Applying Derived Distribution Method to Microlevel Driving Behavior Characteristics to Quantify Uncertainties in Traffic Stream Flow and Density

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.

The study of traffic flow characteristics is essential for designing highway systems for which different traffic influencing variables are of primary importance. The study aims to assess the impact of a range of traffic flow and density levels on the spatio-temporal (lateral placement and time headway) traffic characteristics of highways. A substantial amount of traffic data like speed, time headway (TH), and lateral placement (LP) of vehicles was collected continuously for 12 h on six Indian highway sections. The DPCU for different vehicles with different flow and density rates were calculated. The speed and TH descriptive statistics and probability distribution functions at varying flow and density levels were also analyzed. To determine the most appropriate probability distribution function, the goodness of fit test was used. Furthermore, the impact of traffic speed, flow, and density on the LP of vehicles under different traffic conditions was assessed using descriptive statistics. It was observed that the traffic flow and density levels had an unusual effect on the DPCUs of vehicles. The mean traffic speed under high flow and density decreased by 33.3 and 18.3%, while the mean TH under high flow and density decreased by 28.4 and 29.9%. Also, the results indicated that traffic speed and TH data exhibit a different distribution of probability functions depending on the traffic flow and density. The descriptive statistics on the LP of vehicles show the existence of a significant difference of the same concerning the different ranges of traffic speed, flow, and density.KeywordsSpeedFlowDensityTime headwayLateral placementMixed traffic

PCE are used as the factors to transfigure a traffic stream which is composed of various class of vehicles into an equivalent traffic stream composed absolutely of passenger cars. Based on various parameters of traffic stream traffic speed, traffic density, traffic flow and traffic headways PCE values are calculated. Present study analyses that various methods have been used to forecast the PCE values. It is too tough to estimate the traffic volume and traffic capacity of carriageway under heterogeneous traffic conditions. So, to get a standard vehicular unit, PCE has been developed to compare the congestion caused by different vehicles. The PCE values as introduced by developed countries are inappropriate for Indian mixed traffic condition because traffic is more heterogeneous and miscellaneous in nature. HCM utilizes PCE factors to estimate the effect of heavy vehicles on traffic stream under mixed traffic conditions. Different vehicles have significantly various driving performance characteristics to that of passenger cars. Therefore, these vehicles can have a major impact on traffic operations. It is therefore important to account properly for this impact in the traffic stream analysis in order to reflect the functioning quality of carriageway as close as possible. The primary objective of the research is to determine the appropriate PCE values of different vehicles. Additionally, a general PCE value with only one vehicle category was developed for planning purposes. The development of PCE values were based on the relative headway concept. For analysis of PCE, a significant amount of field data was collected during both peak and off-peak hour for the purpose of calibration. The PCE values determined from this study were 4.57, 3.76, 2.13, 0.28, 0.53 and 1.38 for peak hour and 4.6, 3.63, 2.14, 0.29, 0.55 and 1.33 for off peak hour for truck, bus, LCV, 2-wheeler, cycle and 3-wheeler respectively.KeywordsCapacityPassenger car unitHeadwayHeterogeneous trafficTraffic stream

With the traffic stream data collected in the exit ramp downstream of south Ruijin road located in the Shanghai inner loop expressway, traffic parameters of traffic speed, density, occupancy and volume are analyzed. The time variation of speed and volume, the difference of traffic volume participating rate in the various lanes of road section downstream, the selection and evaluation of density-speed model and the relationship among traffic density, speed and volume are key-points analyzed. The research can provide data support and scientific basis for the design and planning of urban expressway.

In order to study the quantitative influence of driver's behavior on road traffic, the cellular automaton dynamic model with influences on drivers (IDCA) is proposed based on the NaSch model in this paper for the characteristics of the driver's behavior. The improved model combines field force and graph theory to simulate road traffic flow. According to the driver's behavior characteristics, these factors are considered in the model, such as the direct physical and indirect psychological effects on driver, relative speed and vehicle characteristic. The influences of driver types and vehicle flow evolution mechanism on traffic flow are studied based on the model through computer numerical simulation. The simulation results show that the IDCA model can simulate abundant traffic behaviors better than the NaSch model, which reappears synchronous flow and other complex traffic phenomena. Traffic flows have better stability and higher dissolving congestion efficiency in the IDCA model through the analysis of the fluctuation of vehicles' speed and the vehicles' headway-distance. In addition, by using the computer numerical simulation of the IDCA model, the density-velocity and density-flow diagrams of the mixed traffic flow composed of different driver types are obtained. The conclusions are obtained: the larger the proportion of aggressive drivers, the larger the vehicle speed and traffic flow under the same road medium density and high density, and traffic flow decreases with the increase of the proportion of conservative drivers. The phenomenon of high speed car-following is simulated at last. The result of the rate of high speed car-following in the small headway areas more than 7% is obtained, which is consistent with the measured results.

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.

Vehicles offer people many conveniences, but at the same time cause considerable problems, for example for the environment. Today, the electrification of the automobile is considered the best way to solve at least a large part of the environmental problems caused by motor vehicles. Apart from the fact that an electric powertrain produces no local emissions, it also differs from a conventional fossil-fuel powertrain in its propulsion dynamics. It is therefore also to be expected that electric vehicles will influence the flow of traffic due to their different dynamic properties. This thesis focuses on using microscopic traffic simulation to provide a first estimation of how the electrification of vehicles could change the traffic flow and to what extent different traffic situations would influence the fuel and energy consumption of vehicles. In most microscopic traffic flow models, drivers and vehicles are modeled together by a combined model in one simulation object. By adjusting some parameter values, different combinations of driver and vehicle can be roughly distinguished. The values are determined on the basis of observational data of the traffic flow. However, the currently still small share of electric vehicles in road traffic and the fact that they look little different from combustion engine driven and hybrid vehicles make it difficult to collect data from electric vehicles in traffic flow, unless the data is collected in fleet tests. In this thesis, drivers and vehicles were modeled separately. The focus of this thesis is on the assessment of the influence of vehicle propulsion on traffic flow. Therefore, the model developed in this thesis was based on the physical capabilities of both the fossil-fuel powered and the electric powered vehicles. The driver model, which was also required, was created on the basis of data of human drivers, which were determined in an accompanying investigation of a driving simulator. Driver and vehicle model were combined again in the microscopic traffic simulation to a car-following model. For observing the impact of electric vehicles on traffic flow and estimating energy consumption of vehicles, a traffic scenario of a part of the City of Duisburg was established. The effect of electric vehicles on traffic flow, as well as the effect of traffic density on fuel and energy consumption was studied based on this scenario. The simulation results indicate that electric vehicles could raise the average speed of traffic flow to a certain degree, when traffic load and driving styles of the drivers remain the same. The increase of the share of electric vehicles has a positive effect on traffic efficiency. In situations with higher traffic density, energy consumption of electric vehicles is far superior to that of vehicles with internal combustion engine powertrains.

An Analysis on Mixed Traffic Flow of Conventional Passenger Cars and Microcars Using a Cellular Automata Model

Assessing traffic disturbance, efficiency, and safety of the mixed traffic flow of connected vehicles and traditional vehicles by considering human factors

Accurate on-road vehicle speed prediction is important for many intelligent vehicular and transportation applications. It is also challenging because the individual vehicle speed is affected by many factors, e.g., traffic speed, vehicle type, and driver's behavior, in either deterministic or stochastic ways. This paper proposes a novel vehicle speed prediction method in the context of vehicular networks, where the real-time traffic information is accessible. Traffic speeds of following road segments are first predicted by Neural Networks (NNs) based on historical traffic data. Hidden Markov models (HMMs) are trained by the Baum-Welch algorithm with historical traffic and vehicle data to present the statistical relationship between vehicle speed and traffic speed. The forward-backward algorithm is applied on HMMs to extract vehicle's speed on each road segment along the driving route. Simulation is set up on the SUMO microscopic traffic simulator with the application of a real Luxembourg highway network and traffic count data. The vehicle speed prediction result shows that our proposed method outperforms other ones in terms of prediction accuracy.

Based on NS model, a mixed traffic flow on single lane is simulated in the paper, which includes two-classification vehicles owing the different max-velocity. Take the one move 5 grids for one time as quick-vehicle, which is the results of aggressive behavior, such as overtaking and closely following behavior. And take the one which moves 3 grids for one time as slow-vehicle, which is on behalf of normally driving. The relationship among discrete of traffic flow, traffic density and chaos is researched, in the hope of preparing the solid theoretic basis for eliminating the bad chaos in traffic flow. And based on the distance headway condition and velocity condition, Rear-end accident risk assessment coefficient is advanced to research the influence of aggressive behavior on traffic safety lever on the urban road and on the freeway. The results show that aggressive behavior would emphasis the chaos of traffic flow and the happen opportunity is more, the chaos character in traffic flow is more. And aggressive driving behavior would decrease the safety character of traffic flow. The happen opportunity is more, the value of Rear-end accidents is bigger.

This paper studies the reconstruction of road traffic flow state based on multi-source monitoring data and establishes a model. From this, it analyses the degree of road unimpeded and realizes the prediction of road congestion change. Then, it carries out congestion prediction of newly increased vehicles, and verifies the rationality and real-time of the model. The model is the basic model of traffic flow. The model uses traffic volume, speed and traffic density as the basic characteristics of the actual traffic flow state, follows Mamdani-type fuzzy logic inference, and further derives by mathematical and physical analysis method, and obtains the ratio of driving speed and traffic density, which can be used to describe the actual state of traffic flow, and evaluate the degree of road unimpeded.

Traffic Flow Model for Staggered Intersection without Signal Lamp

There is a roundabout connecting the road II/513 and Priemyselna Street near the town of Hlohovec. It connects the D1 motorway with the nearby industrial zone, the shopping centre Vah and the town of Hlohovec. Roundabout are located on roads both in urban and rural areas. Most traffic surveys only measure traffic flow within specific time intervals. However, the data on traffic flow is not enough to create the correct and complete traffic model. In addition to traffic flow, it is necessary to know other characteristics of the traffic flow, especially the speed, which is extremely important. Traffic flow and speed are the basic characteristics of the traffic flow. Traffic density can be calculated using these two variables.

Vision‐based traffic surveillance plays an important role in traffic management. However, outdoor illuminations, the cast shadows and vehicle variations often create problems for video analysis and processing. Thus, the authors propose a real‐time cost‐effective traffic monitoring system that can reliably perform traffic flow estimation and vehicle classification at the same time. First, the foreground is extracted using a pixel‐wise weighting list that models the dynamic background. Shadows are discriminated utilising colour and edge invariants. Second, the foreground on a specified check‐line is then collected over time to form a spatial–temporal profile image. Third, the traffic flow is estimated by counting the number of connected components in the profile image. Finally, the vehicle type is classified according to the size of the foreground mask region. In addition, several traffic measures, including traffic velocity, flow, occupancy and density, are estimated based on the analysis of the segmentation. The availability and reliability of these traffic measures provides critical information for public transportation monitoring and intelligent traffic control. Since the proposed method only process a small area close to the check‐line to collect the spatial–temporal profile for analysis, the complete system is much more efficient than existing visual traffic flow estimation methods.