Traffic Planning Research Articles

TRAFFIC AND ROAD STUDY IN CONSTANTINE CITY: ANALYSIS OF TRAFFIC IN CONSTANTINE CITY

This study primarily aims to shed light on the traffic and road conditions in the city of Constantine. As a crucial measure in the transportation sector, traffic and road studies play a pivotal role in evaluating the efficiency of existing road networks, determining current and future traffic volumes, identifying bottlenecks, and proposing solutions through traffic redistribution and planning. To assess the current state of traffic and roads in Constantine and their adequacy in relation to the city's area and population, this study was conducted. Constantine was chosen due to its unique position as a hub for numerous major roads with high traffic volumes. Six out of the seven national roads traversing the entire province pass through Constantine. To comprehensively address the subject, the research is divided into three main sections: - Section One: Provides essential definitions related to the topic, such as road network, road capacity, traffic volume, etc. - Section Two: Delves into the details of traffic volume, its types, methods, and the significance of its study. - Section Three: Conducts a detailed analysis of traffic volume and roads in Constantine. Initially, the study relies on the administrative classification of roads to identify those within the study area. Subsequently, it analyzes the average traffic volume and traffic density on these roads.

DGTNet:dynamic graph attention transformer network for traffic flow forecasting

Abstract Graph-based traffic flow prediction plays a crucial role in urban traffic management and planning. In this paper, we propose a novel Dynamic Graph Attention Transformer Network (DGTNet), which is designed to address the issue of inadequate integration of temporal and spatial dimensions in traditional models. DGTNet maintains temporal continuity while revealing the complex dynamic relationships between key nodes in the urban traffic system, capturing the periodic changes in the rhythm of city life. Specifically, this study adopts adaptive signal decomposition technology to decompose traffic data into multiple Intrinsic Mode Functions (IMFs), effectively capturing the dynamic changes in traffic flow. This decomposition method is key to the implementation of DGTNet’s dynamic graph construction, enabling the analysis of traffic flow at different time scales, thereby providing a new perspective for traffic flow prediction research. In the traffic prediction module, we comprehensively consider node, edge, and graph structural information, adopting a multi-head self-attention mechanism to achieve direct cross-modeling in both temporal and spatial dimensions. Finally, we introduce a position-wise feedforward network layer to integrate different types of data and capture nonlinear relationships. The experimental results, based on public transportation network datasets METR_LA, PEMS_BAY, PEMS03, and PEMS07, demonstrate that DGTNet exhibits notable enhancements in three evaluation indicators, namely the Mean Absolute Percentage Error (MAPE), the Root Mean Square Error (RMSE), and the Mean Absolute Error (MAE). The pertinent code has been made available for public access at https://github.com/chenjing0616/DGTNet.

Traffic Prediction of Urban Intersections based on ARIMA Model

In the urban planning, road traffic planning as an important part, through the formation of traffic layout network, can further accelerate the pace of urban development. However, the blockage will cause the car to start and stop regularly, and the car engine will emit a large amount of exhaust gas in these two stages, resulting in increased environmental pollution. Therefore, the focus needs to be on how to predict traffic flow efficiently. This paper focuses on the traffic flow density of intersections in one month, presents the change of traffic density by means of mapping, reasonably divides the training set and the test set, and uses ARIMA model to make effective prediction. It shows the changing relationship between traffic flow and time. Through reasonable prediction, the congestion problem is found in the first time, and at the same time, the solution measures are given, the traffic infrastructure is scientifically allocated, and the safety control system is set up, which provides reference for the later urban traffic development.

Self‐similar traffic prediction for LEO satellite networks based on LSTM

AbstractTraffic prediction serves as a critical foundation for traffic balancing and resource management in Low Earth Orbit (LEO) satellite networks, ultimately enhancing the efficiency of data transmission. The self‐similarity of traffic sequences stands as a key indicator for accurate traffic prediction. In this article, the self‐similarity of satellite traffic data was first analyzed, followed by the construction of a satellite traffic prediction model based on an improved Long Short‐Term Memory (LSTM). An early stopping mechanism was incorporated to prevent overfitting during the model training process. Subsequently, the Diebold‐Mariano (DM) test method was applied to assess the significance of the prediction effect between the proposed model and the comparison model. The experimental results demonstrated that the improved LSTM satellite traffic prediction model achieved the best prediction performance, with Root Mean Squared Error values of 18.351 and 8.828 on the two traffic datasets, respectively. Furthermore, a significant difference was observed in the DM test compared to the other models, providing a solid basis for subsequent satellite traffic planning.

Spatio-temporal attention based collaborative local–global learning for traffic flow prediction

Traffic flow prediction is crucial for intelligent transportation systems (ITS), providing valuable insights for traffic control, route planning, and operation management. Existing work often separately models the spatial and temporal dependencies and primarily relies on predefined graphs to represent spatio-temporal dependencies, neglecting the traffic dynamics caused by unexpected events and the global relationships among road segments. Unlike previous models that primarily focus on local feature extraction, we propose a novel collaborative local–global learning model (LOGO) that employs spatio-temporal attention (STA) and graph convolutional networks (GCN). Specifically, LOGO simultaneously extracts hidden traffic features from both local and global perspectives. In local feature extraction, a novel STA is devised to directly attend to spatio-temporal coupling interdependencies instead of separately modeling temporal and spatial dependencies, and to capture in-depth spatio-temporal traffic context with an adaptive graph focusing on the dynamics in traffic flow. In global feature extraction, a global correlation matrix is constructed and GCNs are utilized to propagate messages on the obtained matrix to achieve interactions between both adjacent and similar road segments. Finally, the obtained local and global features are concatenated and fed into a gated aggregation to forecast future traffic flow. Extensive experiments on four real-world traffic datasets sourced from the Caltrans Performance Measurement System (PEMS03, PEMS04, PEMS07, and PEMS08) demonstrate the effectiveness of our proposed model. LOGO achieves the best performance over 18 state-of-the-art baselines and the best prediction performance with the highest improvement of 6.06% on the PEMS07 dataset. Additionally, two real-world case studies further substantiate the robustness and interpretability of LOGO.

A novel tucker decomposition driven taxi travel demand forecasting algorithm

<p>This research aims to enhance taxi travel demand forecasting for sustainable urban traffic management and planning. We extend the Seasonal Autoregressive Integrated Moving Average model into a high-dimensional tensor form by treating the urban transport network as a Euclidean space and introducing Tucker decomposition. This novel approach, both theoretically significant and practically applicable, represents time series data as tensors to better capture multimodal structures and correlations, improving predictive accuracy. Tucker decomposition reduces computational complexity and memory requirements, making it ideal for large-scale urban traffic network prediction. Experiments on six real-world datasets show that the model’s MAE and RMSE are reduced by about 39.43% and 27.01% on average, respectively, compared to the baseline model. Notably, the model is computationally very efficient and takes only a relatively short time to train., suitable for real-time traffic management, congestion mitigation, and resource optimization. In summary, this work innovates time series analysis, providing an efficient and precise tool for urban traffic management and planning, contributing to sustainable urban transportation advancement.<strong></strong></p>

An OpenStreetMap derived building classification dataset for the United States

Building classification is crucial for population estimation, traffic planning, urban planning, and emergency response applications. Although essential, such data is often not readily available. To alleviate this problem, this work presents a comprehensive dataset by providing residential/non-residential building classification covering the entire United States. We developed a dataset of building types based on building footprints and the available OpenStreetMap information. The dataset is validated using authoritative ground truth data for select counties in the U.S., which shows a high precision for non-residential building classification and a high recall for residential buildings. In addition to the building classifications, this dataset includes detailed information on the OpenStreetMap data used in the classification process. A major result of this work is the resulting dataset of classifying 67,705,475 buildings. We hope that this data is of value to the scientific community, including urban and transportation planners.

Integrated Analysis of Rockfalls and Floods in the Jiului Gorge, Romania: Impacts on Road and Rail Traffic

This study examines the impact of rockfalls and floods on road and rail traffic in the Jiului Gorge, Romania, a critical transportation corridor. Using Sentinel-1 radar imagery processed through ESA SNAP and ArcGIS Pro, alongside traffic detection facilitated by YOLO models, we assessed susceptibility to both rockfalls and floods. The primary aim was to enhance public safety for traffic participants by providing accurate hazard mapping. Our study focuses on the area from Bumbești-Jiu to Petroșani, traversing the Southern Carpathians. The results demonstrate the utility of integrating remote sensing with machine learning to improve hazard management and inform more effective traffic planning. These findings contribute to safer, more resilient infrastructure in areas vulnerable to natural hazards.

TPTrans: Vessel Trajectory Prediction Model Based on Transformer Using AIS Data

The analysis of large amounts of vessel trajectory data can facilitate more complex traffic management and route planning, thereby reducing the risk of accidents. The application of deep learning methods in vessel trajectory prediction is becoming more and more widespread; however, due to the complexity of the marine environment, including the influence of geographical environmental factors, weather factors, and real-time traffic conditions, predicting trajectories in less constrained maritime areas is more challenging than in path network conditions. Ship trajectory prediction methods based on kinematic formulas work well in ideal conditions but struggle with real-world complexities. Machine learning methods avoid kinematic formulas but fail to fully leverage complex data due to their simple structure. Deep learning methods, which do not require preset formulas, still face challenges in achieving high-precision and long-term predictions, particularly with complex ship movements and heterogeneous data. This study introduces an innovative model based on the transformer structure to predict the trajectory of a vessel. First, by processing the raw AIS (Automatic Identification System) data, we provide the model with a more efficient input format and data that are both more representative and concise. Secondly, we combine convolutional layers with the transformer structure, using convolutional neural networks to extract local spatiotemporal features in sequences. The encoder and decoder structure of the traditional transformer structure is retained by us. The attention mechanism is used to extract the global spatiotemporal features of sequences. Finally, the model is trained and tested using publicly available AIS data. The prediction results on the field data show that the model can predict trajectories including straight lines and turns under the field data of complex terrain, and in terms of prediction accuracy, our model can reduce the mean squared error by at least 6×10−4 compared with the baseline model.

Do the sparser access points have less impact on arterial traffic? A microscopic simulation-based study

A dynamic microscopic traffic flow simulation within a ring arterial context was developed to investigate the effects of access point spacing on urban arterial flow under a right-in-right-out access management system. The microscopic traffic flow model, centered on car-following and lane-changing behaviors, was established based on vehicle interactions. The car-following aspect encompasses free driving, car-following behavior, and deceleration and braking states while lane-changing considerations include decision-making and acceptable gap assessment. Experimental scenarios account for arterial traffic density, access traffic demand intensity, average access point spacing, and variation coefficient of access point spacing. The traffic flow and speeds within the ring arterial were evaluated across 5040 operational conditions (equating to 5880 simulation hours). The traffic flow trends and speed variations with density across different access spacing scenarios were analyzed. We made an intriguing discovery: the impact on arterial traffic flow increases with larger average access point spacing, challenging conventional traffic planning recommendations that advocate for greater spacing. Additionally, access traffic minimally affects the overall arterial flow when arterial traffic volume is low. By highlighting these critical insights, this study introduces novel considerations for designing and managing access points.

A hybrid model for missing traffic flow data imputation based on clustering and attention mechanism optimizing LSTM and AdaBoost

Reliable traffic flow data is not only crucial for traffic management and planning, but also the foundation for many intelligent applications. However, the phenomenon of missing traffic flow data often occurs, so we propose an imputation model for missing traffic flow data to overcome the randomness and instability bands of traffic flow. First, k-means clustering is used to classify road segments with traffic flow belonging to the same pattern into a group to utilize the spatial characteristics of roads fully. Then, the LSTM networks optimized with an attention mechanism are used as the base learner to extract the temporal dependence of the traffic flow. Finally, the AdaBoost algorithm is used to integrate all the LSTM-attention networks into a reinforced learner to impute the missing data. To validate the effectiveness of the proposed model, we use the PeMS dataset for validation, we impute the data with missing data rate from 10 to 60% under three missing modes, and we use multiple baseline models for comparison, which confirms that our proposed model improves the stability and accuracy of imputing the missing data of the traffic flow with different scenarios.

An Intelligent Mobile Prediction method with Mini-batch HTIA-based Seq2Seq Networks

The prediction of individual mobility has been shown to hold significant commercial and social value in traffic planning and location advertising. However, the prediction of individual mobility is prone to substantial errors and high overhead time because of online learning or long input sequences. We propose an innovative sequence-to-sequence model with mini-batch hierarchical temporal incidence attention (HTIA) to address this issue. This model effectively captures long-term and short-term dependencies underlying individual mobility patterns. We perform mini-batch training via sequence padding in HTIA to increase the model's efficiency while maintaining interpretability. Through extensive experiments conducted on three public datasets exhibiting different degrees of uncertainty, we demonstrated that our proposed approach outperforms state-of-the-art competing schemes, reducing the best mean relative error results by more than 70.8%, 60.8%, and 69.9%.

Hybrid deep learning model with VMD-BiLSTM-GRU networks for short-term traffic flow prediction

Accelerating urbanization and the rapid development of intelligent transportation systems have rendered short-term traffic flow prediction an important research field. Accurate prediction of traffic flow is beneficial for the optimization of traffic planning, improvement of road utilization, reduction of traffic congestion, and reduction in the incidence of traffic accidents. However, data pertaining to traffic flow are typically influenced by a multitude of factors, resulting in data that exhibit a considerable degree of nonlinearity and complexity. To address the issue of noise in raw traffic flow data, this study proposes a hybrid model that combines variational modal decomposition (VMD), a bidirectional long short-term memory network (BiLSTM), and a gated recurrent unit (GRU) for short-term traffic flow prediction. To validate the effectiveness of the model, an experimental validation was conducted based on traffic flow data from UK highways, and the performance of the model was compared with common benchmark models. The experimental results demonstrate that the proposed method yields superior prediction results in terms of mean absolute error, coefficient of determination, and root-mean-square error compared to existing prediction techniques, thereby substantiating its efficacy in short-term traffic flow prediction.

Refining Cellular Data for Accurate Trip Chain Identification: A Novel Approach for Urban Travel Analysis

Cellular data play a crucial role in supporting travel demand assessment and urban traffic planning because of their cost-effectiveness and extensive coverage. However, inherent inaccuracies, such as imprecise positioning, data duplication, and abnormal communication frequencies, hinder their ability to depict travelers’ trips accurately. In this paper, we present a novel approach to mitigate these issues by employing base station mapping to refine positioning data, eliminating duplicates and abnormal frequency records. This refinement enables more effective utilization of cellular data for trip chain identification. We address the ping-pong handover effect by employing a finite automaton machine and an approximate nearest neighbor searching method with carefully selected seeds to identify activities. Our method’s accuracy is validated through ground truth value analysis, focusing on permanent resident population estimation and metro passenger flow estimation. Furthermore, we demonstrate the practical effectiveness and value of our proposed method through a series of representative applications in a real-world case study in the city of Nanning, Guangxi Province, China.

Impact of Debris Removal Post-Wildfires on Pavement Fatigue and Rutting Lives: Case Studies of California’s Camp and Carr Fires

Between 2017 and 2018, California experienced a series of four devastating fires, including the Camp and Carr Fires, which ranked among the most destructive fires in U.S. history. During these fires, roads were critical in the evacuation, rescue operations, goods transportation, and access to critical services. Additionally, postfire, road infrastructure became crucial for removing hazardous and nonhazardous waste from fire-affected areas to major landfills and recycling facilities. Despite the significance of pavements in this process, previous studies have not quantitatively assessed the potential damage caused to pavements by the additional trucks used in debris removal operations. This research aimed to address this knowledge gap by collecting precise traffic data for the routes taken to waste management facilities, including data on the number of trips involved in debris transportation. The traffic information was then utilized to calculate changes in equivalent single axle loads and traffic index values for pavement design. Pavement structures were obtained from the available core database. Pavement simulation results showed that of the nine studied highways, only one exhibited a reduction in cracking life of about 2 years. However, Skyway, the main artery in the town of Paradise, demonstrated a significantly accelerated fatigue cracking failure by 14.3 years. A sensitivity analysis of fire intensity showed other highway sections that were structurally adequate could be affected by larger fires. The presented methodology could be used in traffic planning as part of debris management operations to avoid vulnerable pavement sections.

Analysis of the environmental factors contributing to road traffic accidents involving school-aged pedestrian children in urban Algerian settings

Despite efforts by the Algerian government and civil society to improve road safety, traffic accidents remain a significant issue, particularly affecting children and youth. Data from the National Delegation for Road Safety (NDSR) reveals that this demographic is especially vulnerable, with 371 child fatalities recorded during the 2020-2021 school year. This study evaluated road safety around twelve primary schools in Oued El Abtal district, Mascara province, and aimed to identify effective interventions through a participatory approach involving 1,074 students. An anonymous questionnaire was used to capture students' perceptions of road risks, and the data were analyzed using IBM SPSS Statistics V27. Chi-square and Fisher’s exact tests were applied to examine associations between gender and variables like travel mode, school accompaniment, student behavior, and crossing locations. The sample was balanced by gender, consisting of 50.9% boys and 49.1% girls. Walking was the predominant travel mode (75.2%; n=808). Key risk factors included inadequate infrastructure (3.2%), insufficient signage (11.7%), sidewalk obstructions (5.1%), dangerous driving behaviors (12.2%, with speeding at 29.5%), heavy traffic (16.2%), and poor urban planning (e.g., lack of speed bumps and low visibility). These findings highlight the urgent need for targeted interventions to improve the safety of school commutes. This research identifies critical risk factors in school travel, providing a foundation for developing strategies aimed at enhancing road safety for children in Algeria.

Research on Smart City Road Network Capacity Optimization Configuration Based on Deep Learning Algorithms

At present, the urban traffic system is faced with the problems of congestion and low efficiency, and the traditional methods have certain limitations when dealing with the complex urban road network. This paper aims to explore a new method of capacity Optimization of Road Networks in a Smart City environment and proposes a method based on a Graph Neural network, named “Smart City Road optimization Graph Neural Network” (SCRO-GNN). SCRO-GNN first collects and preprocesses multi-source data, including road network data, traffic flow, accident records, environmental factors, etc. The key node and edge features, including the number of lanes at the intersection, the traffic flow of the section, and the adjacency matrix of the road network are defined to characterize the structure of the road network. Then, the graph neural network model is constructed and trained to predict the traffic flow of different sections and evaluate the road capacity by using the graph structure of the road network. This paper tests the performance of SCRO-GNN on real road networks in multiple cities. The results show that, compared with the traditional traffic flow prediction model, SCRO-GNN can significantly improve the prediction accuracy, especially when dealing with the highly complex urban road network structure. Based on these predictions, the proposed optimization strategy performed well in reducing traffic congestion and improving the efficiency of road use. The research in this paper not only demonstrates the potential of graph neural networks in smart city road network optimization, but also provides a new direction for future traffic system research. The successful implementation of the SCRO-GNN approach is expected to provide more efficient and intelligent solutions for urban traffic management and planning.

MODELS FOR REAL-TIME TRAFFIC FLOW MANAGEABILITY AND DECISION-MAKING IN INTELLIGENT TRANSPORTATION SYSTEMS

This article explores models in Intelligent Transportation Systems for real-time traffic flow manageability, focusing on decision-making processes. It covers forecasting, planning, implementing, and controlling strategies to manage traffic flow and ease congestion. Traffic flow prediction models, like dynamic route guidance and traffic flow prediction, utilize historical data and real-time inputs for proactive decision-making. Traffic flow planning models, such as dynamic route guidance index and route efficiency factor, aid in route selection and signal timing optimization. In order to streamline the boundless complexity, the authors assume that it is effective to delineate the managerial capacity paradigm of intelligent transportation systems into the two separate scenarios of “stable and known situation” and “unstable and with large uncertainty situation”. The article proposes a hypothesis to improve the decision-making process in traffic flow. The distinction between these two situations is essential for the smooth running of the business and requires a thorough understanding of the traffic flow in real time, making decisions in intelligent transport systems in order to direct the traffic. The article focuses on data-driven decisions for smoother traffic flow.

The Use of Smart Sensors to Enhance Road Safety: An Experimental Study to Explore the Usability of Smart Traffic Light Systems with Emergency Vehicles

The evolving landscape of transportation systems is being reshaped by the integration of sensors and Internet of Things (IoT) technology, particularly in the context of traffic light systems. These innovations aim to transform traditional traffic lights into smart devices capable of being controlled by emergency vehicles (EVs) to ensure safe passage, thereby enhancing road safety, and reducing intersection accidents. However, there are challenges in implementing such technologies and gaining the trust of EV drivers navigating busy intersections. The traditional fixed-time traffic signal plans are contrasted with the dynamic adjustment made possible by smart sensors and applications, which collect real-time traffic data and adapt signal timings to changing conditions, reducing congestion, and enhancing traffic flow. However, security concerns arise, necessitating the strengthening of systems with advanced technologies to address potential security and safety issues. The technological advancements have a significant impact on the transportation industry. Smart sensors in streetlights play a crucial role in solving long-standing transportation issues, and machine learning is employed for data analysis and traffic forecasting, benefiting both regular and EV drivers. Further research is needed to advance technologies like IoT and smart sensors and to integrate GSM sim cards and GPS to support EV drivers in finding the quickest route to their final destinations. EVs are becoming more than vehicles, evolving into smart devices equipped with technical tools allow drivers to control traffic lights, ensuring a clear path and more safety.

Enhancing Traffic Management in Cyber Physical Systems – A Gradient Based Fuzzy Controller Approach

Traffic forecast is a critical aspect of effective traffic management and planning in cyber-physical systems (CPS). In this study, we present a novel approach to traffic prediction and regulation within cyber-physical systems (CPS), introducing the Gradient Rule based Fuzzy Controller. This innovative methodology utilizes dynamic fuzzy logic control enhanced with gradient-based rules to adapt signal timings in real-time, effectively addressing the variable nature of traffic. Our results demonstrate significant improvements in reducing total queue length and delay at intersections, with reductions of up to 91.23%. Furthermore, extensive simulations and evaluations underscore the superiority of our approach compared to state-of-the-art models, highlighting its flexibility and adaptability to diverse traffic scenarios. This research emphasizes the novelty of integrating gradient-based rules into fuzzy control techniques, offering a promising avenue for advancing traffic management systems in CPS environments.