Inferring hybrid transportation modes from sparse GPS data using a moving window SVM classification

Understanding travel behaviour and travel demand is of constant importance to transportation communities and agencies in every country. Nowadays, attempts have been made to automatically infer transportation modes from positional data, such as the data collected by using GPS devices so that the cost in time and budget of conventional travel diary survey could be significantly reduced. Some limitations, however, exist in the literature, in aspects of data collection (sample size selected, duration of study, granularity of data), selection of variables (or combination of variables), and method of inference (the number of transportation modes to be used in the learning). This paper therefore, attempts to fully understand these aspects in the process of inference. We aim to solve a classification problem of GPS data into different transportation modes (car, walk, cycle, underground, train and bus). We first study the variables that could contribute positively to this classification, and statistically quantify their discriminatory power. We then introduce a novel approach to carry out this inference using a framework based on Support Vector Machines (SVMs) classification. The framework was tested using coarse-grained GPS data, which has been avoided in previous studies, achieving a promising accuracy of 88% with a Kappa statistic reflecting almost perfect agreement

Geocontext extraction methods analysis for determining the new approach to automatic semantic places recognition

Goal of this paper is to determine actual trends in geocontext extraction methods and to understand which types of geocontext information are the most interesting for users. For this purposes comparison of recent researches about geocontext analysis was done. Researches were compared by the type of achieved result, used formalism, source data and limitations. As the main result of comparison new approach for automatic semantic places recognition was proposed. This approach is based on geotags markup with semantic user-defined tags. The solution allows extracting information (coordinates and a set of corresponding semantic tags on the natural language) about locations which are interesting for the location-based services users. The main advantage of the approach is its simplicity - the method does not rely on any syntax analysis algorithms during the semantic labeling stage. For illustrating the approach an example of the general purpose accidents monitoring service for the Geo2Tag platform was described

Inferring the transportation mode from sparse GPS data

Understanding travel behaviour and travel demand is of constant importance to transportation communities and agencies in every country. Nowadays, attempts have been made to automatically infer the modes of transport from positional data (such as GPS data) to significantly reduce the cost in time and budget of conventional travel diary surveys. Some limitations, however, exist in the literature, in aspects of data collection (spatio-temporal sample distribution, duration of study, granularity of data, device type), data pre-processing (managing GPS errors, choice of modes, trip information generalisation, data labelling strategy), the classification method used and the choice of variables used for classification, track segmentation methods used (clustering techniques), and using transport network datasets. Therefore, this research attempts to fully understand these aspects and their effect on the process of inference of mode of transport. Furthermore, this research aims to solve a classification problem of sparse GPS data into different transportation modes (car, walk, cycle, underground, train and bus). To address the data collection issues, we conduct studies that aim to identify a representative sample distribution, study duration, and data collection rate that best suits the purpose of this study. As for the data pre-processing issues, we standardise guidelines for managing GPS errors and the required level of detail of the collected trip information. We also develop an online WebGIS-based travel diary that allows users to view, edit, and validate their track information to assure obtaining high quality information. After addressing the validation issues, we develop an inference framework to detect the mode of transport from the collected data. We first study the variables that could contribute positively to this classification, and statistically quantify their discriminatory power using ANOVA analysis. We then introduce a novel approach to carry out this inference using a framework based on Support Vector Machines (SVMs) classification. The classification process is followed by a segmentation phase that identifies stops, change points and indoor activity in GPS tracks using an innovative trajectory clustering technique developed for this purpose. The final phase of the framework develops a network matching technique that verifies the classification and segmentation results by testing their obedience to rules and restrictions of different transport networks. The framework is tested using coarse-grained GPS data, which has been avoided in previous studies, achieving almost 90% accuracy with a Kappa statistic reflecting almost perfect agreement

A methodology for train trip identification in mobility campaigns based on smartphones

Nowadays, mobility campaigns use mobile phones as sensors for travel surveys aimed at gathering chronological information, patterns and modes used by citizens. Train trip travel identification is one of the issues present in this new schema. Differentiating train and car trips is challenging because in many cases railways and roads are side by side and their individual travels have similar speed. In this paper, we describe a methodology based on a speed-based filter and geospatial operation using the OSM network to determine possible train trip segments in data gathered in a mobility campaign. We evaluated our method using over 9,683 segments, which have been gathered by 239 devices. The results show that the proposed approach successfully detects 76.14% of the train trip segments labeled by users. This methodology can be used as a post-processing step to classify train segments in big data of smar cities

Transportation Mode Detection Based on Permutation Entropy and Extreme Learning Machine

With the increasing prevalence of GPS devices and mobile phones, transportation mode detection based on GPS data has been a hot topic in GPS trajectory data analysis. Transportation modes such as walking, driving, bus, and taxi denote an important characteristic of the mobile user. Longitude, latitude, speed, acceleration, and direction are usually used as features in transportation mode detection. In this paper, first, we explore the possibility of using Permutation Entropy (PE) of speed, a measure of complexity and uncertainty of GPS trajectory segment, as a feature for transportation mode detection. Second, we employ Extreme Learning Machine (ELM) to distinguish GPS trajectory segments of different transportation. Finally, to evaluate the performance of the proposed method, we make experiments on GeoLife dataset. Experiments results show that we can get more than 50% accuracy when only using PE as a feature to characterize trajectory sequence. PE can indeed be effectively used to detect transportation mode from GPS trajectory. The proposed method has much better accuracy and faster running time than the methods based on the other features and SVM classifier

Automatic Transportation Mode Recognition on Smartphone Data Based on Deep Neural Networks

In the last few years, with the exponential diffusion of smartphones, services for turn-by-turn navigation have seen a surge in popularity. Current solutions available in the market allow the user to select via an interface the desired transportation mode, for which an optimal route is then computed. Automatically recognizing the transportation system that the user is travelling by allows to dynamically control, and consequently update, the route proposed to the user. Such a dynamic approach is an enabling technology for multi-modal transportation planners, in which the optimal path and its associated transportation solutions are updated in real-time based on data coming from (i) distributed sensors (e.g., smart traffic lights, road congestion sensors, etc.); (ii) service providers (e.g., car-sharing availability, bus waiting time, etc.); and (iii) the user’s own device, in compliance with the development of smart cities envisaged by the 5G architecture. In this paper, we present a series of Machine Learning approaches for real-time Transportation Mode Recognition and we report their performance difference in our field tests. Several Machine Learning-based classifiers, including Deep Neural Networks, built on both statistical feature extraction and raw data analysis are presented and compared in this paper; the result analysis also highlights which features are proven to be the most informative ones for the classification

Travel Mode Identification with Smartphone Sensors

Personal trips in a modern urban society typically involve multiple travel modes. Recognizing a traveller\u27s transportation mode is not only critical to personal context-awareness in related applications, but also essential to urban traffic operations, transportation planning, and facility design. While the state of the art in travel mode recognition mainly relies on large-scale infrastructure-based fixed sensors or on individuals\u27 GPS devices, the emergence of the smartphone provides a promising alternative with its ever-growing computing, networking, and sensing powers. In this thesis, we propose new algorithms for travel mode identification using smartphone sensors. The prototype system is built upon the latest Android and iOS platforms with multimodality sensors. It takes smartphone sensor data as the input, and aims to identify six travel modes: walking, jogging, bicycling, driving a car, riding a bus, taking a subway. The methods and algorithms presented in our work are guided by two key design principles. First, careful consideration of smartphones\u27 limited computing resources and batteries should be taken. Second, careful balancing of the following dimensions (i) user-adaptability, (ii) energy efficiency, and (iii) computation speed. There are three key challenges in travel mode identification with smartphone sensors, stemming from the three steps in a typical mobile mining procedure. They are (C1) data capturing and preprocessing, (C2) feature engineering, and (C3) model training and adaptation. This thesis is our response to the challenges above. To address the first challenge (C1), in Chapter 4 we develop a smartphone app that collects a multitude of smartphone sensor measurement data, and showcase a comprehensive set of de-noising techniques. To tackle challenge (C2), in Chapter 5 we design feature extraction methods that carefully balance prediction accuracy, computation time, and battery consumption. And to answer challenge (C3), in Chapters 6,7 and 8 we design different learning models to accommodate different situations in model training. A hierarchical model with dynamic sensor selection is designed to address the energy consumption issue. We propose a personalized model that adapts to each traveller\u27s specific travel behavior using limited labeled data. We also propose an online model for the purpose of addressing the model updating problem with large scaled data. In addressing the challenges and proposing solutions, this thesis provides an comprehensive study and gives a systematic solution for travel mode detection with smartphone sensors