Apichon Witayangkurn

Fieldservers and sensor service grid as real-time monitoring infrastructure for ubiquitous sensor networks.

The fieldserver is an Internet based observation robot that can provide an outdoor solution for monitoring environmental parameters in real-time. The data from its sensors can be collected to a central server infrastructure and published on the Internet. The information from the sensor network will contribute to monitoring and modeling on various environmental issues in Asia, including agriculture, food, pollution, disaster, climate change etc. An initiative called Sensor Asia is developing an infrastructure called Sensor Service Grid (SSG), which integrates fieldservers and Web GIS to realize easy and low cost installation and operation of ubiquitous field sensor networks.

Understanding tourist behavior using large-scale mobile sensing approach

This article describes a framework that capitalizes on the large-scale opportunistic mobile sensing approach for tourist behavior analysis. The article describes the use of massive mobile phone GPS location records to study tourist travel behavior, in particular, number of trips made, time spent at destinations, and mode of transportation used. Moreover, this study examined the relationship between personal mobility and tourist travel behavior and offered a number of interesting insights that are useful for tourism, such as tourist flows, top tourist destinations or origins, top destination types, top modes of transportation in terms of time spent and distance traveled, and how personal mobility information can be used to estimate the likelihood in tourist travel behavior, i.e., number of trips, time spent at destinations, and trip distance. Furthermore, the article describes an application developed based on the analysis in this study that allows the user to observe touristic, non-touristic, and commuting trips along with home and workplace locations as well as tourist flows, which can be useful for urban planners, transportation management, and tourism authorities.

Large-Scale Auto-GPS Analysis for Discerning Behavior Change during Crisis

Auto-GPS is a new type of mobile sensing data used to discern human mobility and behavior during a large-scale crisis. Using data collected after the 2011 Great Japan Earthquake, useful information is revealed on how humans react in disaster scenarios and how the evacuation process can be monitored in near real time.

Anomalous event detection on large-scale GPS data from mobile phones using hidden markov model and cloud platform

Anomaly detection is an important issue in various research fields. An uncommon trajectory or gathering of people in a specific area might correspond to a special event such as a festival, traffic accident or natural disaster. In this paper, we aim to develop a system for detecting such anomalous events in grid-based areas. A framework based on a hidden Markov model is proposed to construct a pattern of spatio-temporal movement of people in each grid during each time period. The numbers of GPS points and unique users in each grid were used as features and evaluated. We also introduced the use of local score to improve the accuracy of the event detection. In addition, we utilized Hadoop, a cloud-computing platform, to accelerate the processing speed and allow the handling of large-scale data. We evaluated the system using a dataset of GPS trajectories of 1.5 million individual mobile phone users accumulated over a one-year period, which constitutes approximately 9.2 billion records.

Understanding User Attributes from Calling Behavior: Exploring Call Detail Records through Field Observations

Mobile phones are arguably one of the most prolific sources of large-scale human mobility data. The availability of this data has generated a massive body of research focused on understanding the dynamics and patterns of human mobility. However, it is increasingly evident that additional value can be derived from such data. This paper proposes a novel approach for understanding the attributes of mobile users by analyzing calling behavior derived from field survey data, in combination with call detail records (CDRs). Our survey reveals distinctive traits in calling behavior that correspond to user attributes. Analysis results demonstrate that frequent call locations, the variability in call time distributions, and the locations from which calls are made around midday are all keys to distinguishing gender. In addition, the location of calls initiated during the morning hours is a key to analyzing income levels for males.

Understanding the unobservable population in call detail records through analysis of mobile phone user calling behavior: A case study of Greater Dhaka in Bangladesh

The understanding of mass population movements has greatly advanced with the rapid spread of ubiquitous devices. Anonymized call detail records (CDRs) for mobile phones have enabled us to not only trace individual trajectories but also approximate activity patterns, including significant locations such as homes and workplaces. The majority of studies analyzing CDRs attempt to utilize the mobility patterns of anonymized crowds to improve transportation and public health. This is quite reasonable because CDRs can capture the movements of people at given times and places, whereas general statistics usually account for a population based on their locations of residence. However, it has also been pointed out that there are discrepancies between the movements of people as observed through CDRs and those of an entire population in a given area. This is because CDRs only represent device users. In fact, we can never learn about the population that is unobservable through CDRs only by analyzing CDRs. Therefore, this study attempts to provide clues to help us understand the whereabouts of the unobservable population by analyzing two months of the CDRs for 58 volunteers with mobile device service from a major telecommunications company in combination with field survey data from Dhaka. We surveyed the personal and household attributes of mobile users in relation to their calling behavior. The analysis results show that per mobile user observed in CDRs, there is an average of roughly 2.4 to 2.8 unobservable people. Their age groups and gender composition are also provided. We find that male and female users exhibit opposite trends in call locations according to the presence of children within the household. In addition, based on field observations, we find that the location and time distributions of small children follow some specific routines. Our findings contribute to the understanding of the whereabouts of the unobservable population, the majority of whom are children and are considered to be vulnerable to disasters or infectious diseases but are difficult to locate through CDRs alone.

Performance comparisons of spatial data processing techniques for a large scale mobile phone dataset

Mobile technology, especially mobile phone, is very popular nowadays. Increasing number of mobile users and availability of GPS-embedded mobile phones generate large amount of GPS trajectories that can be used in various research areas such as people mobility and transportation planning. However, how to handle such a large-scale dataset is a significant issue particularly in spatial analysis domain. In this paper, we aimed to explore a suitable way for extracting geo-location of GPS coordinate that achieve large-scale support, fast processing, and easily scalable both in storage and calculation speed. Geo-locations are cities, zones, or any interesting points. Our dataset is GPS trajectories of 1.5 million individual mobile phone users in Japan accumulated for one year. The total number was approximately 9.2 billion records. Therefore, we conducted performance comparisons of various methods for processing spatial data, particularly for a huge dataset. In this work, we first processed data on PostgreSQL with PostGIS that is a traditional way for spatial data processing. Second, we used java application with spatial library called Java Topology suite (JTS). Third, we tried on Hadoop Cloud Computing Platform focusing on using Hive on top of Hadoop to allow SQL-like support. However, Hadoop/Hive did not support spatial query at the moment. Hence, we proposed a solution to enable spatial support on Hive. As the results, Hadoop/hive with spatial support performed best result in large-scale processing among evaluated methods and in addition, we recommended techniques in Hadoop/Hive for processing different types of spatial data.

UAV-Based Sensor Web Monitoring System

An unmanned aerial vehicle- (UAV-) based monitoring system is developed as an intermediate system between aerial survey and ground survey. All the measurement tools are mounted on the UAV to acquire detailed information from low altitudes which is different from a satellite or a plane. The monitoring is carried out from the sky, but the spatial and temporal resolutions are freely selected near the ground. In this study, the data is easily acquired with safety and mobility by the utilization of a sensor web. A sensor web is a type of sensor network which is well suited for environmental monitoring. Sensor nodes are spatially distributed and wirelessly communicate with each other. In this study, the UAV-based system is considered as a mobile sensor node. This study proposes a combination of UAV-based monitoring with a ubiquitous sensor network.

Agent-Based Modeling of Taxi Behavior Simulation with Probe Vehicle Data

Taxi behavior is a spatial–temporal dynamic process involving discrete time dependent events, such as customer pick-up, customer drop-off, cruising, and parking. Simulation models, which are a simplification of a real-world system, can help understand the effects of change of such dynamic behavior. In this paper, agent-based modeling and simulation is proposed, that describes the dynamic action of an agent, i.e., taxi, governed by behavior rules and properties, which emulate the taxi behavior. Taxi behavior simulations are fundamentally done for optimizing the service level for both taxi drivers as well as passengers. Moreover, simulation techniques, as such, could be applied to another field of application as well, where obtaining real raw data are somewhat difficult due to privacy issues, such as human mobility data or call detail record data. This paper describes the development of an agent-based simulation model which is based on multiple input parameters (taxi stay point cluster; trip information (origin and destination); taxi demand information; free taxi movement; and network travel time) that were derived from taxi probe GPS data. As such, agent’s parameters were mapped into grid network, and the road network, for which the grid network was used as a base for query/search/retrieval of taxi agent’s parameters, while the actual movement of taxi agents was on the road network with routing and interpolation. The results obtained from the simulated taxi agent data and real taxi data showed a significant level of similarity of different taxi behavior, such as trip generation; trip time; trip distance as well as trip occupancy, based on its distribution. As for efficient data handling, a distributed computing platform for large-scale data was used for extracting taxi agent parameter from the probe data by utilizing both spatial and non-spatial indexing technique.

A Taxi Zoning Analysis Using Large-Scale Probe Data: A Case Study for Metropolitan Bangkok

Taxis are considered one of the most convenient means of transportation, especially when people have to travel off-route, where public transportation is not a feasible option, and also when they need to reach a destination according to what is most convenient for them. However, many issues exist about taxi services, such as the problems of passengers who are unable to get taxi service at the location of their choice, or problems concerning when they need the taxi service to arrive. These problems may be due to the unavailability of the taxi at that particular location or due to the taxi driver not wanting to provide service. A taxi driver may not want to provide service to a potential passenger, because they may have preferences on the direction and areas they want to go or because of the different types of service zoning. Understanding the behaviors of taxi drivers and the characteristics of the trip/travel might be helpful to solving such issues. In this study, we conducted an analysis from a questionnaire survey and large-scale taxi probe data to understand taxi service behavior, travel characteristics, and to discover taxi service zoning characteristics. As a result, four types of taxi service zones including isolated zones, interactive zones, special service zones, and target zones were encountered. Travel characteristics were calculated and analyzed at different criteria, such as weekdays, weekends, and various time windows in a single day. The result of these characteristics was explained according to their similarities and dissimilarities in each type of zone. The discovery of the different zones and their respective definitions might be a good initiative for further development of a policy for taxi drivers to provide better service for passengers.