What do riders tweet about the people that they meet? Analyzing online commentary about UberPool and Lyft Shared/Lyft Line

Pratt, Angela; Morris, Eric A.; Zhou, Ying; Khan, Sakib Mahmud; Chowdhury, Mashrur

doi:10.1016/j.trf.2019.01.015

Cited by 26 publications

(25 citation statements)

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“…The underlying incentives explain this phenomenon: Ideally, a user wants to book a shared ride (financial incentive) but without actually sharing the ride (inconvenience and detour). This discrepancy is consistently observable also in public vocalization of user sentiment about shared ride experiences 21 , 24 , 28 , and exemplarily summarized by the user quote ’Every time I take a [shared ride] and it ends up being just me the entire ride I feel like a genius’ 27 . The expected detour and inconvenience mediate a repulsive interaction between the sharing decisions of ride-hailing users, turning ride-sharing decisions into a complex anti-coordination game.…”

Section: Resultsmentioning

confidence: 98%

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Incentive-driven transition to high ride-sharing adoption

2021

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Ride-sharing—the combination of multiple trips into one—may substantially contribute towards sustainable urban mobility. It is most efficient at high demand locations with many similar trip requests. However, here we reveal that people’s willingness to share rides does not follow this trend. Modeling the fundamental incentives underlying individual ride-sharing decisions, we find two opposing adoption regimes, one with constant and another one with decreasing adoption as demand increases. In the high demand limit, the transition between these regimes becomes discontinuous, switching abruptly from low to high ride-sharing adoption. Analyzing over 360 million ride requests in New York City and Chicago illustrates that both regimes coexist across the cities, consistent with our model predictions. These results suggest that even a moderate increase in the financial incentives may have a disproportionately large effect on the ride-sharing adoption of individual user groups.

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Section: Resultsmentioning

confidence: 98%

“…Inconvenience: Sharing a ride with another user may be inconvenient due to spending time in a crowded vehicle or due to loss of privacy 22 , 24 , 25 . This disincentive scales with the distance or duration d inc users ride together.…”

Section: Resultsmentioning

confidence: 99%

Incentive-driven transition to high ride-sharing adoption

2021

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“…In shared ridehailing services, there are/can be multiple users paired with a driver, while in ride-hailing services, a single user is paired with a driver. Moreover, shared ride-hailing offers additional discounts to the riders for agreeing to the vehicle sharing policies (Pratt, Morris, Zhou, Khan, & Chowdhury, 2019). Furthermore, shared ride-hailing services are different from traditional ride-hailing.…”

Section: Introductionmentioning

confidence: 99%

“…influence the attitude to share these rides (Moody et al, 2019). In addition, Pratt et al (2019) also highlighted the issues of increased travel time and uncertainty while using shared ride-hailing. Considering the potential of shared ride-hailing services in addressing the traffic congestion problem, and limited empirical work on this area of research, it would be interesting to see the extent to which the shared ride-hailing service will replace the solo ride-hailing service.…”

Section: Introductionmentioning

confidence: 99%

Does India Need a Shared Ride-Hailing Now More than Ever? Understanding Commuter’s Intentions to Share Ridesailing Now than Ever ? Understanding Commuters’ Intention to Share Rides

Goel¹,

Haldar²

2020

AJBA

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Manuscript type: Research paper Research aims: This study aims to investigate the factors influencing shared ride-hailing services, amongst commuters in India. Design/Methodology/Approach: Data collected across 355 respondents were analysed using structural equation modelling (SEM). Research findings: The findings reveal that all the four factors, i.e., the perceived use, perceived ease of use, trust and perceived risk, significantly influence commuter's intentions to use shared ridehailing service. Gender is found to moderate the relationship between perceived use, and the intention to use, as well as perceived risk and intention to use. This study however, does not find any empirical evidence to establish the moderation impact of car ownership on the relationship between perceived risk and the intention to use. Theoretical contribution/Originality: This study expands the previous literature by incorporating trust and perceived risk in examining the intention to use shared ride-hailing services. It also supplements previous works by examining gender and car ownership as the moderating variable. Practitioner/Policy implication: The findings suggest that marketers need to focus on strategies to reduce the risks associated with using shared ride-hailing services, especially amongst women riders.

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“…Also, labeling of 700K tweets with multiple human annotators is a challenging and timeconsuming task. In a parallel study, manual annotation of Tweet dataset on UberPool is conducted with a small sample size (i.e., 1000 tweets), where three different annotators were involved (Pratt et al, 2019). This study reported the final labels from these three annotators based on the weighted average method.…”

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confidence: 99%

Multi-class twitter data categorization and geocoding with a novel computing framework

Khan

Chowdhury

Ngo

et al. 2020

Cities

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This study details the progress in transportation data analysis with a novel computing framework in keeping with the continuous evolution of the computing technology. The computing framework combines the Labelled Latent Dirichlet Allocation (L-LDA)-incorporated Support Vector Machine (SVM) classifier with the supporting computing strategy on publicly available Twitter data in determining transportation-related events to provide reliable information to travelers. The analytical approach includes analyzing tweets using text classification and geocoding locations based on string similarity. A case study conducted for the New York City and its surrounding areas demonstrates the feasibility of the analytical approach. Approximately 700,010 tweets are analyzed to extract relevant transportation-related information for one week. The SVM classifier achieves more than 85% accuracy in identifying transportation-related tweets from structured data. To further categorize the transportation-related tweets into sub-classes: incident, congestion, construction, special events, and other events, three supervised classifiers are used: L-LDA, SVM, and L-LDA incorporated SVM. Findings from this study demonstrate that the analytical framework, which uses the L-LDA incorporated SVM, can classify roadway transportation-related data from Twitter with over 98.3% accuracy, which is significantly higher than the accuracies achieved by standalone L-LDA and SVM.

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What do riders tweet about the people that they meet? Analyzing online commentary about UberPool and Lyft Shared/Lyft Line

Cited by 26 publications

References 10 publications

Incentive-driven transition to high ride-sharing adoption

Incentive-driven transition to high ride-sharing adoption

Does India Need a Shared Ride-Hailing Now More than Ever? Understanding Commuter’s Intentions to Share Ridesailing Now than Ever ? Understanding Commuters’ Intention to Share Rides

Multi-class twitter data categorization and geocoding with a novel computing framework

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