Ride-hailing services: Competition or complement to public transport to reduce accident rates. The case of Madrid (original) (raw)
Related papers
Analysis of the Impact of Ride-Hailing Services on Motor Vehicles Crashes in Madrid
Sustainability, 2021
In most cities, discretionary passenger transport by car is predominantly supplied by taxi services. These services face competition from new digital platforms (UBER, Cabify, etc.) that connect users with the services offered by authorized drivers with a license for rented vehicles with drivers (VTC). However, very little is known about the impacts that these services produce in cities where they operate. So far, most studies on this issue have focused on cities of the United States of America, and they broadly found a positive impact in terms of road safety. Road safety has become one of the priority focuses for ensuring social welfare, to the point of being integrated into the Sustainable Development Goals as a primary value to achieve sustainable, safe and responsible mobility. Within this context, the objective of this paper is to analyze the impact of ride-hailing platforms on the frequency of traffic accidents with at least one fatally or seriously injured person in the munici...
Does the Implementation of Ride-Hailing Services Affect Urban Road Safety? The Experience of Madrid
International Journal of Environmental Research and Public Health, 2022
In recent years, changes have occurred in consumption, ownership, and social relations, giving rise to new economic models in which technology enables new ways of connecting, creating, and sharing value. The nature of transport has transformed with the emergence of mobile applications, such as Uber and Cabify, which offer an alternative to the services traditionally provided by the taxi and chauffeur-driven hire vehicle (CDV) sectors. These services have developed within a context of market regulation of the taxi and CDV which are subject to considerable unjustified restrictions for entering and operating in the market, including the numerus clausus of licenses, the limited geographical scope of the license and, in the case of taxis, the regulation of prices as inflexible public rates. Bearing in mind the latest legislative changes affecting mostly the provision of the services of these platforms, this study analyzes whether the number of traffic accident victims has fallen since th...
Ride-hailing vs. Taxi Services: a Survey-based Comparison
Journal of Tourism and Services
As being an important player of sharing economy, ride sourcing services provide benefits for socio-economic and environmental issues of countries and make positive contributions for sustainability. To indicate socioeconomic and environmental influences of these services, this paper aims to make comparison between some alternative Transportation Network Companies, namely, Taxify, Uber and Liftago and standard taxi services regarding their drivers’, vehicles’ and trips’ characteristics. In line with this objective, this research investigates 84 drivers of ride sourcing services and standard taxi industry from two various cities of the Czech Republic, namely, Prague and Ostrava. The data of this paper was collected by using a self-administered questionnaire and the researchers applied multiple correspondence analysis in R-software to make the analyses of this research. The results of these analyses show that different from standard taxi services, most of the drivers of Taxify in Prague...
The Cost of Convenience: Ridesharing and Traffic Fatalities
SSRN Electronic Journal, 2018
We examine the effect of the introduction of ridesharing services in U.S. cities on fatal traffic accidents. The arrival of ridesharing is associated with an increase of approximately 3% in the number of motor vehicle fatalities and fatal accidents. This increase is not only for vehicle occupants but also pedestrians. We propose a simple conceptual model to explain the effects of ridesharing's introduction on accident rates. Consistent with the notion that ridesharing increases congestion and road use, we find that its introduction is associated with an increase in arterial vehicle miles traveled, excess gas consumption, and annual hours of delay in traffic. On the extensive margin, ridesharing's arrival is also associated with an increase in new car registrations. These effects are higher in cities with prior higher use of public transportation and carpools, consistent with a substitution effect, and in larger cities and cities with high vehicle ownership. The increase in accidents appears to persist-and even increase-over time.
Urban Transport XXIV, 2018
Shared ride-hailing services are discreetly emerging in cities all over the world, with the purpose of providing a transport service more flexible than the public bus and cheaper than the regular taxi. Hence, it is important to identify and include the relevant and key factors of such a service to ensure its success. The aim of this study was to investigate the utility of an on-demand shared ride-hailing commuting service and determine the design factors for an appropriate service from the users' perspective. For one week, a pilot of a shared ride-hailing services to commute from the city centre of Barcelona to the most western district of the city was conducted. Although the sample used in this study was modest in scale, it enabled to draw preliminary conclusions. Users valued very positively the comfort provided but they would not pay more than the double of the price of the public transport. This indicates that despite having a high number of users, such a low price could complicate the profitability of the service unless it was partially subsidized.
Transport Policy, 2016
In this study, we present exploratory evidence of how "ridesourcing" services (app-based, on-demand ride services like Uber and Lyft) are used in San Francisco. We explore who uses ridesourcing and for what reasons, how the ridesourcing market compares to that of traditional taxis, and how ridesourcing impacts the use of public transit and overall vehicle travel. In spring 2014, 380 completed intercept surveys were collected from three ridesourcing "hot spots" in San Francisco. We compare survey results with matched-pair taxi trip data and results of a previous taxi user survey. We also compare travel times for ridesourcing and taxis with those for public transit. The findings indicate that, despite many similarities, taxis and ridesourcing differ in user characteristics, wait times, and trips served. While ridesourcing replaces taxi trips, at least half of ridesourcing trips replaced modes other than taxi, including public transit and driving. Impacts on overall vehicle travel are unclear. We conclude with suggestions for future research.
Does Uber affect bicycle-sharing usage? Evidence from a natural experiment in Budapest
Transportation Research Part A: Policy and Practice, 2020
We are grateful for the comments by Péter Bucsky on our article on Uber's effects on bicycle sharing usage (Bakó et al., 2020). Unfortunately, we believe that the concerns raised, and criticisms made by Bucsky (2020) are either based on misunderstandings of our results or are irrelevant to the analysis. In the following we address each objection made by the assessor and further clarify our results. 2. Missing relationship between Uber and BSS Bucsky (2020) argues that it is "highly questionable" whether riders combine Uber and BSS within a trip. This argument might be valid, yet this was not the subject of our analysis. Our paper was not intended to analyse the relationship between Uber and BSS within one single trip. We applied a wider, more systemic perspective. As we pointed out in the original paper, "the nature of complementarity between ride-sharing and bike-sharing services is best characterized as a type of temporal complementarity" (Bakó et al., 2020, p. 291). This means that many riders use both BSS and Uber, but at different times and during different trips. To illustrate again our logic with an example different from what we had given in the article consider someone who may use BSS to visit a friend after work, but she goes home using Uber, either because it is late in the night or because she consumed some alcohol or due to any other reasons. Another reason why city dwellers might use BSS especially during the afternoon commuting time instead of driving or using ride-sharing services can also be explained by traffic congestions. As McKenzie (2020) showed, BSS can be faster than Uber in commuting peaks. This temporal complementarity between Uber and BSS is also strongly supported by the data. The temporal distribution of BSS and Uber usage differs significantly as was reported by Rao (2018), who analysed JUMP (Uber's BSS service) and Uber usage in San Francisco. Regarding BSS usage, the San Francisco data show substantial similarities with data from Budapest (see Fig. 3 in Bakó et al., 2020), however it is fair to note that the temporal distribution of Uber usage is not available for Budapest. Yet, given the similarities in Uber's usage pattern across major cities (see e.g., Hall et al., 2018 or Mohamed et al., 2020), we can safely assume a similar usage pattern in Budapest as in San Francisco. Moreover, we believe that the assessor misunderstands our argument regarding the overlap between the user bases of Uber and
Linköping University, 2021
The growing population and motorization generate more movements. In many cities, the increase of population and motorization is much greater than the development of the capacity of the transportation network. For unprotected road users, the risk of getting in a traffic accident increases and the risk of being more severely injured in an accident. In March 2020, a pandemic was declared because of a Coronavirus. More people started to work/study from home to prevent the virus from spreading by avoiding unnecessary trips, gatherings, and crowded areas. Therefore, travel behaviours have shifted during the pandemic compared to previous years. This project aims to get knowledge of how mobility and traffic accidents are affected by significant shifts of travel flow, predict the effect of traffic accidents based on mobility, and evaluate the risk of travelling on a particular road segment. Mobility data has been collected from Google Mobility, Apple Mobility, the Environmental Barometer, Trafikkontoret, and traffic accident data collected from STRADA. Mobility and traffic accident data have been analysed using Excel, QGIS, and PTV Visum Safety. The accident rates have been calculated to determine if the accident rate has changed during the pandemic, and three scenarios for 2021 have been predicted. A risk analysis model has also been used to calculate the risk of being involved in an accident on particular streets using a car, cycle, or walking. It was found that mobility has decreased, and the usage of transportation modes has shifted. During the pandemic, it has been more popular to cycle, which is also reflected in the traffic accident data, where the percentages of cyclists being involved in traffic accidents have increased. No matter the degree of injury or transportation mode, the total number of traffic accidents had decreased in 2020. However, the number of severe accidents is almost the same as in previous years. Males are overrepresented in traffic accidents, and the differences are even more significant for 2020. In 2020, the travel speed on the roads increased, which might be due to decreased traffic volume, making it possible to drive faster. The percentage of accidents involving alcohol also increased. The results from the risk analysis show whether the risk of getting into a traffic accident on a particular street using a specific transportation mode has increased or decreased depending on the street and transportation mode. Three scenarios (better, same, worse) of the risk of travelling on the particular road stretches in 2021 have also been calculated. To make better predictions, additional years should be considered. For future work, it would also be interesting to consider the weather since it greatly impacts the transportation mode used and the risk of accidents. The infrastructure was not considered in this project, which would be interesting since the transportation mode, route used, and speed might depend on the infrastructure and current constructions.
Analysis of Factors Affecting Real-Time Ridesharing Vehicle Crash Severity
Sustainability
The popular real-time ridesharing service has promoted social and environmental sustainability in various ways. Meanwhile, it also brings some traffic safety concerns. This paper aims to analyze factors affecting real-time ridesharing vehicle crash severity based on the classification and regression tree (CART) model. The Chicago police-reported crash data from January to December 2018 is collected. Crash severity in the original dataset is highly imbalanced: only 60 out of 2624 crashes are severe injury crashes. To fix the data imbalance problem, a hybrid data preprocessing approach which combines the over- and under-sampling is applied. Model results indicate that, by resampling the crash data, the successfully predicted severe crashes are increased from 0 to 40. Besides, the G-mean is increased from 0% to 73%, and the AUC (area under the receiver operating characteristics curve) is increased from 0.73 to 0.82. The classification tree reveals that following variables are the prima...
2021
As transit ridership continues to fall in many cities across the globe, key policy debates continue around whether Uber and other ride-hailing services are contributing to this trend. This research explores the effects of the introduction of ride-hailing to Colombian cities on public transportation ridership using Ubers timeline as case study. We test the hypothesis that ride-hailing may either substitute or compete with public transit, particularly in cities with large transit service gaps in coverage or quality. Our analysis builds on historic transit ridership data from national authorities and uses a staggered difference-in-difference model that accounts for fixed effects, seasonality, socioeconomic controls, and the presence of integrated transport systems. Despite large reductions in transit ridership in most cities, our results suggest that Uber is not statistically associated with the observed drop in ridership. Moreover, consistent with evidence from previous research, publ...