Correlation analysis of day-to-day origin-destination flows and traffic volumes in urban networks

Barroso, Joana Maia Fernandes; Albuquerque-Oliveira, João Lucas; Oliveira-Neto, Francisco Moraes

doi:10.1016/j.jtrangeo.2020.102899

Cited by 13 publications

(4 citation statements)

References 34 publications

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“…They have also made a similar conclusion as in [184], that is, most travelers still rely on their past travel experiences, which have lost efficiency in making route choices when encountering local emergencies [185]. Barroso et al have broadly pointed out the difficulties that local road congestion caused by traffic accidents, temporary commercial activities, and other factors in cities cause for day-to-day travelers and have pointed out that travelers who obtain such local traffic information still tend to rely more on their experiences and habits to make travel decisions [186].…”

Section: The Impact Of Local Traffic Information On Day-to-day Travelersmentioning

confidence: 99%

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Urban Day-to-Day Travel and Its Development in an Information Environment: A Review

Nai,

Yang,

et al. 2024

Sustainability

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Urban day-to-day travel systems generally exist in various types of cities. Their modeling is difficult due to the uncertainty of individual travelers in micro travel decision-making. Moreover, with the advent of the information age, intelligent connected vehicles, smartphones, and other types of intelligent terminals have placed urban day-to-day travel systems in an information environment. In such an environment, the travel decision-making processes of travelers are significantly affected, making it even more difficult to give theoretical explanations for urban day-to-day travel systems. Considering that analyzing urban day-to-day travel patterns in an information environment is of great significance for governing the constantly developing and changing urban travel system and, thus, of great importance for the sustainable development of cities, this paper gives a systematic review of the theoretical research on urban day-to-day travel and its development in an information environment over the past few decades. More specifically, the basic explanation of an information environment for urban day-to-day travel is given first; subsequently, the theoretical development of micro decision-making related to individual day-to-day travelers in an information environment is discussed, and the theoretical development related to changes in urban macro traffic flow, which can be recognized as the aggregation effect formed by individual micro decision-making, is also discussed; in addition, the development of understanding different types of traffic information that travelers may obtain in an information environment is discussed; finally, some important open issues related to the deep impact of information environment on urban day-to-day travel systems that require further research are presented. These valuable research directions include using information methods to fit day-to-day travel patterns of cities and implementing macro and micro integrated modeling for urban day-to-day travel systems based on complex system dynamics and even quantum mechanics.

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Section: The Impact Of Local Traffic Information On Day-to-day Travelersmentioning

confidence: 99%

“…Local emergencies in urban road network [184,185] Local congestion caused by temporary traffic accidents or commercial activities [186] Shared travel strategy between travelers [187][188][189][190] Inertial information from certain vehicle operation organizations [191]…”

Section: Information Types Specific Information Literaturementioning

confidence: 99%

Urban Day-to-Day Travel and Its Development in an Information Environment: A Review

Nai,

Yang,

et al. 2024

Sustainability

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“…Tuli, Mitra and Crews [11] employed a random-effect negative binomial (RENB) model to investigate the demand for shared bicycles. Barroso, Albuquerque-Oliveira and Oliveira-Neto [12] introduced clustering methods to define traffic profiles and the daily traffic periods in trip analyses based on OD data. Chang, Huang, Chan et al [13] introduced long-memory properties to investigate road fatality factors.…”

Section: Introductionmentioning

confidence: 99%

A Complex Empirical Mode Decomposition for Multivariant Traffic Time Series

Shen

Zhang

2023

Electronics

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Data-driven modeling methods have been widely used in many applications or studies of traffic systems with complexity and chaos. The empirical mode decomposition (EMD) family provides a lightweight analytical method for non-stationary and non-linear data. However, a large amount of traffic data in practice are usually multidimensional, so the EMD family cannot be used directly for those data. In this paper, a method to calculate the extremum point and the envelope-like function (series) from the complex function (series) is proposed so that the EMD family can be applied to two-variate traffic time-series data. Compared to the existing multivariate EMD, the proposed method has advantages in computational burden, flexibility and adaptivity. Two-dimensional trajectory data were used to test the method and its oscillatory characteristics were extracted. The decomposed feature can be used for data-driven traffic analysis and modeling. The proposed method also extends the utilization of EMD to multivariate traffic data for applications such as traffic data denoising, pattern recognition, traffic flow dynamic evaluation, traffic prediction, etc.

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“…For example, round trips between residential and working regions are the most important activities in people's daily lives (Ekman, Keränen, Karvo, & Ott, 2008), and numerous interactions within or among popular working zones form part of the most important economic activities (Yang, Sun, Shang, Wang, & Zhu, 2019) happening every day. These daily regular mobilities also lead to the high similarity of the 24-h fluctuation of the volume of OD flows in each day (Barroso, Albuquerque-Oliveira, & Oliveira-Neto, 2020). Also, daily periodicity is often used as an important feature in the field of deep learning for OD-related problems (Wang, Fu, Zhang, Li, & Li, 2018).…”

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confidence: 99%

Recurrent origin–destination network for exploration of human periodic collective dynamics

Chen

Xie

Xiao

et al. 2021

Transactions in GIS

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Correlation analysis of day-to-day origin-destination flows and traffic volumes in urban networks

Cited by 13 publications

References 34 publications

Urban Day-to-Day Travel and Its Development in an Information Environment: A Review

Urban Day-to-Day Travel and Its Development in an Information Environment: A Review

A Complex Empirical Mode Decomposition for Multivariant Traffic Time Series

Recurrent origin–destination network for exploration of human periodic collective dynamics

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