Capturing correlation with a mixed recursive logit model for activity-travel scheduling

Zimmermann, Maëlle; Västberg, Oskar Blom; Frejinger, Emma; Karlström, Anders

doi:10.1016/j.trc.2018.05.032

Cited by 31 publications

(6 citation statements)

References 36 publications

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“…We close by noting that a recently increasingly popular approach for problems similar to ours is the formulation of activity chains within a recursive logit (RL) framework (Zimmermann et al 2018 ; Hidaka et al 2019 ; Västberg et al 2020 ; Gao and Schmöcker 2021 ). With such an approach path or activity choice probabilities can be obtained for large network applications.…”

Section: Discussionmentioning

confidence: 87%

Calibration of sightseeing tour choices considering multiple decision criteria with diminishing reward

et al. 2022

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For an increasing number of cities, managing tourism becomes an important task and accordingly better understanding of touristic travel patterns is required. We model the sightseeing-tour choice within a city as a utility maximization problem. For this, attractions and their intrinsic utilities as well as tourists’ preferences are evaluated over multiple dimensions in order to explain the variance in tourists’ choice of POIs (points of interest) including the visiting order. Furthermore, the choice of destinations is considered “history-dependent” in that there is diminishing marginal utility gained by visiting additional POIs. Given the many potential sights, this leads to a large combinatorial problem. We solve this with a variant of a TTDP (tourist trip design problem) with the modified distance that evaluates omitted POIs and geographical distance between estimated and observed tours. The approach is applied to revealed-preference survey data from Kyoto, Japan, where tourists stated their visited attractions among 37 touristic areas. We discuss model fit and scenarios with the existing and a modified transport network.

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Section: Discussionmentioning

confidence: 87%

Calibration of sightseeing tour choices considering multiple decision criteria with diminishing reward

et al. 2022

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“…Based on the development of NGEV route choice models in the literature Akamatsu, 2012, 2014;Papola and Marzano, 2013;Mai, 2016), our contributions open up the applicability of NGEV-based models in the traffic assignment context. The framework can also be applied not only to vehicular networks, but also to various types of networks, such as transit networks based on frequency (e.g., Lam et al, 1999;Ma and Fukuda, 2015) or with a timetable (e.g., Nielsen and Frederiksen, 2006;Nuzzolo et al, 2001), urban pedestrian networks (e.g., Hato, 2016, 2018), and activity-based networks (e.g., Zimmermann et al, 2018). It should be noted that, although the MTA models are mathematically rigid, they generally require more computational effort than Dial's model.…”

Section: Discussionmentioning

confidence: 99%

Markovian Traffic Equilibrium Assignment based on Network Generalized Extreme Value Model

Oyama,

Hara,

Akamatsu

2020

Preprint

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This study establishes a novel framework of Markovian traffic equilibrium assignment based on the network generalized extreme value (NGEV) model, which we call NGEV equilibrium assignment. The use of the NGEV model in traffic assignment has recently been proposed and enables capturing the path correlation without explicit path enumeration. However, the NGEV equilibrium assignment has never been investigated in the literature, which has limited the practical applicability of the NGEV-based models. We address this gap by providing the necessary development for the NGEV equilibrium assignment. We first show that the NGEV assignment can be formulated and solved under the same path algebra with the Markovian traffic assignment models. We then provide the equivalent optimization formulations to the NGEV equilibrium assignment, from which both primal and dual types of solution algorithms are derived. In particular, we are the first to propose an efficient algorithm based on an accelerated gradient method in the traffic assignment field. The convergence and complementary relationship of the proposed primal-dual algorithms are shown through numerical experiments.

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“…Baqueri et al extended the model to study the limitation of the size of the study area, which is the defect of the activity-based model, and considered the activity trips outside the study area in the trip chain [11]. In addition, in the analysis of activity trip and influencing factors, Zimmerman et al use the hybrid recursive logit model to comprehensively consider the activity, time, place, trip, and other aspects [12] and improve the recursive logit model proposed by Fosgerau et al to release the limitation of IIA, making the model more effective in explanation and prediction [13]. In order to explore the complexity of individual activity travel decision-making dimensions, Fu Xuemei conducted a joint modeling analysis on the five decision-making dimensions of activity travel, and confirmed that there is an interaction between different decision-making dimensions.…”

Section: Activity-based Travel Modelmentioning

confidence: 99%

Impact of Shared Bus on Campus Travel and Space Optimization Based on Activity Travel Behavior

Chen

Zhang

Wang

et al. 2021

Journal of Advanced Transportation

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Based on the travel analysis of students in the Jiulonghu campus, this paper constructs a small-scale shared bus in the campus, explores the impact of shared traffic on campus travel, promotes the optimization design of campus space environment, and creates a more comfortable and convenient travel space. In order to overcome the shortcomings of the traditional trip-based research, this paper analyzes travel behavior from the perspective of activity-based travel concept framework, considering more activities and travel information. At the same time, in order to improve the richness of information, the explicit preference survey (RP) and the declarative preference survey (SP), which are added to the bus sharing customized travel platform, are used to collect data of personal attributes and travel diary. Firstly, based on the ArcGIS platform, this paper constructs the activity travel path generation tool, dynamic activity density distribution tool, and spatiotemporal path clustering tool and comprehensively analyzes the activity travel mode from the aspects of time distribution, spatial distribution, and category characteristics. Secondly, based on the travel activities, the location selection model (S-MNL) considering the heterogeneity of SP and RP data sources and the activity duration planning model (Cox regression), considering the nonnormal distribution of activity duration are established to analyze the impact of shared bus on students’ travel distance, travel time, and travel frequency. Finally, according to the analysis of modeling results, the impact of shared traffic on campus travel is analyzed, and the optimal design scheme of campus space is given. This paper uses the survey method based on declarative preference SP and the survey method based on explicit preference (RP) to get the actual and hypothetical travel response of travelers, which improves the data richness.

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Capturing correlation with a mixed recursive logit model for activity-travel scheduling

Cited by 31 publications

References 36 publications

Calibration of sightseeing tour choices considering multiple decision criteria with diminishing reward

Calibration of sightseeing tour choices considering multiple decision criteria with diminishing reward

Markovian Traffic Equilibrium Assignment based on Network Generalized Extreme Value Model

Impact of Shared Bus on Campus Travel and Space Optimization Based on Activity Travel Behavior

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