million) Daily T/R Trips (million) 3 2001 (Taxi) # % 0.7 0.7 1.0 0.4 1.6 0.2 2009 (Taxi) # % 0.8 0.8 1.2 0.5 1.9 0.2 2017 (Taxi & Ridesourcing) # % 1.9 1.6 2.8 0.9 5.1 0.5 Patterns of Taxi/Ridesourcing Tours Given the fact that nearly 40% of daily travels involved five or more trips (among all observations from the 2001, 2009 and 2017 surveys), a tour-based analysis can help to explore the linkages between trips. More specifically, this section attempts to explore the following questions: (1) How are the origins and destinations of T/R trips distributed by type of activities and time of the day? (2) How do T/R services impact the connectivity and complexity of daily travels? (3) How do T/R services integrate with other modes to serve daily travel? 4
3.1 Asymmetric OD Trip Flow based on Activities and Time-of-Day Involved To explore activity patterns, each T/R trip is classified based on the categories and precedence of the activities at its origin and destination: (1) Home activities only take place at home location; (2) Mandatory activities have fixed frequency, location and timing (e.g. going to work/school); (3) Flexible activities are performed on a regular basis for household-sustaining purposes but have certain flexible characteristics (e.g. banking, grocery shopping); (4) Optional activities can vary for all characteristics (e.g. recreation, visiting friends); (5) Mode transfer indicates the process of changing travel mode during one trip, which was only recorded in the 2017 survey. FIGURE 1 depicts the chord diagrams of average daily T/R trip flow between each origin and destination pair in 2001, 2009 and 2017. The original statistics are available in the Appendix. The circumference of each ring in FIGURE 1 is proportional to the actual trip flow in the respective year. The trend is quite visually intuitive; taxi trips stayed relatively constant from 2001 to 2009 (actually decreased by 1%), while taxi plus ridesourcing trips grew explosively from 2009 to 2017 (increased by 170%). Each segmented and colored arc in the periphery of the ring indicates the distribution of five types of activities (i.e. home activities, mandatory activities, flexible activities, optional activities and mode transfer) that took place right before or after the T/R trips. The numbers around the arcs indicate the percentage of T/R trips with that OD pair among total T/R trips. For instance, home-based T/R trips took the largest share in all three surveyed years, ranging from 30% to nearly 40% of total T/R trips. Bands linked to the arc with the same color represent trips that end with that type of activity (i.e. trip purpose). For example, home activities are represented by green arcs in each diagram, so the three distinct green bands represent T/R trips with following directed flows respectively: Mandatory Home, Flexible Home and Optional Home. In fact, the demand for T/R services for all OD pairs went up since 2009. Especially, daily trips with the following directed flows Optional Home, Mandatory Mandatory, Mandatory Home, Home Optional and Home Mandatory all showed significant increases with a magnitude of hundreds of thousands of trips per day. However, in terms of the percentage of increase, Optional Flexible and Flexible Optional had the fastest growth since 2009 by 786% and 235%, respectively. Except for the differences by activities, the demands for T/R trips were also found to be imbalanced in directions, indicated by the different thickness of the two bands linked with the same two arcs, but with two distinct colors. For example, Flexible Home trips are represented by the green band linked with Home activities and Flexible activities, whereas Home Flexible trips are represented by the purple band linked with Home activities and Flexible activities. Overall, home-based trips in 2017 were distributed the most asymmetrically, with more trips going toward home than away (NFlexible Home / NHome Flexible 1.9, NOptional Home / NHome Optional 1.4, NMandatory Home / NHome Mandatory 1.3, NMode Transfer Home / NHome Mode Transfer 1.2). In other words, there was a 90% higher demand for T/R trips from flexible activities to home than for the reverse. Although mode transfer only accounted for 12% of total trips in 2017, nearly half (48%) of those were home-based, with 20% more trips ending at home than starting from home. After data inspection, two observations may help to explain those findings. First, some T/R trips were a part of complex tours with mixed activities, therefore, a tour like H-M-O-H can generate more O-H trips than H-O trips; this will be further explained in Section 3.2. Second, some travelers left home with public transportation in the daytime, then used T/R as the 5
alternative to return home at night when public transportation service is poor. Therefore, T/R services may complement or substitute the mode used for trips in the opposite direction. 2001 2009 2017 FIGURE 1 Chord diagram of average daily T/R trip flow by origin and destination types. (The circumference of each ring is proportional to the actual trip flow in the respective year. Bands linked to the arc with the same color represent trips that end with that type of activity. The numbers around the ring indicate the percentage of trips with that OD pair among total trips. The total trip counts are reported in tabular form in the Appendix for readers unable to distinguish the colors.) 6
This speculation is bolstered by findings shown in FIGURE 2. By parsing home-based T/R trips based on time of the day, a disproportionate demand can be found for different trip purposes. For instance, trips from home to mandatory activities took place mostly in the morning, while most of the return trips took place in the afternoon and at night, which is consistent with the working schedule of the majority of the population. Similar trends also applied to flexible activities, with the majority of the Flexible Home trips taking place in the evening, after people leave work. This tidal effect may potentially increase the burden of road traffic during peak hours. However, the most salient imbalance was actually related to optional activities. They had relatively consistent demand during daytime, yet a huge spike during late night from 10 pm to 4 am, which accounted for 15% of total T/R trips. This finding is in concordance with the results of other studies mentioned earlier (Clewlow & Mishra, 2017b; Hampshire et al., 2017; Murphy, 2016). The asymmetric service demand geographically may result in more deadheading of those for-hire vehicles, as they will tend to travel farther to pick up the next passenger after dropping off the last one. FIGURE 2 Distribution of travel time of home-based T/R trips (all bars add up to 100%) 3.2 Taxi/Ridesourcing Increases Simple & Short Tours While we have seen so far that T/R services are disproportionately used for different trip purposes, this does not fully characterize the niche these services fill in people’s daily travel. Besides, looking at individual trips in isolation may not be adequate for a thorough knowledge of users’ travel behavior. Therefore, for each individual, the OD and travel mode of their each trip were linked chronologically into tours, which comprise a sequence of trips to a single or multiple anchor destinations, beginning and ending at home (Hensher & Reyes, 2000; McGuckin et al., 2005). Based on this definition, individuals may have one or multiple tours each day. Those who didn’t initiate or end their travel day at home were removed from the analysis (around 8% of total respondents from three surveys). Next, all tours were categorized into following five types. A simple tour is only associated with one type of activity, while a complex tour includes two or 7
more types of activities. A tour that includes at least one optional and at least one flexible activity, we term a complex non-mandatory tour. A tour that includes at least one mandatory activity, along with at least one optional or flexible activity, we term a complex mandatory tour. Comparing the T/R-containing tours with the general tours (including those that contain a T/R trip and those that do not), a similar trend can be observed in terms of the total count, which grew slowly from 2001 to 2009, then rapidly from 2009 to 2017. A more divergent trend can be observed, however, regarding the simple tours (i.e. simple optional simple flexible simple mandatory), which declined among general tours in each year, but increased among T/Rcontaining tours, especially from 2009 to 2017. This suggests that due to the increasing accessibility and convenience of RS services, people may be thinking less about chaining their daily activities to reduce trips and stops. FIGURE 3 depicts tours containing at least one T/R trip. In 2017, the mandatory and non-mandatory tours made up 38% and 62% of the total, respectively, and the simple and complex tours made up 68% and 32%, respectively. While all tour types increased concurrently, the simple optional (H-O-H) tours possessed the largest share of 24%, followed by simple flexible (H-F-H) and simple mandatory (H-M-H) tours. Simple mandatory tours showed the fastest growth from 2009 to 2017, with a rate of 70%. This suggests that RS has become a reliable commute mode for many travelers, which is consistent with the study by Pew Research Center (Smith, 2016). Moreover, the shares of different tour types have become more uniform since 2009, indicating that the T/R services are not anymore limited to a specific tour type. Tour Type Simple Optional H-O-H Simple Flexible H-F-H Simple Mandatory H-M-H Complex Non-mandatory H- F&O -H Complex Mandatory H- (F/O)&M -H (H-home, M-mandatory, F-flexible, O-optional) FIGURE 3 Average daily count and percentage of tours containing at least one T/R trip (Only tours beginning from and ending at home are included) It has been observed that geographic locations can heavily influence TNC adoption and usage since their services exist mainly in densely populated areas where better access to public transit and higher cost of parking also incentivize more multimodal travel. Here we further investigate the distinct changes of tour patterns from 2009 to 2017 across regions categorized by population density and different levels of transit usage, respectively. The population density in each census block group of the households’ home location was categorized into low ( 2000 person/sqmi), medium (2000-10,000 person/sqmi) and high 8
( 10,000 person/sqmi) levels. The transit mode share of each Core Based Statistical Area (CBSA) of the households’ home location was calculated based on overall trips reported in the Trip table. Since there is no universal definition of low or high levels of transit usage, all CBSAs in both survey years were categorized into low ( 3.5%, N 18), medium (3.5%-5%, N 16) and high ( 5%, N 17) relative levels of transit usage based on their overall transit mode shares in 2017. Finally, the increased count of each tour type from 2009 to 2017 was weighted by the total population within those three types of CBSAs. As FIGURE 4 and FIGURE 5 plot, the growth of each tour type from 2009 to 2017 was different across regions, but mainly located within highly populated areas and transit-oriented CBSAs. CBSA Population Density FIGURE 4 Average per-capita increase in tours containing at least one T/R trip, from 2009 to 2017, grouped by population density of the CBSAs FIGURE 5 Average per-capita increase in tours containing at least one T/R trip, from 2009 to 2017, grouped by transit mode share of the CBSAs 3.3 Multi-modal Integration of RS with Traditional Modes To explore how different modes have been linked during people’s daily travel, all modes used during a tour were identified. FIGURE 6 compares the distribution of different mode 9
combinations for all tours from 2001 to 2017. Both Panel a and Panel b only depict the eight types with the highest mode shares. Auto only had the largest share since 2001, yet decreased by 2% in each following wave of the survey. In contrast, biking/walking (B/W) increased by 1% in each wave of the survey and transit only increased by 1% from 2009 to 2017. The rest were fairly consistent over time. Even though T/R related tours only made up a tiny portion of the mode share per se, the percentage increased from 0.4% in 2009 to 1% in 2017 (i.e. 250% increase), by far the largest proportional change across all mode combinations and all NHTS survey years examined. By parsing those tours based on mode combinations, Panel b of FIGURE 6 identifies detailed changes measured in ‱ (basis points or hundredths of a percent). Only 4‱ of tours used taxi as the sole travel mode in 2001 and 2009, yet the share of T/R increased to 11‱ in 2017. All modal combinations involving T/R increased from 2009 to 2017. Tours by T/R bike/walk showed the largest absolute growth while tours using T/R only and T/R transit showed the largest relative gains. Survey Year 2001 2009 2017 Panel a: Mode combinations in all tours (in %) Panel b: Mode combinations in all T/R related tours (in ‱ or hundredths of a percent) FIGURE 6 Mode share of travel mode combinations in tours (only 8 combinations with the highest mode share are presented in both panels) The RS services were found to be used for mode transfer in 11% of all T/R-containing tours, yet the direct transfer between RS and transit only accounted for 0.9% of T/R trips, which 10
is similar to the findings from the 2017 New York City Mobility Survey; for trips by for-hire vehicles, 0.4% connected to transit and 0.9% connected from transit (New York City Department of Transportation, 2018). Of this 0.9%, 0.8% occurred within the highly transit-oriented CBSAs identified in Section 3.2, and only 0.1% occurred in other areas. Within those transit-oriented CBSAs, 1.5% of T/R trips were for direct transfer between T/R and transit. Although higher than the national average, this is much lower than the 5% reported in the intercept survey administered near RS “hot spots” in downtown San Francisco (Rayle et al., 2016), and much, much lower than implied by Uber and Lyft’s statements that 30% and 25% of their trips, respectively, started or ended near a transit station (within 200 meters or 656 feet identified by Uber) (Coleman, 2017; Macku, 2017). In an urban area, places with high concentrations of points of interest and activities are usually overlapped with high density of transit stations, which serve as more central and convenient locations for pick-up and drop-off, but not necessarily the actual destinations. Moreover, 200 meters is a rather large distance within which to be inferring a mode connection. Nevertheless, approximately one-third of all T/R-containing tours were also partially served by public transit, suggesting an indirect connection at the tour level that was still significant. Taking both panels into account, the share of tours with transit only or B/W only increased, and tours with T/R transit or T/R B/W also increased. This indicates that overall, RS services appear to be used in conjunction with public transit and active modes at the tour level. In contrast, the share of tours containing auto either decreased or kept the same, while the share of tours containing T/R auto increased. This suggests that directionally, RS services may help transform a certain proportion of monomodal car users into multimodal users at the tour level. However, these findings (1) apply to people’s mode choices at the tour level rather than the trip level, so they are not sufficient for assessing the actual frequency of use of each mode, and (2) represent the overall nationwide trend of T/R, and situations may vary case by case locally. At the same time, 40% of all T/R-containing tours also involved auto trip(s), indicating that even with T/R services, private vehicles were still used in many cases. However, we also find that (1) during 44% of those auto trips, people were actually passengers; (2) during 56% of the rest of auto trips when people were drivers, more than half of trips had multiple passengers (including both household and non-household members); (3) even for those drive-alone trips, the three most common trip purposes are going to work, changing type of transportation, and picking-up/dropping-off someone. In all these cases, people may have a way to not keep the vehicle with them for the rest of the tour, which increases their possibility of taking TNC trips (e.g. other travelers can drive the car after the trip, or people can park their car in their workplace or a park-and-ride before taking a TNC). FIGURE 7 shows how people were combining auto with T/R on a tour in the three survey years. The most common practice for people in 2017 was to leave home with an auto yet return home with a T/R fleet, which accounts for 40% of the cases, doubling its share in 2001. Th
as home - work - home). Tours involving T/R grew from 0.4% of all tours in 2009 to 1% of all tours in 2017, mostly within densely populated and transit-oriented regions. Although less than 1% of T/R trips involved a direct transfer to or from transit, one-third of all tours containing T/R also included transit.
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booking. Booking an occasional trip. An occasional trip is one that only oc. curs once. If you want to book a single trip, follow the instructions below. If you want to book a trip that repeats on the same day at the same time, please see the next section, " Booking a regular trip ". You can book a one-way trip, round trip or multi-trip.
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