Impacts of Shared Autonomous Vehicles on the Travelers’ Mobility

Hamadneh, Jamil; Esztergár-Kiss, Domokos

doi:10.1109/mtits.2019.8883392

Cited by 22 publications

(12 citation statements)

References 18 publications

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“…The main differences from previous studies are the mode choice mechanisms of multi-agents. The mode is chosen by either discrete choice modeling [23] or utility scoring [24,25]. While using such a simulation toolkit, various studies have also differed in terms of evaluation targets.…”

Section: Literature Review 21 Agent-based Simulations For Impact Analysismentioning

confidence: 99%

The City-Wide Impacts of the Interactions between Shared Autonomous Vehicle-Based Mobility Services and the Public Transportation System

et al. 2021

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When attempts are made to incorporate shared autonomous vehicles (SAVs) into urban mobility services, public transportation (PT) systems are affected by the changes in mode share. In light of that, a simulation-based method is presented herein for analyzing the manner in which mode choices of local travelers change between PT and SAVs. The data used in this study were the modal split ratios measured based on trip generation in the major cities of South Korea. Subsequently, using the simulated results, a city-wide impact analysis method is proposed that can reflect the differences between the two mode types with different travel behaviors. As the supply–demand ratio of SAVs increased in type 1 cities, which rely heavily on PT, use of SAVs gradually increased, whereas use of PT and private vehicles decreased. Private vehicle numbers significantly reduced only when SAVs and PT systems were complementary. In type 2 cities, which rely relatively less on PT, use of SAVs gradually increased, and use of private vehicles decreased; however, no significant impact on PT was observed. Private vehicle numbers were observed to reduce when SAVs were operated, and the reduction was a minimum of thrice that in type 1 cities when SAVs and PT systems interacted. Our results can therefore aid in the development of strategies for future SAV–PT operations.

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Section: Literature Review 21 Agent-based Simulations For Impact Analysismentioning

confidence: 99%

The City-Wide Impacts of the Interactions between Shared Autonomous Vehicle-Based Mobility Services and the Public Transportation System

et al. 2021

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“…In the time allocation theory, travel time is considered as a consumption, which means an expense rather than the gaining of money, just like in the case of activity time [23]. Reduction in the travel time suggests an increase in the opportunity cost (i.e., the obtained benefit from choosing other alternatives) generated from the allocated time to the work to increase the income [30][31][32]. Factors that make the perceived travel time worth less, such as the carried tools that travelers bring onboard and multitasking, are discussed in this paper.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Malokin et al [24] show that autonomous vehicles might attract people who want to multitask during traveling. Autonomous vehicles can give travelers pleasant time because these remove the stress of driving and enable passengers to be involved in multitasking during the travel more than other modes [31,51,52]. Simoni et al [53] state that the VOT is decreased when travelers use autonomous vehicles.…”

Section: Literature Reviewmentioning

confidence: 99%

The Effects of Multitasking and Tools Carried by Travelers Onboard on the Perceived Trip Time

Hamadneh

Esztergár-Kiss

2021

Journal of Advanced Transportation

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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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“…Besides, autonomous vehicles are a promising technology that will enter the market soon, and this technology can be used to reduce the area of P&R facilities, because AVs do not park if a traveler needs to be served. The acceptance of AVs depends on several factors, such as the trip time, cost, trip purpose, sociodemographic factors, and sharing acceptability [12]. In the meantime, people can use AVs as a full replacement for their normal transport modes.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Fegnant et al produced a strategy that allowed for a lower waiting time than that of a random distribution of SAVs on the road network of a city, without giving any priority to a specific zone or block [28]. The study of Hamadneh and Esztergár-Kiss (2019) showed that one SAV can replace up to 8 conventional private vehicles, with an acceptable average waiting time of 7 to 10 min [12].…”

Section: Literature Reviewmentioning

confidence: 99%

Simulation of the Daily Activity Plans of Travelers Using the Park-and-Ride System and Autonomous Vehicles: Work and Shopping Trip Purposes

et al. 2020

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The preferences of travelers determines the utility of daily activity plans. Decision-makers can affect the preference of travelers when they force private car users to use park-and-ride (P&R) facilities as a way of decreasing traffic in city centers. The P&R system has been shown to be effective in reducing uninterrupted increases in traffic congestion, especially in city centers. Therefore, the impacts of P&R on travel behavior and the daily activity plans of both worker and shopper travelers were studied in this paper. Moreover, autonomous vehicles (AVs) are a promising technology for the coming decade. A simulation of the AV as part of a multimodal system, when the P&R system was integrated in the daily activity plans, was carried out to determine the required AV fleet size needed to fulfill a certain demand and to study the impacts of AVs on the behavior of travelers (trip time and distance). Specifically, a group of travelers, who use private cars as their transport mode, was studied, and certain modifications to their daily activity plans, including P&R facilities and changing their transport mode, were introduced. Using the MATSim open-source tool, four scenarios were simulated based on the mentioned modifications. The four scenarios included (1) a simulation of the existing transport modes of the travelers, (2) a simulation of their daily activity plans when their transport modes were changed to AVs, (3) a simulation of the travelers, when P&R facilities were included in their activity chain plans, and (4) a simulation of their daily activity plans, when both P&R and AVs were included in their activity chain plans. The result showed that using the P&R system increased overall travel time, compared with using a private car. The results also demonstrated that using AVs as a replacement for conventional cars reduced travel time. In conclusion, the impact of P&R and AVs on the travel behavior of certain travelers was evaluated in this paper.

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Impacts of Shared Autonomous Vehicles on the Travelers’ Mobility

Cited by 22 publications

References 18 publications

The City-Wide Impacts of the Interactions between Shared Autonomous Vehicle-Based Mobility Services and the Public Transportation System

The City-Wide Impacts of the Interactions between Shared Autonomous Vehicle-Based Mobility Services and the Public Transportation System

The Effects of Multitasking and Tools Carried by Travelers Onboard on the Perceived Trip Time

Simulation of the Daily Activity Plans of Travelers Using the Park-and-Ride System and Autonomous Vehicles: Work and Shopping Trip Purposes

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