Multi-objective trajectory optimization in planning for sequential activities across space and through time

Feng, Xiyun; Wang, Shaohua; Murray, Alan T.; Cao, Yuanpei; Gao, Song

doi:10.1177/2399808320913300

Cited by 4 publications

(1 citation statement)

References 30 publications

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“…In addition to classical research on socioeconomic outcomes, increasing research attention has been given to neighborhood effects on residents’ health and wellbeing (Ludwig et al, 2012). As suggested by some recent studies, individuals’ activity space, that is, the space within which individuals perform their daily activities, both inside and outside their residential neighborhoods, is of great significance to understanding the inequalities in people’s life quality and wellbeing (Feng et al, 2020; Perchoux et al, 2016). There is also clear evidence of neighborhood effects on people’s daily activities such as travel and commute (Cao, 2010; Gehrke and Wang, 2020) and yet, we still know little about the extent to which neighborhood effects shape the spatial pattern of people’s daily movements and activities, let alone their causal mechanisms and socioeconomic outcomes.…”

Section: Introductionmentioning

confidence: 99%

Examining neighborhood effects on residents’ daily activities in central Shanghai, China: Integrating “big data” and “thick data”

Xiao

et al. 2022

Environment and Planning B: Urban Analytics and City Science

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This research combines “big data” and “thick data” approaches to examine the correlation and causation between residential neighborhood features and people’s daily commuting and traveling patterns by integrating two datasets: household survey data and mobile phone data. We focus on “lilong” neighborhoods—a primary form of traditional residential neighborhood in central Shanghai. The characteristics of lilong neighborhoods are assessed using “thick data” from surveys in 105 lilongs, while residents’ daily activities are mapped out using “big data” from two weeks of mobile phone usage. We match these two datasets at neighborhood level based on their geospatial references. Four multinomial logistic regression models are developed to examine neighborhood effects on lilong residents’ daily activities. Our research confirms the major mechanisms of neighborhood effects and unravels their relative importance in shaping the patterns of residents’ daily activities. Conceptually, this study sheds new light on the understanding of how people’s life quality and wellbeing are affected by neighborhood characteristics through highlighting the importance of social interactions and the access to/quality of public facilities. Methodologically, incorporating household survey data (thick data) and mobile phone data (big data) is proven to be a novel and effective approach for examining neighborhood effects at a relatively large scale.

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Section: Introductionmentioning

confidence: 99%

Examining neighborhood effects on residents’ daily activities in central Shanghai, China: Integrating “big data” and “thick data”

Xiao

et al. 2022

Environment and Planning B: Urban Analytics and City Science

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Solving the tourist trip planning problem with attraction patterns using meta-heuristic techniques

Sylejmani,

Abdurrahmani,

Ahmeti

et al. 2024

Inf Technol Tourism

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A Deep Reinforcement Learning Approach to Solving the Digital Billboard Optimal Placement Problem Based on Multi-Source Spatiotemporal Data Fusion

Zhou,

Wang,

Yan

et al. 2024

Preprint

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The study presents a study on optimizing the spatial location of digital billboards in urban environments using multi-source big data and deep reinforcement learning methods. Focusing on the Fifth Ring Road in Beijing, China, the study aims to solve the Maximum Coverage-Digital Billboard Localization Problem (MC-DBLP) model. This paper describes the data collection and preprocessing process, the use of geo-detectors and attention models, and an evaluation of the effectiveness of deep reinforcement learning methods compared to traditional optimization solvers. The authors propose a new solution model for the digital billboard placement problem. The method is universal and scalable, which can provide a reference for similar problems in other fields. Multi-sourced spatiotemporal data are incorporated to unify the data format of factors and, an attention model multi-decoder (AMMD), Gurobi solver, and a heuristic algorithm are used to solve the problem. The results show that the Gurobi solver produces the best solution results, and the optimal digital billboard locations are obtained. In the future, the authors plan to select more various and complex influencing factors and explore more efficient and accurate algorithm models to better solve location optimization problems.

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Multi-objective trajectory optimization in planning for sequential activities across space and through time

Cited by 4 publications

References 30 publications

Examining neighborhood effects on residents’ daily activities in central Shanghai, China: Integrating “big data” and “thick data”

Examining neighborhood effects on residents’ daily activities in central Shanghai, China: Integrating “big data” and “thick data”

Solving the tourist trip planning problem with attraction patterns using meta-heuristic techniques

A Deep Reinforcement Learning Approach to Solving the Digital Billboard Optimal Placement Problem Based on Multi-Source Spatiotemporal Data Fusion

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