The prediction of human and vehicle mobility in a city is becoming attracting field. This topic attracts researchers in broad field from the behavioral science, where understanding the complexity of the human mobility behavior is one of the hot topics, to industrial field, which apply the result to many beneficial applications. Recent progress to sensing human mobility via smartphones is boosting this trend. However, due to the complexity and context-dependence of human behavior and the incompleteness and noise of geospatial data collecting from various sensors, the prediction of human and vehicle mobility is still far from solved. This workshop aims at collecting contributions on the cutting-edge studies in human mobility description, modeling, intelligent computational method which can advance the human and vehicle prediction research.
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Grab-Posisi: An Extensive Real-Life GPS Trajectory Dataset in Southeast Asia
- Xiaocheng Huang,
- Yifang Yin,
- Simon Lim,
- Guanfeng Wang,
- Bo Hu,
- Jagannadan Varadarajan,
- Shaolin Zheng,
- Ajay Bulusu,
- Roger Zimmermann
Real-world GPS trajectory datasets are essential for geographical applications such as map inference, map matching, traffic detection, etc. Currently only a handful of GPS trajectory datasets are publicly available and the quality of these datasets ...
Predicting Traffic Accidents with Event Recorder Data
Large amounts of data on accidents are continually being collected by dashboard cameras (dashcams). In this paper, we address the problem of predicting the occurrence of accidents: Our goal is to predict when accidents will occur based on stored dashcam ...
Traveler's Next Activity Predication with Location-Based Social Network Data
The rise of technology and the internet provides powerful means for people from all around the world to communicate and connect with one another. Online social network platforms become go-to places for users to express and share their individuality, ...
Context-based Markov Model toward Spatio-Temporal Prediction with Realistic Dataset
This paper describes a method that simultaneously predicts the next visiting location and time of mobile users, i.e., spatio-temporal prediction (STP) from global positioning system (GPS) log dataset acquired from users' smartphones.
The GPS dataset ...
Using Mobile Sensing Technology for Capturing People Mobility Information
The detection and analysis of human crowds have been widely used from urban design and traffic management to disaster evacuation and mobility prediction. Currently, several common methods of crowd flow detection have different performances in terms of ...
Bike-Share Demand Prediction using Attention based Sequence to Sequence and Conditional Variational AutoEncoder
In recent years, bicycle sharing services (bike-shares) have been established worldwide. One important aspect of bike-share management is to periodically rebalance the positions of the available bikes. Because the bike demand varies by and over time, ...
Spatio-Temporal Clustering of Traffic Data with Deep Embedded Clustering
Traffic data is a challenging spatio-temporal data, and a multivariate time series data with spatial similarities. Clustering of traffic data is a fundamental tool for various machine learning tasks including anomaly detection, missing data imputation ...
A Novel Approach to Approximate Crime Hotspots to the Road Network
- Francisco C. F. Nunes Junior,
- Ticiana L. Coelho da Silva,
- José F. de Queiroz Neto,
- José Antônio F. de Macêdo,
- Wellington Clay Porcino
Crimes (e.g., assault, arson, harassment, and murder) have emerged as one of the most critical problems countries face. In particular, in Brazil, crime is a theme of growing interest and the prime concern in some cities, due to the high crime rates, the ...
Graph Analyses of Phone-Based Origin-Destination Data for Understanding Urban Human Mobility in Seoul, Korea
High ownership rate of smartphones in South Korea makes the phone-based human mobility information reliable. By creating a large directed graph, the dynamic of urban human mobility can be interpreted. In this research, graph analysis was applied to ...
Enhancing a Crowd-based Delivery Network with Mobility Predictions
In this paper, a new application domain for mobility predictions is presented. Based on the application domain new challenges arise in terms of when and how the mobility prediction has to be done. This results in three cases for mobility predictions, ...