Showing papers on "Traffic wave published in 2016"

Failure of classical traffic flow theories: Stochastic highway capacity and automatic driving

Abstract: In a mini-review Kerner (2013) it has been shown that classical traffic flow theories and models failed to explain empirical traffic breakdown — a phase transition from metastable free flow to synchronized flow at highway bottlenecks. The main objective of this mini-review is to study the consequence of this failure of classical traffic-flow theories for an analysis of empirical stochastic highway capacity as well as for the effect of automatic driving vehicles and cooperative driving on traffic flow. To reach this goal, we show a deep connection between the understanding of empirical stochastic highway capacity and a reliable analysis of automatic driving vehicles in traffic flow. With the use of simulations in the framework of three-phase traffic theory, a probabilistic analysis of the effect of automatic driving vehicles on a mixture traffic flow consisting of a random distribution of automatic driving and manual driving vehicles has been made. We have found that the parameters of automatic driving vehicles can either decrease or increase the probability of the breakdown. The increase in the probability of traffic breakdown, i.e., the deterioration of the performance of the traffic system can occur already at a small percentage (about 5%) of automatic driving vehicles. The increase in the probability of traffic breakdown through automatic driving vehicles can be realized, even if any platoon of automatic driving vehicles satisfies condition for string stability.

Urban real-world driving traffic emissions during interruption and congestion

Abstract: On-road emissions from urban traffic during interrupted and congested flow conditions are too high as compared to free-flow condition and often influenced by accelerating and decelerating speed due to frequent stop-and-go. In this study, we measured emissions from passenger cars and auto-rickshaws during peak and off-peak hours and analyzed according to different mileages with the instantaneous speed and acceleration for interrupted and congested traffic conditions. It was found that during flow, several short-events lasting over fractions of a second each lead to a sharp increase in pollutant emissions, indicating episodic conditions. The emission levels are sensitive to frequency and intensity of acceleration and deceleration, in accordance with the traffic-flow patterns and speed, besides mileages. Further, congestion conditions occur during both peak and off-peak hours, but last for different durations. The results are important in the sense that instantaneous estimates of pollutant emissions are necessary for the assessment of air quality in urban centers and for an effective traffic management plan.

Real-time path planning to prevent traffic jam through an intelligent transportation system

Abstract: Congestion is a major problem in large cities. One of the main causes of congestion is the sudden increase of vehicle traffic during peak hours. Current solutions are based on perceiving road traffic conditions and re-routing vehicles to avoid the congested area. However, they do not consider the impact of these changes on near future traffic patterns. Hence, these approaches are unable to provide a long-term solution to the congestion problem, since when suggesting alternative routes they create new bottlenecks at roads closer to the congested one, thus just transferring the problem from one point to another. With this issue in mind, we propose an intelligent traffic system called CHIMERA, which improves the overall spatial utilization of a road network and also reduces the average vehicle travel costs by avoiding vehicles from getting stuck in traffic. Simulation results show that our proposal is more efficient in forecasting congestion and is able to re-route vehicles appropriately, performing a proper load balance of vehicular traffic.

Efficient Driving on Multilane Roads Under a Connected Vehicle Environment

Abstract: Traffic anticipation enhances driving intelligence and strengthens the ability to take early vehicle control action, e.g., lane change and speed adjustment, in a dynamically varying traffic environment. This paper presents an efficient vehicle driving system, based on detailed anticipation of surrounding traffic, that aims at optimizing the driving performance of individual vehicles and smoothening traffic flows on multilane roads. More elaborately, under a connected vehicle environment, the system receives the states of all vehicles that exist within its communication range. Based on their predicted states in a look forward horizon, the system generates the optimal acceleration and makes lane change decision simultaneously in the model predictive control framework. A fast hierarchical optimization scheme is used in the framework for its onboard implementation. The proposed efficient driving system is applied to a fraction of traffic, and both the individual and overall traffic performances are evaluated using a microscopic traffic simulator. It is revealed that the vehicles under the proposed efficient driving system improve their fuel economy and travel efficiency, significantly. In the mixed traffic, by the influence of the vehicle with the proposed driving system, the other traditionally driven vehicles also improve their performance.

Day-to-Day Flow Dynamics and Congestion Control

Abstract: For a predetermined set of an upper bound of link flows, this paper proposes a price-based congestion control scheme for achieving such a restraint target of traffic flow that evolves from day to day. On each day, drivers have to pay a toll selected from a feasible set. The tolls on each day are determined by the link flows and toll charges on the previous day and the predetermined upper bound of link flows. Several properties of the dynamical system model with the control scheme are analyzed, including the invariance of its evolutionary trajectories; the equivalence between its stationary state and user equilibrium under toll charge; the uniqueness, existence, and boundedness of its stationary state; and the convergence of its evolutionary trajectories. A special case of the model and implementation of the control scheme for several alternative targets are also given. Finally, application of the model to a traffic network is demonstrated with a numerical example. The study is helpful for better understanding the mechanism of congestion control under day-to-day traffic flow dynamics.

Analysis of traffic congestion induced by the work zone

Abstract: Based on the cellular automata model, a meticulous two-lane cellular automata model is proposed, in which the driving behavior difference and the difference of vehicles’ accelerations between the moving state and the starting state are taken into account. Furthermore the vehicles’ motion is refined by using the small cell of one meter long. Then accompanied by coming up with a traffic management measure, a two-lane highway traffic model containing a work zone is presented, in which the road is divided into normal area, merging area and work zone. The vehicles in different areas move forward according to different lane changing rules and position updating rules. After simulation it is found that when the density is small the cluster length in front of the work zone increases with the decrease of the merging probability. Then the suitable merging length and the appropriate speed limit value are recommended. The simulation result in the form of the speed–flow diagram is in good agreement with the empirical data. It indicates that the presented model is efficient and can partially reflect the real traffic. The results may be meaningful for traffic optimization and road construction management.

Traffic prediction and control system for vehicle traffic flows at traffic intersections

Abstract: A method and a traffic prediction and control system (TPCS) for predicting and controlling vehicle traffic flow through a traffic intersection dynamically with proximal traffic intersections are provided. The TPCS dynamically receives sensor data from sensors at a local traffic intersection, determines traffic flow parameters, and determines a traffic flow flux using the traffic flow parameters. The TPCS dynamically receives analytical parameters from sensors at proximal traffic intersections and determines a minimum safe driving distance between leading and trailing vehicles, a traffic free flow density, a synchronized traffic flow density, and a traffic jam density to predict transitions of the vehicle traffic flow across traffic flow phases through the local traffic intersection. The TPCS controls the vehicle traffic flow by dynamically adjusting duration of traffic signals of the local traffic intersection and transmitting traffic signal time adjustment instructions to the proximal traffic intersections to maintain an optimized traffic flow flux.

Improved 2D intelligent driver model in the framework of three-phase traffic theory simulating synchronized flow and concave growth pattern of traffic oscillations

Abstract: This paper firstly show that 2 Dimensional Intelligent Driver Model (Jiang et al., 2014) is not able to replicate the synchronized traffic flow. Then we propose an improved model by considering the difference between the driving behaviors at high speeds and that at low speeds, which is in the framework of three-phase traffic theory. Simulations show that the improved model can reproduce the phase transition from synchronized flow to wide moving jams, the spatiotemporal patterns of traffic flow induced by traffic bottleneck, and the concave growth pattern of traffic oscillations (i.e. the standard deviation of the velocities of vehicles increases in a concave/linear way along the platoon). Validating results show that the empirical time series of traffic speed obtained from Floating Car Data can be well simulated as well.

Modeling and analyses of driver’s characteristics in a traffic system with passing

Abstract: This paper investigates the effect of aggressive or timid characteristics of driver’s behavior with passing by means of lattice hydrodynamic traffic flow model. The effect of driver’s characteristic on the stability of traffic flow is examined through linear stability analysis. It is shown that for both the cases of passing or without passing the stability region significantly enlarges (reduces) as the proportion of aggressive (timid) drivers increases. To describe the propagation behavior of a density wave near the critical point, nonlinear analysis is conducted and mKdV equation representing kink–antikink soliton is derived. It is observed that jamming transition occurs between uniform flow and kink jam phase with increase in aggressive driver’s characteristics for smaller values of passing. When passing constant is greater than a critical value, jamming transitions occur among uniform traffic flow and kink-Bando traffic wave through chaotic phase. Numerical simulation is carried out to validate the theoretical findings which confirm that traffic jam can be suppressed efficiently by considering the driver’s characteristics in a single-lane traffic system with or without passing.

Detecting and positioning of traffic incidents via video-based analysis of traffic states in a road segment

Abstract: Non-recurrent traffic incidents (accidents, stalled vehicles and spilled loads) often bring about traffic congestion and even secondary accidents. Detecting and positioning them quickly and accurately has important significance for early warning, timely incident-disposal and speedy congestion-evacuation. This study proposes a video-based detecting and positioning method by analysing distribution characteristics of traffic states in a road segment. Each lane in the monitored segment is divided into a cluster of cells. Traffic parameters in each cell, including flow rate, average travel speed and average space occupancy, are obtained by detecting and tracking traffic objects (vehicles and spilled loads). On the basis of the parameters, traffic states in the cells are judged via a fuzzy-identification method. For each congested cell, a feature vector is constructed by taking its state together with states of its upstream and downstream neighbouring cells in the same lane. Then, a support vector machine classifier is trained to detect incident point. If a cell is judged to be corresponding to an incident point at least for two successive time periods, an incident is detected and its position is calculated based on the identity number of the cell. Experiments prove the efficiency and practicability of the proposed method.

Cellular automata model for urban road traffic flow considering pedestrian crossing street

Abstract: In order to analyze the effect of pedestrians’ crossing street on vehicle flows, we investigated traffic characteristics of vehicles and pedestrians. Based on that, rules of lane changing, acceleration, deceleration, randomization and update are modified. Then we established two urban two-lane cellular automata models of traffic flow, one of which is about sections with non-signalized crosswalk and the other is on uncontrolled sections with pedestrians crossing street at random. MATLAB is used for numerical simulation of the different traffic conditions; meanwhile space–time diagram and relational graphs of traffic flow parameters are generated and then comparatively analyzed. Simulation results indicate that when vehicle density is lower than around 25 vehs/(km lane), pedestrians have modest impact on traffic flow, whereas when vehicle density is higher than about 60 vehs/(km lane), traffic speed and volume will decrease significantly especially on sections with non-signal-controlled crosswalk. The results illustrate that the proposed models reconstruct the traffic flow’s characteristic with the situation where there are pedestrians crossing and can provide some practical reference for urban traffic management.

Estimating link travel time functions for heterogeneous traffic flows on freeways

Abstract: Summary Oversized vehicles, such as trucks, significantly contribute to traffic delays on freeways. Heterogeneous traffic populations, that is, those consisting of multiple vehicles types, can exhibit more complicated travel behaviors in the operating speed and performance, depending on the traffic volume as well as the proportions of vehicle types. In order to estimate the component travel time functions for heterogeneous traffic flows on a freeway, this study develops a microscopic traffic-simulation based four-step method. A piecewise continuous function is proposed for each vehicle type and its parameters are estimated using the traffic data generated by a microscopic traffic simulation model. The illustrated experiments based on VISSIM model indicate that (i) in addition to traffic volume, traffic composition has significant influence on the travel time of vehicles and (ii) the respective estimations for travel time of heterogeneous flows could greatly improve their estimation accuracy. Copyright © 2016 John Wiley & Sons, Ltd.

Travel time estimation method for urban road based on traffic stream directions

Abstract: Traffic flow on a given link in an urban road network can be divided into several traffic streams, depending on their turning manoeuvres when entering and leaving the link. These traffic streams may experience various travel times due to multiple reasons, such as fluctuations in traffic demand/supply and stochastic arrivals/departures at signalised intersections. However, the current travel time estimation methods take traffic flow as a whole and produce a single estimation value. This approach can produce large errors. Furthermore, given the travel time information of each traffic stream, the results of dynamic traffic assignment models can be made much more accurate and the effect of signal controls improved. In this paper, a comparison analysis is conducted to verify the significant difference in link travel times of different traffic streams. Then, link travel time is redefined in consideration of traffic stream directions. This process is also successful in filtering noise, as shown in the nu...

Traffic flow on pedestrianized streets

Abstract: Giving pedestrians priority to cross a street enhances pedestrian life, especially if crosswalks are closely spaced. Explored here is the effect of this management decision on car traffic. Since queuing theory suggests that for a given pedestrian flux the closer the crosswalk spacing the lower the effect of pedestrians on cars, scenarios where pedestrians can cross anywhere should be best for both cars and pedestrians. This is the kind of pedestrianization studied. Analytic formulas are proposed for a pedestrianized street’s capacity, free-flow speed and macroscopic fundamental diagram. Of these, only the free-flow speed formula is exact. The analytic form of the capacity formula is inspired by analytic upper and lower bounds derived with variational theory for a version of the problem where cars are treated as a fluid. The formula is then calibrated against microscopic simulations with discrete cars. The MFD for the fluid version of the problem is shown to be concave and have a certain symmetry. These two geometrical properties, together with the formulae for capacity and free-flow speed, yield a simple approximation for the MFD. Both the capacity and MFD formulae match simulations with discrete cars well for all values of the pedestrian flux – errors for the capacity are well under 0.2% of the capacity before pedestrianization. Qualitatively, the formulas predict that the street’s capacity is inversely proportional to the square root of the pedestrian flux for low pedestrian fluxes, and that pedestrians increase the cars’ free-flow pace by an amount that is proportional to the pedestrian flux.

A Self-Adaptive Traffic Light Control System Based on Speed of Vehicles

Abstract: Intelligent transportation is a typical case of cyber-physical system (CPS). Due to the rapid increasing of the number of vehicles in city, problems caused by vehicles, like congestion and environment pollution, are becoming more and more serious. Traffic light control system is often used to control the vehicles passing for a solution of the congestion in the city. Present control systems used are normally assigned as to be static, i.e., traffic light signal changes in a static way. The aim of this paper is to propose a dynamical traffic light control system, i.e., change the traffic light signals in real time following the speed of vehicles. This system is an instance of V2I(Vehicle to Infrastructure) communication model, realizing data transmission between vehicles and traffic lights. Vehicles send speed messages to the traffic light when passing an intersection, then the traffic light analyzes the information and adjusts the signal time in real time. Each traffic light in each direction has a control strategy of itself without the orthogonal requirement. Therefore, the traffic light is a kind of cyber-physical system. This traffic light control system can maximize the number of vehicles passing intersection, and as a result, minimize the congestion and pollution. A traffic light control algorithm based on speed of vehicles and its simulation are presented. The safety and liveness of this control system are discussed too.

Car-Following and Lane-Changing Behavior Involving Heavy Vehicles

Abstract: This paper presents empirical findings on car-following and lane-changing behavior involving heavy vehicles; trajectory data from the next generation simulation program were used in the study. It was found that when following passenger cars, heavy vehicles tended to reduce speed variations caused by traffic disturbances and thereby dampened traffic oscillations. In contrast, passenger cars following heavy vehicles tended to amplify traffic disturbances, although with lower probability and magnitude compared with the dampening effect. Moreover, heavy vehicles tended to discourage lane changes, especially behind them. This finding has convoluted implications: although reduced lane changes can improve traffic stability by preventing or reducing disturbances, large gaps can persist behind heavy vehicles and contribute to underutilization of road capacity.

Hybrid short-term prediction of traffic volume at ferry terminal based on data fusion

Abstract: Ferry traffic prediction is an important consideration for urban transportation authorities. A novel methodology which considers cyclical patterns in ferry on-board vehicle traffic and improves on this method by incorporating traffic data from nearby freeway loop detectors is proposed. Since vehicles arriving in ferry terminals often originate from nearby freeways, the study of freeway traffic flow can improve ferry traffic prediction, especially during peak travel periods. In this study, the authors first identify periodic patterns in ferry traffic through analysis in both the time and frequency domain, and propose a modelling methodology which considers these patterns in short-term traffic prediction. In the proposed model, the ferry traffic volume is divided into periodic and dynamic components, with the periodic component approximated by trigonometric functions. Second, this model is expanded to incorporate measured traffic data on nearby freeways. Specifically, the correlation between freeway traffic and ferry traffic volume is studied, and an enhanced model is proposed which integrates the predictions obtained through historical ferry data and from freeway traffic during peak travel time periods. Validation results demonstrate that consideration of periodic patterns increases prediction accuracy and that the proposed model further improves the ferry traffic prediction accuracy during peak time periods.

Car-Following and Lane-Changing Behavior Involving Heavy Vehicles

Abstract: This paper presents empirical findings on car-following and lane-changing behavior involving heavy vehicles; trajectory data from the next generation simulation program were used in the study. It was found that when following passenger cars, heavy vehicles tended to reduce speed variations caused by traffic disturbances and thereby dampened traffic oscillations. In contrast, passenger cars following heavy vehicles tended to amplify traffic disturbances, although with lower probability and magnitude compared with the dampening effect. Moreover, heavy vehicles tended to discourage lane changes, especially behind them. This finding has convoluted implications: although reduced lane changes can improve traffic stability by preventing or reducing disturbances, large gaps can persist behind heavy vehicles and contribute to underutilization of road capacity.

An empirical study on the traffic state evolution and stop-and-go traffic development on freeways

Abstract: Stop-and-go traffic, related to traffic breakdown and instability, is the core mechanism of traffic state transition to congestion. The purpose of this paper is to provide improved empirical understanding on the development of the stop-and-go traffic from asymmetric theory's viewpoint. We analyse traffic state transition before and after stop-and-go wave by observing platoon trajectories using NGSIM data. We observe that stop-and-go wave develops into growth (dissipation) in unstable (stable) traffic, which becomes stable (unstable) by passing the wave, respectively. Alternating patterns can explain recurring oscillatory traffic. Besides, we investigate the relationship between the evolution of stop-and-go wave and lane-changes. We can find that the growth region in the flow-density plane is located on the higher flow and density area above dissipation region. Also, as the number of lane-change increases (decreases), traffic will be developed to growth (dissipation), respectively. The findings of this res...

Stochastic macroscopic analysis and modelling for traffic management

Abstract: When congestion becomes a problem on a road or road network, there are generally three main solution areas available to tackle it: construction, pricing or traffic management. Traffic management became an increasingly preferred option towards the end of the twentieth century as an alternative to construction in many cases. Traffic management proves a more efficient alternative and focusses on influencing traffic flows such that the existing road and network capacity is more effectively utilised resulting in a reduction in congestion. The effectiveness of traffic management is dependent on the ability to influence traffic flow. However, traffic contains a relatively large amount of stochastic behaviour, which is connected to human driving behaviour. The fluctuations that occur in traffic flow due to this stochastic behaviour have a large effect on the effectiveness of traffic management. Furthermore, uncertainty between time dependant scenarios has also shown to have a large influence on the outcome of the analysis of traffic management measures. In the past, little attention has been paid to these effects. Therefore, the main objective of this thesis is to give insight into the stochastic fluctuations and uncertainty in traffic flow for the application of traffic management measures and to propose tools that allow these effects to be analysed and subsequently modelled in aggregated macroscopic flows. In doing this, the necessity to consider uncertainty and fluctuations for traffic management is also demonstrated. Stochastic processes are considered as uncertainty, which describes day-to-day uncertainties between traffic flows, and fluctuations, which describes microscopic variability in the traffic flow. Three main areas are focussed on: the analysis of variations in traffic, modelling fluctuations and uncertainty in traffic, and the visual communication of uncertainty from traffic models.

Method for traffic control

Abstract: Described is a method to optimize traffic light activity and minimize traffic congestion. Traffic conditions are monitored by sensors and a controller dynamically controls the green light time to account for traffic conditions and enhance the traffic flow. In one example, the green light time of each lane is reduced or increased according to traffic flow in the lane.

Multi Layer Perception Type Artificial Neural Network based Traffic Control

Abstract: Real time traffic control is a main criteria of the urban traffic signal control system, and giving viable ongoing traffic signal control for a substantial complex traffic system is a testing issue. The objective of the work is to find and adjust the timing of signals based on the traffic density. Such a situation arises in a city where outbound vehicles during morning time and inbound vehicles during evening time is more while the vehicular movement in the opposite direction is less. To predict and adjust the timings of the signals on both sides of the road at the same time, Artificial Neural Network technique is used. A real time traffic survey of Light Motor Vehicle, Heavy Motor Vehicle, two and three wheeler vehicular movement in Thanjavur city is done. The number of vehicles (cars, auto, bikes, trucks and buses) and width of the road was given as a input and the output predicted was in terms of timing for the traffic signal at any particular place and for any particular width of the road. The width of the road is also taken into account which is essential in planning a city based traffic consisting of different road widths.

A feature-based method for traffic anomaly detection

Abstract: The wide spread use of GPS-enabled devices facilitates us to sense the movement of vehicles. Detecting anomalous movement behavior on road segments can benefit both drivers and transportation authorities. An important challenge behind this is how to detect these anomalies effectively and timely under large scale of raw GPS trajectories. In this light, we propose a Feature-Based method for Traffic Anomaly Detection (FBTAD). A key observation is that road segments, where these incidents turns up, tend to have their vehicle flow features changed in a short period of time. For example, a traffic accident may immediately and significantly slow down the travel speed on a road segment. In this paper, we first map-match raw trajectories. Then we calculate the road segments' traffic features in each time slot, e.g., 10 minutes, and introduce an offline spatial-temporal index for speeding up the anomaly detection process. Finally, we retrieve anomaly candidates by checking the road segment's traffic flow acceleration based on the index built above, and examine candidates' density change ratio or moving objects' outflow ratio to further infer traffic anomalies. The effectiveness and efficiency of our approach is validated by extensive experimentation. Our evaluations show that the method proposed in this paper is able to detect traffic anomalies more efficiently as well as earlier than the baseline method.

Modeling and simulating traffic congestion propagation in connected vehicles driven by temporal and spatial preference

Abstract: Differing from the traditional traffic, connected vehicles enable information sharing between vehicles at vicinity to facilitate cooperative path planning, which may positively affect the congestion propagation process. In this paper, we propose to modeling and simulating traffic congestion propagation in such new situation where the path planning is driven by a temporal or spatial preference with aims at investigating the effects of various factors on traffic congestion, e.g. traffic light, mobility pattern, traffic density and communication radius. Simulations show that the traffic congestion is indeed affected by the concerned factors; however, the traffic congestion fails to be mitigated persistently as the communication radius increases beyond a certain threshold. The result is helpful for understanding the traffic congestion propagation in connected vehicles.

Cyber-physical systems: A framework for dynamic traffic light control at road intersections

Abstract: Traffic control at road intersections is based on traffic lights (TLs). The control mechanism typically used for traffic lights operates based on a periodic schedule to change the light (red/yellow/green). In many cases, a different schedule is used in late night/early morning hours. This fixed control mechanism does not adequately account for changing traffic conditions, and is unaware of/unresponsive to congestion. In this work, we propose a framework for dynamic traffic light control at intersections. The framework relies on a simple sensor network to collect traffic data and includes novel protocols for traffic flow control to handle congestion and facilitate flow. We show that our proposed algorithms have low overhead and are practical to employ in live traffic flow scenarios. Through extensive simulations, we demonstrate the benefits of our framework in optimizing traffic flow metrics, such as traffic throughput, average vehicle waiting time, and vehicle waiting line length.

Saturation Flow Rate Analysis at Signalized Intersections for Mixed Traffic Conditions in Motorcycle Dependent Cities

Abstract: This article describes the results of a comprehensive saturation flow rate analysis at signalized intersections in motorcycle dependent cities. Most of current capacity analysis methods for signalized intersections consider vehicles moving on lanes, and they assume lane discipline and high driver discipline regarding traffic regulations. Moreover, for modeling mixed traffic conditions, the not lane-based heterogeneous real traffic flow is transferred into lane-based homogeneous flow by using Passenger Car Units. So far, available methods of capacity analysis do not consider the specific conditions of driver behaviour and traffic flow which are dominant e.g. in Vietnam or other countries where motorcycles have the major share in traffic. Consequently, there is a need to develop a proper method to allow such saturation flow rate analysis. The methodology of this research was developed with consideration of the specific traffic situation in Hochiminh City, Vietnam, a motorcycle dependent city. First, the article describes the unique aspects of traffic flow and saturation flow rate analysis under such mixed traffic conditions compared with car-dominated traffic, using data collected by observations of saturated flow conditions at 12 signalized intersections. Then, saturation flow rate models using a regression method are presented and described. The term of Motorcycle Unit (MCU) is introduced, and a set of factors which mainly affect the saturation flow in such mixed conditions is developed. These factors reflect major differences between the traffic flows under saturated conditions at signalized intersections in motorcycle dependent cities and car-dominated cites. For example, this includes the influence of the approach width, the impact of four-wheel vehicles (including car, bus, and truck) on motorcycles, the very specific interactions of left-turning and opposite straight-moving traffic streams, the relationship between right-turning 4-wheel vehicles and motorcycles moving straight in the same direction, and the phenomenon of capacity drop within the green time. Finally, a procedure to calculate the saturation flow rate for specific traffic situations is provided, and the application of the proposed model is depicted. The conducted research indicates that the proposed saturation flow rate analysis model is an appropriate approach to calculate the saturation flow rate for traffic streams at signalized intersections under such mixed traffic conditions. The set of motorcycle equivalent units used for analysis is also proved to be suitable for this model. Equations and graphs to determine the appropriate values for the identified influencing factors are presented. According to each specific traffic situation, calculation procedures are provided to determine the value of the saturation flow rate.