Travel Mode Recognition from GPS Data Based on LSTM

A large amount of GPS data contains valuable hidden information. With GPS trajectory data, a Long Short-Term Memory model (LSTM) is used to identify passengers' travel modes, i.e., walking, riding buses, or driving cars. Moreover, the Quantum Genetic Algorithm (QGA) is used to optimize the LSTM model parameters, and the optimized model is used to identify the travel mode. Compared with the state-of-the-art studies, the contributions are: 1. We designed a method of data processing. We process the GPS data by pixelating, get grayscale images, and import them into the LSTM model. Finally, we use the QGA to optimize four parameters of the model, including the number of neurons and the number of hidden layers, the learning rate, and the number of iterations. LSTM is used as the classification method where QGA is adopted to optimize the parameters of the model. 2. Experimental results show that the proposed approach has higher accuracy than BP Neural Network, Random Forest and Convolutional Neural Networks (CNN), and the QGA parameter optimization method can further improve the recognition accuracy

IMPUTING SOCIAL DEMOGRAPHIC INFORMATION BASED ON PASSIVELY COLLECTED LOCATION DATA AND MACHINE LEARNING METHODS

Multiple types of passively collected location data (PCLD) have emerged during the past 20 years. Its capability in travel demand analysis has also been studied and revealed. Unlike the traditional surveys whose sample is designed efficiently and carefully, PCLD features a non-probabilistic sample of dramatically larger size. However, PCLD barely contains any ground truth for both the human subjects involved and the movements they produce. The imputation for such missing information has been evaluated for years, including origin and destination, travel mode, trip purpose, etc. This research intends to advance the utilization of PCLD by imputing social demographic information, which can help to create a panorama for the large volume of travel behaviors observed and to further develop a rational weighting procedure for PCLD. The Conditional Inference Tree model has been employed to address the problems because of its abilities to avoid biased variable selection and overfitting

Evaluating car-sharing switching rates from traditional transport means through logit models and Random Forest classifiers

Positive impacts of car-sharing, such as reductions in car ownership, congestion, vehicle-miles-traveled and greenhouse gas emissions, have been extensively analyzed. However, these benefits are not fully effective if car-sharing subtracts travel demand from existing sustainable modes. This paper evaluates substitution rates of car-sharing against private cars and public transport using a Random Forest classifier and Binomial Logit model. The models were calibrated and validated using a stated-preference travel survey and applied to a revealed-preference survey, both administered to a representative sample of the population living in Turin (Italy). Results of the two models show that the predictive power of both models is comparable, albeit the Logit model tends to estimate predictions with a higher reliability and the Random Forest model produces higher positive switches towards car-sharing. However, results from both models suggest that the substitution rate of private cars is, on average, almost five times that of public transport

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

From Accessibility and Exposure to Engagement: A Multi-scalar Approach to Measuring Environmental Determinants of Children’s Health Using Geographic Information Systems

A growing body of research suggests that increasing the accessibility to health-related environmental features and increasing exposure to and engagement in outdoor environments leads to positive benefits for the overall health and well-being of children. Additionally, research over the last twenty-five years has documented a decline in the time children spend outdoors. Outdoor activity in children is associated with increased levels of physical fitness, and cognitive well-being. Despite acknowledging this connection, problems occur for researchers when attempting to identify the child’s location and to measure whether a child has made use of an accessible health-related facility, or where, when and for how long a child spends time outdoors. The purpose of this thesis is to measure children’s accessibility to, exposure to, and engagement with health-promoting features of their environment. The research on the environment-health link aims to meet two objectives: 1) to quantify the magnitude of positional discrepancies and accessibility misclassification that result from using several commonly-used address proxies; and 2) to examine how individual-level, household-level, and neighbourhood-level factors are associated with the quantity of time children spend outdoors. This will be achieved by employing the use of GPS tracking to objectively quantify the time spent outdoors using a novel machine learning algorithm, and by applying a hexagonal grid to extract built environment measures. This study aims to identify the impact of positional discrepancies when measuring accessibility by examining misclassification of address proxies to several health-related facilities throughout the City of London and Middlesex County, Ontario, Canada. Positional errors are quantified by multiple neighbourhood types. Findings indicate that the shorter the threshold distance used to measure accessibility between subject population and health-related facility, the higher the proportion of misclassified addresses. Using address proxies based on large aggregated units, such as centroids of census tracts or dissemination areas, can result in vast positional discrepancies, and therefore should be avoided in spatial epidemiologic research. To reduce the misclassification, and positional errors, the use of individual portable passive GPS receivers were employed to objectively track the spatial patterns, and quantify the time spent outdoors of children (aged 7 to 13 years) in London, Ontario across multiple neighbourhood types. On the whole, children spent most of their outdoor time during school hours (recess time) and the non-school time outdoors in areas immediately surrounding their home. From these findings, policymakers, educators, and parents can support children’s health by making greater efforts to promote outdoor activities for improved health and quality of life in children. This thesis aims to advance our understanding of the environment and health-link and suggests practical steps for more well-informed decision making by combining novel classification and mapping techniques

Transportation mode detection based on mobile sensor data

This thesis addresses transportation mode detection based primarily on mobile phone data using machine learning methods. Our approach uses short samples of accelerometer readings taken while traveling in a vehicle to distinguish between three modalities --- car, bus, and train. We use gravity estimation to pre-process the samples. We extract features from statistical, frequency-based, and peak-based domain. With statistical analysis of the features we gain an introspective into the data. To additionally analyze the features we construct several feature sets for classification. As a classifier we use random forest, support vector machine, and neural network. Our approach correctly classifies 65% cars, 63% buses, and 18% trains using neural network

Transportation mode detection based on mobile sensor data

This thesis addresses transportation mode detection based primarily on mobile phone data using machine learning methods. Our approach uses short samples of accelerometer readings taken while traveling in a vehicle to distinguish between three modalities --- car, bus, and train. We use gravity estimation to pre-process the samples. We extract features from statistical, frequency-based, and peak-based domain. With statistical analysis of the features we gain an introspective into the data. To additionally analyze the features we construct several feature sets for classification. As a classifier we use random forest, support vector machine, and neural network. Our approach correctly classifies 65% cars, 63% buses, and 18% trains using neural network