In transport modeling and prediction, trip purposes play an important role since mobility choices (e.g. modes, routes, departure times) are made in order to carry out specific activities. Activity based models, which have been gaining popularity in recent years, are built from a large number of observed trips and their purposes. However, data acquired through traditional interview-based travel surveys lack the accuracy and quantity required by such models. Smartphones and interactive web interfaces have emerged as an attractive alternative to conventional travel surveys. A smartphone-based travel survey, Future Mobility Survey (FMS), was developed and field-tested in Singapore and collected travel data from more than 1000 participants for multiple days. To provide a more intelligent interface, inferring the activities of a user at a certain location is a crucial challenge. This paper presents a learning model that infers the most likely activity associated to a certain visited place. The data collected in FMS contain errors or noise due to various reasons, so a robust approach via ensemble learning is used to improve generalization performance. Our model takes advantage of cross-user historical data as well as user-specific information, including socio-demographics. Our empirical results using FMS data demonstrate that the proposed method contributes significantly to our travel survey application.
Several recent pilot studies combined Global Positioning System (GPS) technology with travel survey data collection to evaluate opportunities for improving the quantity and accuracy of travel data. These studies used GPS to supplement traditional data elements collected in paper or electronic travel diaries. Although many traditional trip elements can be obtained from the GPS data, trip purpose has remained an important element, requiring the use of a diary to continue. Presented are the results of a proof-of-concept study conducted at the Georgia Institute of Technology that examined the feasibility of using GPS data loggers to completely replace, rather than supplement, traditional travel diaries. In this approach, all GPS data collected must be processed so that all essential trip data elements, including trip purpose, are derived. If this processing is done correctly and quickly, then the computer-assisted telephone interview retrieval call could be shortened significantly, reducing both respondent bu... GPS (Globe Positioning System) trajectory data provide a new way for city travel analysis others than traditional travel diary data. But generally raw GPS traces do not include information on trip purposes or activities. Earlier studies addressed this issue through a combination of manual and computer-assisted data processing steps. Nevertheless, geographic context databases provide the possibility for automatic activity identification based on GPS trajectories since each activity is uniquely defined by a set of features such as location and duration. Distinguished with most existing methods using two dimensional factors, this paper presents a novel approach using spatial temporal attractiveness of POIs (Point of Interests) to identify activity-locations as well as durations from raw GPS trajectory. We also introduce an algorithm to figure out how the intersections of trajectories and spatial-temporal attractiveness prisms indicate the potential possibilities for activities. Finally, Experiments using real world GPS tracking data, road networks and POIs are conducted for evaluations of the proposed approach.   The recent Swedish Intelligent Speed Adaptation (ISA) study included a component that involved the installation of units based on the Global Positioning System (GPS) in hundreds of cars in three Swedish cities, Borlange, Lund, and Lidkoping; these vehicles were observed for up to 2 years. In Borlange, the speed and location data of each vehicle were transmitted at regular intervals to a central server and stored for later analysis. This data set contains a wealth of travel behavior information that had not been available before. However, a data set of this magnitude introduces a major need for automated processes that can glean travel behavior details from the trip summary and collected GPS point files. A summary is presented of characteristics of and issues with the Borlange GPS data set, which included 186 personal vehicles with at least 30 days of travel data and corresponding household sociodemographic data. (These 186 vehicles recorded 49,667 vehicle days of travel and 240,435 trips inside the study ... In the late 1990s, global positioning system (GPS) devices began to be used as a method for measuring personal travel. Early devices were for in-vehicle use only and derived their power from the accessory socket of the car. In the early 2000s, the first wearable devices appeared, using battery power from rechargeable batteries. The early wearable devices were heavy and ungainly, and success in having people use the devices was limited. In 2005, the Institute of Transport and Logistics Studies (ITLS) and NeveITS pioneered the use of a much smaller device with its own internal battery, similar in weight and dimensions to a mobile telephone. Subsequent to the initial deployment of this device, there have been further advances in the sensitivity of the antenna receiver and we have developed with NeveITS a number of improvements to software. Most recently, another device called a Starnav, has been developed for ITLS in Taiwan, and offers further sophistication and user friendliness than the Neve devices. This paper describes these GPS devices and demonstrates the capability of these devices to provide detailed and accurate data on travel movements. We provide a brief description of the software we have developed and continue to improve for analysing the resulting data. The latest technologies for GPS devices indicate the potential to replace many conventional methods of data collection that are flawed because of known errors and inaccuracies. In the past few decades, travel patterns have become more complex and policy makers demand more detailed information. As a result, conventional data collection methods seem no longer adequate to satisfy all data needs. Travel researchers around the world are currently experimenting with different Global Positioning System (GPS)-based data collection methods. An overview of the literature shows the potential of these methods, especially when algorithms that include spatial data are used to derive trip characteristics from the GPS logs. This article presents an innovative method that combines GPS logs, Geographic Information System (GIS) technology and an interactive web-based validation application. In particular, this approach concentrates on the issue of deriving and validating trip purposes and travel modes, as well as allowing for reliable multi-day data collection. In 2007, this method was used in practice in a large-scale study conducted in the Netherlands. In total, 1104 respondents successfully participated in the one-week survey. The project demonstrated that GPS-based methods now provide reliable multi-day data. In comparison with data from the Dutch Travel Survey, travel mode and trip purpose shares were almost equal while more trips per tour were recorded, which indicates the ability of collecting trips that are missed by paper diary methods. The collection of huge amount of tracking data made possible by the widespread use of GPS devices, enabled the analysis of such data for several applications domains, ranging from traffic management to advertisement and social studies. However, the raw positioning data, as it is detected by GPS devices, lacks of semantic information since this data does not natively provide any additional contextual information like the places that people visited or the activities performed. Traditionally, this information is collected by hand filled questionnaire where a limited number of users are asked to annotate their tracks with the activities they have done. With the purpose of getting large amount of semantically rich trajectories, we propose an algorithm for automatically annotating raw trajectories with the activities performed by the users. To do this, we analyse the stops points trying to infer the Point Of Interest (POI) the user has visited. Based on the category of the POI and a probability measure based on the gravity law, we infer the activity performed. We experimented and evaluated the method in a real case study of car trajectories, manually annotated by users with their activities. Experimental results are encouraging and will drive our future works.