A simulation-based approach to investigate the driver route choice behavior under the connected vehicle environment

Dai, Rongjian; Lu, Yingrong; Ding, Chuan; Lu, Guangquan; Wang, Yunpeng

doi:10.1016/j.trf.2018.04.008

Cited by 13 publications

(9 citation statements)

References 28 publications

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“…Using a simulation-and a stated preference-based approach, numerous attempts were made to econometrically address the various behavioral mechanisms of drivers' route choice with real-time information. The studied behavioral mechanisms involved logical choice [14,15], inertia choice [11,16], switching behavior [17][18][19], habit and learning [20,21], and others [22][23][24][25][26][27]. Specifically, Karthik et al [16] designed an inertia behavior simulation study and demonstrated that user experiences decreased inertia behavior in day-to-day variation.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Specifically, Karthik et al [16] designed an inertia behavior simulation study and demonstrated that user experiences decreased inertia behavior in day-to-day variation. The travel time information was demonstrated by many studies to effectively move route choice towards rationality ( [14,15,17,19,25,26,[28][29][30][31][32]), however, the effect of information strongly depends on other factors, such as personal traits, trip characteristics, and other decision considerations. From the personal trait perspective, Jou et al [17] concluded that elderly travelers would be less likely to switch due to the habitual and risk-aversive effects, and male travelers would be more likely to switch to the best route.…”

Section: Literature Reviewmentioning

confidence: 99%

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Empirical Study of Effect of Dynamic Travel Time Information on Driver Route Choice Behavior

Wang

2020

Sensors

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The objective of this paper is to study the effect of travel time information on day-to-day driver route choice behavior. A real-world experimental study is designed to have participants repeatedly choose between two alternative routes for five origin-destination pairs over multiple days after providing them with dynamically updated travel time information (average travel time and travel time variability). The results demonstrate that historical travel time information enhances behavioral rationality by 10% on average and reduces inertial tendencies to increase risk seeking in the gain domain. Furthermore, expected travel time information is demonstrated to be more effective than travel time variability information in enhancing rational behavior when drivers have limited experiences. After drivers gain sufficient knowledge of routes, however, the difference in behavior associated with the two information types becomes insignificant. The results also demonstrate that, when drivers lack experience, the faster less reliable route is more attractive than the slower more reliable route. However, with cumulative experiences, drivers become more willing to take the more reliable route given that they are reluctant to become risk seekers once experience is gained. Furthermore, the effect of information on driver behavior differs significantly by participant and trip, which is, to a large extent, dependent on personal traits and trip characteristics.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Empirical Study of Effect of Dynamic Travel Time Information on Driver Route Choice Behavior

Wang

2020

Sensors

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“…The development of connected vehicles (CVs), which can communicate with each other and supply crucial information such as vehicle location, heading speed, traffic conditions, and distance, is a potential solution to the adaptive route-planning problem in large-scale traffic flow due to their distributed computing and independent decision-making abilities [15]. A well-known approach for coordinating multiple CVs is to design a distributed framework in which each CV acts as a powerful traffic information collector and computing node [16,17]. This can significantly reduce the computational burden on route planning centers [18].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, each CV can independently plan its own route according to local information from neighboring vehicles [15] using wireless communication and distributed computing technologies. Route-planning strategies with different travel demands, such as travel time [19,20], route stability [21], fuel consumption [22], security [23], and traveler habits [17], have been extensively studied in distributed frameworks.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Route Planning Method of Connected Vehicles for Improving the Transport Efficiency

Long

Deng

Yang

et al. 2022

IJGI

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In recent years, the route-planning problem has gained increased interest due to the development of intelligent transportation systems (ITSs) and increasing traffic congestion especially in urban areas. An independent route-planning strategy for each in-vehicle terminal improves its individual travel efficiency. However, individual optimal routes pursue the maximization of individual benefit and may contradict the global benefit, thereby reducing the overall transport efficiency of the road network. To improve traffic efficiency while considering the travel time of individual vehicles, we propose a new dynamic route-planning method by innovatively introducing a bidding mechanism in the connected vehicle scenario for the first time. First, a novel bidding-based dynamic route planning is proposed to formulate vehicle routing schemes for vehicles affected by congestion via the bidding process. Correspondingly, a bidding price incorporating individual and global travel times was designed to balance the travel benefits of both objectives. Then, in the bidding process, a new local search algorithm was designed to select the winning routing scheme set with the minimum bidding price. Finally, the proposed method was tested and validated through case studies of simulated and actual driving scenarios to demonstrate that the bidding mechanism would be conducive to improving the transport efficiency of road networks in large-scale traffic flow scenarios. This study positively contributes to the research and development of traffic management in ITSs.

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“…e results demonstrated that with the increase of the CAV popularization rate, traffic safety had been significantly improved. Dai et al [14] studied the connection between a traffic system performance and the driver's path choice under a connected vehicle environment. e dynamic travel of commuter's daily path choice in the connected vehicle environment was investigated.…”

Section: Introductionmentioning

confidence: 99%

VISSIM-Based Simulation and Analysis of Upstream Segments in Ramp Areas for Optimizing Vehicle Group Lane-Changing Behaviors

Huang

Zou

et al. 2020

Journal of Advanced Transportation

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The traffic congestion in ramp areas is becoming increasingly prominent. In the upstream segments of ramp areas, effective management and control of lane-changing behaviors can improve the road capacity and make full use of the existing road resource. With the continuous development and application of connected vehicle technologies, lane-changing behaviors can be performed by vehicle groups. Under a connected vehicle environment, the lane-changing behaviors by vehicle groups are controlled in the upstream segment in a ramp area, and the lane-changing behaviors can be completed prior to entering the ramp area. Finally, lane-changing strategies are optimized and identified. VISSIM simulates these proposed strategies. This paper considers the delay as the output index for analyzing and comparing various strategies. The results demonstrate that the delays of different lane-changing strategies are also different. If the delays of ramp areas are to be substantially reduced, it is necessary to continuously optimize the lane-changing strategies by vehicle groups in the upstream segments. This optimization of lane-changing strategies will effectively regulate drivers’ lane-changing behaviors, improve road safety, and increase traffic capacity.

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A simulation-based approach to investigate the driver route choice behavior under the connected vehicle environment

Cited by 13 publications

References 28 publications

Empirical Study of Effect of Dynamic Travel Time Information on Driver Route Choice Behavior

Empirical Study of Effect of Dynamic Travel Time Information on Driver Route Choice Behavior

An Adaptive Route Planning Method of Connected Vehicles for Improving the Transport Efficiency

VISSIM-Based Simulation and Analysis of Upstream Segments in Ramp Areas for Optimizing Vehicle Group Lane-Changing Behaviors

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