Ridesourcing mode choice: A latent class choice model for UberX in Chile

García-Melero, Gustavo; Sainz-González, Rubén; Coto-Millán, Pablo; Valencia, Alejandra

doi:10.1016/j.trip.2022.100722

Cited by 4 publications

(3 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Preferences for bike-sharing services were generally investigated using a latent class model (LCM) [38]. In general, preferences are often complex and multidimensional.…”

Section: Methodsmentioning

confidence: 99%

Exploring Tourists’ Preferences for Bike-Sharing Services in the Context of Tourism

Tru,

Ngoc

2024

Sustainability

View full text Add to dashboard Cite

This study explores tourists’ preferences for bike-sharing services in the context of tourism. Based on a sample of 800 individuals who visited Da Nang, Vietnam between July and August 2023, a latent class behavior model was developed to investigate preferences for bike-sharing services from tourists’ point of view. The results show that tourists prefer a bike-sharing service that ensures round-the-clock availability, is accessible within a 5-min walk from both the origin and destination, features bikes stationed at specific designated locations, and provides a variety of payment options at an affordable rate of USD 1.00 per h. Under these attributes, about 41.63% of tourists are likely to choose a bike-sharing option for their travel tours. These findings offer valuable insights for traffic management authorities and policymakers, demonstrating that bike-sharing services can be hailed as an effective complement to existing transportation modes and can help bridge the gap between supply and demand in tourist cities.

show abstract

“…Preferences for bike-sharing services were generally investigated using a latent class model (LCM) [38]. In general, preferences are often complex and multidimensional.…”

Section: Methodsmentioning

confidence: 99%

Exploring Tourists’ Preferences for Bike-Sharing Services in the Context of Tourism

Tru,

Ngoc

2024

Sustainability

View full text Add to dashboard Cite

show abstract

“…Other classes are price insensitive. In Chile, online revealed preference surveys with UberX users were conducted to ascertain who choose ride‐sourcing and why [22]. A latent class choice model was established, and two latent classes were determined.…”

Section: Literature Reviewmentioning

confidence: 99%

Investigating heterogeneity in travel behaviour change when implementing soft transport interventions: A latent class choice model

Fan

Chen

et al. 2023

IET Intelligent Trans Sys

View full text Add to dashboard Cite

Attracting more travellers to shift towards green modes plays a significant role in sustainable transportation development. Soft transport interventions are important strategies for facilitating voluntary travel behaviour change. This study investigates the effects of two soft transport interventions, information intervention and public transport service improvement, on heterogeneous traveller groups’ behaviour change. Beijing, China was selected as the case study site. Firstly, three heterogenous traveller groups were identified based on latent class cluster analysis: Group A (20.4%, travel in low‐frequency and prefer multimode), Group B (30.3%, travel in middle‐frequency and prefer the car) and Group C (49.3%, travel in high frequency and prefer green modes). Then, latent class choice models under two transport interventions were established respectively. The findings indicate that information intervention decreases Group B's preference for car use and exclusion of bicycles. Little effect is observed on travel behaviour change of Group A and C. Public transport service improvement significantly reduces car use of Group B and C. This study demonstrates a limited influence of information intervention on travel behaviour change and public transport service improvement is effective in achieving car use reduction. The results provide decision makers with the information needed to target interventions for different traveller groups.

show abstract

“…Latent class analysis was introduced initially in 1950 (28) and evolved over the past few decades to account for selecting the number of classes, indicator variables, and including covariates (26). Of the different formats of these models, the developments in supervised, unsupervised, and clustering classes are notable in transportation research (29)(30)(31)(32) for various mode choice applications. Considering the advantages of using latent class models over standard regression models, the authors employed LCM to identify the unobserved groups among the influential variables in coverage and speed bias data sets.…”

Section: Modeling the Performance Metricsmentioning

confidence: 99%

Exploring the Efficacy of Large-Scale Connected Vehicle Data in Real-Time Traffic Applications

Kandiboina,

Knickerbocker,

Bhagat

et al. 2023

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

Transportation agencies strive to optimize their spending on data collection by exploring efficient techniques that provide reliable traffic data. In recent years, the automobile industry has experienced vast developments in wireless technology that enables agencies to collect valuable traffic data in large volumes from connected vehicles (CVs). Unlike traditional data collection techniques, the CV or probe data are economically feasible for wide-area coverage. Therefore, this study aims to explore the CV data provided by Wejo Connected Vehicle Data Solutions for their feasibility in real-time traffic applications. The large volumes of the CV data are compared against a ground reference sensor to assess their reliability. The performance metrics such as market penetration rates, speed bias, and latency are used to understand the efficacy of the data for their usage over infrastructure-mounted sensors in regular traffic operations. The analysis resulted in an average market penetration of 6.3% in the study area with a mean speed error of less than 1 mph. The data also expressed potential event detection capabilities with relatively lower latencies. Furthermore, latent class models are developed on the penetration rate and speed bias data sets to identify the unobserved groups within the data, resulting in five-class models for both data sets. The paper concludes by summarizing the potential benefits of the CV data concerning the assessed metrics and provides opportunities to replace or augment the data to existing infrastructure-mounted traffic sensors.

show abstract

Ridesourcing mode choice: A latent class choice model for UberX in Chile

Cited by 4 publications

References 36 publications

Exploring Tourists’ Preferences for Bike-Sharing Services in the Context of Tourism

Exploring Tourists’ Preferences for Bike-Sharing Services in the Context of Tourism

Investigating heterogeneity in travel behaviour change when implementing soft transport interventions: A latent class choice model

Exploring the Efficacy of Large-Scale Connected Vehicle Data in Real-Time Traffic Applications

Contact Info

Product

Resources

About