Development of Origin-Destination

Matrices Using Mobile Phone Call Data

Md Shahadat Iqbal, BUET

Charisma F Choudhury, UoL

Pu Wang, MIT

Marta Gonzalez, MIT

Bangladesh University

of Engineering and

Technology

Massachusetts

Institute of

Technology

University of

Leeds

Data sources Motivation

Traditional approaches of developing OD matrices rely on roadside and

household surveys, and/or traffic

counts

Limited sample sizes

Prone to sampling biases, non-response bias and reporting errors

Lower update frequencies

High data collection costs

Mobile phone CDR Mobile Phone CDR

Mobile phone CDR Mobile Phone CDR

Coarse granularity

May not be the final origin/destination

Only transient ODs (t-Ods)

False displacements

Penetration and user bias

Location bias

Data 2: Call Detail Records

Mobile phone CDR Challenges

B (t-O)

C

A (O)

D

F (D)

E (t-D)

Proposed framework

Actual OD = t-OD * penetration factor * phone usage

factor * vehicle usage factor

Scaling factor adjusted to match ground truth (traffic counts)

- Microsimulation tool

Framework

Proposed framework

Convert tower-to-tower

transient OD to node-to-

node transient OD

Determine scaling factor

using simulator

CDR data

Traffic count data

Network data

Generate tower-to-tower

transient OD matrix

Actual OD matrix

Mobile phone CDR Proposed Framework

Study area

Central part of the Dhaka city

- Area: 300km2 , Pop.:10.7million

- No automated data collection

system in place

Mobile phone penetration rate more than 90%

Calls from 6.9 million users (65% of the population of the

study area) over a month

- 971.33 million anonymized call records

Mobile phone CDR Case Study

Step 1: Generate tower-to-tower t-OD Step 1: Generate Tower-to-tower t-OD

Step 2: Convert to node-to-node t-OD

Step 2: Convert to Node-to-Node t-OD

Step 2: Convert to node-to-node t-OD

Step 2: Convert to Node-to-Node t-OD

Step 3: Determine scaling factor

t-OD from step 2 Seed OD in MITSIMLab

Scaled up using an optimization based approach

Step 3: Determine Scaling Factor

Step 3: Determine scaling factor Step 3: Determine Scaling Factor

Step 3: Determine scaling factor Step 3: Determine Scaling Factor

Results 7:00-9:00

t-OD actual OD

Results

7:00-9:00

Validation

Scaled up ODs have been applied to simulate the traffic between 9:00-12:00 in MITSIMLab

Simulated traffic counts are compared against the observed counts from these locations on a different day

- Root Mean Square Error 335.09

- Root Mean Square Percent Errors 13.59%

Validation

Summary

Mobile phone CDR and limited traffic count data can be successfully combined to generate OD matrices

More economic than the traditional approaches (CDR already recorded for billing purposes)

Convenient for periodic update of the OD matrix

Extendable for dynamic OD estimation

Particularly effective for generating complex OD matrix where land use pattern is heterogeneous and asymmetry in

travelling pattern prevails throughout the day but there is a

limitation of traditional data sources

Summary

Session 842

Forthcoming issue of Transportation Research Part C

Email: cfc@alum.mit.edu

Questions?