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While ride-hailing has become ubiquitous in most cities, there is a dearth of studies using individual survey level data to better understand what drives the demand for such services.
While ride-hailing has become ubiquitous in most cities, there is a dearth of studies using individual survey level data to better understand what drives the demand for such services.
Understanding consumer motivations and preferences is not only critical to the sharing platform but also to their rivals who want to retrieve or gain market share. While survey data provides a window into ride-hail travelers the implications are limited to the case study, with the majority in the US. Notably, the results of different studies across the world have yielded slightly different socio-demographic indicators as travelers in different regions may possess different attitudes towards this new mode of transport.
These differences necessitate country-specific studies and ours is the first investigating the characteristics of ride-hailing travelers in Australia. Furthermore, the ride-hailing studies to date are by and large based on metropolitan demand and do not shed light on differences if any that exists between metropolitan and regional areas.
This paper contributes to this knowledge gap by examining the regional/metropolitan dimension in the factors influencing the frequency of ride-hailing use and the demand for pooled and autonomous vehicle (AV) rides in Australia. Through individual-level survey data, we found that there is indeed a divide between factors affecting not only the frequency of using ride-hailing in the status quo but also in the future adoption of new services of pooled and AV-based services.
The success of on-demand sharing economy ride-hailing platforms such as Uber in recent years has rapidly disrupted the traditional taxi market. As of June 2020, Uber operates in more than 700 cities across 63 countries, with about 14 million daily trips, 91 million active passengers and
3.9 million drivers. Australia is no exception. According to a 2019 nation-wide survey commissioned by the Victorian Government in Australia, the ride-hailing platform is the second-largest platform (about 24.8%) after Airtasker (34.8%) that is used by Australian residents.
With the growing demand for ride-hailing services, ride- hailing companies are recently offering a pooling (ride- splitting) option such as UberPool in some cities. Pooled services with relatively lower costs have the potential to draw users from active/public transit modes and increase vehicle occupancy. Such services can be used for door-to-door trips, and/or for first- and last-mile connectivity to transit hubs (if they are available in lower cost and well-integrated). Nevertheless, the fundamental question here rests on the key factors that make people to embrace these shared economy services (that is, pooling rides with others for passengers or a mix passenger-parcel delivery), and how these services should be subsidized to provide a complementary and/or substitution, when necessary.
Also, with the emergence of autonomous vehicles, AV ride-hailing rides will become a reality soon. Given the benefits, start-up industries have already taken steps towards this future, and it is likely that local markets will also opt for such technological changes. While it is known that the shared economy and the disruptive technologies will be increasingly relevant to the transport sector, the impact is not yet well understood, particularly on-demand services. Many issues regarding privacy concerns, equity, accessibility, digital and regional divide remain unaddressed.
While ride-hailing has become ubiquitous in most metropolitan areas, it is still unclear how it prevails in the regional areas, especially after the emergence of new services of pooling or self-driving rides. In this paper we attempt to address these knowledge gaps.
In mid-2019, we conducted a web-based survey administrated by a survey sampling firm in the Australian states of South Australia, Queensland, Tasmania and Western Australia who have used/are familiar with ride-hailing platforms including Uber. These states were chosen as - unlike New South Wales and Victoria - Uberpool (shared ride-hailing trips) had not begun to operate, and one aim of our survey was to gather stated preferences for this particular feature. In addition, responses from residents in both metropolitan and regional areas in these states were obtained. The survey collected 777 complete questionnaires (406 metropolitan and 371 regional) in five Australian states. According to the Australian Bureau of Statistics (ABS), metropolitan areas represent Greater Capital City Statistical Areas, and regional areas identify the rest of state. These are geographical areas that have been identified by the ABS based on the functional or socio-economic characteristics of these areas. The sample was chosen to represent the most recently available 2016 Australian population census by age and gender demographics. This was done using screening questions related to gender and age in the beginning of the survey, which was administrated by the survey sampling firm.
Developing an integrated latent variable and ordered logit models, as shown in Figure. 1, we investigated five research questions to identify: (i) the determinants of the frequency of ride-hailing use in the status quo; (ii) the likelihood of adopting pooled ride-hailing services; (iii) the likelihood of adopting AV ride-hailing services; and (iv) the likelihood of adopting AVPool ride-hailing service.
Factor analysis was undertaken to re-identify the number of unobserved behavioural factors from the indicators based on their interdependencies. The latent variable Tech-centric is defined with the indicators of favouring technology. Individuals from metropolitan areas are by and large in favour of technology. Notably, several indicators dropped out in the regional dataset. The latent variable of Anti-driving represents the main reasons of using Uber, namely the lack of accessibility of PT, scarcity and expensive parking. The indicator of ‘Using Uber due to being unable to drive due to alcohol’ dropped out from the Anti-driving latent variable in the metropolitan dataset. The latent variable Security-cautious represents the indicators related to concerns. There is no significant difference between individuals from regional and metropolitan areas in terms of their concerns using such platforms. Similarly, two latent variables given by Pro-Pooling and Pro-AV were defined with indicators favouring the pooled and AV ride-hailing services. These two latent variables were only used in the model estimations of pooled and AV-based ride-hailing adoption.
The results obtained in this study provide some key insights and policy implications.
Results indicated that PT commuters are more likely to use ride-hailing frequently in both regional and metropolitan areas.
This implies that those who have already given up door-to-door driving have greater tendency to switch to ride-hailing. Given the door-to-door convenience and relatively lower waiting time and cost, this may lead to PT substitution in the near future, which is particularly concerning for city planners, given the externalities of ride-hailing such as deadhead mile travelled, increasing congestion and pollution.
Given the substitution of ride-hailing and pooled ride- hailing with PT in metropolitan areas leading to a fall in demand for PT, governments need to rethink their policies, budgetary concerns and infrastructural planning in moving forward to ensure that PT does not remain underutilised and thereby cost-inefficient. In the rural areas however, taxis as a form of substitute for ride- hailing need to consider competitive pricing or some form of price discrimination (different pricing strategies for different services/routes) and improve their services to raise customer satisfaction to lure users away from ride-hailing.
The results also showed that the regional multimodal PT travellers and metropolitan residents with tech-centric and anti-driving attitudes are more likely to adopt pooled services for all four trip purposes.
Given these two latent attitudinal constructs are correlated with being a PT user, it implies that pooled ride-hailing services may substitute PT in the future. While pooled ride-hailing has the potential to take on a paratransit mobility role, it is not clear how sustainable this is compared to PT and active transport.
Our survey showed that 49% -57% of Australian metropolitan or regional residents are somewhat or extremely likely to use pooled ride-hailing services for work and recreational trips. This percentage is slightly higher for trips to airport or a cruise terminal where 62% and 63% of regional and metropolitan residents are somewhat or extremely likely to use such services.
The survey also provides insight on the effect of ride attributes making pooled services attractive and identifies areas in which PT can be improved to lure commuters away from ride-hailing.
It was found that walking distance to/from pick up/drop off points is likely to be a deterrent factor in metropolitan work-related and trips to terminals, while it is only significant in the latter for the regional residents.
Delays were more significant barriers in regional recreational trips and priority drop off were influential in attractiveness of metropolitan trips to terminals.
In terms of the number of passengers and price discount, it did matter for regional work-related trips and for metropolitan terminal trips, where sharing with two passengers at 50% discount increased the likelihood of sharing a ride.
The results indicated that AV ride-hailing services are likely to be considered as a mobility option by regional and metropolitan residents, and the latter are likely to do so for trips to PT stations.
It was found that regional residents with tech-centric attitudes and metropolitan residents with anti-driving attitude are more likely to use AV-based services for all trips’ purposes. This implies that AV uptake in regional areas is likely to be correlated with being young, female, multimodal traveller and car owner. While AV uptake in metropolitan areas is more likely among those who driving is not their first choice either due to difficulty or cost of parking, stress of driving, or not being able to utilise travel time more effectively. Notably, this group are multimodal PT users in the status quo and the likelihood of substituting PT with AV ride-hailing service could bring about further externalities in the near future in our metropolitan areas, if PT fails to keep its market share. However, the stated intention to swap ride-hailing with AV ride-hailing showed that around 40% of metropolitan residents and more than 50% of regional residents would not choose AV service, even with price discounts up to 50%. This indicates a potential problem in AV uptake in Australia.
On the other hand, ride-hailing companies wanting to promote pooled services should note concerns related to walking distance to pick up points and devise some incentives perhaps in the form of loyalty points to encourage customers to utilise this service. With the AV option, ride-hailing platforms should not be ambitious and embark on this on a grand scale but rather to complement commute trips to PT stations.
The results highlight the unlikely adoption of AVPool services by the majority of metropolitan and regional residents. While metropolitan PT users/commuters are more likely to adopt AVPool for recreational and trips to terminals, the only factor that may increase the uptake by regional residents is an anti-driving attitude. The stated intention to swap pooled services with AVPool was be similar to AV adoption.
Our results showed that in metropolitan areas, ride- hailing can replace PT with a higher percentage in metropolitan areas while in the regional areas the substitution of private cars occurs with a higher percentage. Expectedly, taxi is another major competitor. Ride-hailing has also induced a more significant reduction in active mode usage and only a small percentage stated that ride-hailing had increased their use of PT, walking or biking, which reflects the complementary role of ride-hailing in low PT accessibility regions. By and large, these results confirm the finding from previous studies that ride-hailing may have negative externalities as a result of deadheading, increased vehicle kilometres travelled, creating extra trips and substituting from more sustainable modes such as active transport modes and PT. On the other hand, it may bring about some positives impacts, such as reducing the demand for parking and car ownership.
Lastly, in line with the literature, younger generations are more likely to share the ride either with fellow passengers or a parcel and are also more likely to adopt AV services.
Notably, this one-off survey only provides a snapshot about the perceptions of respondents towards the pooled services. It has however, shown that the impact of ridesharing in all its forms on transport usage and patterns is complex.
Larger sample size and preferably a panel data analysis should be deployed in future research to provide clearer policy and forecasting implications.
These perceptions may change once the technology is up and running, as Ian Ayres says:
“Humans not only are prone to make biased predictions, we’re also damnably overconfident about our predictions and slow to change them in the face of new evidence. In fact, these problems of bias and overconfidence become more severe the more complicated the prediction.”
