Predicting passenger origin-destination in online taxi-hailing systems

Because of transportation planning, traffic management, and dispatch optimization importance, passenger origin-destination prediction has become one of the most important requirements for intelligent transportation systems management. In this paper, we propose a model to predict the next specified time window travels' origin and destination. To extract meaningful travel flows, we use K-means clustering in four-dimensional space with maximum cluster size limitation for origin and destination zones. Because of the large number of clusters, we use non-negative matrix factorization to decrease the number of travel clusters. Also, we use a stacked recurrent neural network model to predict travel count in each cluster. Comparing our results with other existing models shows that our proposed model has 5-7% lower mean absolute percentage error (MAPE) for 1-hour time windows, and 14% lower MAPE for 30-minute time windows.Comment: 25 pages, 20 figure

The Possibility of Big Data Spatio-Temporal Analytics for Understanding Human Behavior and Their Spatial Patterns in Urban Area

13301甲第4630号博士（学術）金沢大学博士論文本文Ful

Investigations of outdoor mobility patterns of taxicabs in urban scenarios

This thesis investigates various outdoor mobility patterns of taxicabs in urban environments based on open-data real traces and it proposes a suitable outdoor mobility model to fit the provided measurement data. This thesis is processing user traces of taxicabs of two major cities: Rome and San Francisco downloaded from CRAWDAD open-source repository, which is responsible for sharing data from real networks and real mobile users across the various research communities around the world. There are numerous sources of collecting traces of users in a city, such as mobile devices, vehicles, smart cards, floating sensors etc. This thesis presents a comparative analysis of the mobility patterns of various taxicabs from Rome and San Francisco cities based on data collected via GPS-enabled mobile devices. Finding suitable mobility models of taxicabs to represent the travelling patterns of users moving from one location to another with respect to their varying time, location and speed can be quite helpful for the advanced researches in the diverse fields of wireless communications, such as better network planning, more efficient smart city design, improved traffic flows in cities. Also other applications such as weather forecasting, cellular coverage planning, e-health services, prediction of tourist areas, intelligent transport systems can benefit from the information hidden in user traces and from being able to find out statistically valid mobility models. The work here focused on extracting various mobility parameters from the crowdsourced open-source data and trying to model them according to various mobility models existing in the literature. The measurement analysis of this thesis work was completed in Matlab

Investigations of outdoor mobility patterns of taxicabs in urban scenarios

This thesis investigates various outdoor mobility patterns of taxicabs in urban environments based on open-data real traces and it proposes a suitable outdoor mobility model to fit the provided measurement data. This thesis is processing user traces of taxicabs of two major cities: Rome and San Francisco downloaded from CRAWDAD open-source repository, which is responsible for sharing data from real networks and real mobile users across the various research communities around the world. There are numerous sources of collecting traces of users in a city, such as mobile devices, vehicles, smart cards, floating sensors etc. This thesis presents a comparative analysis of the mobility patterns of various taxicabs from Rome and San Francisco cities based on data collected via GPS-enabled mobile devices. Finding suitable mobility models of taxicabs to represent the travelling patterns of users moving from one location to another with respect to their varying time, location and speed can be quite helpful for the advanced researches in the diverse fields of wireless communications, such as better network planning, more efficient smart city design, improved traffic flows in cities. Also other applications such as weather forecasting, cellular coverage planning, e-health services, prediction of tourist areas, intelligent transport systems can benefit from the information hidden in user traces and from being able to find out statistically valid mobility models. The work here focused on extracting various mobility parameters from the crowdsourced open-source data and trying to model them according to various mobility models existing in the literature. The measurement analysis of this thesis work was completed in Matlab

Analysis of Lisbon visitors’ internet access behavior: behavior analysis through the identification of clusters

Project Work presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Marketing IntelligenceThis master's thesis focuses on clustering the internet access behavior of urban visitors in the Lisbon urban area. To promote smart city development, the study aims to provide insights into visitors' behaviors while accessing the internet in Lisbon, enabling improved decision-making processes for city management, and enhancing the overall online and offline experience for visitors. The over-tourism phenomenon has put a strain on infrastructure, public transportation, and cultural heritage sites. Therefore, innovative methods are needed for effective smart city management, particularly in urban mobility. The increasing availability of Wi-Fi networks during travel has generated valuable data that can be used to develop groundbreaking approaches to understanding visitors’ behaviors and mobility patterns in urban areas. This knowledge enables the analysis and clustering of urban visitors' behavior, contributing to improved decision-making processes in smart city management

A spatiotemporal analysis of the impact of lockdown and coronavirus on London’s bicycle hire scheme: from response to recovery to a new normal

The coronavirus pandemic that started in 2019 has had wide-ranging impacts on many aspects of people’s daily lives. At the peak of the outbreak, lockdown measures and social distancing changed the ways in which cities function. In particular, they had profound impacts on urban transportation systems, with public transport being shut down in many cities. Bike share systems (BSS) were widely reported as having experienced an increase in demand during the early stages of the pandemic before returning to pre-pandemic levels. However, the studies published to date focus mainly on the first year of the pandemic, when various waves saw continual relaxing and reintroductions of restrictions. Therefore, they fall short of exploring the role of BSS as we move to the post-pandemic period. To address this gap, this study uses origin-destination (O-D) flow data from London’s Santander Cycle Hire Scheme from 2019–2021 to analyze the changing use of BSS throughout the first two years of the pandemic, from lockdown to recovery. A Gaussian mixture model (GMM) is used to cluster 2019 BSS trips into three distinct clusters based on their duration and distance. The clusters are used as a reference from which to measure spatial and temporal change in 2020 and 2021. In agreement with previous research, BSS usage was found to have declined by nearly 30% during the first lockdown. Usage then saw a sharp increase as restrictions were lifted, characterized by longer, less direct trips throughout the afternoon rather than typical peak commuting trips. Although the aggregate number of BSS trips appeared to return to normal by October 2020, this was against the backdrop of continuing restrictions on international travel and work from home orders. The period between July and December 2021 was the first period that all government restrictions were lifted. During this time, BSS trips reached higher levels than in 2019. Spatio-temporal analysis indicates a shift away from the traditional morning and evening peak to a more diffuse pattern of working hours. The results indicate that the pandemic may have had sustained impacts on travel behavior, leading to a “new normal” that reflects different ways of working

A Smartphone-Based Prototype System for Incident/Work Zone Management Driven by Crowd-Sourced Data

This project develops a smartphone-based prototype system that supplements the 511 system to improve its dynamic traffic routing service to state highway users under non-recurrent congestion. This system will save considerable time to provide crucial traffic information and en-route assistance to travelers for them to avoid being trapped in traffic congestion due to accidents, work zones, hazards, or special events. It also creates a feedback loop between travelers and responsible agencies that enable the state to effectively collect, fuse, and analyze crowd-sourced data for next-gen transportation planning and management. This project can result in substantial economic savings (e.g. less traffic congestion, reduced fuel wastage and emissions) and safety benefits for the freight industry and society due to better dissemination of real-time traffic information by highway users. Such benefits will increase significantly in future with the expected increase in freight traffic on the network. The proposed system also has the flexibility to be integrated with various transportation management modules to assist state agencies to improve transportation services and daily operations

Curated Landscapes: The Evolution of the Postcard Shot

This research examines traveling landscape-objects in tourist environments and their impact on cultural identity in America. Traveling landscape-objects include any form of tourist paraphernalia or representation of cultural landscapes. For these purposes, I studied different forms of tourist representation of the Natchez Trace Parkway, an entity of the National Park Service. Research areas include the content, location, and changing medium of traveling landscape-objects, while also addressing their meaning, frequency, quality, role in non-representational arenas, and the future of tourist representations. Methods include detailed cataloguing and analysis of over one thousand images of various shapes and forms ⎯ ranging from early illustrations of the Natchez Trace Parkway, to historic photographs, postcards and finally digital images found on flickr.com. Results suggest that we can identify prominent cultural landscape icons by acknowledging where tourists collected the most representations or traveling landscape-objects. In addition, the form or medium of traveling landscape-objects affects their meaning, frequency, and quality in that tourists value the tactile quality of representations. Lastly, the intrinsic value of representations (even in non-representational arenas) is confirmed, and their future secured

Identifying the gaps between needs, expectations, and views of different stakeholders related to car-sharing, bike-sharing, and scooter-sharing systems

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