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Travelers choose transport modes on the basis of their preferences in which the features onboard vehicles affect the choice of transport modes. In economic theory, it is found that travelers seek to maximize their travel utilities. Autonomous vehicles (AVs) are to be on the market when machines will replace human drivers. AVs are to be used for different purposes, such as transporting passengers and goods. In literature, some research studied AVs in combination with other transport modes, whereas fewer studies have considered the impact of a camera onboard carsharing and privately shared autonomous vehicles (PSAVs) on traveler choices. This research was developed to predict the impact of traveler preferences on the availability of PSAV and carsharing considering certain variables, such as travel time, travel cost, waiting/walking time, and the availability of camera onboard (i.e., as an indication of comfort and privacy factors). A stated preference (SP) survey was designed in which sociodemographic and travel variables were included. A discrete choice experiment (DCE) was designed and included in the SP survey. The SP survey was distributed in Budapest, Hungary, where 313 respondents were collected. The discrete choice modeling approach is used, in which the multinomial logit (MNL) model was applied in developing a transport mode choice model. The developed model estimated the impact of different variables on transport modes as well as the willingness of travelers to use carsharing and PSAVs. The model explained the effects of trip length, income, gender, education, occupation, frequent current transport mode, and trip purpose on travelers' selection of transport mode. In conclusion, it demonstrated that people tend to use carsharing more than PSAVs, and the availability of cameras onboard decreases the probability of using a transport mode. [ABSTRACT FROM AUTHOR]

Travelers conduct onboard activities while using the tools they bring with them onboard to convert part of their travel time to a productive time. Productive travel time contributes to the reduction in the disutility of travel time. This paper discusses the influence of travelers' onboard activities and the tools carried by travelers on the perceived trip time. 10 onboard activities and 12 tools carried by travelers are introduced and studied in this work. A questionnaire focusing on the main trip of each respondent in urban areas is conducted, where a sample size of 525 participants is collected. Statistical methods such as central tendency, chi-square, exploratory factor analysis (EFA), rank-based nonparametric test, and multivariate analysis of variance (MANOVA) are applied. The main findings are the following: almost all of the onboard activities and the tools carried by travelers impact the trip time positively (i.e., the perception is enhanced). For each transport mode, the most frequent onboard activities that impact the trip time positively is obtained, and the connection between each onboard activity and each tool carried by travelers is found (i.e., moderate to strong association). EFA uncovers the underlying relationship between those onboard activities and those tools carried by travelers that influence travelers' perception. In this case, instead of the full list, fewer onboard activities and tools carried by travelers are produced to simplify the finding of their impacts on the perceived trip time. The participation in onboard activity is ranked across certain groups, such as the tendency of women to be engaged in onboard activities is higher than men's tendency. Regarding the positive impact on trip time, a statistical difference is demonstrated between groups, where the use of the tools carried by travelers is varied across the transport mode, trip purpose, and trip time, gender, age, education, and job variable. Besides, the involvement in onboard activities is statistically dependent across the transport mode, gender, income, and car ownership variable. The output of this study helps decision-makers and mobility planners in understanding the behavior of travelers onboard in more detail, such as the availability of onboard tools affecting the choice of transport mode., Author(s): Jamil Hamadneh (corresponding author) [1]; Domokos Esztergár-Kiss [1] 1. Introduction Travel behavior is affected by the advancement of technology, such as the availability of information and communication technology (ICT) [...]