Profiling Public Transit Passenger Mobility Using Adversarial Learning
Bibliographic Data
| ID | 22033766 |
|---|---|
| Authors | Yicong Li (0000-0001-7905-4885, PowerChina (China)), Tong Zhang (0000-0003-1637-0666, Wuhan University, corresponding author), Xiaofei Lv (0000-0002-1712-630X, PowerChina (China)), Yingxi Lu (Hunan City University), Wangshu Wang (0000-0003-2307-155X, TU Wien) |
| Year | 2023 |
| Volume | 12 |
| Issue | 8 |
| Pages | 338 |
| Publication date | 2023-08-12 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi12080338 |
| OpenAlex | W4385811608 |
| Language | EN |
| Citations received | 1 |
| References cited | 38 |
It is important to capture passengers’ public transit behavior and their mobility to create profiles, which are critical for analyzing human activities, understanding the social and economic structure of cities, improving public transportation, assisting urban planning, and promoting smart cities. In this paper, we develop a generative adversarial machine learning network to characterize the temporal and spatial mobility behavior of public transit passengers, based on massive smart card data and road network data. The Apriori algorithm is extended with spatio-temporal constraints to extract frequent transit mobility patterns of individual passengers based on a reconstructed personal trip dataset. This individual-level pattern information is used to construct personalized feature vectors. For regular and frequent public transit passengers, we identify similar transit mobility groups using spatio-temporal constraints to construct a group feature vector. We develop a generative adversarial network to embed public transit mobility of passengers. The proposed model’s generator consists of an auto-encoder, which extracts a low-dimensional and compact representation of passenger behavior, and a pre-trained sub-generator containing generalization features of public transit passengers. Shenzhen City is taken as the study area in this paper, and experiments were carried out based on smart card data, road network data, and bus GPS data. Clustering analysis of embedding vector representation and estimation of the top K transit destinations were conducted, verifying that the proposed method can profile passenger transit mobility in a comprehensive and compact manner
Cluster analysis · Computer network · Computer security · Data mining · Feature learning · Feature vector · Machine learning · Public transport · Smart card · Transport engineering · Computer Science · Engineering · Human Mobility and Location-Based Analysis · Traffic Prediction and Management Techniques · Urban Transport and Accessibility · Artificial Intelligence
Understanding individual human mobility patterns
Unravelling daily human mobility motifs
Human mobility
Limits of Predictability in Human Mobility
The origin of bursts and heavy tails in human dynamics
A universal model for mobility and migration patterns
Smart card data use in public transit
The scaling laws of human travel
Graph-based mobility profiling
Measuring urban regional similarity through mobility signatures
Does the returners and explorers dichotomy in urban human mobility depend on the observation duration? An empirical study in Guangzhou, China
Defining and understanding trip chaining behaviour
Understanding commuting patterns using transit smart card data
The P 1 P 2 D Hypothesis
| Citations per year | 1 |
|---|---|
| Citation span | 2025 - 2025 (1) |
| Highly cited | No |