Inferring travel activity pattern from smartphone sensing data using deep learning

Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2018.Cataloged from PDF version of thesis.Includes bibliographical references (pages 77-85).Understanding the travel routine of the individuals is important in many domains. In transport research understanding daily travel routine is crucial for modeling the travel behavior of the individuals. Such models help predict the travel demand and develop strategies for managing that demand. Understanding travel patterns of the individuals is also important to develop effective incentive mechanisms. Location-based services like personal digital assistants and journey planners use historical travel routine to build preferences of the user and make useful recommendations. In health sciences logging the routine travel behavior is important to monitor health of the patients and make recommendations wherever necessary. Several fitness tracking applications available on smartphones utilize the travel activity diary to evaluate the fitness of the individuals and make recommendations. The proliferation of sensing-enabled smartphone devices engendered the development of tools for logging travel routine of individuals. The research in this thesis uses the sensor data collected from smartphone devices to develop a travel activity inference algorithm. Presently, the research into travel activity inference has been focused on developing supervised learning algorithms. These algorithms require a large amount of labeled data for training algorithms that generalize well. Generalization in personalized travel activity inference is a challenging problem due to the concept drift. The problem of concept drift is magnified as the more personalized information is introduced in the input variables. Once the users start using the applications they are constantly generating new data. Expecting the users to label all the data generated by them is impractical. Instead, it would be useful to identify only those examples which would help most improve the algorithm and have the user label such instance. This reduces the burden on the user and does not discourage them from participating in the data collection process. In other words, we need a model that is identifies concept drift in data and adapts accordingly. There has been advances in the deep learning research in last few years. The deep learning algorithms provide a framework for learning feature representation from raw data. The convolutional neural networks have been particularly effective in learning feature representations on many datasets. These models have achieved significant improvement on many complex problems over other machine learning approaches. For the sequential classification problems like the travel activity inference, the recurrent neural network like long short term memory networks are particularly suitable. This thesis proposes to use the deep learning algorithms for travel activity inference. To develop an end-to-end deep learning algorithm that learns feature representations from raw sensor data and incorporates different sensors with differing frequencies. The research proposes using a combination of convolutional neural network for feature representation learning in both time and frequency domain and long short term memory network for sequential classification. In practical situations, the users of the smartphones cannot be asked to carry their smartphones in a fixed position every time. The proposed algorithm for travel activity inference need to be robust to changes in orientation of the smartphones. We compared the performance of the proposed deep learning algorithm against a baseline model based on the current supervised machine learning approaches. The deep learning algorithm achieved an overall average accuracy of 95.98% compared to the baseline method which achieved an overall average accuracy of 89%. We also show that the proposed deep learning algorithm is robust to changes in the orientation of the smartphone.by Ajinkya Ghorpade.S.M. in Transportatio

Stop Detection in Smartphone-based Travel Surveys

Future Mobility Sensing (FMS) is a smartphone-based travel survey system that employs a web-based prompted-recall interaction to correct automatically inferred information. A key component of FMS is a stop detection algorithm that derives the users' activity locations and times based on the raw data collected by their phones. Output of this algorithm is presented in the Activity Diary for the users to validate, and its accuracy has a significant impact on user burden. In this paper, we present FMS' stop detection algorithm and its performance during testing by volunteers and public users during a large-scale field test

Activity recognition for a smartphone and web-based human mobility sensing system

Activity-based models in transport modeling and prediction are built from a large number of observed trips and their purposes. However, data acquired through traditional interview-based travel surveys is often inaccurate and insufficient. Recently, a human mobility sensing system, called Future Mobility Survey (FMS), was developed and used to collect travel data from more than 1,000 participants. FMS combines a smartphone and interactive web interface in order to better infer users activities and patterns. This paper presents a model that infers an activity at a certain location. We propose to generate a set of predictive features based on spatial, temporal, transitional, and environmental contexts with an appropriate quantization. In order to improve the generalization performance of the proposed model, we employ a robust approach with ensemble learning. Empirical results using FMS data demonstrate that the proposed method contributes significantly to providing accurate activity estimates for the user in our travel-sensing application

Smartphone-Based Survey for Real-Time and Retrospective Happiness Related to Travel and Activities

Understanding and incorporating measures of travel and activity well-being in transportation research are critical for the design and evaluation of policies. In recent years, several efforts have been made to quantify travelers’ subjective well-being by using a self-reported state of happiness during participation in various activities or travel patterns. The inadequacies of these conventional survey methods in collecting uninterrupted and comprehensive information have restricted the number of such studies. In this study, a smartphone-based sensing platform was adapted to collect mobility information and measure happiness. Two surveys were conducted with respondents from five continents. Real-time and retrospective happiness measures are compared and explained. Results indicate that different cognitive biases affect the levels of happiness reported by the individuals. In comparison with staying at home, performing work and education activities tends to result in lower levels of happiness, while performing other activities tends to result in higher levels of happiness. Activity duration has a significant effect on real-time happiness but is less significant for retrospective happiness

Assessing the representativeness of a smartphone-based household travel survey in Dar es Salaam, Tanzania

Abstract The household travel survey (HTS) finds itself in the midst of rapid technological change. Traditional methods are increasingly being sidelined by digital devices and computational power—for tracking movements, automatically detecting modes and activities, facilitating data collection, etc.. Smartphones have recently emerged as the latest technological enhancement. FMS is a smartphone-based prompted-recall HTS platform, consisting of an app for sensor data collection, a backend for data processing and inference, and a user interface for verification of inferences (e.g., modes, activities, times, etc.). FMS, has been deployed in several cities of the global north, including Singapore. This paper assesses the first use of FMS in a city of the global south, Dar es Salaam. FMS in Dar was implemented over a 1-month period, among 581 adults chosen from 300 randomly selected households. Individuals were provided phones with data plans and the FMS app preloaded. Verification of the collected data occurred every 3 days, via a phone interview. The experiment reveals various social and technical challenges. Models of individual likelihood to participate suggest little bias. Several socioeconomic and demographic characteristics apparently do influence, however, the number of days fully verified per individual. Similar apparent biases emerge when predicting the likelihood of a given day being verified. Some risk of non-random, non-response is, thus, evident