A tutorial on recursive models for analyzing and predicting path choice behavior

Zimmermann, Maëlle; Frejinger, Emma

doi:10.1016/j.ejtl.2020.100004

Cited by 37 publications

(10 citation statements)

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“…We refer to that work for further details on the model and the literature on both choice set generation and route choice models. We also believe that a comparison with alternative models (Nested Logit, see Nassir et al (2015) , or Recursive Logit, see Zimmermann and Frejinger (2020) ) is out of the scope of this paper.…”

Section: State Of the Artmentioning

confidence: 99%

The impact of COVID-19 pandemic on public transport usage and route choice: Evidences from a long-term tracking study in urban area

2022

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The COVID-19 pandemic strongly affected mobility around the world. Public transport was particularly hindered, since people may perceive it as unsafe and decide to avoid it. Moreover, in Switzerland, several restrictions were applied at the beginning of the first pandemic wave (16/03/2020), to reduce the contagion. This study observes how the pandemic affected travel behaviour of public transport users, focusing on route choice and recurrent trips. We conducted a travel survey based on GPS tracking during the first pandemic wave, following 48 users for more than 4 months. The very same users were also tracked in spring 2019, allowing a precise comparison of travel behaviour before and during the pandemic. We analyse how the pandemic affected users, in terms of travel distance, mode share and location during the day. We specifically focus on recurrent trips, commuting and non-commuting, observing how mode and route changed between the two different periods. Finally, we estimate a route choice model for public transport (Mixed Path Size Logit), based on trips during the two different years, to identify how the route choice criteria changed during the pandemic. The main differences identified in travel behaviour during the pandemic are a different perception of costs of transfers and of travel time in train, and that users no longer have a clear preferred route for a recurrent trip, but often choose different routes.

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Section: State Of the Artmentioning

confidence: 99%

The impact of COVID-19 pandemic on public transport usage and route choice: Evidences from a long-term tracking study in urban area

2022

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“…The synthetic data methodology has been widely applied in the robotics field. Using MATLAB software, the synthetic data has been integrated into the comprehensive systems in solving adaptive trajectory planning [18], Unmanned Aerial Vehicle (UAV) coordination [19], Wide Area Aerial surveillance [17], and route choice problem in transportation science [20]…”

Section: Find X X Xδ Based On the Fitness Value Yesmentioning

confidence: 99%

Untitled

2021

Int.J.Artif.Intell.Robot.

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I. INTRODUCTIONNowadays, sustainability becomes a general requirement in engineering and sciences, including in path planning technologies. Path planning is very important for an object, which needs to generate motion, such as a vehicle robot, an arm robot manipulator, a missile, a ship, a submarine, and an aircraft. It is a process to design a feasible path that becomes a trace for vehicles or robots.One of the advantages of using a Bezier curve for path planning is its adaptability to change the control points. It has been widely used as a manufacturing path in additive manufacturing [1,2,3], vehicle route path of an aircraft [4], and vessel path motion [5]. The goal of sustainability in the manufacturing system is still in the way of the process. In the future, additive manufacturing will be developed into sustainable manufacturing via path planning technologies [1]. Thus, research on the path planning methodologies that address a sustainability goal is highly important.Sustainable manufacturing refers to applying an approach for manufacturing that has minimal harm to environments and mitigated bad impacts on human health and nature [6]. For the robots and vehicle systems, which commonly operate in obstacle environments, it is necessary to achieve the safety goal by generating a collision-free path. Collision avoidance strategies become an important issue in path planning. The concept of collision avoidance dynamic critical area for vessel path planning was introduced in [5]. Collision avoidance for human-robot interactions in adaptive manufacturing was presented in [3]. Saeed et al.[7] proposed a boundary node method to solve mobile robot path planning in static obstacle environments. Kala et al. [8] applied multi-neuron heuristic search to solve the path planning in the static obstacle environment.The GA as one of the evolutionary methods has been applied to solve the point-to-point path planning. Linquan et al.[9] proposed to use GA for path planning of robot soccer systems in a dynamic environment. Choi et al. [10] presented two-path planning algorithms based on Bezier curves for autonomous vehicles with waypoints and corridor constraints. Lil et al. 11] assumed the existence of an obstacle-avoiding polyline path and replaced the polyline path with a G 2 cubic spline curve to avoid the obstacles.

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“…Several recursive models have been developed afterwards to deal with, for instance, correlations between path utilities (Mai, 2016, Mai et al, 2015, 2018), dynamic networks (de Moraes Ramos et al, 2020, stochastic time-dependent networks (Mai et al, 2021), or discounted behavior (Oyama and Hato, 2017), with applications in, for instance, traffic management (Baillon andCominetti, 2008, Melo, 2012) or network pricing (Zimmermann et al, 2021). We refer the reader to Zimmermann and Frejinger (2020) for a comprehensive review.…”

Section: Introductionmentioning

confidence: 99%