Potential of cellular signaling data for time-of-day estimation and spatial classification of travel demand: a large-scale comparative study with travel survey and land use data

Fekih, Mariem; Bonnetain, Loïc; Furno, Angelo; Bonnel, Patrick; Smoreda, Zbigniew; Galland, Stéphane; Bellemans, Tom

doi:10.1080/19427867.2021.1945854

Cited by 8 publications

(8 citation statements)

References 49 publications

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“…For example, considering the high popularity of smartphones nowadays, with travelers participating in day-to-day travel systems, mobile communication operators can obtain natural desensitized travel data-mobile signaling data (MSD)-in their backend more conveniently than through traditional travel surveys [192]. Although not very precise, MSD can at least reflect the changes in the location and movement process of smartphone users (namely, the travelers) under sectors of base stations.…”

Section: Using Increasingly Diverse Information Technology Methods Fo...mentioning

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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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: Using Increasingly Diverse Information Technology Methods Fo...mentioning

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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“…BN capture the dependencies between variables over time, allowing for the representation of dynamic systems and the modelling of complex temporal relationships. However, they require a linearity hypothesis and are not able to capture complex patterns [16,22,25,27,33,34,45].…”

Section: Methodsmentioning

confidence: 99%

“…The purposes of the studies can be divided between individual mobility prediction, which captures regularities, and tendencies of individual's mobility behaviours using mobility data, and population mobility prediction, which captures mobility behaviours at a population/group of individual level, capturing aggregated trends. The first predictions are carried out mainly by means of statistic or machine learning techniques according to the data availability [8][9][10][11][12][13][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36], while the latter can also exploit data mining techniques or agent-based modelling [6,11,24,[37][38][39][40][41][42][43][44][45][46]. The identified purposes can be further segmented from a spatial perspective by varying the unit of analysis, resulting in three prediction outcomes per purpose, i.e., trajectory recognition, next location prediction, and next trip prediction.…”

Section: Objectives and Predictionsmentioning

confidence: 99%

Next Place Prediction Model: A Literature Review

Garola,

Siragusa,

Seghezzi

et al. 2024

Advances in Transdisciplinary Engineering

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Given the rising attention towards the understanding of people’s mobility, this paper focuses on the study of next-place prediction models for mobility demand and path estimation. A systematic literature review was conducted to classify the existing methodologies from a quantitative and qualitative perspective. The findings highlight the availability of several models to study next-place prediction which varies according to the objectives of the study and on the data used, with no specific dominant approach. In conclusion, the study proposes a first attempt of classification, by developing a conceptual framework, that on the one hand explains the relationship between the models’ characteristics and on the other hand guides its selection according to the users’ needs.

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“…Zheng et al [35] used data from automated number plate recognition (ANPR) cameras to study the travel time reliability. Fekih et al [36] proposed a framework to extract dynamic trip fows and travel demand patterns from cellular signaling data to estimate aggregate trip by time of day.…”

Section: Avi Data In Travel Flowmentioning

confidence: 99%

Investigating Travel Flow Differences between Peak Hours with Spatial Model with Endogenous Weight Matrix Using Automatic Vehicle Identification Data

Zhou

Chen

et al. 2022

Journal of Advanced Transportation

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The rapid urbanization has brought great challenges to the transportation network. However, travel flow at peak hours is not always the same. It is important to investigate how travel flow differs between peak hours to capture travel flow patterns and influential factors to facilitate traffic management and urban planning. This paper establishes a spatial model with endogenous weight matrix (SARBP-EWM) to investigate the travel flow differences between morning and evening peaks on both weekday and weekend based on automatic vehicle identification (AVI) data and point of interest (POI) data in Xuancheng, China. The results confirm strong spatial effects and endogeneity issue. Besides, facility variables such as number of offices and number of clinics reveal strong negative impacts on travel flow differences on both weekday and weekend, while the number of middle school shows significantly positive relation with travel flow differences. In addition, the endogenous weight matrix on both weekday and weekend is successfully estimated and compared. It is found that TAZ pairs tend to be clustered with lower spatial weights on weekday, while they are more randomly distributed with higher spatial weights at weekend. Based on the results above, the policies proposed from Xuancheng 14th Five-Year Plan are evaluated and discussed. The above empirical analysis quantifies impacts from key factors on urban travel flow differences between peak hours and provides important references for urban planning and policy making.

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Potential of cellular signaling data for time-of-day estimation and spatial classification of travel demand: a large-scale comparative study with travel survey and land use data

Cited by 8 publications

References 49 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

Next Place Prediction Model: A Literature Review

Investigating Travel Flow Differences between Peak Hours with Spatial Model with Endogenous Weight Matrix Using Automatic Vehicle Identification Data

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