Trip Distribution Modeling Using Mobile Phone Data: Emphasis on Intra-Zonal Trips

Demissie, Merkebe Getachew; Phithakkitnukoon, Santi; Kattan, Lina

doi:10.1109/tits.2018.2868468

Cited by 39 publications

(25 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The use of mobile phone data has been explored for the development of large scale mobility sensing since the early 2000s (Caceres et al, 2008). The data have been used to investigate various aspects of transportation issues: large-scale urban sensing (Calabrese et al, 2011a;Ratti et al, 2005); traffic parameter estimation ( Bar-Gera, 2007;Demissie et al, 2013a;Demissie et al, 2013b;Liu et al, 2008); origin-destination trip estimation (Calabrese et al, 2011b;Demissie et al, 2016a;Demissie et al, 2018;White and Wells, 2002); land use inference (Demissie et al, 2015;Toole et al, 2012); travel demand estimation (Alexander et al, 2015;Colak et al, 2015;Demissie et al, 2016b;Demissie et al, 2018;Gundlegård et al, 2016;Phithakkitnukoon et al, 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Regardless of the recent advances on the use of mobile phone data for travel demand modeling such as OD flows of different modes (Phithakkitnukoon et al, 2017); OD estimation by purposes and time of day (Alexander et al, 2015;Colak et al, 2015;Demissie et al, 2018;Gundlegård et al, 2016;Toole et al, 2015), there is still work to be done to explore the usage of mobile phone to develop travel demand models. Undoubtedly, we are building on previous studies of the same topic.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Because of the nature of the CDR data in our study, we cannot follow the stay location detection procedure provided by Zheng et al (2009). Instead, we followed the distance threshold modification suggested by Demissie et al (2018) and Wang et al (2017) such as the 200meters distance threshold is replaced by a cell tower location. Note that more than 99% of the distances (Euclidean) between the cell towers are longer than 500 meters, so we did not merge traces from multiple cell towers to estimate a stay location.…”

Section: Stay and Pass-by Area Detectionmentioning

confidence: 99%

“…Traditional methods for collecting trips origin and destination such as surveys are costly, laborious and take the time of trip makers. In recent years, mobile phone data, which includes the passively recorded spatiotemporal trajectories of large portion of the population, have emerged as promising inputs for travel demand model development (Alexander et al, 2015;Demissie, 2014;Demissie et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Understanding Human Mobility Patterns in a Developing Country Using Mobile Phone Data

Demissie

Phithakkitnukoon

Kattan

et al. 2019

Data Science Journal

Self Cite

View full text Add to dashboard Cite

This study demonstrates the use of mobile phone data to derive country-wide mobility patterns. We identified significant locations of users such as home, work, and other based on a combined measure of frequency, duration, time, and day of mobile phone interactions. Consecutive mobile phone records of users are used to identify stay and pass-by locations. A stay location is where users spend a significant amount of their time measured through their mobile phone usage. Trips are constructed for each user between two consecutive stay locations in a day and then categorized by purpose and time of the day. Three measures of entropy are used to further understand the regularity of user's spatiotemporal mobility patterns. The results show that user's in a high entropy cluster has high percentage of non-home based trips (77%), and user's in a low entropy cluster has high percentage of commuting trips (49%), indicating high regularity. A set of doubly constrained trip distribution models is estimated. To measure travel cost, the concept of a centroid point that assumes the origins and destinations of all trips are concentrated at an arbitrary location such as the centroid of a zone is replaced by multiple origins and destinations represented by cell tower locations. Note that a cell tower location can only be used as trips origin/destination location when a stay is detected. The travel cost measured between cell tower locations has resulted in shorter trip distances and the model estimation shows less sensitivity to the distance-decay effect.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Stay and Pass-by Area Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Understanding Human Mobility Patterns in a Developing Country Using Mobile Phone Data

Demissie

Phithakkitnukoon

Kattan

et al. 2019

Data Science Journal

Self Cite

View full text Add to dashboard Cite

show abstract

“…Classifier design is the rule designed for classifying objects. Traditional methods for mode identification often use statistical regression, such as linear regression, a polynomial logit model [27], etc. However, these models have unrealistic assumptions and require a predefined relationship between the dependent variable and the explanatory variables.…”

Section: Problem Descriptionmentioning

confidence: 99%

A Random Forest Model for Travel Mode Identification Based on Mobile Phone Signaling Data

Zhang

Xia

et al. 2019

Sustainability

View full text Add to dashboard Cite

Identifying and detecting the travel mode and pattern of individual travelers is an important problem in transportation planning and policy making. Mobile-phone Signaling Data (MSD) have numerous advantages, including wide coverage and low acquisition cost, data stability and reliability, and strong real-time performance. However, due to their noisy and temporally irregular nature, extracting mobility information such as transport modes from these data is particularly challenging. This paper establishes a travel mode identification model based on the MSD combined with residents' travel survey data, Geographic Information System (GIS) data, and navigation data. Using the data obtained from Kunshan, China in 2017, enriched with variables on the travel mode identification, the model achieved a high accuracy of 90%. The accuracy is satisfactory for all of the transport modes other than buses. Furthermore, among the explanatory variables such as the built environment factors (e.g., the coverage rate of a bus stop) are in general more significant, in contrast with other attributes. This indicates that the land use functions are more influential on the travel mode selection as well as the level of travel demand.

show abstract

People Flow Analysis Based on Anonymous OD Trip Data

Sun

Zhao

Lian

2020

Communications in Computer and Information Science

View full text Add to dashboard Cite

Trip Distribution Modeling Using Mobile Phone Data: Emphasis on Intra-Zonal Trips

Cited by 39 publications

References 47 publications

Understanding Human Mobility Patterns in a Developing Country Using Mobile Phone Data

Understanding Human Mobility Patterns in a Developing Country Using Mobile Phone Data

A Random Forest Model for Travel Mode Identification Based on Mobile Phone Signaling Data

People Flow Analysis Based on Anonymous OD Trip Data

Contact Info

Product

Resources

About