Tracking a system of shared autonomous vehicles across the Austin, Texas network using agent-based simulation

Li, Jun; Kockelman, Kara M.; Boesch, Patrick M.; Ciari, Francesco

doi:10.1007/s11116-017-9811-1

Cited by 168 publications

(85 citation statements)

References 13 publications

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“…Existing research on AV mainly focuses on macro-level analysis, including policy issues [7,16], legislative supervision [17], SAV dispatch optimization [5,18,19] and the impact on traditional transportation system [5,20,21]. In the study of user behavior at the micro-level, individuals' travel mode choice behavior in AV and SAV mobility behavior has already become a hot research topic and recently get more attention.…”

Section: Introductionmentioning

confidence: 99%

“…In the study of user behavior at the micro-level, individuals' travel mode choice behavior in AV and SAV mobility behavior has already become a hot research topic and recently get more attention. However, the travel mode model used in the study of AV choice behavior mainly regards socioeconomic attribute and travel mode attribute as the explanatory variables of the model [19,[22][23][24][25][26], while ignoring the intrinsic influence brought by the individual's preference heterogeneity. In recent years, only a few studies have focused on psychological factors such as safety, technology interest, or environmental concern [13,27,28].…”

Section: Introductionmentioning

confidence: 99%

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Exploring the Factors Affecting Mode Choice Intention of Autonomous Vehicle Based on an Extended Theory of Planned Behavior—A Case Study in China

et al. 2019

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Autonomous vehicle (AV) is an innovative transport option that has the potential to disrupt all industries tied to transportation systems. The advent of AV technology will bring a novel on-demand mobility pattern such as shared autonomous vehicle (SAV). To promote AV technology, it is important to understand which factors influence travelers’ intention to use AVs and SAVs. This paper collected literature from databases such as Scopus, Web of Science and ScienceDirect, and made a systematic review. The study aims to explore the determinants that influence travelers’ behavioral intentions towards use AVs and SAVs based on an extended version of the theory of planned behavior, which incorporates knowledge and perceived risk. This study was tested empirically using a valid survey sample collected from 906 respondents in China. Structural equation model was conducted to investigate the predictors of intentions to use AVs and SAVs. Results showed that knowledge about AV technology and perceived risk are the two main potential obstacles for travelers to use AVs and SAVs. Attitude significantly affects AVs and SAV choice intentions. Subjective norm is the most critical factor affecting the travelers' intention to use AVs. Perceived behavioral control potentially stymie the travelers' intention to use SAVs. The findings will enhance the understanding of travelers’ choice motivation from psychological and service perspectives, and provide data support for governments and companies in improving travel management strategies and product services.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploring the Factors Affecting Mode Choice Intention of Autonomous Vehicle Based on an Extended Theory of Planned Behavior—A Case Study in China

et al. 2019

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“…One step into this direction has already been taken by Liu et al [29], who addressed the problem from the perspective of mode shifts and empty rides.…”

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confidence: 99%

Autonomous vehicles: The next jump in accessibilities?

Meyer

Becker

Bösch

2017

Research in Transportation Economics

281

127

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Autonomous vehicles are expected to offer a higher comfort of traveling at lower prices and at the same time to increase road capacity -a pattern recalling the rise of the private car and later of motorway construction. Using the Swiss national transport model, this research simulates the impact of autonomous vehicles on accessibility of the Swiss municipalities. The results show that autonomous vehicles could cause another quantum leap in accessibility. Moreover, the spatial distribution of the accessibility impacts implies that autonomous vehicles favor urban sprawl and may render public transport superfluous except for dense urban areas.

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“…Therefore, the model can be used to simulate traffic flow and residents' behaviors in urban transport, including the residents' choice of travel modes [31] and carpooling models [32]. Other research applications are found in the optimization of bus routes [33,34], the simulation of the functioning of the urban composite transportation system [35], the evaluation of the impact of the intercity high-speed railway on the ecological environment [36], etc. From the perspective of development trends in research, studies are moving from the simulation of individual decision-making to that of the composite flow of urban traffic, with increasing complexity of simulation.…”

mentioning

confidence: 99%

An Agent-based Model Simulation of Human Mobility Based on Mobile Phone Data: How Commuting Relates to Congestion

Liu

et al. 2019

IJGI

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The commute of residents in a big city often brings tidal traffic pressure or congestions. Understanding the causes behind this phenomenon is of great significance for urban space optimization. Various spatial big data make the fine description of urban residents' travel behaviors possible, and bring new approaches to related studies. The present study focuses on two aspects: one is to obtain relatively accurate features of commuting behaviors by using mobile phone data, and the other is to simulate commuting behaviors of residents through the agent-based model and inducing backward the causes of congestion. Taking the Baishazhou area of Wuhan, a local area of a mega city in China, as a case study, we simulated the travel behaviors of commuters: the spatial context of the model is set up using the existing urban road network and by dividing the area into space units. Then, using the mobile phone call detail records of a month, statistics of residents' travel during the four time slots in working day mornings are acquired and then used to generate the Origin-Destination matrix of travels at different time slots, and the data are imported into the model for simulation. Under the preset rules of congestion, the agent-based model can effectively simulate the traffic conditions of each traffic intersection, and can induce backward the causes of traffic congestion using the simulation results and the Origin-Destination matrix. Finally, the model is used for the evaluation of road network optimization, which shows evident effects of the optimizing measures adopted in relieving congestion, and thus also proves the value of this method in urban studies.residents' mobility over time and space can be used for urban geographic mapping [13], epidemiological analysis [14,15], real-time urban monitoring [16], etc. and can also be used for recognition of urban spatial features [17][18][19] or measurement of urban vibrancy [20]. Another important scenario of application is the study of residents' commuting and urban transport, including the identification of commuting areas and commuting distances [21,22], and the acquisition of commuter Origin-Destination (OD) matrices [23][24][25]. Among various sources of location data, mobile phone data have been widely employed in studies such as residents' commuting thanks to its extensive coverage, passive data collection, and the fact that its data acquisition requires no extra equipment. In comparison, alternative data sources, such as smart card data or taxi GPS data, have equivalent difficulties in data coverage but much smaller population coverage [26]. Results of studies based on new data have been shown to have higher accuracy compared to those that are based on statistical data or measured data, proving the effectiveness of big data application in urban studies. In general, most of these studies are at an early stage of describing urban phenomena through data, few studies attempt to go further such as using big data to identify the connection between residents' travel and traffic c...

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Tracking a system of shared autonomous vehicles across the Austin, Texas network using agent-based simulation

Cited by 168 publications

References 13 publications

Exploring the Factors Affecting Mode Choice Intention of Autonomous Vehicle Based on an Extended Theory of Planned Behavior—A Case Study in China

Exploring the Factors Affecting Mode Choice Intention of Autonomous Vehicle Based on an Extended Theory of Planned Behavior—A Case Study in China

Autonomous vehicles: The next jump in accessibilities?

An Agent-based Model Simulation of Human Mobility Based on Mobile Phone Data: How Commuting Relates to Congestion

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