Modeling and Analysis of Airport Surface Traffic Flow Based on Variable Cell Transmission Model

In this paper, the characteristics of traffic flow on urban expressway in Beijing are investigated. Based on the real data, some classical traffic flow models are used to explore the relationships among traffic flow, speed and time occupancy. The FCM (fuzzy C-means method) clustering algorithm is applied to classify the traffic flow state. The results show that the Greenshields model is the best suitable model for the mathematical description of traffic flow, i.e. traffic speed and time occupancy is in linear relation while traffic volume and time occupancy (traffic speed) is in parabolic relation. Four different traffic flow states, including free flow, steady flow, synchronized flow and congested flow, are identified according to the FCM clustering method. For the free traffic flow, the time occupancy cluster center is equal to 0.16, and the traffic volume cluster center is equal to 980 pcu/h. For the steady traffic flow, the time occupancy cluster center is equal to 0.30, and the traffic volume cluster center is equal to 1737 pcu/h. For the synchronized traffic flow, the time occupancy cluster center is equal to 0.50, and the traffic volume cluster center is equal to 1896 pcu/h. For the congested traffic flow, the time occupancy cluster center is equal to 0.79, and the traffic volume cluster center is equal to 1184 pcu/h. The analysis of traffic flow characteristics in this study can provide the method and data foundation to evaluate the traffic service level and enhance the traffic organization and management.

This paper studies the risk assessment of traffic flow on major highways under high-speed driving conditions and the safety warning after traffic incidents. Firstly, by analyzing the operation characteristics of the main line traffic flow and the evolution characteristics of the upstream traffic flow after the event, a collaborative control strategy for the dynamic assessment and accurate early warning of the main line traffic flow is proposed. Secondly, based on the operating speed, traffic flow and head-time distance state parameters of the main traffic flow, the risk assessment model of the main traffic flow is established, which provides an algorithm basis for the safe operation status evaluation and active control of the main traffic flow. Third, based on the driver’s reaction process after the traffic incident and the evolution process of main traffic flow, a traffic incident safety warning distance function is proposed, which provides a quantitative function for the accurate early warning and prevention of traffic accidents in the upstream traffic flow after traffic incidents. Through the dynamic assessment and control of operation risks of expressway main line traffic flow, the accurate risk warning of expressway main line traffic flow under the traffic event conditions will significantly improve the dynamic safety control level and risk warning ability of the expressway main line.

The prevailing roadway and traffic conditions on expressways in India are vastly different when compared with the other roads in India and also, there is no perfect lane-discipline. The knowledge of roadway capacity is an important basic input required for planning, design, analysis, and operation of roadway systems. Hence, this work aims to model traffic flow on Indian urban expressways with specific reference to Delhi–Gurgaon expressway and estimate its capacity using the micro-simulation model using VISSIM 5·40. For this purpose, the field data collected on traffic-flow characteristics on expressways was used in calibration and validation of the simulation model. The validated simulation model was then used to develop fundamental traffic–flow relationships, namely, speed–flow, speed–area occupancy, and flow–area occupancy for the traffic-flow levels, starting from near-zero until the capacity of the facility. The capacity of an eight-lane divided urban expressway in level terrain with 14·0-m wide road space (one direction of traffic flow) was found to be in the range of 9700–10 000 vehicles/h. Moreover, through sensitivity analysis using simulation model, the following relationships, (i) percentage trucks in the traffic stream and capacity-level flow and (ii) percentage of trucks in the traffic stream and capacity-level traffic-stream speed, were developed. The results and the overall methodology followed in this study for modeling traffic flow on expressways in India to determine the capacity estimates under varying traffic compositions may be very useful for the practitioners and also for the continuing efforts to prepare an Indian Highway Capacity Manual.

A real-time control method-based simulation for high-speed trains on large-scale rail network

Today, traffic is an integral part of every active human life. People prefer individual car transport to public transport. This fact increases the number of cars on the roads. This article focuses on the basic characteristics between moving vehicles on the road, traffic flow characteristics - intensity, speed and density. Their values are obtained from traffic survey and radar Sierzega. In addition, a simulation was made from the measured values and the basic characteristics of the traffic flow were evaluated using mathematical statistics

Connected automated vehicles (CAVs) can significantly shorten the headway of car following, thereby effectively improving the traffic capacity and injecting new power to alleviate traffic congestion. To investigate the congestion characteristics of mixed traffic flow with CAVs and human-driven vehicles (HDVs), this paper proposes a cell transmission model to capture and simulate traffic congestion for mixed traffic flow. Firstly, the Newell, adaptive cruise control (ACC), and cooperative adaptive cruise control (CACC) models are adopted to capture the car-following behavior of different vehicles. Secondly, the fundamental diagram under different penetration rates of CAVs is derived based on car-following models. Then, the cell transmission model (CTM) of mixed traffic flow is developed based on the classical CTM and fundamental diagram of mixed traffic flow. Finally, two simulation methods, mixed traffic flow CTM and micro-simulation, are designed to verify the effectiveness of the proposed model. Moreover, taking the moving bottleneck on the expressway as an example, the congestion characteristics of mixed traffic flow are analyzed using multiple indexes, such as average travel speed, congestion delay, and congestion scale. The results show the following: (i) CAVs can significantly alleviate traffic congestion, (ii) the duration of the bottleneck is positively correlated with the degree of traffic congestion, and (iii) The traffic congestion assessment results under different model parameters slightly differ, but the impact is negligible.

One of the key reasons why the road capacity of urban roads in China often fails to meet the designed capacity is the mixture of heavy vehicles (slow-moving) and light vehicles (fast-moving). This paper presents a two-lane cellular automaton model suitable for simulating urban road traffic that includes intersections, based on the NaSch model. The model comprehensively takes into account multiple key factors, such as vehicle safety distance, speed differences between adjacent vehicles, the acceleration and deceleration performance of different types of vehicles, and driver reaction time, enabling it to more realistically reproduce the complex characteristics of mixed traffic flows on urban roads. The paper investigates and analyzes the influence of traffic flow density and the proportion of heavy vehicles on the moving bottleneck effect in urban roads from several aspects, including space–time evolution diagrams, traffic flow, average speed, and lane-changing rates. The results indicate that the model established in this paper successfully simulates the characteristics of mixed traffic flows on urban roads, and the simulation results align with actual traffic conditions, achieving the expected simulation effects. Before the traffic volume reaches saturation, the higher the proportion of heavy vehicles on urban roads, the stronger the moving bottleneck effect. This confirms the necessity of conducting research on the phenomenon of moving bottlenecks and the mechanisms of traffic impacts in urban roads, providing a scientific basis for formulating effective traffic dispersion measures and alleviating traffic congestion in the future. This research possesses significant practical meaning and value.

The impacts on traffic flows by Fuel Service Stations (FSS) located within 50 m vicinity of four (4) signalized intersections in Kumasi City were assessed. Changes in saturated flow rates, delays and lost times were the im-pact assessment indicators. Reduction in saturation flow rates ranged from low of 2 % to high of 56 %, depend-ing on orientations of the FSS to the intersections. Access to the FSS facilitated circumvention of the intersecti-ons, thereby easing congestion and delays. At one FSS, there was 7.9 % overall reduction in intersection delays, while in the other 3 FSS, the overall intersection delays were increased by 8 - 30 %. The actual lost times excee-ded the allotted signal operational time of 4 s, thereby reducing the effective green times. The existing traffic conditions were simulated for optimal traffic flow using Synchro 6.0 plus Sim Trafficware with traffic flow cha-racteristics, signal control data and geometric features of the intersections as inputs to determine the clearance lost times of critical movements. Comparison of outputs of the simulation runs of existing and control (steady flow states or intersections without FSS) indicated that if the operational signal timings were optimized to com-mensurate the actual lost times, the existing situations would experience reduced delays and congestions.

Road network traffic flow prediction: A personalized federated learning method based on client reputation

This paper focuses on the traffic operation scenario of freeway weaving areas, taking the macro traffic flow of mixed manual and autonomous driving as the research object, and conducts research on traffic operation risk assessment methods. Considering the characteristics of traffic flow parameters such as existing road environment, traffic flow state, traffic composition, and traffic conflicts, a multi-dimensional index system for the operational characteristics of mixed traffic flow in weaving areas is established. Parameters significantly related to risks are screened out through correlation analysis, and a comprehensive risk assessment model is constructed to output the traffic operation risk status.

This research investigated behaviors and characteristics of traffic flow by analyzing speed-flow rate relationships, lane change, deceleration, and speed deviation at freeway merging sections. Results indicated that the traffic characteristics of merging section are relatively different from those of basic section in terms of the shape of speed-flow rate curve. In addition, it is shown that remarkable lane changes and deceleration at adjacent merging sections caused to turbulence phenomena and that the severity of turbulence could be recognized by analyzing the standard deviation of speed traps of 50m lane by lane within the influence zone by entrance ramp.

Lagrangian generic second order traffic flow models for node

In recent years, due to the implementation of the highway-free policy during holidays, the travel demand and traffic volume during holidays have increased significantly, and the resulting congestion problem is more serious. To better analyze and study the traffic flow characteristics of expressway hot toll stations during holidays, and put forward targeted optimization strategies to achieve the purpose of improving the efficiency of highway operation during holidays, this paper takes the toll collection data of more than 5 million lines of expressways in Jilin Province in 25 days from September to October as the research object. By using the characteristics of traffic flow on different characteristic days, this paper summarizes the characteristic data of traffic flow by statistical means and depicts the trend chart of hot toll stations based on geographic information using the digital visualization method. Based on basic data cleaning and classification analysis, a flow rate of hot toll station phase change analysis, according to the characteristic of geographic information data, and Jilin province, based on the three-dimensional map heat flow variation, the comprehensive finish holiday hot spots for highway toll station traffic flow characteristics of the impression. Based on this, the paper puts forward the optimization policy of highway holiday travel optimization.

Traffic information prediction is one of the most essential studies for traffic research, operation and management. The successful prediction of traffic speed is increasingly significant for the benefits of both road users and traffic authorities. However, accurate prediction is challenging, due to the stochastic feature of traffic flow and shallow model structure. Furthermore, environmental factors, such as rainfall influence, should also be incorporated to improve accuracy. Inspired by deep learning, this paper investigates the performance of deep belief network (DBN) and long short‐term memory (LSTM) to conduct short‐term traffic speed prediction with the consideration of rainfall impact as a non‐traffic input. The deep learning models have the ability to learn complex features of traffic flow pattern under various rainfall conditions. To validate the performance of rainfall‐integrated DBN and LSTM, the traffic detector data from an arterial in Beijing are utilised for model training and testing. The experiment results indicate that with the combination input of speed and additional rainfall data, deep learning models have better prediction accuracy over other existing models, and also yields improvements over the models without rainfall input. Furthermore, the LSTM can outperform the DBN to capture the time‐series characteristics of traffic speed data.

Local data are essential for the analysis of abrupt conditions in traffic flow. In this research, we address the shortcomings of real-time traffic flow detection data, which inadequately represent the density characteristics of vehicular traffic. To mitigate this issue, we establish mathematical relationships among traffic flow density, time headway, and vehicle speed, based on the fundamental characteristics of traffic flow dynamics. This framework allows for the estimation of density across various road segments. Recognizing the nonlinear dynamics that characterize traffic flow, we introduce a microscopic traffic flow cusp catastrophe potential function that incorporates headway, utilizing cusp catastrophe theory alongside microscopic traffic flow parameters to analyze real-time variations in traffic flow potential characteristics. In the experimental section, we developed fitting regression functions for traffic flow parameters using road detection data from the cities of Nanjing and Yueyang in China. We also compared outcomes derived from the theoretical model with the parameter estimation results obtained from the data, which facilitated the calculation of traffic flow characteristics, such as cusp density and volume over a one-month period. The results derived from the proposed potential function exhibit the functions’ greater capacity to capture the complex fluctuations in traffic flow compared to traditional traffic potential functions, thereby providing a valuable instrument with which to evaluate traffic flow characteristics in conjunction with real-time monitoring data.