Using Longitudinal Mobile Phone Data to Understand the Stability of Individual Travel Patterns

2017 ◽  
Vol 2643 (1) ◽  
pp. 166-177 ◽  
Author(s):  
Zhengyu Duan ◽  
Chun Wang ◽  
H. Michael Zhang ◽  
Zengxiang Lei ◽  
Haifeng Li ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Travel Demand ◽  
Activity Space ◽  
Mobile Phone Data ◽  
Travel Patterns ◽  
Trip Chain ◽  
Demand Models ◽  
The Stability ◽  
Daily Trip ◽  
Daily Travel

Most travel demand models assume that individuals’ daily travel patterns are stable or follow a fixed routine. This hypothesis is being questioned by more and more researchers. In this study, longitudinal mobile phone data were used to study the stability of individual daily travel patterns from three aspects, including activity space, activity points, and daily trip-chain patterns. The activity space was represented by the number of nonhome activity points, the radius of nonhome activity points, and the distance from home. The visitation pattern of activity points was analyzed by entropy and predictability measures. The stability of trip-chain patterns was described by the number of distinct trip chains, the typical trip chain, and the typical trip-chain ratio. Analysis of 21 days of mobile phone data from three communities in Shanghai, China, revealed that individuals’ daily travel patterns showed considerable variation. Although individuals’ visitation patterns to activity points were very regular, the day-to-day variations of individual trip-chain patterns were quite significant. On average, an individual exhibited about eight types of daily trip chains during the 21-day period. The daily travel patterns of residents in the outskirts were more stable than those of residents in the city center. Individuals’ travel patterns on weekdays were more complex than those on weekends. As individuals’ activity spaces increased, the stability of their travel patterns decreased.

scholarly journals Optimal Driving Range for Battery Electric Vehicles Based on Modeling Users’ Driving and Charging Behavior

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Zhenbo Lu ◽  
Qi Zhang ◽  
Yu Yuan ◽  
Weiping Tong
Keyword(s):  
Electric Vehicles ◽  
Travel Demand ◽  
Copula Function ◽  
Battery Electric Vehicles ◽  
Trip Chain ◽  
Behavioral Variables ◽  
Driving Range ◽  
Optimal Driving ◽  
Daily Trip ◽  
Daily Travel

This paper proposes a simulation approach for the optimal driving range of battery electric vehicles (BEVs) by modeling the driving and charging behavior. The driving and charging patterns of BEV users are characterized by reconstructing the daily travel chain based on the practical data collected from Shanghai, China. Meanwhile, interdependent behavioral variables for daily trips and each trip are defined in the daily trip chain. To meet the goal of the fitness of driving range, a stochastic simulation framework is established by the Monte Carlo method. Finally, with consideration of user heterogeneity, the optimal driving range under different charging scenarios is analyzed. The findings include the following. (1) The daily trip chain can be reconstructed through the behavioral variables for daily trips and each trip, and there is a correlation between the variables examined by the copula function. (2) Users with different daily travel demand have a different optimal driving range. When choosing a BEV, users are recommended to consider that the daily vehicle kilometers traveled are less than 34% of the battery driving range. (3) Increasing the charging opportunity and charging power is more beneficial to drivers who are characterized by high daily travel demand. (4) On the premise of meeting travel demand, the beneficial effects of increased fast-charging power will gradually decline.

scholarly journals Validation of MCMC-Based Travel Simulation Framework Using Mobile Phone Data

2021 ◽  
Vol 2 ◽  
Author(s):  
Suxia Gong ◽  
Ismaïl Saadi ◽  
Jacques Teller ◽  
Mario Cools
Keyword(s):  
Mobile Phone ◽  
Travel Demand ◽  
Mobile Phone Data ◽  
Simulation Framework ◽  
Modeling Framework ◽  
Practical Applications ◽  
Mobility Data ◽  
Demand Models ◽  
The Difference ◽  
Using Data

An essential step in agent-based travel demand models is the characterization of the population, including transport-related attributes. This study looks deep into various mobility data in the province of Liège, Belgium. Based on the data stemming from the 2010 Belgian HTS, that is, BELDAM, a Markov chain Monte Carlo (MCMC) sampling method combined with a cross-validation process is used to generate sociodemographic attributes and trip-based variables. Besides, representative micro-samples are calibrated using data about the population structure. As a critical part of travel demand modeling for practical applications in the real-world context, validation using various data sources can contribute to the modeling framework in different ways. The innovation in this study lies in the comparison of outputs of MCMC with mobile phone data. The difference between modeled and observed trip length distributions is studied to validate the simulation framework. The proposed framework infers trips with multiple attributes while preserving the traveler’s sociodemographics. We show that the framework effectively captures the behavioral complexity of travel choices. Moreover, we demonstrate mobile phone data’s potential to contribute to the reliability of travel demand models.

scholarly journals Validating Activity-Based Travel Demand Models Using Mobile Phone Data

2018 ◽  
Author(s):  
Feng Liu ◽  
Ziyou Gao ◽  
Bin Jia ◽  
Xuedong Yan ◽  
Davy Janssens ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Travel Demand ◽  
Mobile Phone Data ◽  
Demand Models

The Potential GTX Travel Demand using Mobile Phone Data

2019 ◽  
Vol 7 (1) ◽  
pp. 77-84
Author(s):  
Jin Ki Eom ◽  
Kwang-Sub Lee ◽  
Ho-Chan Kwak ◽  
Ji Young Song ◽  
Myeong-Eon Seong
Keyword(s):  
Mobile Phone ◽  
Travel Demand ◽  
Mobile Phone Data

scholarly journals Can We Map-Match Individual Cellular Network Signaling Trajectories in Urban Environments? Data-Driven Study

2019 ◽  
Vol 2673 (7) ◽  
pp. 74-88 ◽  
Author(s):  
Loïc Bonnetain ◽  
Angelo Furno ◽  
Jean Krug ◽  
Nour-Eddin El Faouzi
Keyword(s):  
Mobile Phone ◽  
Cellular Network ◽  
Travel Demand ◽  
Transportation Network ◽  
Urban Environments ◽  
Network Control ◽  
Mobile Phone Data ◽  
Transport Systems ◽  
Spatiotemporal Scales ◽  
Temporal Dimensions

Mobile phone data collected by network operators can provide fundamental insights into individual and aggregate mobility of people, at unprecedented spatiotemporal scales. However, traditional call detail records (CDR) have fundamental issues because of low accuracy in both spatial and temporal dimensions, which limits their applicability for detailed studies on mobility, especially in urban scenarios. This paper focuses on a new generation of mobile phone passive data, individual cellular network signaling data, characterized by higher spatiotemporal resolutions than traditional CDR. A framework based on unsupervised hidden Markov model is designed for map-matching such data on a multimodal transportation network, aimed at accurately inferring the complex multimodal travel itineraries and popular paths people follow in their urban daily mobility. This information, especially if computed at large spatiotemporal scales, can represent a solid basis for studying actual and dynamic travel demand, to properly dimension multimodal transport systems and even perform anomaly detection and adaptive network control. The approach is evaluated in a case study based on real cellular traces collected by a major French operator in the city of Lyon, and a validation study at both microscopic and macroscopic levels proposed. The results show that this approach can properly handle sparse and noisy cell phone trajectories in complex urban environments. Moreover, the results are promising concerning popular paths detection and reconstruction of origin–destination matrices.

scholarly journals Mobile phone records to feed activity-based travel demand models: MATSim for studying a cordon toll policy in Barcelona

2019 ◽  
Vol 121 ◽  
pp. 56-74 ◽  
Author(s):  
Aleix Bassolas ◽  
José J. Ramasco ◽  
Ricardo Herranz ◽  
Oliva G. Cantú-Ros
Keyword(s):  
Mobile Phone ◽  
Travel Demand ◽  
Demand Models

Mobile Phone Tower Tracking

2020 ◽  
Author(s):  
Anne Hardy
Keyword(s):  
Mobile Phone ◽  
Travel Behaviour ◽  
Text Messages ◽  
Phone Call ◽  
Mobile Phone Data ◽  
Data Sets ◽  
Travel Patterns ◽  
Mobile Phone Use ◽  
Time Space ◽  
Call Detail Records

Tracking tourists using mobile phone data involves collating mobile phone call detail records (CDR), that can determine travel patterns of mobile phone users. The size of the data involved in this style of research is enormous; Xiao, Wang, and Fang (2019) received 600 – 800 million records per day when they used mobile phone data from Shanghai, resulting in over 10 billion mobile phone trajectories. However, mobile phone data does not provide precise travel itineraries. Rather, the data is a series of time-space points, showing where mobile phone users were when they made or received calls or text messages. Inferences are required to determine which mobile phone users are tourists, and when they entered countries or regions. However, the ubiquity of mobile phone use and the size of the data sets available to researchers means that this form of data can be used as a proxy for accommodation and visitation (Xiao, Wang, and Fang, 2019; Ahas et al., 2008; Ahas et al., 2007). Many significant findings regarding travel behaviour have emerged from this technique, including understandings of the impacts of seasonality, the impacts of nationality, and the impacts of events. This chapter will review these findings as well as the challenges that arise from the use of this data.

scholarly journals Identifying Urban Residents’ Activity Space at Multiple Geographic Scales Using Mobile Phone Data

2020 ◽  
Vol 9 (4) ◽  
pp. 241
Author(s):  
Lunsheng Gong ◽  
Meihan Jin ◽  
Qiang Liu ◽  
Yongxi Gong ◽  
Yu Liu
Keyword(s):  
Built Environment ◽  
Mobile Phone ◽  
Human Life ◽  
Activity Space ◽  
Mobile Phone Data ◽  
Urban Residents ◽  
Activity Spaces ◽  
Point Of Interest ◽  
The Relationship

Residents’ activity space reflects multiple aspects of human life related to space, time, and type of activity. How to measure the activity space at multiple geographic scales remains a problem to be solved. Recently, the emergence of big data such as mobile phone data and point of interest data has brought access to massive geo-tagged datasets to identify human activity at multiple geographic scales and to explore the relationship with built environment. In this research, we propose a new method to measure three types of urban residents’ activity spaces—i.e., maintenance activity space, commuting activity space, and recreational activity space—using mobile phone data. The proposed method identifies the range of three types of residents’ activity space at multiple geographic scales and analyzing the relationship between the built environment and activity space. The research takes Zhuhai City as its case study and discovers the spatial patterns for three activity space types. The proposed method enables us to achieve a better understanding of the human activities of different kinds, as well as their relationships with the built environment.

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