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.
Most of the algorithms used to derive activities in GPS travel surveys are rule-based and rely heavily on GIS information, such as Point Of Interest (POI) and land use information @cite_14 @cite_8 @cite_26 . An early car-based study in America by @cite_14 inferred trip purposes from GPS data and an extensive GIS land use database. In more recent work, POI's attractiveness is defined along time of day to indicate the potential possibilities for activities @cite_4 , and @cite_20 proposed to infer an activity based on the distance between POI and the stop location. Another option is to use individual characteristics as input for activity recognition algorithms. @cite_6 developed a rule based approach to identify activities based on users' home and work locations, and POI land use information in the Swiss. Similar information and rules were used in the GPS survey in the Netherlands @cite_12 . Reference @cite_15 described a more complicated heuristic rule-based method which collects users' workplace or school, the two most frequently used grocery stores, and occupation beforehand to be used to derive trip characteristics.
@cite_14 @cite_4 @cite_26 @cite_8 @cite_6 @cite_15 @cite_12 @cite_20
1998451808 2002313219 1983447652 2146299864
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. Passive global positioning system (GPS) travel survey has proven an innovative alternative to the traditional paper-and-pencil method, as it can record exact time and location of travel activities without incurring much burden on survey respondents. Lying in the core of this technique is how to derive accurate travel information (e.g., trip purpose) from GPS data. Previous studies rely on simple rules that are manually extracted from the inherent structure of GPS data streams. These methods, however, are difficult to generalize for other applications and lack helpful hints for new research. This paper presents a machine learning approach to deriving trip purpose from GPS track data coupled with other relevant data sources. This approach employs a number of attributes (i.e., time stamp and land-use type of trip ends, a set of spatiotemporal indices of travel, and demographic and socioeconomic characteristics of respondents) to construct a decision tree for purpose of classification. Each attribute provides partial evidence to the depiction of a given purpose, but this depiction may or may not be monotonic, and none of them can work alone toward the goal. A reasoning procedure using the adaptive boosting technique was designed to explore how these attributes could work together to achieve trip purpose derivation. This technique generated multiple decision trees to improve the classification results through a mechanism of voting from these trees. Each tree was constructed in the depth-first fashion with the root node and the split of subsequent nodes being determined on the basis of the gain-ratio computed for the relevant attributes. This procedure was implemented in the C5.0 machine learning environment with 226 GPS trip records collected from 36 respondents. The experimental results seemed rather promising: using 10 iterations for adaptive boosting, an overall classification accuracy of 87.6 was achieved. 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. 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.