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

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.

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.

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

Analyzing Passenger Travel Demand Related to the Transportation Hub inside a City Area using Mobile Phone Data

2018 ◽  
Vol 2672 (50) ◽  
pp. 23-34 ◽  
Author(s):  
Gang Zhong ◽  
Jian Zhang ◽  
Linchao Li ◽  
Xiaoxuan Chen ◽  
Fan Yang ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Transportation Planning ◽  
Travel Demand ◽  
Urban Transportation ◽  
Mobile Phone Data ◽  
City Area ◽  
Planning And Management ◽  
Passenger Travel ◽  
Transportation Corridors ◽  
The City

The passenger transportation hub plays a crucial role in the urban transportation system. Analyzing transportation hub related travel demand is necessary to support urban transportation planning and management. However, it is difficult to use the traditional travel survey methods to study travel demand because tracking passenger travel trajectories is a near impossible task. The location information from the cellular system provides a feasible way to solve the problem. This paper concentrates on applying mobile phone data to study passenger travel demand related to the Hongqiao transportation hub in Shanghai, China. First, a method is introduced to collect passenger travel information related to the hub from mobile phone data. Then, travel demand indexes are presented to characterize the travel demand in a visual way. Finally, transportation corridors, which connect the hub and other urban areas, are identified to analyze the distribution of travel demand more thoroughly. The results illustrate that the passenger travel demand shows an obvious tide pattern in the city area with the Hongqiao transportation hub as the center. Moreover, there are two identified transportation corridors which reveal the major distribution directions of the passengers, that is, the city center and the Zizhu industrial development zone. The approach in this study testifies that mobile phone data has great potential for transportation planning and management related to transportation hubs.

PENGEMBANGAN ”REAL TIME TRAFFIC INFORMATION SYSTEM” BAGI PENGGUNA JALAN

2013 ◽  
Vol 12 (3) ◽  
Author(s):  
Rusmadi Suyuti
Keyword(s):  
Information System ◽  
Real Time ◽  
Travel Demand ◽  
Urban Traffic ◽  
Road User ◽  
Traffic Information ◽  
Estimation Methods ◽  
Traffic Counts ◽  
Real Time Traffic ◽  
Demand Models

Traffic information condition is a very useful  information for road user because road user can choose his best route for each trip from his origin to his destination. The final goal for this research is to develop real time traffic information system for road user using real time traffic volume. Main input for developing real time traffic information system is an origin-destination (O-D) matrix to represent the travel pattern. However, O-D matrices obtained through a large scale survey such as home or road side interviews, tend to be costly, labour intensive and time disruptive to trip makers. Therefore, the alternative of using traffic counts to estimate O-D matrices is particularly attractive. Models of transport demand have been used for many years to synthesize O-D matrices in study areas. A typical example of the approach is the gravity model; its functional form, plus the appropriate values for the parameters involved, is employed to produce acceptable matrices representing trip making behaviour for many trip purposes and time periods. The work reported in this paper has combined the advantages of acceptable travel demand models with the low cost and availability of traffic counts. Two types of demand models have been used: gravity (GR) and gravity-opportunity (GO) models. Four estimation methods have been analysed and tested to calibrate the transport demand models from traffic counts, namely: Non-Linear-Least-Squares (NLLS), Maximum-Likelihood (ML), Maximum-Entropy (ME) and Bayes-Inference (BI). The Bandung’s Urban Traffic Movement survey has been used to test the developed method. Based on several statistical tests, the estimation methods are found to perform satisfactorily since each calibrated model reproduced the observed matrix fairly closely. The tests were carried out using two assignment techniques, all-or-nothing and equilibrium assignment.  

scholarly journals Address-based computation of intra-cell distances for travel demand models

2021 ◽  
Vol 184 ◽  
pp. 123-130
Author(s):  
Matthias Heinrichs ◽  
Rita Cyganski ◽  
Daniel Krajzewicz
Keyword(s):  
Travel Demand ◽  
Demand Models

scholarly journals Effects of social distancing on the spreading of COVID-19 inferred from mobile phone data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hamid Khataee ◽  
Istvan Scheuring ◽  
Andras Czirok ◽  
Zoltan Neufeld
Keyword(s):  
Mobile Phone ◽  
Basic Reproductive Number ◽  
Mobile Phone Data ◽  
Reproductive Number ◽  
Expansion Phase ◽  
Social Distancing ◽  
Exponential Expansion ◽  
Total Death ◽  
Mobility Reduction ◽  
Epidemiological Parameters

AbstractA better understanding of how the COVID-19 pandemic responds to social distancing efforts is required for the control of future outbreaks and to calibrate partial lock-downs. We present quantitative relationships between key parameters characterizing the COVID-19 epidemiology and social distancing efforts of nine selected European countries. Epidemiological parameters were extracted from the number of daily deaths data, while mitigation efforts are estimated from mobile phone tracking data. The decrease of the basic reproductive number ($$R_0$$ R 0 ) as well as the duration of the initial exponential expansion phase of the epidemic strongly correlates with the magnitude of mobility reduction. Utilizing these relationships we decipher the relative impact of the timing and the extent of social distancing on the total death burden of the pandemic.

Sign in / Sign up

Export Citation Format

Share Document