Understanding Inequalities in Ride-Hailing Services Through Simulations.
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ABSTRACT:
SUBMITTER: Bokanyi E
PROVIDER: S-EPMC7162900 | biostudies-literature | 2020 Apr
REPOSITORIES: biostudies-literature
Publications
Understanding Inequalities in Ride-Hailing Services Through Simulations.
Bokányi Eszter E Hannák Anikó A
Scientific reports 20200416 1
Despite the potential of ride-hailing services to democratize the labor market, they are often accused of fostering unfair working conditions and low wages. This paper investigates the effect of algorithm design decisions on wage inequality in ride-hailing platforms. We create a simplified city environment where taxis serve passengers to emulate a working week in a worker's life. Our simulation approach overcomes the difficulties stemming from both the complexity of transportation systems and th ...[more]
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Project description:IntroductionThis study aimed to explore the factors influencing people's utilization of ride-hailing services, particularly in the context of the COVID-19 pandemic.MethodsA two-stage survey was conducted among the same group of passengers pre and post COVID-19 pandemic, resulting in a total of 670 valid samples. Exploratory factor analysis (EFA) was applied to the data, followed by the ordered probit and ordered logit models to identify the motivational factors behind passengers' frequency of using ride-hailing.ResultsThe findings indicated that trust and loyalty were the most influential factors in determining passengers' frequency of using ride-hailing services. However, passengers' perception of the COVID-19 pandemic did not have a significant effect on the frequency of using ride-hailing.DiscussionThis research provides empirical evidence and policy implications for understanding people's usage of the ride-hailing services in the context of public-health emergency.
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Project description:Since ride-hailing has become an important travel alternative in many cities worldwide, a fervent debate is underway on whether it competes with or complements public transport services. We use Uber trip data in six cities in the United States and Europe to identify the most attractive public transport alternative for each ride. We then address the following questions: (i) How does ride-hailing travel time and cost compare to the fastest public transport alternative? (ii) What proportion of ride-hailing trips do not have a viable public transport alternative? (iii) How does ride-hailing change overall service accessibility? (iv) What is the relation between demand share and relative competition between the two alternatives? Our findings suggest that the dichotomy-competing with or complementing-is false. Though the vast majority of ride-hailing trips have a viable public transport alternative, between 20% and 40% of them have no viable public transport alternative. The increased service accessibility attributed to the inclusion of ride-hailing is greater in our US cities than in their European counterparts. Demand split is directly related to the relative competitiveness of travel times i.e. when public transport travel times are competitive ride-hailing demand share is low and vice-versa.
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Project description:ObjectiveNightclub patrons who consume alcohol and drugs in these venues would appear to be an important population to target with on-demand ride hailing app (RHA) services to reduce drunk/drugged driving (DUI). The present study is an exploratory examination of RHA use to avoid DUI behavior, as well as the perceived barriers and benefits of such RHA use, among young adult nightclub patrons in Miami who use drugs.MethodsCompleters of a 2011-2015 randomized controlled trial of brief interventions to reduce health risk behaviors among young adult nightclub patrons were recruited to participate in a single self-administered computer-assisted interview about health risks, driving behaviors, and RHA perceptions and use. Recruitment (N = 123) began in June 2016 and ended in July 2017. Bivariable logistic regression and ANOVA models examined group differences between: (a) those who had used an RHA to avoid DUI vs. not; and (b) those who used RHAs as their primary mode of transportation to nightclubs vs. not.ResultsAbout half were female (52.8%); median age was 29; 59.4% Hispanic, 31.7% Black, 8.1% white, 0.8% other race/ethnicity. Recent alcohol and marijuana use were almost universally endorsed, and more than half reported recent use/misuse of cocaine (72.4%), MDMA (63.4%), and prescription benzodiazepines (62.6%) and opioids (56.9%). More than 80% reported driving under the influence of alcohol and/or drugs in the past 12 months, and 17.1% experienced a DUI arrest in the prior two years. Almost two-thirds (65.9%) of participants had used an on-demand RHA to avoid DUI, but self-driving or riding in another's car were the most common (76.4%) primary modes of transportation to clubs. RHAs were the primary mode of travel to clubs for 21 (17.1%) respondents. Participants whose friends strongly disapproved of DUI were more than twice as likely to have used RHAs for this reason compared to those who had not done so. Those reporting RHA use to avoid DUI were less likely than others to have driven under the influence in the past 12 months and were somewhat more likely to endorse DUI-related risks. Those who used alternate modes of transportation were more likely than those who used RHAs as their primary mode of transportation to clubs to endorse the expense of RHAs and the lack of RHA drivers near their favorite clubs as barriers to RHA use to travel to nightclubs.ConclusionsThis novel study among a high-risk population points to the potential for on-demand RHAs to reduce DUI behaviors and arrests among young adult nightclub patrons who consume alcohol and/or drugs in the context of the club experience. Our findings point to key educational, peer support, and structural targets for intervention to increase the use of RHAs among this population, specifically, club-based incentives for increasing RHA availability and affordability. Research is needed to fully elucidate the findings of this exploratory study, including potential differences in intervention approaches depending on the location-specific public transportation options.
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Project description:BackgroundOnline ride-hailing is a fast-developing new travel mode. However, tobacco control policies on its drivers remain underdeveloped. This study aims to reveal the status and determine the influencing factors of ride-hailing drivers' smoking behaviour to provide a basis for the formulation of tobacco control policies.MethodsWe derived our cross-sectional data from an online survey of full-time ride-hailing drivers in China. We used a survey questionnaire to collect variables, including sociodemographic and work-related characteristics, health status, health behaviour, health literacy and smoking status. Finally, we analysed the influencing factors of current smoking by conducting chi-square test and multivariate logistic regression.ResultsA total of 8990 ride-hailing drivers have participated in the survey, in which 5024 were current smokers, accounting to 55.9%. Nearly one-third of smokers smoked in their cars (32.2%). The logistic regression analysis results were as follows: male drivers (OR = 0.519, 95% CI [0.416, 0.647]), central regions (OR = 1.172, 95% CI [1.049, 1.309]) and eastern regions (OR = 1.330, 95% CI [1.194, 1.480]), working at both daytime and night (OR = 1.287, 95% CI [1.164, 1.424]) and non-fixed time (OR = 0.847, 95% CI [0.718, 0.999]), ages of 35-54 years (OR = 0.585, 95% CI [0.408, 0.829]), current drinker (OR = 1.663, 95% CI [1.526, 1.813]), irregular eating habits (OR = 1.370, 95% CI [1.233, 1.523]), the number of days in a week of engaging in at least 10 min of moderate or vigorous exercise ≥3 (OR = 0.752, 95% CI [0.646, 0.875]), taking the initiative to acquire health knowledge occasionally (OR = 0.882, 95% CI [0.783, 0.992]) or frequently (OR = 0.675, 95% CI [0.591, 0.770]) and underweight (OR = 1.249, 95% CI [1.001, 1.559]) and overweight (OR = 0.846, 95% CI [0.775, 0.924]) have association with the prevalence of current smoking amongst online ride-hailing drivers.ConclusionThe smoking rate of ride-hailing drivers was high. Sociodemographic and work-related characteristics and health-related factors affected their smoking behaviour. Psychological and behavioural interventions can promote smoking control management and encourage drivers to quit or limit smoking. Online car-hailing companies can also establish a complaint mechanism combined with personal credit.
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