Related papers: Semantic Exploration of Traffic Dynamics

Data on citywide street-segment traffic volumes are essential for urban planning and sustainable mobility management. Yet such data are available only for a limited subset of streets due to the high costs of sensor deployment and…

Despite the importance of urban traffic flows, there are only a few theoretical approaches to determine fundamental relationships between macroscopic traffic variables such as the traffic density, the utilization, the average velocity, and…

Human mobility regularity is crucial for understanding urban dynamics and informing decision-making processes. This study first quantifies the periodicity in complex human mobility data as a sparse identification of dominant positive…

Short-term traffic volume prediction is crucial for intelligent transportation system and there are many researches focusing on this field. However, most of these existing researches concentrated on refining model architecture and ignored…

Data on human spatial distribution and movement is essential for understanding and analyzing social systems. However existing sources for this data are lacking in various ways; difficult to access, biased, have poor geographical or temporal…

Accurate, scalable traffic monitoring is critical for real-time and long-term transportation management, particularly during disruptions such as natural disasters, large construction projects, or major policy changes like New York City's…

Crowd-sourced traffic data offer great promise in environmental modeling. However, archives of such traffic data are typically not made available for research; instead, the data must be acquired in real time. The objective of this paper is…

This paper employs deep learning in detecting the traffic accident from social media data. First, we thoroughly investigate the 1-year over 3 million tweet contents in two metropolitan areas: Northern Virginia and New York City. Our results…

This work proposes the use of Information Theory for the characterization of vehicles behavior through their velocities. Three public data sets were used: i.Mobile Century data set collected on Highway I-880, near Union City, California;…

Accurate time-series forecasting is vital for numerous areas of application such as transportation, energy, finance, economics, etc. However, while modern techniques are able to explore large sets of temporal data to build forecasting…

We perform a systematic analysis on the large-scale taxi trip data to uncover urban mobility and city dynamics in multimodal urban transportation environments. As a case study, we use the taxi origin-destination trip data and some…

Mobility and network traffic have been traditionally studied separately. Their interaction is vital for generations of future mobile services and effective caching, but has not been studied in depth with real-world big data. In this paper,…

Modern cities are complex systems, evolving at a fast pace. Thus, many urban planning, political, and economic decisions require a deep and up-to-date understanding of the local context of urban neighborhoods. This study shows that the…

Our goal is to use overhead imagery to understand patterns in traffic flow, for instance answering questions such as how fast could you traverse Times Square at 3am on a Sunday. A traditional approach for solving this problem would be to…

Traffic speed data imputation is a fundamental challenge for data-driven transport analysis. In recent years, with the ubiquity of GPS-enabled devices and the widespread use of crowdsourcing alternatives for the collection of traffic data,…

The potential of an efficient ride-sharing scheme to significantly reduce traffic congestion, lower emission level, as well as facilitating the introduction of smart cities has been widely demonstrated. This positive thrust however is faced…

Urban planning is increasingly data driven, yet the challenge of designing with data at a city scale and remaining sensitive to the impact at a human scale is as important today as it was for Jane Jacobs. We address this challenge with…

Non-recurrent and unpredictable traffic events directly influence road traffic conditions. There is a need for dynamic monitoring and prediction of these unpredictable events to improve road network management. The problem with the existing…

Social media outlets such as Twitter constitute valuable data sources for understanding human activities in the virtual world from a geographic perspective. This paper examines spatial distribution of tweets and densities within cities. The…

Traffic forecasting is crucial for smart cities and intelligent transportation initiatives, where deep learning has made significant progress in modeling complex spatio-temporal patterns in recent years. However, current public datasets…