Smartphone-Based Recognition of Access Trip Phase to Public Transport Stops Via Machine Learning Models

The usage of mobile phones is nowadays reaching full penetration rate in most countries. Smartphones are a valuable source for urban planners to understand and investigate passengers’ behavior and recognize travel patterns more precisely. Different investigations tried to automatically extract transit mode from sensors embedded in the phones such as GPS, accelerometer, and gyroscope. This allows to reduce the resources used in travel diary surveys, which are time-consuming and costly. However, figuring out which mode of transportation individuals use is still challenging. The main limitations include GPS, and mobile sensor data collection, and data labeling errors. First, this paper aims at solving a transport mode classification problem including (still, walking, car, bus, and metro) and then as a first investigation, presents a new algorithm to compute waiting time and access time to public transport stops based on a random forest model

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

Custom Dual Transportation Mode Detection by Smartphone Devices Exploiting Sensor Diversity

Making applications aware of the mobility experienced by the user can open the door to a wide range of novel services in different use-cases, from smart parking to vehicular traffic monitoring. In the literature, there are many different studies demonstrating the theoretical possibility of performing Transportation Mode Detection (TMD) by mining smart-phones embedded sensors data. However, very few of them provide details on the benchmarking process and on how to implement the detection process in practice. In this study, we provide guidelines and fundamental results that can be useful for both researcher and practitioners aiming at implementing a working TMD system. These guidelines consist of three main contributions. First, we detail the construction of a training dataset, gathered by heterogeneous users and including five different transportation modes; the dataset is made available to the research community as reference benchmark. Second, we provide an in-depth analysis of the sensor-relevance for the case of Dual TDM, which is required by most of mobility-aware applications. Third, we investigate the possibility to perform TMD of unknown users/instances not present in the training set and we compare with state-of-the-art Android APIs for activity recognition.Comment: Pre-print of the accepted version for the 14th Workshop on Context and Activity Modeling and Recognition (IEEE COMOREA 2018), Athens, Greece, March 19-23, 201

Robust Algorithms for Estimating Vehicle Movement from Motion Sensors Within Smartphones

Building sustainable traffic control solutions for urban streets (e.g., eco-friendly signal control) and highways requires effective and reliable sensing capabilities for monitoring traffic flow conditions so that both the temporal and spatial extents of congestion are observed. This would enable optimal control strategies to be implemented for maximizing efficiency and for minimizing the environmental impacts of traffic. Various types of traffic detection systems, such as inductive loops, radar, and cameras have been used for these purposes. However, these systems are limited, both in scope and in time. Using GPS as an alternative method is not always viable because of problems such as urban canyons, battery depletion, and precision errors. In this research, a novel approach has been taken, in which smartphone low energy sensors (such as the accelerometer) are exploited. The ubiquitous use of smartphones in everyday life, coupled with the fact that they can collect, store, compute, and transmit data, makes them a feasible and inexpensive alternative to the mainstream methods. Machine learning techniques have been used to develop models that are able to classify vehicle movement and to detect the stop and start points during a trip. Classifiers such as logistic regression, discriminant analysis, classification trees, support vector machines, neural networks, and Hidden Markov models have been tested. Hidden Markov models substantially outperformed all the other methods. The feature quality plays a key role in the success of a model. It was found that, the features which exploited the variance of the data were the most effective. In order to assist in quantifying the performance of the machine learning models, a performance metric called Change Point Detection Performance Metric (CPDPM) was developed. CPDPM proved to be very useful in model evaluation in which the goal was to find the change points in time series data with high accuracy and precision. The integration of accelerometer data, even in the motion direction, yielded an estimated speed with a steady slope, because of factors such as phone sensor bias, vibration, gravity, and other white noise. A calibration method was developed that makes use of the predicted stop and start points and the slope of integrated accelerometer data, which achieves great accuracy in estimating speed. The developed models can serve as the basis for many applications. One such field is fuel consumption and CO2 emission estimation, in which speed is the main input. Transportation mode detection can be improved by integrating speed information. By integrating Vehicle (Phone) to Infrastructure systems (V2I), the model outputs, such as the stop and start instances, average speed along a corridor, and queue length at an intersection, can provide useful information for traffic engineers, planners, and decision makers