Inferring transportation mode from smartphone sensors:Evaluating the potential of Wi-Fi and Bluetooth

Understanding which transportation modes people use is critical for smart cities and planners to better serve their citizens. We show that using information from pervasive Wi-Fi access points and Bluetooth devices can enhance GPS and geographic information to improve transportation detection on smartphones. Wi-Fi information also improves the identification of transportation mode and helps conserve battery since it is already collected by most mobile phones. Our approach uses a machine learning approach to determine the mode from pre-prepocessed data. This approach yields an overall accuracy of 89% and average F1 score of 83% for inferring the three grouped modes of self-powered, car-based, and public transportation. When broken out by individual modes, Wi-Fi features improve detection accuracy of bus trips, train travel, and driving compared to GPS features alone and can substitute for GIS features without decreasing performance. Our results suggest that Wi-Fi and Bluetooth can be useful in urban transportation research, for example by improving mobile travel surveys and urban sensing applications

Using attitudes and green consciousness as a determinant of travel behaviour and market segmentation

L'abstract è presente nell'allegato / the abstract is in the attachmen

Analysing Human Mobility Patterns of Hiking Activities through Complex Network Theory

The exploitation of high volume of geolocalized data from social sport tracking applications of outdoor activities can be useful for natural resource planning and to understand the human mobility patterns during leisure activities. This geolocalized data represents the selection of hike activities according to subjective and objective factors such as personal goals, personal abilities, trail conditions or weather conditions. In our approach, human mobility patterns are analysed from trajectories which are generated by hikers. We propose the generation of the trail network identifying special points in the overlap of trajectories. Trail crossings and trailheads define our network and shape topological features. We analyse the trail network of Balearic Islands, as a case of study, using complex weighted network theory. The analysis is divided into the four seasons of the year to observe the impact of weather conditions on the network topology. The number of visited places does not decrease despite the large difference in the number of samples of the two seasons with larger and lower activity. It is in summer season where it is produced the most significant variation in the frequency and localization of activities from inland regions to coastal areas. Finally, we compare our model with other related studies where the network possesses a different purpose. One finding of our approach is the detection of regions with relevant importance where landscape interventions can be applied in function of the communities.Comment: 20 pages, 9 figures, accepte

Morphology, lifecycles, and environmental sensitivities of tropical trimodal convection

Includes bibliographical references.2022 Fall.Convective clouds are ubiquitous in the tropics and typically follow a trimodal distribution of cumulus, congestus, and cumulonimbus clouds. Due to the crucial role each convective mode plays in tropical and global transport of heat and moisture, there has been both historical and recent interest in the characteristics, sensitivities, and lifecycles of these clouds. However, designing novel studies to further our knowledge has been challenging due to several limitations: the extensive computing resources needed to conduct modeling studies at sufficient resolution and scale to capture the trimodal distribution in detail; the lack of analysis tools which can objectively detect and track these clouds throughout their lifetime; and a need for more observational and modeling data of the tropical convective environments that produce these clouds. In this dissertation, three distinct but related studies that address these problems to advance the knowledge of our field on the morphology, lifecycles, and environmental sensitivities of tropical trimodal convection are presented. The first study examines the sensitivities of the tropical trimodal distribution and the convective environment to initial aerosol loading and low-level static stability. The Regional Atmospheric Modeling System (RAMS) configured as a Large Eddy Simulation (LES) is utilized to resolve all three modes in detail through two full diurnal cycles. Three initial static stabilities and three aerosol profiles are independently and simultaneously varied for a suite of nine simulations. This research found that (1) large aerosol loading and strong low-level static stability suppress the bulk environment and the intensity and coverage of convective clouds; (2) cloud and environmental responses to aerosol loading tend to be stronger than those from static stability; (3) the effects of aerosol and stability perturbations modulate each other substantially; (4) the deepest convection and highest dynamical intensity occur at moderate aerosol loading, rather than at low or high loading; and (5) most of the strongest feedbacks due to aerosol and stability perturbations are seen in the boundary layer (the latter being applied within the boundary layer themselves), though some are stronger above the freezing level. The second study presented seeks to further enhance an artificial intelligence analysis tool, the Tracking and Object-Based Analysis of Clouds (tobac) Python package, from both a scientific and procedural standpoint to enable a wider variety of research uses, including process-level studies of tropical trimodal convection. Scientific improvements to tobac v1.5 include an expansion of the tool from 2D to 3D analyses and the addition of a new spectral filtering tool. Procedural enhancements added include greater computational efficiency, data regridding capabilities, and treatments for processing data with singly or doubly periodic boundary conditions (PBCs). My distinct contributions to this work focused on the 2D to 3D expansion and the PBC treatment. These new capabilities are presented through figures, schematics, and discussion of the new science that tobac v1.5 facilitates, such as the analysis of large basin-scale datasets and detailed simulations of layered clouds, that would have been impossible before. Finally, the last study in this dissertation is a process-focused modeling study on the sensitivities of upscale growth of tropical trimodal convection to environmental aerosol loading. This project was enabled by the scientific and procedural improvements to tobac discussed in the second study, in particular the new abilities of tobac to detect and track objects in 3D and with model PBCs. Here, we used a subset of RAMS simulations from the first study, where only aerosol loading was changed and the upscale growth from shallow cumulus through congestus and cumulonimbus during the nighttime hours was investigated. This study revealed that moderately increasing aerosol loading enhances collision-coalescence processes in the middle of the cloud, which delays initial glaciation but promotes it later in the growth period. Greatly increasing aerosol, however, produces a cloud structure with a more extreme aspect ratio and greater entrainment aloft that rapidly loses buoyancy and vertical velocity with height, as well as exhibiting a greater amount of condensate loading towards the top of the cloud. We also found the relative timing of these processes to be especially important, with more rapid initial growth and lofting of condensate often inhibiting deeper convective growth

Incorporation of micro-level analysis in strategic urban transport modelling: with a case study of the Greater Beijing

Many developing countries and regions are suffering from severe urban transport problems arising from accidents, congestion, air pollution, rising carbon intensity, and chronic under-funding of infrastructure and services. The problems make those cities the most polluted and often the least liveable. Strategic transport modelling has been recognised as an effective approach for developing and testing policy options, especially where it is integrated with land use planning and urban design. However, in most developing-country cities strategic transport modelling has been out of reach for practical policy use because of its sophisticated data and skill requirements, which currently imply unaffordable high costs and long durations for model development. This means that strategic urban transport modelling is the least available where it is needed most urgently. Meanwhile, the spread of smart data in mapping and urban activity monitoring has often been just as rapid in developing countries as in the developed. This has triggered new approaches in micro-level analyses of transport networks, personal movements and vehicles. In the most advanced cases, the new analyses have started to influence strategic modelling. The main hypothesis of this dissertation is that an incorporation of the micro-level smart data and analyses in strategic urban transport modelling will make it feasible to establish a sufficiently robust strategic transport model for evidence-based policy analysis with cost, time and skill thresholds that are close to being affordable in developing country cities. In order to test this main hypothesis, a number of novel model development tasks have been carried out which contribute to the field of applied urban modelling. This new approach aims to contribute to the transformation of the prevailing modus operandi where model development could not start in earnest until extensive data collection and skills training have been completed to a situation where a sufficiently robust model can be established cheaply and quickly to support on-going and incremental refinements. More specifically, new modelling tools have been developed as part of this dissertation using sparse GPS taxi traces to identify slow-moving and stopping traffic hotspots using an extended density-based spatial clustering algorithm that is tolerant of significant data noise, and to estimate congested road speeds (which used to be very costly and time-consuming to obtain if at all). The micro-level network, congested speeds and insights into the nature of the congested traffic have been incorporated into a MEPLAN-based strategic transport model interacting with a MEPLAN-based land use and travel demand model. This means that the strategic economic, social and environmental impacts of transport interventions can be tested in a robust way through accounting for the interactions among transport, land-use and background social-technical trends. A new approach to establish the medium to long term visions for alternative travel demand management and transport investment scenarios has been tested using this model. The methods and algorithms have been tested in a case study of the Greater Beijing region, which consists of the municipalities of Beijing and Tianjin together with the surrounding areas in the province of Hebei. The government’s data regulations of restricting overseas studies to using only publicly available data sources have made the case study ideal for testing the new approach. The potential of the new strategic urban transport model has been tested through a wide range of policy scenarios. The results suggest that the new approach developed in this dissertation has made it not only cheaper and faster to develop a robust model, but could also potentially fill a gap in the lack of medium to long term perspectives regarding major road and metro investments over the next two decades. Such analyses could be of critical importance in improving the performance of the transport system in terms of safety, economic efficiency, air quality and carbon reduction given the long lead times to plan and deliver transport infrastructure investments

Evaluating Impacts of Shared E-scooters from the Lens of Sustainable Transportation

As the popularity of shared micromobility is increasing worldwide, city governments are struggling to regulate and manage these innovative travel technologies that have several benefits, including increasing accessibility, reducing emissions, and providing affordable travel options. This dissertation evaluates the impacts of shared micromobility from the perspective of sustainable transportation to provide recommendations to decision-makers, planners, and engineers for improving these emerging travel technologies. The dissertation focuses on four core aspects of shared micromobility as follows: 1) Safety: I evaluated police crash reports of motor vehicle involving e-scooter and bicycle crashes using the most recent PBCAT crash typology to provide a comprehensive picture of demographics of riders crashing and crash characteristics, as well as mechanism of crash and crash risk, 2) Economics: I estimated the demand elasticity of e-scooters deployed, segmented by weekday type, land use, category of service providers based on fleet size using negative binomial fixed effect regression model and K-means clustering, 3) Expanding micromobility to emerging economies: Using dynamic stated preference pivoting survey and panel data mixed logit model, I assessed the intentions to adopt shared micromobility in mid-sized cities of developing countries, where these innovative technology could be the first wave of decarbonizing transportation sector, and 4) Micromobility data application: I identified five usage-clusters of shared e-scooter trips using combination of Principal Component Analysis (PCA) and K-means clustering to propose a novel framework for using micromobility data to inform data-driven decision on broader policy goals. Based on the key findings of the research, I provide five recommendations as follows: 1) decision-makers should be proactive in incorporating new travel technologies like shared micromobility, 2) city governments should leverage shared micromobility usage and operation data to empower the decision-making process, 3) each shared micromobility vehicles should be approached uniquely for improving road safety, 4) city governments should consider regulating the number of service providers and their fleet sizes, and 5) decision-makers should prioritize expanding shared micromobility in emerging economies as one of the first efforts to the decarbonizing transportation sector