Multiple abrupt phase transitions in urban transport congestion
- URL: http://arxiv.org/abs/2005.12902v3
- Date: Mon, 11 Jan 2021 16:59:03 GMT
- Title: Multiple abrupt phase transitions in urban transport congestion
- Authors: Aniello Lampo, Javier Borge-Holthoefer, Sergio G\'omez, Albert
Sol\'e-Ribalta
- Abstract summary: We show that the location of the onset of congestion changes according to the considered urban area.
We introduce a family of planar road network models composed of a dense urban center connected to an arboreal periphery.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: During the last decades, the study of cities has been transformed by new
approaches combining engineering and complexity sciences. Network theory is
playing a central role, facilitating the quantitative analysis of crucial urban
dynamics, such as mobility, city growth or urban planning. In this work, we
focus on the spatial aspects of congestion. Analyzing a large amount of real
city networks, we show that the location of the onset of congestion changes
according to the considered urban area, defining, in turn, a set of congestion
regimes separated by abrupt transitions. To help unveiling these spatial
dependencies of congestion (in terms of network betweenness analysis), we
introduce a family of planar road network models composed of a dense urban
center connected to an arboreal periphery. These models, coined as GT and
DT-MST models, allow us to analytically, numerically and experimentally
describe how and why congestion emerges in particular geographical areas of
monocentric cities and, subsequently, to describe the congestion regimes and
the factors that promote the appearance of their abrupt transitions. We show
that the fundamental ingredient behind the observed abrupt transitions is the
spatial separation between the urban center and the periphery, and the number
of separate areas that form the periphery. Elaborating on the implications of
our results, we show that they may have an influence on the design and
optimization of road networks regarding urban growth and the management of
daily traffic dynamics.
Related papers
- Rethinking Urban Mobility Prediction: A Super-Multivariate Time Series
Forecasting Approach [71.67506068703314]
Long-term urban mobility predictions play a crucial role in the effective management of urban facilities and services.
Traditionally, urban mobility data has been structured as videos, treating longitude and latitude as fundamental pixels.
In our research, we introduce a fresh perspective on urban mobility prediction.
Instead of oversimplifying urban mobility data as traditional video data, we regard it as a complex time series.
arXiv Detail & Related papers (2023-12-04T07:39:05Z) - A shape-based heuristic for the detection of urban block artifacts in
street networks [0.0]
Street networks are ubiquitous components of cities, guiding their development and enabling movement from place to place.
Their graph representation is often designed primarily for transportation purposes.
This representation is less suitable for other use cases where transportation networks need to be simplified.
arXiv Detail & Related papers (2023-09-01T13:11:35Z) - Adaptive Hierarchical SpatioTemporal Network for Traffic Forecasting [70.66710698485745]
We propose an Adaptive Hierarchical SpatioTemporal Network (AHSTN) to promote traffic forecasting.
AHSTN exploits the spatial hierarchy and modeling multi-scale spatial correlations.
Experiments on two real-world datasets show that AHSTN achieves better performance over several strong baselines.
arXiv Detail & Related papers (2023-06-15T14:50:27Z) - Extracting Spatial Interaction Patterns between Urban Road Networks and
Mixed Functions [4.198538504785438]
The more mixed the functions of an area has, the more possible its vitality may be.
Our study shows that the higher the degree of the road network structure has, the more likely it will attract functions' aggregation.
It also reveals that diversified local degree will help gather urban functions.
arXiv Detail & Related papers (2022-11-03T01:41:46Z) - Neural Embeddings of Urban Big Data Reveal Emergent Structures in Cities [7.148078723492643]
We propose using a neural embedding model-graph neural network (GNN)- that leverages the heterogeneous features of urban areas.
Using large-scale high-resolution mobility data sets from millions of aggregated and anonymized mobile phone users in 16 metropolitan counties in the United States, we demonstrate that our embeddings encode complex relationships among features related to urban components.
We show that embeddings generated by a model trained on a different county can capture 50% to 60% of the emergent spatial structure in another county.
arXiv Detail & Related papers (2021-10-24T07:13:14Z) - An Experimental Urban Case Study with Various Data Sources and a Model
for Traffic Estimation [65.28133251370055]
We organize an experimental campaign with video measurement in an area within the urban network of Zurich, Switzerland.
We focus on capturing the traffic state in terms of traffic flow and travel times by ensuring measurements from established thermal cameras.
We propose a simple yet efficient Multiple Linear Regression (MLR) model to estimate travel times with fusion of various data sources.
arXiv Detail & Related papers (2021-08-02T08:13:57Z) - Methodological Foundation of a Numerical Taxonomy of Urban Form [62.997667081978825]
We present a method for numerical taxonomy of urban form derived from biological systematics.
We derive homogeneous urban tissue types and, by determining overall morphological similarity between them, generate a hierarchical classification of urban form.
After framing and presenting the method, we test it on two cities - Prague and Amsterdam.
arXiv Detail & Related papers (2021-04-30T12:47:52Z) - Local Betweenness Centrality Analysis of 30 European Cities [0.0]
We show that road segments important for long-distance transportations are concentrated along larger streets while those for short range transportations form clusters around CBD, historical, or residential districts.
Our findings are useful for urban planners and decision-makers to understand the current situation of the city and make informed decisions.
arXiv Detail & Related papers (2021-03-21T17:08:04Z) - Multi-intersection Traffic Optimisation: A Benchmark Dataset and a
Strong Baseline [85.9210953301628]
Control of traffic signals is fundamental and critical to alleviate traffic congestion in urban areas.
Because of the high complexity of modelling the problem, experimental settings of current works are often inconsistent.
We propose a novel and strong baseline model based on deep reinforcement learning with the encoder-decoder structure.
arXiv Detail & Related papers (2021-01-24T03:55:39Z) - Empowering Urban Governance through Urban Science: Multi-scale Dynamics
of Urban Systems Worldwide [0.0]
The current science of cities can provide a useful foundation for future urban policies.
International comparisons of the evolution of cities often produce uncertain results because national territorial frameworks are not always in strict correspondence with the dynamics of urban systems.
We propose to provide various compositions of systems of cities to better take into account the dynamic networking of cities that go beyond regional and national territorial boundaries.
arXiv Detail & Related papers (2020-05-20T12:47:40Z) - Constructing Geographic and Long-term Temporal Graph for Traffic
Forecasting [88.5550074808201]
We propose Geographic and Long term Temporal Graph Convolutional Recurrent Neural Network (GLT-GCRNN) for traffic forecasting.
In this work, we propose a novel framework for traffic forecasting that learns the rich interactions between roads sharing similar geographic or longterm temporal patterns.
arXiv Detail & Related papers (2020-04-23T03:50:46Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.