A Survey Study Measuring People's Preferences Towards Automated and Non-Automated Ridesplitting

Abstract

Ridesplitting is both common and important as it facilitates daily transportation needs. Alongside an increase in ridesplitting is the introduction of automated driving systems, which together, bring out the possibility of automated ridesplitting. However, previous studies have identified resistance in the acceptance of automated driving systems. In light of past research on automated driving systems, we used a survey to compare people's preferences of automated ridesplitting to non-automated ridesplitting. Statistical and text mining techniques were leveraged to analyze the results. We found similarities in the numeric responses of important factors concerning automated and non-automated ridesplitting whereas there were large differences between automated and nonautomated ridesplitting in the text responses. Additionally, people prioritized cost and time in both automated and non-automated ridesplitting. These results can be used in the design of future ridesplitting services, especially with respect to increasing acceptance of and trust in automated ridesplitting services.

How Do People Prefer Ridesplitting? A Survey Study Focusing on Passenger, Matching, and Trip Characteristics

“…According to our results, an ideal trip for users would be less than 15 miles and take less than 30 minutes while the wait time shall be less than 5 minutes. To address users’ widely reported concerns about uncertainties in a split trip and priorities in travel time (Pratt et al, 2019; Shaheen, Gosselin, Broader, & Cohen, 2022; Zhang et al, 2019), an accurately estimated travel time with variations less than 5 minutes would be recommended based on our findings. Day time and early evenings are the most preferred time for ridesplitting: users might choose the service for the purpose of commuting between home and work, attending social events, etc.…”

“…Researchers have suggested that factors such as trip travel time and distance are important predictors of one’s wiliness to split rides with others (X. (Michael) Chen et al, 2017; Zhang, Roberts, & Goldman, 2019). Findings from research work that focused on general ridesourcing services also showed that the social dynamic introduced by travelling with others, personal preferences of matching, as well as uncertainties about the trip constituted the major concerns and considerations (Deakin, Frick, & Shively, 2010; Pratt, Morris, Zhou, Khan, & Chowdhury, 2019; Sarriera et al, 2017).…”

Strangers On This Road We Are On: A Literature Review of Pooling in On-Demand Mobility Services

“…Ridesplitting choices in AVs may involve the same traveler priorities as non-AVs in respect of travel time and trip cost. For instance, Zhang et al ( 51 ) employed a paired sample t -test and text mining on survey data of residents in Massachusetts and Connecticut and found that respondents were more willing to split rides with more passengers if the cost was lower and there were no large negative impacts on their travel time, regardless of whether the vehicle was automated.…”

Improving Acceptability of Cost Savings Allocation in Ridesharing Systems Based on Analysis of Proportional Methods

“…[18], (4) how can people's preferences relevant to ridesharing be measured? [19], (5) how are travelers' ridesharing decisions affected by social networks? [20], (6) how can cost savings be maximized [21] or travel distance in ridesharing systems reduced?…”