Integrating Activity-Based Models with Mobile Phone Data

1. Introduction

1.1 General

1.1.1 Existing Traffic Models Mismatch the Urban Developed Governance

1.1.2 Advantages of 4G/5G Mobile Networks in Analysis Individual Activity Chains

1.1.3 Next-Generation Disaggregate Traffic Models for Urban Sustainable Development

1.2 Prospects of Mobile Phone Signaling Data

1.2.1 Academic Prospects

1.2.2 Industrial Applications

1.3 Objectives and Value of Mobile Phone Signaling Data Reliability

1.4 Research Framework: From Feature Recognition to Traffic Modeling

1.4.1 Review of Travel Characteristic Recognition and ABM

1.4.2 Technical Principles and Evaluation of 4G/5G Mobile Phone Signaling Data

1.4.3 Accurate Travel Chain Identification based on mobile phone signaling data

1.4.4 Reconstruction of ABM Fused with Multi-Source Data

1.4.5 Construction of Activity Scheduling and Multi-Agent Micro-Simulation Platform based on the historical activity

1.5 Summary


2. Review of Travel Characteristic Recognition and Activity-Based Modeling

2.1 Development of Travel Chain Identification by Mobile Phone Signaling Data

2.1.1 Characteristics Comparison of Various Mobile Phone Positioning Data

2.1.2 Development of Trip Endpoint Identification

2.1.3 Development of Travel Path Identification

2.1.4 Development of Travel Mode Identification

2.2 Development of ABM

2.2.1 Development with Data Evolution

2.2.2 Development with Prediction Methodology

2.2.3 Development with Model Framework

2.3 Development of Activity Scheduling and Multi-Agent Micro-Simulation Platform

2.3.1 Eindhoven University of Technology: From AURORA to FEATHERS

2.3.2 University of Toronto: From TASHA to GTASIM

2.3.3 Technische Universität Berlin: MATSim

2.3.4 MIT: from DynaMIT to SimMobility

2.3.5 Other Platforms

2.4 Summary


3. Technical Principles and Evaluation of 4G/5G Mobile Phone Signaling Data

3.1 Communication Networks Mechanisms

3.1.1 Evolution of Wireless Communication Systems

3.1.2 Structure of 4G/5G Communication Networks

3.2 Spatio-temporal Characteristics of 4G/5G Mobile Phone Signaling Data

3.2.1 Data Generation and Extraction in 4G/5G Communication Networks

3.2.2 Spatial Distribution of 4G/5G Communication Base Stations

3.2.3 Temporal Characteristics of 4G/5G Mobile Phone Signaling Data

3.3 Summary


4. Accurate Travel Chain Identification Based on Mobile Phone Signaling Data

4.1 Framework

4.2 Experiment Design and Data Analysis

4.2.1 Experimental Design Methodology

4.2.2 Characteristic Analysis of 4G/5G Mobile Phone Signaling Data

4.2.3 Pre-processing

4.3 Accurate Travel Endpoint Identification and Localization

4.3.1 Travel Endpoint Identification

4.3.2 Activity Localization

4.3.3 Optimization Strategies

4.3.4 Parameter Calibration

4.3.5 Validation and Evaluation

4.4 Travel Path Identification in Complex Urban Road Networks

4.4.1 Travel Path Fitting Module 4.4.2 Travel Path Identification Module

4.4.3 Parameter Calibration

4.4.4 Validation and Evaluation

4.5 Motorized Travel Mode Identification in Complex Urban Road Networks

4.5.1 Input Data Characteristics

4.5.2 Identification Algorithms

4.5.3 Parameter Calibration

4.5.4 Validation and Evaluation

4.6 Summary


5. ABMs Combined with Mobile Phone Signaling Data

5.1 Model Structure

5.2 Input Data

5.2.1 Historical Individual Activity Chain

5.2.2 Public Transportation

5.2.3 Point-of-Interest (POI)

5.2.4 Urban Road Network

5.3 Basic Information Prediction Module (BIPM)

5.4 Primary Activity Prediction Module (PAPM)

5.4.1 Activity Start Time and Duration Prediction

5.4.2 Activity Location Prediction

5.5 Secondary Activity Prediction Module (SAPM)

5.5.1 Activity Duration Prediction

5.5.2 Activity Location Prediction

5.5.3 Travel Information Prediction

5.6 Time Dynamic Adjustment Module (TDAM)

5.7 Evaluation with Multi-Source Data and Prediction Methods

5.7.1 Verification Method

5.7.2 Evaluation with Multi-Source Data

5.7.3 Evaluation With Various Prediction Methods

5.8 Summary


6. Activity Scheduling and Multi-Agent Micro-Simulation Platform

6.1 Significance of Historical Behavior in Activity Scheduling

6.2 Platform Structure

6.2.1 Input Data

6.2.2 Activity Simulation and Adjustment Module

6.2.3 State Adjustment Module

6.3 Multi-Scenario Simulation and Evaluation

6.3.1 Parameter calibration

6.3.2 Case Study in Chengdu

6.4 Summary


7. Outlook