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From page 33... ... 33 4.1 Roadmap to the Chapter This is the first chapter of a highly technical discussion about cell phone data, inference of locations and activity types, development of origin–destination (O-D) matrices, and comparisons with survey data to evaluate the robustness of the methods and results. Read the entire page →
From page 34... ... 34 Cell Phone Location Data for Travel Behavior Analysis than 400%, from 0.6 billion gallons in 1984 to 3.1 billion gallons in 2014 (Schrank et al. Read the entire page →
From page 35... ... Description of Raw Data 35 level or for traffic analysis zones, which contain on average about 5,000 people. Population at the Census tract level was used to develop expansion factors to translate cell phone–derived estimates of travel to person-trips. Read the entire page →
From page 36... ... 36 Cell Phone Location Data for Travel Behavior Analysis activity diaries of users while requiring limited human inputs for validation purposes (Cottrill et al. Read the entire page →
From page 37... ... Description of Raw Data 37 4.4 A Closer Look at Cell Phone Data 4.4.1 Typical Data Set Layout Each time a phone is positioned, it generates a single record in a mobile phone data set, which is the equivalent of a row in the data set. Each record contains at least three basic pieces of information: an ID number, a unique number associated with the device generating the record; a location that indicates the device's location when this record is generated; and a time stamp that indicates when the record is generated (Table 4-1) Read the entire page →
From page 38... ... 38 Cell Phone Location Data for Travel Behavior Analysis location records greatly depends on the size of these zones. Knowing the connected cell tower is important to the network operator for assigning costs and revenue. Read the entire page →
From page 39... ... Description of Raw Data 39 4.4.4 Uncertainty in Location Estimates Advanced positioning techniques, such as triangulation, are capable of estimating the location of a mobile phone within a cell and produce data sets with a finer spatial resolution than the cell-tower-based positioning method. Calabrese et al. Read the entire page →
From page 40... ... 40 Cell Phone Location Data for Travel Behavior Analysis 4.4.5 Device Oscillation At any given location in a cellular network, there may be several cell towers whose radio signals reach a device. If these multiple cell towers have similar signal strengths, the connection of a device may hop between multiple towers even when the device is stationary. Read the entire page →
From page 41... ... Description of Raw Data 41 to Location B is larger than a predetermined threshold. This method is based on the observation that oscillation results in a location change characterized by an abnormally high speed. Read the entire page →
From page 42... ... 42 Cell Phone Location Data for Travel Behavior Analysis • Sample selection. It is common for researchers to select a study sample from all the subscribers included in a raw mobile phone data set provided by the network operator. Read the entire page →
From page 43... ... Description of Raw Data 43 4.5.1.1 Tower-Based CDR Data The San Francisco Bay area is used as an example to demonstrate the spatial resolution and coverage of tower-based CDR data. In such CDR data, a cellular tower ID is often recorded with a time stamp when a cell phone connects to a cellular network for a call, message, or data transmission. Read the entire page →
From page 44... ... 44 Cell Phone Location Data for Travel Behavior Analysis Figure 4-4. Cellular towers and census tracts in the Bay Area: (a) Read the entire page →
From page 45... ... Description of Raw Data 45 Figure 4-5. Census tract size and cell tower coverage in the San Francisco Bay Area. Read the entire page →
From page 46... ... 46 Cell Phone Location Data for Travel Behavior Analysis 4.5.1.2 Triangulated CDR Data With more advanced technology, a cell phone's locations can be pinpointed more accurately while it connects to an operator's service network. The triangulated CDR data in Table 4-4 provide an example using the Boston region. Read the entire page →
From page 47... ... Source: Jiang et al. Read the entire page →
From page 48... ... 48 Cell Phone Location Data for Travel Behavior Analysis Source: Jiang et al. Read the entire page →
From page 49... ... Description of Raw Data 49 Source: Jiang et al. Read the entire page →
From page 50... ... 50 Cell Phone Location Data for Travel Behavior Analysis Figure 4-10. Population density and cell phone use patterns. Read the entire page →
From page 51... ... Description of Raw Data 51 Figure 4-11. Triangulated cell phone traces of a volunteer individual. Read the entire page →
From page 52... ... 52 Cell Phone Location Data for Travel Behavior Analysis (a) Read the entire page →
From page 53... ... Description of Raw Data 53 (a) Read the entire page →
From page 54... ... 54 Cell Phone Location Data for Travel Behavior Analysis Figure 4-14. Time-of-day cell use: sample and individual user patterns. Read the entire page →
From page 55... ... Description of Raw Data 55 Saturday (one around noon and one around 6 p.m.) and one peak on Sunday around 6 to 7 p.m. Read the entire page →

From page 33...

... 33 4.1 Roadmap to the Chapter This is the first chapter of a highly technical discussion about cell phone data, inference of locations and activity types, development of origin–destination (O-D) matrices, and comparisons with survey data to evaluate the robustness of the methods and results.