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Ride-hailing in Santiago de Chile: Users’ characterisation and effects on travel behaviour
Abstract and Figures
In this paper, an in-depth examination of the use of ride-hailing (ridesourcing) in Santiago de Chile is presented
based on data from an intercept survey implemented across the city in 2017. First, a sociodemographic analysis
of ride-hailing users, usage habits, and trip characteristics is introduced, including a discussion of the substitution
and complementarity of ride-hailing with existing public transport. It is found that (i) ride-hailing is
mostly used for occasional trips, (ii) the modes most substituted by ride-hailing are public transport and traditional
taxis, and (iii) for every ride-hailing rider that combines with public transport, there are 11 riders that
substitute public transport. Generalised ordinal logit models are estimated; these show that (iv) the probability
of sharing a (non-pooled) ride-hailing trip decreases with the household income of riders and increases for
leisure trips, and that (v) the monthly frequency of ride-hailing use is larger among more affluent and younger
travellers. Car availability is not statistically significant to explain the frequency of ride-hailing use when age and
income are controlled; this result differs from previous ride-hailing studies. We position our findings in this
extant literature and discuss the policy implications of our results to the regulation of ride-hailing services in
Chile.
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... De todos modos, este texto se presta como parte de un cuerpo de literatura emergente respecto a la gig economy y los cambios en la naturaleza del trabajo en la última década en América Latina, al igual que la descripción que hacen Hidalgo y Valencia (2019) sobre Uber Eats y Glovo en Quito, o los efectos que Uber ha tenido en los comportamientos de sus usuarios y el tránsito vial (LAGOS, MUÑOZ & ZULEHNER, 2019;TIRACHINI & RÍO, 2019). En particular, el foco en los trabajadores de la gig economy entienden su condición y prácticas puede servir para entender los espacios de tensión y discontinuidad que se producen en sus relaciones laborales, así como en los regímenes discursivos más amplios en los que están insertos. ...
Autopercepción de Conductores de Uber en Chile: Entre la negación de la precariedad y el orgullo del buen servicio Self-perception of Uber drivers in Chile: Between precariousness and pride in good service Autopercepção dos motoristas de Uber no Chile: Entre a negação da precariedade e o orgulho do bom serviço Resumen: El estatus y condiciones de los trabajadores de la "gig economy" ha sido un tópico de debate alrededor del mundo. En particular, la empresa Uber ha estado en el foco de una serie de disputas respecto a la clasificación de sus conductores. Este texto se enfoca en el caso de los conductores de Uber de Santiago de Chile en el año 2019 y cómo estos perciben sus condiciones de empleo, en tanto el país posee una tradición de trabajo precario y/o informal y durante ese periodo un proyecto de ley que apuntaba a codificar el estado legal de los conductores estaba siendo discutido en el Congreso. Para desarrollar la investigación, se realizaron 7 entrevistas semiestructuradas a conductores, variando en afiliación sindical, contacto con otros conductores y tiempo de actividad. Los resultados muestran cómo, a pesar de descartar la categoría de emprendedor, se toman varios elementos asociados a esta que aparecen mediados por la construcción de las condiciones de empleo como riesgosas y/o precarias y a Uber como una compañía negligente y/o explotadora. Por otro lado, los entrevistados construyen la relación que mantienen con los pasajeros como producto de sus características personales y un elemento identitario que sirve para separarlos de otros conductores de plataforma y/o taxistas. En particular, el concepto de agencia permite posicionar estos resultados dentro del marco general del país y cómo esta se asocia a los relatos sobre el trabajo informal en Chile.
... Furthermore, most of these service users are the young generation (16-29 years old) or classified as Gen-Z (77,78%), according to [30,31]. These findings are consistent with studies conducted in other cities, such as Nagpur, India [32], Yogyakarta, Indonesia [33], and Santiago, Chile [34]. Convenience is the primary consideration when users choose the ride-hailing service since it can be accessed from their smartphones. ...
The significant gap between supply and demand of flexible and comfortable public transport within cities in the lower-income countries encourages the opportunity for the private sector to provide a ride-hailing service. Since its introduction in 2010, the ride-hailing service has continued to grow and has shaped urban mobility in the past twenty years. Due to the growth of ride-hailing services, some concerns arise regarding their contribution to the increasing traffic volume and the increased dependency on smaller-capacity vehicles in citizen mobility. This study aims to: 1) analyze the travel patterns of ride-hailing users based on their mobility needs; 2) analyze the relationship between travel characteristics and users’ socio-economic background and travel characteristics; 3) analyze the implication of ride-hailing users in shaping Makassar’s urban mobility. This study focused on the city of Makassar as the case study. The data in this study was obtained from an online survey of ride-hailing users with 270 samples. Several approaches are used for the analysis, i.e., descriptive statistics, spatial analysis, and network analysis. The result shows that 1) Most of the ride-hailing users are young generation (Gen Z). They perform a short-distance trip daily (internal trip within the district). 2) Regardless of the users’ age, income, and private vehicle ownership, ease of service is the foremost preference of the users since it is application-based and can be accessed using a smartphone. Door-to-door service is also one of the advantages provided by ride-hailing services.
... Surveys at major California airports showed that in 2015, 21% and 30% of travelers who used ride-hailing to get to the San Francisco and Oakland airports, respectively, would have used shared transit (mostly BART) were ride-hailing not an option [45]. An intercept survey in Santiago de Chile also finds ride-hailing replaces public transit (37%) and taxis (39%) the most, with 11 riders substituting public transit for every one rider who combines it with transit [46]. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 A c c e p t e d M a n u s c r i p t 16 The remaining studies are associational. [47] use panel data regression analysis to estimate the relationship between modes of transportation using three waves of a transit survey in Seattle, WA, including travel logs. ...
Ride-hailing has expanded substantially around the globe over the last decade and is likely to be an integral part of future transportation systems. We perform a systematic review of the literature on energy and environmental impacts of ride-hailing. In general, empirical papers find that ride-hailing has increased congestion, vehicle miles traveled, and emissions. However, theoretical papers overwhelmingly point to the potential for energy and emissions reductions in a future with increased electrification and pooling. Future research addressing the gap between observed and predicted impacts is warranted.
The challenges of North Sulawesi Province lie in the difficulty of developing sustainable industries and economic structures in this region. With the evolution of technology and the growing government policies, factors such as online motorcycle taxi services (ojek online), village funds (dana desa), business and production incentives from the government, financial technology (fintech), and technological disruption have become increasingly crucial in reshaping the economic activities in Indonesia including the creation of new jobs, disruption in existing jobs, and job transitions. This research, however, aims to pinpoint the main drivers of job transitions after significant changes in technology and government policies. To our knowledge, this is the first study attempting to investigate what leads to a change in the profession or job of the actor of MSMEs considering individuals’ demography characteristics, public insurance, and the advent of technology in business. The findings of this study suggest that the fulfillment of electricity needs, personal income, and business income are among the determinants of the job transitions of individuals in North Sulawesi Province. Additionally, factors that can drive job transitions within the same industry or sector due to the presence of new technologies include age, ownership of the National Health Insurance (BPJS/KIS), and residential and workplace or school locations. Working or studying in urban areas increases the likelihood of changing professions or jobs within the same sector or industry. On the other hand, the results suggest that the factors above, along with marital status and higher education attainment, can also drive changes in professions or jobs in different industries or sectors due to the presence of new technologies.
This research examines whether transportation network companies (TNCs), such as Uber and Lyft, live up to their stated vision of reducing congestion in major cities. Existing research has produced conflicting results and has been hampered by a lack of data. Using data scraped from the application programming interfaces of two TNCs, combined with observed travel time data, we find that contrary to their vision, TNCs are the biggest contributor to growing traffic congestion in San Francisco. Between 2010 and 2016, weekday vehicle hours of delay increased by 62% compared to 22% in a counterfactual 2016 scenario without TNCs. The findings provide insight into expected changes in major cities as TNCs continue to grow, informing decisions about how to integrate TNCs into the existing transportation system.
The widespread adoption of smartphones followed by an emergence of transportation network companies (TNC) have influenced the way individuals travel. The authors use the 2017 National Household Travel Survey to explore socioeconomic, frequency of use, and spatial characteristics associated with TNC users. The results indicate that TNC riders tend to be younger, earn higher incomes, have higher levels of education, and are more likely to reside in urban areas compared to the aggregate United States population. Of the TNC users, 60% hailed a ride three times or less in the previous month, indicating that TNC services are primarily used for special occasions. TNC users use public transit at higher rates and own fewer vehicles compared to the aggregate United States population. In fact, the TNC user population reported similar frequencies of use for both TNC services and public transit during the previous month. Approximately 40% of TNC users reside in regions with population densities greater than 10, 000 persons per square mile compared to only 15% for non-TNC users. Lastly, reported use of public transit for TNC users living in large cities (> 1 million) with access to heavy rail was almost three times greater when compared to similar sized cities without heavy rail. The average monthly frequency of TNC use was also elevated when heavy rail was present.
This paper identifies major aspects of ridesourcing services provided by Transportation Network Companies (TNCs) which influence vehicles miles traveled (VMT) and energy use. Using detailed data on approximately 1.5 million individual rides provided by RideAustin in Austin Texas, we quantify the additional miles TNC drivers travel: before beginning and after ending their shifts, to reach a passenger once a ride has been requested, and between consecutive rides (all of which is referred to as deadheading); and the relative fuel efficiency of the vehicles that RideAustin drivers use compared to the average vehicle registered in Austin. We conservatively estimate that TNC drivers commute to and from their service areas accounts for 19% of the total ridesourcing VMT. In addition, we estimate that TNC drivers drove 55% more miles between ride requests within 60 minutes of each other, accounting for 26% of total ridesourcing VMT. Vehicles used for ridesourcing are on average two miles per gallon more fuel efficient than comparable light-duty vehicles registered in Austin, with twice as many are hybrid-electric vehicles. New generation battery electric vehicles with 200 miles of range would be able to fulfill 90% of full-time drivers’ shifts on a single charge. We estimate that the net effect of ridesourcing on energy use is a 41% to 90% increase compared to baseline, pre-TNC, personal travel.
Many authors have pointed out the importance of determining the impact of ride-hailing (ridesourcing) on vehicle kilometers traveled (VKT), and thus on transport externalities like congestion. However, to date there is scant evidence on this subject. In this paper we use survey results on Uber use by residents of Santiago, Chile, and information from other studies to parameterize a model to determine whether the advent of ride-hailing applications increases or decreases the number of VKT. Given the intrinsic uncertainty on the value of some model parameters, we use a Monte Carlo simulation for a range of possible parameter values. Our results indicate that unless ride-hailing applications substantially increase average occupancy rate of trips and become shared or pooled ride-hailing, the impact is an increase in VKT. We discuss these results in light of current empirical research in this area.
Public transit ridership in major US cities has been flat or declining over the past few years. Several authors have attempted both to explain this trend and to offer policy recommendations for how to respond to it. Past writing on the topic is dominated by theoretical arguments that identify possible explanations, with the few empirical analyses excluding the most recent data, from 2015-2018, where the decline is steepest. This research conducts a longitudinal analysis of the determinants of public transit ridership in major North American cities for the period 2002-2018, segmenting the analysis by mode to capture differing effects on rail versus bus.
Our research finds that standard factors, such changes in service levels, gas price and auto ownership, while important, are insufficient to explain the recent ridership declines. We find that the introduction of bike share in a city is associated with increased light and heavy rail ridership, but a 1.8% decrease in bus ridership. Our results also suggest that for each year after Transportation Network Companies (TNCs) enter a market, heavy rail ridership can be expected to decrease by 1.3% and bus ridership can be expected to decrease by 1.7%. This TNC effect builds with each passing year and may be an important driver of recent ridership declines.
App-based ride-hailing is a newly emergent mode of travel that is expected to influence passengers’ travel behavior significantly. In this study, we define app-based ride-hailing services to include hailing of taxis through smartphone apps and sharing of private vehicles (car sharing is not included). This paper is one of the first quantitative studies to examine how app-based ride hailing would impact passengers’ choice of travel mode and change car-purchasing behaviors. China is taken as the empirical context, and the data are from a large-scale app-based survey conducted via the largest app-based ride-hailing platform in the world. Drawing on these rich survey data, the investigation covers both the short-term travel mode choices and long-term car-purchasing behaviors of app-based ride-hailing users. The research findings are helpful in understanding changes in urban residents’ travel behaviors under in context of app-based ride hailing, and provide valuable in-depth insights for both governments and app-based ride-hailing service providers (i.e., enterprises) for the management and regulation of ride hailing.
The recent and dramatic growth in ride-hailing activity is a bellwether of a coming transportation revolution driven by on-demand services. The impacts of ride-hailing services on the transportation system have been immediate and major. Yet, public agencies are only beginning to understand their magnitude because the private ride-hailing industry has provided limited amounts of meaningful data. Consequently, public agencies responsible for managing congestion and providing transit services are unable to clearly determine who uses ride-hailing services and how their adoption influences established travel modes, or forecast the potential growth of this emergent mode in the future. To address these pressing questions, an intercept survey of ride-hailing passengers was conducted in the Greater Boston region in fall 2017. Ten ride-hailing drivers, recruited and trained by the authors, asked passengers to complete surveys during their ride-hailing trip. The tablet-based survey instrument recorded nearly 1,000 passenger responses with regard to socioeconomic background, mobility options, and trip context. These responses, which enabled a robust description of ride-hailing passengers for the region, were used to analyze how new on-demand mobility services such as Uber and Lyft may be substituting travel by other modes. The study substantiates previous findings and advances knowledge of who is utilizing this new mobility option and what factors influence its adoption over public and active transportation modes. The results are intended to inform public policies ensuring that shared mobility technologies will complement existing multimodal landscapes and not worsen existing environmental concerns or equity gaps related to individual mobility. © National Academy of Sciences: Transportation Research Board 2019.
Convenience and low prices have enabled ride-hailing companies, such as Uber and Lyft, to position themselves amongst the most valuable companies within the transportation sector. They now account for the lion share of activities in the platform economy and play an increasing role within our cities. Despite this, very little is known about the type of people that use them, nor the purpose and timing of trips. In addition to this, their effect on other modes, such as taxis and public transit, remains, for the most part, widely unexplored. By comparing the socioeconomic and trip characteristics of ride-hailing users to that of other mode users, we find ride-hailing to be a wealthy younger generation phenomenon. While our results show that ride-hailing is too minute and inconsequential to influence the ridership level of other more substantial modes of travel overall, when considering specific market segments, the rise of ride-hailing corresponds to a significant decrease in taxi ridership and a rise in active modes of travel. Moreover, due to the specific age, timing, and purpose of our subsample, we believe that ride-hailing may effectively reduce drunk-driving, and are convinced that as this mode increases in importance in the future, it will have a much more pronounced effect on the level of ridership of other modes as well.
How Uber affects public transit ridership is a relevant policy question facing cities worldwide. Theoretically, Uber's effect on transit is ambiguous: while Uber is an alternative mode of travel, it can also increase the reach and flexibility of public transit's fixed-route, fixed-schedule service. We estimate the effect of Uber on public transit ridership using a difference-in-differences design that exploits variation across U.S. metropolitan areas in both the intensity of Uber penetration and the timing of Uber entry. We find that Uber is a complement for the average transit agency, increasing ridership by five percent after two years. This average effect masks considerable heterogeneity, with Uber increasing ridership more in larger cities and for smaller transit agencies.