310 research outputs found
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
マルチスケールの視点からみた中国における都市開発と人口移動の関係に関する研究
Development is the main problem facing cities in the world today. Urban development is inseparable from the support of labor. The population movement between regions provides a guarantee for the sustainable development of the city. Therefore, the interactive relationship between urban development and population mobility needs more in-depth research. This research combines official statistics and emerging big data to study the interactive relationship between urban development and population mobility from the macro, meso and micro levels. In addition, with the help of exploratory spatial data analysis methods, the spatial effects between urban development and population mobility can be captured, including spatial dependence and spatial heterogeneity. The use of spatial econometric models reveals the driving forces that affect population mobility. The results of the empirical analysis can provide a theoretical reference for the future development of China’s urbanization.北九州市立大
IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation
During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture
Multimodal urban mobility and multilayer transport networks
Transportation networks, from bicycle paths to buses and railways, are the
backbone of urban mobility. In large metropolitan areas, the integration of
different transport modes has become crucial to guarantee the fast and
sustainable flow of people. Using a network science approach, multimodal
transport systems can be described as multilayer networks, where the networks
associated to different transport modes are not considered in isolation, but as
a set of interconnected layers. Despite the importance of multimodality in
modern cities, a unified view of the topic is currently missing. Here, we
provide a comprehensive overview of the emerging research areas of multilayer
transport networks and multimodal urban mobility, focusing on contributions
from the interdisciplinary fields of complex systems, urban data science, and
science of cities. First, we present an introduction to the mathematical
framework of multilayer networks. We apply it to survey models of multimodal
infrastructures, as well as measures used for quantifying multimodality, and
related empirical findings. We review modelling approaches and observational
evidence in multimodal mobility and public transport system dynamics, focusing
on integrated real-world mobility patterns, where individuals navigate urban
systems using different transport modes. We then provide a survey of freely
available datasets on multimodal infrastructure and mobility, and a list of
open source tools for their analyses. Finally, we conclude with an outlook on
open research questions and promising directions for future research.Comment: 31 pages, 4 figure
A graph deep learning method for short-term traffic forecasting on large road networks
Short‐term traffic flow prediction on a large‐scale road network is challenging due to the complex spatial–temporal dependencies, the directed network topology, and the high computational cost. To address the challenges, this article develops a graph deep learning framework to predict large‐scale network traffic flow with high accuracy and efficiency. Specifically, we model the dynamics of the traffic flow on a road network as an irreducible and aperiodic Markov chain on a directed graph. Based on the representation, a novel spatial–temporal graph inception residual network (STGI‐ResNet) is developed for network‐based traffic prediction. This model integrates multiple spatial–temporal graph convolution (STGC) operators, residual learning, and the inception structure. The proposed STGC operators can adaptively extract spatial–temporal features from multiple traffic periodicities while preserving the topology information of the road network. The proposed STGI‐ResNet inherits the advantages of residual learning and inception structure to improve prediction accuracy, accelerate the model training process, and reduce difficult parameter tuning efforts. The computational complexity is linearly related to the number of road links, which enables citywide short‐term traffic prediction. Experiments using a car‐hailing traffic data set at 10‐, 30‐, and 60‐min intervals for a large road network in a Chinese city shows that the proposed model outperformed various state‐of‐the‐art baselines for short‐term network traffic flow prediction
Algorithmic Analysis of Intermodal Transport Network
Tato práce je zaměřena na analýzu intermodální dopravní sítě pomocí multikriteriálního algoritmu s ohledem na priority města. Nejprve popisujeme reprezentaci intermodální dopravní sítě. Poté definujeme úlohu analýzy nad danou reprezentací. Jedná se o algoritmickou analýzu, tedy na základě zadané poptávky cestujících vyhodnocujeme klíčové indikátory. Mezi zahrnuté indikátory patří počet přeplněných úseků spojů, doba jízdy všech cestujících a celkové náklady všech cestujících. Cílem analýzy je optimalizovat počet přeplněných úseků dopravní sítě tím, že nabídneme cestujícím alternativní jízdy. Tyto cesty se snaží vyhnout úsekům dopravní sítě, kde jsou spoje přeplněné. Vyhnout se lze vybráním jiného spoje veřejné dopravy, jízdou na kole, nebo využitím taxi služby. Popisujeme multikriteriální algoritmus, který pro každého cestujícího vyhledá vhodnou cestu, přičemž optimalizuje čtyři kritéria: obsazenost vozu, dobu jízdy, cestovní náklady a počet přestupů. Také implementujeme nástroj pro analýzu, který obsahuje tento multikriteriální algoritmus a z nalezených cest vypočítá chtěné klíčové indikátory. Pomocí našeho nástroje provádíme analýzu intermodální dopravní sítě hlavního města Prahy. Při evaluaci námi vygenerované poptávky cestujících dosahujeme snížení počtu přeplněných úseků spojů v intermodální dopravní síti o 79,4 %.This work focuses on the analysis of the intermodal transport network using a multi-criteria algorithm that considers preferences of the city. To perform the analysis, we first describe the representation of the intermodal transport network. Given the representation, we define the intermodal transport network analysis problem with preferences of the city. We aim at algorithmic analysis, which computes key performance indicators using given travel demand. Thus, we provide various key performance indicators, e.g., the number of overcrowded trip segments, the total duration of all passenger journeys, and the total costs of passenger journeys. The goal of the analysis is to optimize the number of overcrowded parts of the public transport network. To achieve the goal, we offer passengers alternative journeys. These journeys try to avoid public transport vehicles with occupancy beyond a certain level of comfort. In other words, a passenger may choose another public transport connection, ride a bike, or use a taxi service. We propose a multi-criteria algorithm that finds a suitable journey for each passenger while optimizing four criteria, i.e., vehicle occupancy, duration, costs, and the number of interchanges. We also implement an analysis tool that includes the multi-criteria algorithm and calculates the required key performance indicators. By using the analysis tool, we perform an analysis using the intermodal transport network of the capital city of Prague. In the evaluation, we achieve the reduction in the number of overcrowded trip segments in the intermodal transport network by 79.4 % on randomly generated travel demand
Regional Transport and Its Association with Tuberculosis in the Shandong Province of China, 2009-2011
Human mobility has played a major role in the spread of infectious diseases such as tuberculosis (TB) through transportation; however, its pattern and mechanism have remained unclear. This study used transport networks as a proxy for human mobility to generate the spatial process of TB incidence. It examined the association between TB incidence and four types of transport networks at the provincial level: provincial roads, national roads, highways, and railways. Geographical information systems and geospatial analysis were used to examine the spatial distribution of 2217 smear-positive TB cases reported between 2009 and 2011 in the Shandong province. The study involved factors such as population density and elevation difference in conjunction with the types of transport networks to predict the disease occurrence in space. It identified spatial clusters of TB incidence linked not only with transport networks of the regions but also differentiated by elevation. Our research findings provide evidence of targeting populous regions with well-connected transport networks for effective surveillance and control of TB transmission in Shandong.postprin
Geo Data Science for Tourism
This reprint describes the recent challenges in tourism seen from the point of view of data science. Thanks to the use of the most popular Data Science concepts, you can easily recognise trends and patterns in tourism, detect the impact of tourism on the environment, and predict future trends in tourism. This reprint starts by describing how to analyse data related to the past, then it moves on to detecting behaviours in the present, and, finally, it describes some techniques to predict future trends. By the end of the reprint, you will be able to use data science to help tourism businesses make better use of data and improve their decision making and operations.
Multi-headed self-attention mechanism-based Transformer model for predicting bus travel times across multiple bus routes using heterogeneous datasets
Bus transit is a crucial component of transportation networks, especially in urban areas. Bus agencies must enhance the quality of their real-time bus travel information service to serve their passengers better and attract more travelers. Various models have recently been developed for estimating bus travel times to increase the quality of real-time information service. However, most are concentrated on smaller road networks due to their generally subpar performance in densely populated urban regions on a vast network and failure to produce good results with long-range dependencies. This paper develops a deep learning-based architecture using a single-step multi-station forecasting approach to predict average bus travel times for numerous routes, stops, and trips on a large-scale network using heterogeneous bus transit data collected from the GTFS database and the vehicle probe data. Over one week, data was gathered from multiple bus routes in Saint Louis, Missouri. This study developed a multi-headed self-attention mechanism-based Univariate Transformer neural network to predict the mean vehicle travel times for different hours of the day for multiple stations across multiple routes. In addition, we developed Multivariate GRU and LSTM neural network models for our research to compare the prediction accuracy and comprehend the robustness of the Transformer model. To validate the Transformer Model's performance more in comparison to the GRU and LSTM models, we employed the Historical Average Model and XGBoost model as benchmark models. Historical time steps and prediction horizon were set up to 5 and 1, respectively, which means that five hours of historical average travel time data were used to predict average travel time for the following hour. Only the historical average bus travel time was used as the input parameter for the Transformer model. Other features, including spatial and temporal information, volatility measures (e.g., the standard deviation and variance of travel time), dwell time, expected travel time, jam factors, hours of a day, etc., were captured from our dataset. These parameters were employed to develop the Multivariate GRU and LSTM models. The model's performance was evaluated based on a performance metric called Mean Absolute Percentage Error (MAPE). The results showed that the Transformer model outperformed other models for one-hour ahead prediction having minimum and mean MAPE values of 4.32 percent and 8.29 percent, respectively. We also investigated that the Transformer model performed the best during different traffic conditions (e.g., peak and off-peak hours). Furthermore, we also displayed the model computation time for the prediction; XGBoost was found to be the quickest, with a prediction time of 6.28 seconds, while the Transformer model had a prediction time of 7.42 seconds. The study's findings demonstrate that the Transformer model showed its applicability for real-time travel time prediction and guaranteed the high quality of the predictions produced by the model in the context of a complicated extensive transportation network in high-density urban areas and capturing long-range dependencies.Includes bibliographical references
Book of abstracts of the 24th Euro Working Group on Transportation Meeting
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