Aug 2026· Applied and Computational Engineering· Vol 253, pp. 180-186· 0 citations
TL;DR
A logic-oriented analysis of urban traffic spatiotemporal data examines three temporal evolution characteristics: periodicity, trend, and abrupt change and extracts a logical framework for traffic condition prediction.
Abstract
Urban traffic flow data constitutes a complex dynamic system, exhibiting both temporal evolution and spatial dependence. In the context of high-density metropolitan areas such as the Greater Bay Area in China and the construction of emerging smart cities, extracting operational patterns from historical traffic observation data and inferring future traffic conditions has become a core issue in urban computing and intelligent transportation. This paper conducts a logic-oriented analysis of urban traffic spatiotemporal data. First, it examines three temporal evolution characteristics: periodicity, trend, and abrupt change. Then, it analyzes how road network topology affects the spatial dependence between road segments. Based on this, the paper explores the coupling mechanism between the temporal and spatial dimensions from the perspective of congestion propagation dynamics and extracts a logical framework for traffic condition prediction. This paper also discusses key challenges such as cross-regional multi-modal analysis, the integration of domain knowledge and data-driven methods, and the shift from open-loop prediction to closed-loop decision support, aiming to provide a structured analytical framework that contributes to understanding the intrinsic mechanisms of urban traffic system operation.
Examination of the spatiotemporal evolution of urban traffic congestion in Beijing from a deep learning perspective based on multi-source data suggests that traffic congestion in Beijing displays a pronounced “dual-peak” pattern associated with daily commuting activities.
Zihan Zhou· Computers and artificial int...· 0 citations
Urban traffic congestion remains a critical challenge for smart city development, particularly in the context of real-time monitoring and prediction for intelligent transportation systems. To address this issue, this study proposes a Digital Twin-based urban traffic prediction framework using a lightweight Diffusion Convolutional Recurrent Neural Network (DCRNN-Lite). The proposed model integrates spatial dependencies among road segments through diffusion convolution and temporal traffic dynamics through recurrent modeling, enabling effective spatiotemporal traffic forecasting with reduced computational complexity. Experiments were conducted on the real-world METR-LA dataset, consisting of traffic speed data from 207 sensors deployed across the Los Angeles highway network. The experimental results demonstrate that DCRNN-Lite achieves stable prediction performance, as reflected by low Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), consistent convergence behavior, and strong correlation between predicted and actual traffic speeds at both node and city-wide levels. Despite its simplified architecture, the model effectively captures local traffic variations and global mobility trends, making it suitable for real-time deployment. The findings indicate that the proposed approach provides a favorable balance between accuracy and efficiency, highlighting its potential as a core component for digital twin-enabled smart cities and metaverse-based urban traffic management systems.
H. Awad· International Journal Resear...· 0 citations
OBJECTIVES
Identifying the hotspots is crucial to prevent the hazardous effects of Road Traffic Accidents (RTAs). This research based on the significant current and future hotspots and temporal patterns of the RTAs of two years (2022-2023) by integrating the GIS tools with statistical analysis, investigates and reveals the future hotspots and road intersections and segments having high density of RTAs in Faisalabad city, Pakistan. The aim of this research is to identify the spatial hotspots of RTAs by analyzing the RTAs density along road network intersections, and to predict the future hotspots of the RTAs. It provides the insights for a thorough understanding of the road network's aspects leading RTAs.
METHODS
To examine this, we have acquired the datasets of total 38,865 RTAs incidents, city area administrative boundaries, 4,458 km road network having 64,557 segments. Three advanced spatio-temporal analysis tools are employed. Network Kernel Density Estimation (NetKDE) to identify the dense accident segments, Repeat and Near Repeat analysis tool integrating with Predictive Zones analysis tool for predicting future hotspots are used. These tools allow to identify the highly effected road intersections and future hotspots locations and temporally variations of RTAs.
RESULTS
This research seeks attention to the density of accidents at specific intersections and road sections, examining the part that city's road infrastructure and rush-hour traffic play in the frequencies of accidents. Important City's intersections and arterial network, such as West Canal Road, Chenab Chowk, and Clock Tower, were the primary locations of accidents. The most accidents are concentrated near commercial areas or roads that are commonly used for commuting. The X-intersection accounts for 27.4% and Crossroad intersection accounts for 24.1% of total accidents. The accuracy index of predictive model reached at the point of 1.78, that is indicating the 78% better efficiency of this model rather than randomly allocating the future hotspots. The results reveal a continuous pattern of accidents at these segments throughout both years, it also provides the precise locations, peak time and risk zones of accidents. That requires the traffic control during peak time and improvements in road infrastructure the installation of traffic signals and availability of proper walkable spaces and maintenance of footpaths.
CONCLUSION
This research provides a thorough examination of the spatio-temporal aspects of RTAs with futuristic approach, providing reliable and practical findings for enhancing the public safety, improving traffic management and infrastructure, for the well-being of the urban environment and living standard.
Abdullah Munif, Shoaib Khalid, Fariha Zameer et al.· Traffic Injury Prevention· 0 citations
The prediction of human movement and crowd density is critical for enhancing urban planning and managing
traffic in smart cities. Due to the rise of digital technologies and location data, it has been easier to track how humans interact
in various geographical locations over different time intervals. Unfortunately, static methods and straightforward statistical
models cannot predict crowd movement since they are too rigid to capture the dynamism of real-world scenarios. The
current study aims to present a pragmatic solution to predicting crowd density through a data-driven approach that involves
machine learning and contextual logic. The algorithm relies on location, date, and time inputs to identify key features related
to temporal factors, seasonality, and location attributes. Predictions are initially generated by employing a random forest
regression method before optimizing results using behavioral rules. For better convenience, the proposed method is
implemented within a web interface, which offers not only predictions but also map visualization and hotspots. It helps the
user comprehend the concentration and distribution of crowd density in various locations. Our method is scalable and
applicable in a practical environment as it does not require any costly big data or infrastructural resources. On the whole,
our work shows the benefits of applying machine learning together with logical rules to urban mobility forecasting
problems.
Meghna Beeram, M. Shreyas, Mohana Hiranmayi et al.· International Journal of Inn...· 0 citations
A dynamic monitoring and emergency dispatch model based on high-precision spatio-temporal big data based on a better non-dominated sorting genetic algorithm is adopted for multi-objective adaptive emergency resource scheduling.
Urban traffic congestion continues to increase travel time, fuel consumption, and environmental impact in modern cities, and Tbilisi is no exception: traffic intensity at signalized intersections varies sharply depending on time of day, direction, and the workday–weekend cycle. This study proposes an integrated, AI-assisted framework that connects real-time route-level traffic observation, short-term forecasting, and adaptive signal control within a single decision-support pipeline. Traffic data were collected at five-minute intervals for twelve origin–destination route pairs surrounding one critical intersection in Tbilisi using the Google Routes API, with automated acquisition and storage implemented through AWS Lambda, EventBridge, and Amazon S3, producing a structured dataset of 23,556 observations and 75 variables. A LightGBM regression model forecasts short-term delay at two horizons (t+15 and t+30 minutes), while a two-stage multi-class classifier separates normal from congested traffic states and, within the congested subset, distinguishes slow traffic from jam conditions. Forecasts are translated into operational signal-control actions through a rule-based decision layer and evaluated in a SUMO/TraCI microscopic simulation environment against a fixed-time baseline, a max-pressure-style pressure-adaptive controller, a bridge-model hybrid controller, and an experimental Deep Q-Network (DQN) reinforcement-learning controller. Results show that LightGBM achieved R² = 0.82 and R² = 0.77 at the two forecasting horizons, while the two-stage classifier reached 90.6% overall accuracy. In simulation, the pressure-adaptive controller reduced mean waiting time by approximately 33.6% relative to the fixed-time baseline, while the DQN controller achieved an average reduction of approximately 23.3% across multiple random seeds. The proposed framework demonstrates a reproducible, data-driven pathway from real-time observation to adaptive traffic-signal decision-making, without removing engineering judgment from the control loop.
Nikoloz Patatishvili, Besik Tabatadze· Computational and Applied Sc...· 0 citations
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