The introduction of shared autonomous vehicles into the transport system is suggested to bring significant impacts on traffic conditions, road safety and emissions, as well as overall reshaping travel behaviour. Compared with a private autonomous vehicle, a shared automated vehicle (SAV) is associated with different willingness-to-adopt and willingness-to-pay characteristics. An important aspect of future SAV adoption is the presence of other passengers in the SAV—often people unknown to the cotravellers. This study presents a cross-country exploration of user preferences and WTP calculations regarding mode choice between a private non-autonomous vehicle, and private and shared autonomous vehicles. To explore user preferences, the study launched a survey in seven European countries, including a stated-preference experiment of user choices. To model and quantify the effect of travel mode attributes and socio-demographic characteristics, the study employs a mixed logit model. The model results were the basis for calculating willingness-to-pay values for all countries and travel modes, and provide insight into the significant heterogeneous, gender-wise effect of cotravellers in the choice to use an SAV. The study results highlight the importance of analysis of the effect of SAV attributes and shared-ride conditions on the future acceptance and adoption rates of such services.
Introducing autonomous vehicles (AVs) on the market is likely to bring changes in the mobility of travelers. In this work, extensive research is conducted to study the impact of different levels of automation on the mobility of people, and full driving automation needs further study because it is still under development. The impacts of AVs on travel behavior can be studied by integrating AVs into activity-based models. The contribution of this study is the estimation of AVs’ impacts on travelers’ mobility when different travel demands are provided, and also the estimation of AVs’ impact on the modal share considering the different willingness of pay to travel by AVs. This study analyses the potential impacts of AVs on travel behavior by investigating a sample of 8500 travelers who recorded their daily activity plans in Budapest, Hungary. Three scenarios are derived to study travel behavior and to find the impacts of the AVs on the conventional transport modes. The scenarios include (1) a simulation of the existing condition, (2) a simulation of AVs as a full replacement for conventional transport modes, and (3) a simulation of the AVs with conventional transport modes concerning different marginal utilities of travel time in AVs. The simulations are done by using the Multi-Agent Transport Simulation (MATSim) open-source software, which applies a co-evolutionary optimization algorithm. Using the scenarios in the study, we develop a base model, determine the required fleet size of AVs needed to fulfill the demand of the different groups of travelers, and predict the new modal shares of the transport modes when AVs appear on the market. The results demonstrate that the travelers are exposed to a reduction in travel time once conventional transport modes are replaced by AVs. The impact of the value of travel time (VOT) on the usage of AVs and the modal share is demonstrated. The decrease in the VOT of AVs increases the usage of AVs, and it particularly decreases the usage of cars even more than other transport modes. AVs strongly affect the public transport when the VOT of AVs gets close to the VOT of public transport. Finally, the result shows that 1 AV can replace 7.85 conventional vehicles with acceptable waiting time.
This research focuses on the preferences of micro-mobility users in urban areas, specifically shared electric bikes (e-bikes), shared conventional bikes (bike), and shared electric scooters (e-scooters). It is found that previous scholars study traveler preferences of traditional transport modes while limited attention has been given to preferences of travelers toward micro-mobility considering electric scooters and bikes over conventional bikes. In order to address this gap, a discrete choice modeling approach is used to study the preferences of people through developing a transport choice model. A discrete choice experiment (DCE) is designed where choice sets that combine the shared micro transport modes where three associated attributes and four levels are included in the DCE for each alternative. A stated preference (SP) survey is designed and distributed in Budapest, Hungary. This research focuses on urban areas where travel time is relatively short. Multinomial Logit (MNL) model is applied where a transport choice model is developed. The effect of several factors on the preferences of people toward the three micro transport modes are evaluated. The developed transport choice model includes trip time, trip cost, walking distance, parking characteristics, and socio-demographic factors. The results indicate that travelers prefer using bikes more than e-bikes and e-scooters. Furthermore, it is found that e-scooter is the least favored by travelers. It is noteworthy that car drivers, individuals with access to or frequent usage of micro-mobility, graduate students, full-time workers, males, and young people are more willing to use shared electric micro-mobility services. The probability of choosing a transport mode based on the changes on parking type attribute is estimated in this research. The results show that travelers prefer free floating parking when they use shared electric micro-mobility services. This research underscores the significance of parking type (docks or dockless) and socio-demographic variables when it comes to micro-mobility modes in urban areas. It is evident that shared electric micro-mobility options require more effort and policy support to be effectively implemented, as shared conventional bikes appear more appealing to users. Overall, these findings contribute to the understanding of micro-mobility preferences and highlight areas for further exploration and potential policy interventions.
Autonomous Vehicles (AVs) have been designed to make changes in the travel behaviour of travellers. These changes can be interpreted using transport models and simulation tools. In this study, the daily activity plans were used to study the possibility of increasing the utility of travellers through minimizing the travel time by using AVs. Three groups of travellers were selected based on the benefits that they can obtain when AVs are on the market. The groups are (a) long-trip travellers (b) public transport riders, and (c) travellers with specified characteristics. Each group is divided into one or more scenarios based on the definition of each group and the collected data. A total of seven scenarios were derived from the collected data and simulated twice to include the existing transport modes and the presence of AVs. The simulations were conducted using Multi-Agents Transport Simulation (MATSim) that applies the concept of a co-evolutionary algorithm. MATSim simulates the current plans and the ones where AVs replace all or part of the existing conventional transport modes in the daily activity plans. The results have shown a reduction in the trip time: 13% to 42% for group (a), 33% for group (b), and 16% to 28% for group (c) compared with the original trip times. In conclusion, it can be claimed that AVs could reduce the travel time in all cases, which provides benefits for people to increase their utilities.
In this paper, the Park-and-Ride (P&R) facilities are considered as short time activities in the daily activity plans of travelers. The purpose of integrating the P&R system into the daily activity plans is its benefits of reducing the undesirable effects of using private vehicles, such as pollution and traffic congestion, in the city center. Previous works did not extensively study the influence of the P&R operational strategy on the travel behavior of workers and shoppers; while this study not only covers this underexamined area of research but focuses on the impact of extending the duration of the P&R facility on traveler mobility. The study is conducted by integrating 13 P&R facilities in Budapest, Hungary into the daily activity plans belonging to the traveler groups of workers and shoppers. The study points out the changes in the travel pattern when P&R is enforced to be used by travelers to hinder travelers from entering the city center or park their cars on street. Besides, a comparison is made with a previously published work to study the impact of changing the P&R activity time and the existing condition where no P&R is provided. The multi-agent transport simulation (MATSim) software is used to conduct the simulation. The results support that using the P&R system increases the total travel time, decreases the number of Vehicle-Miles-Traveled (VMT), and changes the travel pattern. Moreover, the results reveal 5.75 minutes increments in the average trip time, when the P&R duration is increased from 4.5 minutes to 10 minutes. The result led to the conclusion that the operational strategy of the P&R facilities impacts the mobility of travelers, and the P&R system changes the travel pattern, such as the duration of peak periods and the number of vehicles en route.
The availability of autonomous vehicles (AVs) on the market will create a novel situation for every stakeholder. A lot of research has been conducted on developing the AVs and the consequences of having AVs on the market, but comprehensive studies concerning various viewpoints on the introduction of AVs into the market can be scarcely found. This research is examining different actors' viewpoints on the acceptance of privately shared autonomous vehicles (PSAVs). Four groups of stakeholders are identified as the following: users, legislators, operators, and manufacturers. The multi-actor multi-criteria analysis (MAMCA) is used, where the analytical hierarchy process (AHP) and the parsimonious AHP (PAHP)methods are applied to evaluate each actor's objectives and criteria. From each group, a representative sample is collected, the actors evaluate a set of pairwise comparison matrices (PCMs), and their consistency is continuously checked. As a result, the objectives and the criteria are ranked and presented. The main finding of the objectives' analysis presents that the safety concerns receive the highest ranking (i.e., weight is 0.085), while the ease of use and the interoperability across the borders have the lowest rankings ((i.e., weight is 0.048, and 0.0515, respectively). The main finding of criteria's analysis shows that ease of use has the lowest ranking (i.e., weight is 0.0024), and the highest rank is related to the reduction in vehicle accidents caused by the malfunction (i.e., weight is 0.0495). Thus, it seems that safety related issues are the most important factor in accepting PSAV. The result of this study is useful for decision-makers and transport planners to form policies, regulations, and guidelines regarding the future implementations of PSAVs before their arrival to the market.
An agent-based transport simulation model is used to examine the impacts of Autonomous Vehicles (AVs) on the mobility of certain groups of people. In the state of the art, it has been found that the researchers primarily have simulation studies focusing on the impacts of AVs on people regardless of certain groups. However, this study focuses on assessing the impacts of AVs on different groups of users, where each group is affected variously by the introduction of different penetration levels of AVs into the market. The Multi-Agent Transport Simulation (MATSim) software, which applies the co-evolutionary algorithm and provides a framework to carry out large-scale agent-based transport simulations, is used as a tool for conducting the simulations. In addition to the simulation of all travellers, 3 groups of users are selected as potential users of AVs, as follow: (1) long commuters with high-income, (2) elderly people who are retired, and (3) part-time workers. Budapest (Hungary) is examined in a case study, where the daily activity plans of the households are provided. Initially, the existing daily activity plans (i.e., the existing condition) of each group are simulated and assessed before the introduction of AVs into the market. After that, the AVs are inserted into the road network, where different fleet sizes of AVs are applied based on the demand of each group. The marginal utility of the travel time spent in case of a transport mode, the AV fleet size, and the cost of the travel are the key variables that determine the use of a transport mode. The key variables are set based on the characteristics of the case study (i.e., demand) and the AVs. The results of the simulations suggest that the AVs have different degrees of influences on certain groups as demonstrated in the occurred changes on the modal share. The value of changes depends on the Value of Travel Time (VOT) of people and the used fleet size of AVs. Moreover, the influence of the traveller’s characteristics on the AVs is manifested, such as different values of fleet utilization. Furthermore, the study demonstrates that an increase in the fleet size of AVs beyond 10% of the demand does not significantly raise the modal share of AVs. The outcome of this paper might be used by decision-makers to define the shape of the AVs’ use and those groups who are interested in using AVs.
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.
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