Traffic Flow Characteristics Research Articles

Study of Urban Arterial Road Off-Ramp Configuration in Highway Municipal Reconstruction

To reduce traffic congestion and improve the overall traffic capacity of roads, the municipal reconstruction of arterial roads from high-grade highways often involves setting up auxiliary roads on both sides of the road. This paper analyzes the existing methods of determining the configuration of urban arterial road off-ramps and proposes a novel design method, includes adding a “waiting for merging” section. Based on the traffic flow characteristics of the main roads, auxiliary roads, as well as off-ramps, second-stage deceleration theory and the insertable gap theory are applied. Considering the worst-case of diverted vehicles, a calculation model for the length of the urban arterial road off-ramps is proposed and utilized to calculate the advisory values for each segment of the off-ramps. Taking the reconstruction of an expressway in Hubei Province, China, as a case study, Vissim traffic simulation software was used to compare the proposed method with the traditional design method under four different traffic volumes. The results show that the proposed design method greatly improves the overall traffic capacity of roads compared with the traditional method, with a reduction of approximately 40% and 60% in delay time and queue length for the overall traffic, respectively. Furthermore, the research results can provide direct and effective design solutions and a basis for decision-making for setting the length of main and auxiliary road off-ramps.

A combined short- and medium-term traffic flow prediction method for proactive traffic control at expressway toll stations

Traffic flow prediction can support proactive control strategies to alleviate queues and delays at expressway toll stations. The prediction should be enough accurate and advanced that leaves sufficient preparation time to implement control measures for upcoming congestions. The accuracy of prediction and the lead time for accurate prediction are contradictory. To balance both aspects, a control-oriented combined prediction method was proposed in this study. This method automatically tuned prediction horizons based on predicted level of congestion. It used long short-term memory (LSTM) neural networks to learn the spatial–temporal characteristics of historical traffic flow and predict short-term and medium-term traffic flow at toll stations. Then, the level of congestion was determined for graded congestion warning and dynamic tuning of prediction horizons. To validate the proposed method, the Ganggou toll station was selected as the study site. The proposed integrated model can facilitate reliable proactive control measures at expressway toll stations.

Pre-disaster resource allocation based on network topology and flow features

Protecting transportation systems from severe damage is crucial for their post-disaster functionality. We adopt network topological metrics and generalised topological metrics involving demand and traffic flow characteristics to identify critical links for enhancing post-event transportation network performance. A single-parameter, non-monotonic function is proposed to allocate limited resources based on these metrics. Monte Carlo simulation generates failure scenarios to evaluate residual network performance through network topological measures and network level of service indicators. This work introduces ‘Trip Efficiency’, a novel level of service indicator that considers both demand connectivity and travel efficiency for a network with multiple links interrupted. In Anaheim case study network, betweenness-based resource allocation leads to better performance on network topological measures; while generalised topological metrics result in more connected OD pairs and higher travel efficiency. The experimental results demonstrate the effectiveness of the proposed method in enhancing network resilience against multisite disruptive events through proactive resource allocations.

Research on the Supply of Shared Bicycles in Subway Hub Stations-Taking the Subway Station of Zhengzhou People 's Hospital as an Example

In recent years, with the rapid development of shared bicycles, it has played a great role in alleviating urban traffic pressure and facilitating residents to go out to a certain extent. However, while solving the problem of the last kilometer of travel, the uneven distribution of shared bicycles in subway hub stations is becoming more and more serious, which has a certain impact on the quality of residents ' travel. This paper takes the supply of shared bicycles in urban subway hub stations as the research object, and observes and summarizes the characteristics of slow traffic flow near the entrances and exits of subway hub stations in the morning and evening peak hours. On the premise of ensuring the right of way for pedestrians, the supply and demand of shared bicycles in the subway hub station and the configuration of shared bicycles in the surrounding space of the subway hub station are comprehensively considered, and the supply of shared bicycles is predicted by the estimation model and the field data of the subway hub station are investigated, taking the subway station of Zhengzhou People 's Hospital as an example to carry out field research and predict the actual demand and supply of shared bicycles in the subway hub. Finally, the problems existing in the research on the supply of shared bicycles in metro hub stations and possible future research directions are summarized.

Investigating built environment and traffic flow impact on crash frequency in urban road networks

While numerous studies have examined the factors that influence crash occurrence, there remains a gap in understanding the intricate relationship between built environment, traffic flow, and crash occurrences across different spatial units. This study explores how built environment attributes, and dynamic traffic flow characteristics affect crash frequency by focusing on proposed traffic density-based zones (TDZs). Utilizing a comprehensive dataset from Greater Melbourne, Australia, this research emphasizes on the dynamic traffic flow variables and insights from the Macroscopic Fundamental Diagram model, considering parameters such as shockwave velocity and congestion index. The association between the potential influencing factors and crash frequency is examined using a random parameter negative binomial regression model. Results indicate that the data segmentation based on TDZs is instrumental in establishing a more refined crash model compared to traditional planning-based zones, as demonstrated by improved goodness-of-fit measures. Factors including density (e.g., employment density), network design (e.g., road density and highway density), land use diversity (e.g., job-housing balance and land use mixture), and public transit accessibility (e.g., bus route density) are significantly associated with crash occurrence. Furthermore, the unobserved heterogeneity effects of the shockwave velocity and congestion index on crashes are revealed. The study highlights the significance of incorporating dynamic traffic flow variables in understanding crash frequency variations across different spatial units. These findings can inform optimal real-time traffic monitoring, environmental design, and road safety management strategies to mitigate crash risks.

An improved eco-driving strategy for mixed platoons of autonomous and human-driven vehicles

The emergence of autonomous vehicles (AVs) could transform traffic flow characteristics and affect energy consumption subsequently. The primary objective of this study is to propose an improved eco-driving strategy for the mixed platoons by exploring the impacts of AVs on energy consumption characteristics. Initially, leveraging precise AV control, this study utilizes a Model Predictive Control (MPC) system to optimize AV trajectories, aiming to minimize energy consumption within mixed platoons of AVs and human-driven vehicles (HDVs). Given the differing collaborative capabilities of HDVs and AVs in mixed platoons, exceptional situations may arise at intersections where not all platoon vehicles cross the stop line during the same green light cycle (GLC). To address these challenges, we then propose a splitting approach to optimize the platoon operation mode. This approach allows for flexible splitting decisions based on AVs market penetration and current traffic conditions. The simulation results indicate that the energy consumption reduction ranges from 15.35% to 35.39% and 2.75–3.64% compared to the traditional mode and the eco-driving mode, respectively. In addition, through an analysis of the energy consumption of various vehicle orders within a mixed platoon, we determined that positioning the second AV in the middle platoon maximizes stability, resulting in a more consistent energy consumption pattern. These findings highlight the advantages of the strategy in terms of energy consumption, providing theoretical support management of mixed platoons.

LSTM deep learning long-term traffic volume prediction model based on Markov state description

ABSTRACT When applying long short-term memory (LSTM) neural network model to traffic prediction, there are limitations in exploiting spatial-temporal traffic state features. The interpretability of models has not received enough attention. This study suggests an LSTM traffic flow prediction model that can anticipate traffic volume 24 h in advance. The model makes use of the traffic flow state information obtained from the fuzzy C-means clustering method by clustering the multi-day historical traffic flow data. Markov chain is used to capture the label feature of traffic flow using transition probability matrix information. To show the efficacy of the suggested technique, experiments were conducted using real traffic volume data from a city in China. The simulation results demonstrate that the proposed model can attain greater prediction accuracy, and the network training time may be significantly reduced.

Traffic Flow Prediction Research Based on an Interactive Dynamic Spatial–Temporal Graph Convolutional Probabilistic Sparse Attention Mechanism (IDG-PSAtt)

Accurate traffic flow prediction is highly important for relieving road congestion. Due to the intricate spatial–temporal dependence of traffic flows, especially the hidden dynamic correlations among road nodes, and the dynamic spatial–temporal characteristics of traffic flows, a traffic flow prediction model based on an interactive dynamic spatial–temporal graph convolutional probabilistic sparse attention mechanism (IDG-PSAtt) is proposed. Specifically, the IDG-PSAtt model consists of an interactive dynamic graph convolutional network (IL-DGCN) with a spatial–temporal convolution (ST-Conv) block and a probabilistic sparse self-attention (ProbSSAtt) mechanism. The IL-DGCN divides the time series of a traffic flow into intervals and synchronously and interactively shares the captured dynamic spatiotemporal features. The ST-Conv block is utilized to capture the complex dynamic spatial–temporal characteristics of the traffic flow, and the ProbSSAtt block is utilized for medium-to-long-term forecasting. In addition, a dynamic GCN is generated by fusing adaptive and learnable adjacency matrices to learn the hidden dynamic associations among road network nodes. Experimental results demonstrate that the IDG-PSAtt model outperforms the baseline methods in terms of prediction accuracy. Specifically, on METR-LA, the mean absolute error (MAE) and root mean square error (RMSE) induced by IDG-PSAtt for a 60 min forecasting scenario are reduced by 0.75 and 1.31, respectively, compared to those of the state-of-the-art models. This traffic flow prediction improvement will lead to more precise estimates of the emissions produced by mobile sources, resulting in more accurate air quality forecasts. Consequently, this research will greatly support local environmental management efforts.

A Short-Term Traffic Flow Prediction Method for Airport Group Route Waypoints Based on the Spatiotemporal Features of Traffic Flow

To fully leverage the spatiotemporal dynamic correlations in air traffic flow and enhance the accuracy of traffic flow prediction models, thereby providing a more precise basis for perceiving congestion situations in the air route network, a study was conducted on a traffic flow prediction method based on deep learning considering spatiotemporal factors. A waypoint network topology graph was constructed, and a neural network model called graph convolution and self-attention-based long short-term memory neural network (GC-SALSTM) was proposed. This model utilized waypoint flow and network efficiency loss rate as input features, with graph convolution extracting spatial features from the waypoint network. Additionally, a long short-term memory network based on a self-attention mechanism was used to extract temporal features, achieving accurate prediction of waypoint traffic. An example analysis was performed on a typical busy sector of airports in the Central and Southern China region. The effectiveness of adding the network efficiency loss rate as an input feature to improve the accuracy of critical waypoint traffic prediction was validated. The performance of the proposed model was compared with various typical prediction models. The results indicated that, with the addition of the network efficiency loss rate, the root mean square error (RMSE) for eight waypoints decreased by more than 10%. Compared to the historical average (HA), autoregressive integrated moving average (ARIMA), support vector regression (SVR), long short-term memory (LSTM), and graph convolution network and long short-term memory network (GCN-LSTM) models, the RMSE of the proposed model decreased by 11.78%, 5.55%, 0.29%, 2.53%, and 1.09%, respectively. This suggests that the adopted network efficiency loss rate indicator effectively enhances prediction accuracy, and the constructed model exhibits superior predictive performance in short-term waypoint traffic forecasting compared to other prediction models. It contributes to optimizing flight paths and high-altitude air routes, minimizing flight delays and airborne congestion to the greatest extent, thus enhancing the overall efficiency of the entire aviation system.

Analyzing single-lane roundabout traffic and environmental impacts through cellular automaton: A focus on U-turn effects

In this paper, we examine the impact of vehicles executing a full turn or U-turn in a single-lane roundabout system using a cellular automaton model. We investigate how increasing the number of these U-turning vehicles affects traffic flow characteristics and energy dissipation. Our findings reveal that as the prevalence of U-turning vehicles rises, the phase diagram undergoes significant changes; the maximum current phase expands, detrimentally impacting the free flow and congestion phases, while giving rise to a jamming phase. This shift results in a gradual increase in capacity at circulating lanes and a steady decline at entry/exit lanes. We also explore the role of aimless vehicles — those circulating without a fixed destination. As the proportion of such vehicles augments, it fosters an enlargement of the maximum current phase at the expense of the free flow and congestion phases. Furthermore, these vehicles influence energy dissipation across the three lanes of a roundabout. Significantly, we elucidate that variations in the percentage of these specific vehicular groups critically affect CO2 emissions. Our research unravels vital insights into optimizing roundabout management to enhance traffic flow and reduce environmental impact.

LSTTN: A Long-Short Term Transformer-based spatiotemporal neural network for traffic flow forecasting

Accurate traffic forecasting is a fundamental problem in intelligent transportation systems and learning long-range traffic representations with key information through spatiotemporal graph neural networks (STGNNs) is a basic assumption of current traffic flow prediction models. However, due to structural limitations, existing STGNNs can only utilize short-range traffic flow data; therefore, the models cannot adequately learn the complex trends and periodic features in traffic flow. Besides, it is challenging to extract the key temporal information from the long historical traffic series and obtain a compact representation. To solve the above problems, we propose a novel LSTTN (Long-Short Term Transformer-based Network) framework comprehensively considering the long- and short-term features in historical traffic flow. First, we employ a masked subseries Transformer to infer the content of masked subseries from a small portion of unmasked subseries and their temporal context in a pretraining manner, forcing the model to efficiently learn compressed and contextual subseries temporal representations from long historical series. Then, based on the learned representations, long-term trend is extracted by using stacked 1D dilated convolution layers, and periodic features are extracted by dynamic graph convolution layers. For the difficulties in making time-step level prediction, LSTTN adopts a short-term trend extractor to learn fine-grained short-term temporal features. Finally, LSTTN fuses the long-term trend, periodic features and short-term features to obtain the prediction results. Experiments on four real-world datasets show that in 60-minute-ahead long-term forecasting, the LSTTN model achieves a minimum improvement of 5.63% and a maximum improvement of 16.78% over baseline models. The source code is availble at https://github.com/GeoX-Lab/LSTTN.

Car-Following-Response-Based Vehicle Classification via Deep Learning

The driving characteristics of individual vehicles in the flow have been shown to influence the aggregate traffic flow characteristics. This is true both for individual human drivers as well as vehicles with some level of automation, such as adaptive cruise control (ACC). Knowledge of the individual constituents of the traffic flow will allow for more advanced traffic control strategies that are tailored to the individual vehicles and their respective driving characteristics. Therefore, there is a need to rapidly assess the car-following dynamics of individual vehicles and identify their level of automation based on their car-following trajectory. This study proposed a time-series based deep learning classification method to classify and identify human-driven and driver-assist vehicles in real-time from driving data. Powered by the recent advances in deep learning, we are able to identify individual vehicles in the flow using only car-following trajectory data and identify both ACC vehicles and human drivers. This article represents the first step toward assessing vehicle characteristics in real time. Furthermore, the proposed method can classify vehicles within a couple of seconds with high accuracy. Comparison with existing state-of-the-art methods shows the superior performance of the proposed method.

A hybrid deep learning model for urban expressway lane-level mixed traffic flow prediction

Precise real-time traffic flow prediction is crucial for route guidance and traffic fine control. With the development of autonomous driving, the mixed traffic flow state composed of Connected Automated Vehicles (CAVs) and Human-driven Vehicles (HVs) provides new insight into traffic flow prediction. In this paper, we innovatively consider the interaction between heterogeneous traffic flow as well as the mutual effect of traffic flow on different lanes and develop a hybrid model based on deep learning for urban expressway lane-level mixed traffic flow prediction, including three modules. First, the feature selection module is applied to screen the features with a high spatio-temporal correlation to the prediction object and construct the input matrix. Then, it is input to the feature attention module to quantify the importance of the input features on the prediction object, thereby assigning attention weights. Finally, the spatio-temporal information fusion module is adopted to capture the global spatio-temporal dynamics of traffic flow at horizontal and vertical spatial scales, as well as learn the complex coupling characteristics of heterogeneous traffic flow, thus obtaining predictions. An urban expressway mixed traffic flow simulation environment is built to collect experimental datasets for prediction accuracy evaluation. The results indicate that the proposed model outperforms the benchmarks in single-step and multi-step mixed traffic flow predictions on each lane. Furthermore, the proposed model shows the best performance and strong robustness under different penetration rates of connected automated vehicles.

Estimation of travel flux between urban blocks by combining spatio-temporal and purpose correlation

Understanding the travel flux between urban blocks is fundamental for traffic demand prediction, urban area planning and urban traffic management. However, the uncertainty of human mobility patterns and the complexity of urban transportation systems usually yield challenges in accurately estimating the travel flux within a city. Thus, we propose a novel travel flux estimation method that integrates traffic flow characteristics (traffic volume and travel time), spatio-temporal autocorrelation, and travel purpose correlation. First, the geographically weighted method was used to model and verify the spatio-temporal autocorrelation of origin–destination flows, whereas the purpose correlation of origin–destination flows was expressed through the function feature vectors of urban blocks. Then, a multi-objective bi-level programming model, according to the generalized least squares method, was constructed to estimate the travel flux between blocks. This was used to solve the problem of accurate estimation of travel flux by combining microscopic traffic flow characteristics with macroscopic spatio-temporal and purpose characteristics. Finally, an empirical analysis of the Hankou district, Wuhan City, demonstrated that in contrast to the existing method, the accuracy of the proposed method for predicting the human travel flux improved by approximately 20%. The estimated results were consistent with the spatial distribution pattern of human travel. Moreover, these results can provide targeted decision support for planning urban spaces, allocating urban resources, and guiding vehicular traffic.

PATTERNS OF SPEED CHANGES TRAFFIC FLOWS IN CITIES

The relevance of the research topic is substantiated and a brief review of previously developed methods for assessing
 the influence of the parameters of urban transport networks on the characteristics of traffic flows is given. The
 patterns of changes in the speed of traffic flows in cities obtained as a result of research are given. The developed
 mathematical models of changes in the speed of traffic flows depending on the density of the transport network of the
 city and the level of transport demand are presented.

Analyzing the Impact of Road Accidents on Carbon Dioxide Emissions in Freeway Traffic: A Simulation and Statistical Modeling Approach

Roadway accidents significantly contribute to intermittent congestion and increased CO2 emissions on freeways. This research introduces a statistical approach designed to predict the rise in CO2 emissions resulting from traffic disturbances or jams triggered by such incidents. It also assesses the influence of varying levels of accident management effectiveness in different situations. To construct these scenarios, the study employs VISSIM, a traffic modeling software, incorporating diverse factors such as traffic volume, vehicle types, incident durations, and freeway lane counts. It then produces traffic flow characteristics in the form of vehicle paths. The emission estimates are derived by correlating these simulated vehicle paths with emission rates from the MOVES model. The study then applies a regression analysis to examine the connection between the increase in emissions and various influencing factors. The findings indicate that this approach efficiently reflects the impact of variables like accident duration, vehicle mix, and traffic volume on CO2 emissions across different lane configurations. The accuracy of these predictions is also confirmed. The outcomes suggest the model’s potential usage in guiding efforts to lower emissions and determining the optimal duration of incident management, particularly in terms of lane closure, to mitigate emission impacts. This paper also discusses the limitations of the model and the future improvement direction.

CVDMARL: A Communication-Enhanced Value Decomposition Multi-Agent Reinforcement Learning Traffic Signal Control Method

Effective traffic signal control (TSC) plays an important role in reducing vehicle emissions and improving the sustainability of the transportation system. Recently, the feasibility of using multi-agent reinforcement learning technology for TSC has been widely verified. However, the process of mapping road network states onto actions has encountered many challenges, due to the limited communication between agents and the partial observability of the traffic environment. To address this problem, this paper proposes a communication-enhancement value decomposition, multi-agent reinforcement learning TSC method (CVDMARL). The model combines two communication methods: implicit and explicit communication, decouples the complex relationships among the multi-signal agents through the centralized-training and decentralized-execution paradigm, and uses a modified deep network to realize the mining and selective transmission of traffic flow features. We compare and analyze CVDMARL with six different baseline methods based on real datasets. The results show that compared to the optimal method MN_Light, among the baseline methods, CVDMARL’s queue length during peak hours was reduced by 9.12%, the waiting time was reduced by 7.67%, and the convergence algebra was reduced by 7.97%. While enriching the information content, it also reduces communication overhead and has better control effects, providing a new idea for solving the collaborative control problem of multi-signalized intersections.

Multi-faceted spatio-temporal network for weather-aware air traffic flow prediction in multi-airport system

As one of the core modules for air traffic flow management, Air Traffic Flow Prediction (ATFP) in the Multi-Airport System (MAS) is a prerequisite for demand and capacity balance in the complex meteorological environment. Due to the challenge of implicit interaction mechanism among traffic flow, airspace capacity and weather impact, the Weather-aware ATFP (Wa-ATFP) is still a nontrivial issue. In this paper, a novel Multi-faceted Spatio-Temporal Graph Convolutional Network (MSTGCN) is proposed to address the Wa-ATFP within the complex operations of MAS. Firstly, a spatio-temporal graph is constructed with three different nodes, including airport, route, and fix to describe the topology structure of MAS. Secondly, a weather-aware multi-faceted fusion module is proposed to integrate the feature of air traffic flow and the auxiliary features of capacity and weather, which can effectively address the complex impact of severe weather, e.g., thunderstorms. Thirdly, to capture the latent connections of nodes, an adaptive graph connection constructor is designed. The experimental results with the real-world operational dataset in Guangdong-Hong Kong-Macao Greater Bay Area, China, validate that the proposed approach outperforms the state-of-the-art machine-learning and deep-learning based baseline approaches in performance. The case study of convective weather scenarios further proves the adaptability of the proposed approach.

Research on spatiotemporal distribution characteristics of ship traffic flow in port waters based on AIS data

With the gradual increase of ship traffic, water traffic management has become increasingly important. This paper is based on AIS data and uses the Analytic Hierarchy Process to comprehensively evaluate the relative hazard level of a specific area, in order to determine the relative consequences of regional hazards (RCORH). Next, we will use speed and traffic volume characteristics as indicators to construct a linear regression model for spatial relationship analysis. The research results indicate that the linear regression model proposed in this article can effectively identify areas with high collision risks, which has positive significance for improving the navigation safety management of port waters.

Motorized and non-motorized mixed traffic characteristics and lateral opening spacing calculation for the section of arterial highway through small towns

A large number of motor vehicles and non-motorized vehicles mixed in the section of arterial highway crossing the town leads to many traffic problems. Therefore, it is necessary to set up a side divider between motorized and non-motorized lanes, and the appropriate spacing of lateral crossing openings to meet the needs of non-motorized vehicles crossing the highway has become a key issue that must be resolved. This paper investigates the traffic flow characteristics of mixed traffic flow on arterial highways through small town sections, and from the two dimensions of highway access efficiency and the psychological characteristics of cyclists, it calibrates the setting range of the spacing of non-motorized lateral crossing openings under different design speeds, which is used to regulate the behavior of non-motorized vehicles crossing the street, improve the safety level of the highway, reduce the lateral interference of the highway, and improve the road access efficiency. The accuracy of the research results is verified by microscopic simulation experiments, which proves that they can meet good expectations in practical engineering. The research results have theoretical significance and reference value for improving the status quo of machine-non-mixed traffic in the section of arterial highway passing through small towns, and enhancing the efficiency and safety of highway traffic. It also provides corresponding reference for the areas facing similar problems worldwide.