Intersection Signal Research Articles

Setting the Intermittent Bus Approach of Intersections: A Novel Lane Multiplexing-Based Method with an Intersection Signal Coordination Model

Intermittent bus lanes (IBLs) can alleviate the contradiction between bus priority and the urgent demand of general vehicles for road resources. However, existing IBL strategies seldom pay attention to the setting method of the dynamic bus lanes at intersections, which leads to the still serious delay of buses at intersections in the traffic congestion environment. To tackle this issue, this research explores a novel method of setting the intermittent bus approach (IBA) of intersections for lane sharing and bus priority at intersections. In particular, a time slice division strategy with an intersection signal coordination model is developed to fully and reasonably allocate the idle time of bus lanes at intersections. Besides, considering the lane-changing demands of general vehicles at intersections, the parameters of the IBA lane system are modeled and optimized. For testing and verifying the feasibility of the proposed method, comparative experiments are conducted through microscopic traffic simulation. Results show that the proposed IBA setting method can effectively solve the problem of bus priority failure at intersections. It can maintain the continuity of vehicle running on intersection sections, which better exerts the operational benefits of dynamic bus lanes.

EVALUATION OF THE OPERATIONAL METHODS FOR THE ANALYSIS OF SIGNALISED INTERSECTIONS

The problem of traffic congestion in urban areas is worse at road intersections. Junction design, traffic light capacity, and driver behaviour are prevalent influences on traffic congestion. Since the traffic signal is one of the traffic controls to alleviate road congestion, adequate traffic capacity with a good design and an optimum delay is the key to improving the signalised junction. Hence, this research aims to determine the performance of signalized junctions with different traffic methods compared to on-site data. Two methods were selected for the analysis: Signalised Intersection Design and Research Aid (SIDRA) and the Malaysia Highway Capacity Manual (MHCM). Prominent signalised intersections along Jalan Kempas in the Kempas neighbourhood of Johor Bahru were selected for this study to measure the length of the vehicle line-up during rush hour. Based on these two (2) methods used, the significant different are related to traffic parameters, namely capacity analysis and level of service and by different conditions of traffic, geometric and type of traffic signal. It was determined that the parameter used for both SIDRA and MHCM is suitable; however, the output of SIDRA resulted in a more similar performance to the site observation. Based on the findings, this research is able to measure the efficiency of traffic assessment tools compared to the actual situation on site and assist the traffic engineer in efficiently investigating and evaluating the performance at signalised junctions.

Network-Wide Implementation of Roundabouts Versus Signalized Intersections on Urban Streets: Analytical and Simulation Comparison

This paper examines the impact of roundabouts implemented at intersections throughout a dense urban network on its operational performance. Metrics considered include the average free-flow speed, flow-moving capacity, trip-serving capacity, and fuel consumption rate. Three intersection strategies are compared: signalized intersections allowing left turns in a permitted manner (TWs), signalized intersections prohibiting left turns (TWLs), and modern roundabouts (RBs). Using the approaches of macroscopic fundamental diagrams and network exit functions, both analytical investigations and microscopic traffic simulations for grid networks were conducted. In general, the results from both analyses agree well. The results reveal that when single-lane roundabouts are applied in networks with a single travel in each direction, the RB network outperforms the TW network for all operational metrics. The RB network also outperforms the TWL network in free-flow speed and flow-moving capacity and has a similar trip-serving capacity as the TWL network. However, when roundabouts with two travel lanes are applied on multi-lane networks, the TWL network exceeds the RB network in both flow-moving and trip-serving capacities. This decrease in the performance of the RB network could possibly come from the complexity imposed on the entering vehicle that wants to use the inner lane. Moreover, because vehicles in the RB network need to accelerate/decelerate more frequently those in the other networks, the RB network generates a higher fuel consumption rate in uncongested and capacity conditions. The findings suggest intersections of roundabouts could be beneficial for networks with a single travel lane in each direction.

Modeling Adjustment Factors for Pedestrians and Bicycles on Turning Vehicle Movements at Signalized Intersections

At signalized intersections, the capacity of turning vehicle streams can be affected by conflicting pedestrians and bicycles who share the same signal phase. To analyze this effect, measurements at 12 pedestrian and bicycle crossings at signalized intersections in Germany covering more than 4,300 signal cycles were conducted. The reduction in capacity can be accounted for by estimating the blockage time of possible turning vehicle departures. In addition to the pedestrian or bicycle volume, the empirical analyses revealed several other parameters influencing the blockage time such as the duration of the green time and the cycle time of signal control as well as the width of the pedestrian crossing. The influencing parameters were further analyzed by microscopic simulation. Since the current quality-of-service assessment procedures in the U.S. Highway Capacity Manual (HCM) and the German Highway Capacity Manual (HBS) do not consider all of these parameters sufficiently, a new model based on the gap-acceptance theory has been derived. The new model uses the relevant influencing parameters directly as input variables so that a precise calculation of the blockage time is achieved. The new model has been derived in such a way that it can be incorporated into the existing quality-of-service assessment procedures to determine the adjustment factors of the saturation flow rate or the capacity for turning movements. For practical applications, a simplification of the exact model is also presented with a good fit of the data.

Impacts of Red-Light Enforcement Cameras on Start-Up Lost Time at Urban Signalized Intersections

Red-light cameras (RLCs) are used extensively in numerous countries as a tool to regulate traffic and enhance safety at signalized urban intersections. In this regard, a case study conducted in the Dammam metropolitan area has uncovered a potential unintended consequence of RLC usage. The presence of RLCs can lead to an increase in first-vehicle reaction time (FVRT), resulting in a higher start-up lost time (SLT) at these intersections. To investigate this phenomenon, data were collected from two distinct types of locations wherein three intersections were equipped with RLCs and three were without RLCs. A total of 311 observations or cycles were recorded, with 72 belonging to the first type and 239 belonging to the second type. The analysis revealed that the average FVRT for the intersections with RLCs was 4.27 s. For those without RLCs, it was slightly lower, at 3.68 s. Similarly, the average SLT for the RLC-equipped intersections was 3.63 s, while it measured 2.73 s at the non-RLC intersections. These findings highlighted a noticeable disparity in SLT between the two types of intersections. Further statistical analysis confirmed the existence of significant differences in the means of SLT and FVRT for the two categories of intersections. Specifically, for every 1-second delay in FVRT per location, the total SLT at intersections with RLCs increased by 7.5 s. These results underscored the potential impact of RLCs on traffic efficiency and supported the recommendation for implementing a red-light countdown system at intersections with RLCs. Such a system has the potential to reduce the FVRT and enhance traffic flow. These findings emphasize a careful consideration of the implications of RLC implementation and provide valuable insights for traffic management authorities.

Study on the installation of bus lane entrances at signal intersections

This article analyzes the feasibility of setting up bus lanes at intersections. Firstly, a multi-objective optimization model for signal control is established. Vissim simulation software is used to output delay data for different types of vehicles at intersections, and the feasibility of bus lanes at intersections is analyzed. The results show that setting bus lanes can improve bus speed and shorten travel time to a certain extent. The calculation results of the theoretical model can provide a reference for parameter calibration of the simulation model, and the simulation results can be compared and analyzed with the calculation results of the theoretical model to verify the effectiveness of the theoretical model and ensure the accuracy of the final benefit evaluation results. Provide theoretical reference for the setting of bus lanes.

Cooperative control of multiple intersections combining agent and chaotic particle swarm optimization

The regional intersection can be regarded as an agent model, that should be controlled and optimized. The traditional particle swarm optimization algorithm has been enhanced through optimization techniques and the inclusion of chaos algorithms and agent interactive and cooperative control technology, resulting in the development of a chaotic particle swarm optimization algorithm. Multi-agent is applied to the collaborative control of intersections to realize the collaborative decision-making of multi-intersections. The state and action of intersection signal control agent are defined to reduce the dimension of state-action space and speed up the convergence rate. The proposed method takes vehicle delay, vehicle queue length and regional access ratio as optimization objectives, implements regional traffic signal control strategy and improves overall traffic efficiency. By constructing a simulation environment based on Paramics software, the effectiveness of the regional traffic signal control method based on chaotic particle swarm optimization algorithm is verified, and the convergence efficiency and signal timing optimization have interactive effects.

Queue Length Estimation Based on Probe Vehicle Data at Signalized Intersections

Queue length is one of the important indexes to evaluate the operation efficiency of signalized intersection and also the key parameter of intersection signal control optimization. Traditional queue length estimation models are mostly based on fixed detection equipment, and the models assumptions are too harsh; there are certain limitations. Based on the probe vehicle data, this paper establishes a model of queue length estimation for signalized intersection based on shockwave theory. First, based on the speed and location data of the probe vehicle, the vehicle density is calculated to estimate the intersection stop line. A real-time calculation method of vehicle arrival rate is proposed to improve the applicability of the model. Then, based on the shockwave theory, the meeting time of the queue forming wave and the queue discharging wave are calculated after the green light is on. Finally, the queue length is summed in sections, including the distance between the last queued probe vehicle and the stop line during the red light period, the length of the subsequent vehicles arriving during the residual red light time, and the newly increased queue length within the queue discharging time. This paper uses the VISSIM software to simulate the actual intersection. The simulation results show that when the penetration of probe vehicle is 50%, 25%, and 10%, their corresponding mean absolute relative error are 11.27%, 27.77%, and 39.12%, respectively. It can be seen that with the increase of penetration, the error gradually decreases. The average absolute relative error is within the acceptable range. After analyzing the existing similar methods, although the accuracy of the method proposed in this paper does not reach the highest level, it has the advantages of simple operation, less computation, and good real-time computation. Relevant research results can provide support for traffic control at signalized intersections.

Network-Wide Coordinated Control Based on Space-Time Trajectories

Under a connected environment, demand information such as vehicle routes in urban networks is accessible. Based on space-time trajectories, this article proposes a real-time network-wide coordinated control strategy within a rolling horizon scheme. A traffic demand framework involving route and arrival and departure times at each intersection is introduced to describe the space-time trajectories of vehicles in the network. In the coordinated control optimization algorithm, the following two steps are iterated: first, the departure time of vehicles with minimal total delay is optimized by a nonfixed intersection signal control model; then, the arrival time is updated according to the departure time and the link travel time. The intersection signal control strategy is composed of a duration division, vehicle aggregates, and an optimization model, where the signal timing plan with minimal delay can be obtained via the departure time. The simulation results indicate that the proposed approach is capable of optimizing the signal timing plan and decreasing vehicle delay given the demand information of vehicles.

Predicting traffic propagation flow in urban road network with multi-graph convolutional network

AbstractTraffic volume propagating from upstream road link to downstream road link is the key parameter for designing intersection signal timing scheme. Recent works successfully used graph convolutional network (GCN) and specific time-series model to forecast traffic flow by capturing the spatial–temporal features. However, accurately predicting traffic propagation flow (tpf) is challenging, since the classical GCN model only considers the influence of adjacent road link. In complex urban road network, specific traffic propagation flow (tpf) is affected by various spatial features, such as adjacent tpf, which influences from tpf with same upstream link and tpf with same downstream link. Thus, we proposed a multi-graph learning-based model named TPP-GCN (traffic propagation prediction-graph convolutional network) in this paper to predict the traffic propagation flow in urban road network. The TPP-GCN model captures not only the temporal features but also multi-spatial features based on multi-layer convolution. We validated the model using real-world traffic flow data derived from taxi GPS data in Shenzhen, China. Finally, we compare and evaluate the proposed model with the existing models across several prediction scales.

Vehicle Trajectory Control and Signal Timing Optimization of Isolated Intersection under V2X Environment

This study proposes a two-level optimization model system for vehicle control and signal timing at isolated signal intersections under the mixed traffic flow environment composed of intelligent connected autonomous vehicles (CAVs) and connected human-driven vehicles (CHVs), to minimize the energy consumption and vehicle delay at intersections. The proposed two-layer optimization model is composed of a two-layer vehicle trajectory control model and a fuzzy control signal timing optimization model. The two-layer vehicle trajectory control model includes a signal-oriented vehicle trajectory control model and a car-following oriented vehicle trajectory control model. The former calculates expected acceleration and speed commands at each time step according to the coming signal information, to help vehicle pass the closest signal intersection without stopping during the green light interval; the latter uses the variable headway (VTH) strategy to follow the preceding vehicle by maintaining a safe distance. A microscopic simulator based on SUMO is developed to test the performance of the proposed optimization algorithm. In the simulation experiment, with the driving characteristics of CHV drivers considered, the results show that our model performs well under a CAV penetration rate of 30%–60% and under small or moderate levels of traffic flow. The average waiting time of vehicles is reduced by about 25% compared with the uncontrolled scheme. Under the condition of penetration rate of 60%, the average energy consumption of vehicles in the proposed model is 17.56% lower than that of the uncontrolled scheme. In addition, the proposed model reduces by 21.94% compared with the scheme of only controlling vehicles. When the traffic flow is at a low or medium level, the average energy consumption and waiting time of vehicles are reduced by nearly 35% with the proposed model.

Fuel consumption at signalized intersections: Investigating the impact of different signal indication settings

The fuel consumption of vehicles depends on various factors including vehicle design, driving style, traffic management, and road design. Many manufacturers have been developing efficient and smart vehicles, which contributes to minimizing vehicle fuel consumption. However, traffic management and control could restrict the efficiency of having a sustainable mobility system. Intersections are considered as critical locations, in terms of fuel consumption, due to the significant impact of traffic control at these locations on the vehicle maneuver either by stopping or acceleration to clear these bottleneck points. Analyzing the effect of different intersection signal settings is, therefore, important to optimize vehicle fuel consumption. In this study, we used simulator data of sixty-six drivers going through signalized intersections equipped with two different signal indication settings, namely, control and flashing green conditions. We calculated total fuel consumption using the VT-CPFM and COPERT models and then applied GLME with two different model distributions: normal and log-normal to study the correlation between the two treatments and fuel consumption. Results showed that by displaying the remaining green time, flashing green treatment (i.e., signals with traffic light sequence: green, flashing green, yellow and red-green) produced a lower fuel consumption in comparison to control condition (green, yellow and red sequence), yielding to a similar performance of eco-driving. It was found that as drivers become aware ahead of time when the traffic light will be turning red due to the flashing green signal indication, eventually they either speed up a little to cross the intersection in time, or they early start decelerating, which creates a more optimal deceleration pattern. Results also showed that the VT-CPFM model resulted in more realistic results than COPERT due to its ability to capture the transient changes in speed and acceleration.

Comprehensive Investigation of Commercial Motor Vehicle Crashes at Signalized and Unsignalized Intersections in Kentucky

This study develops safety performance functions (SPFs) for “fatal and injury” (FI) commercial motor vehicle (CMV) crashes (here, a CMV is defined as a large truck or a bus) at both signalized and unsignalized intersections in Kentucky. Five count-response regression models—negative binomial, Conway–Maxwell–Poisson, heterogeneous Conway–Maxwell–Poisson, zero-inflated Conway–Maxwell–Poisson, and zero-inflated heterogeneous Conway–Maxwell–Poisson (ZI-HTCMP)—were developed and compared at each of the selected signalized and unsignalized intersections. FI CMV intersection-related crash data in Kentucky over a period of five years (2015 to 2019) were used. Information on road-specific characteristics, for example, shoulder width, median width and type, International Roughness Index (IRI) value, and traffic volume (including annual average daily traffic [AADT] and heavy vehicle percentage [HVP]) was collected. Additionally, information on intersection-specific characteristics, for example, number of through lanes and presence of exclusive left- and right-turn lanes on major and minor roads, was collected using Google Maps’ street view time slider back to the crash year. For both signalized and unsignalized intersections, the ZI-HTCMP model (with a varying dispersion parameter) outperformed the other comparative models. With regard to signalized intersections, a speed limit of greater than 45 mph on the major road, a speed limit greater than 45 mph on the minor road, and a median on the major road wider than 3 ft were significantly associated with increased FI CMV crashes. As far as unsignalized intersections were concerned, higher major road AADT, 4-legged configuration, a speed limit greater than 45 mph on the major road, and an IRI value of greater than 100 for the major road were significantly associated with increased FI CMV crashes. The SPF results (for each of the signalized and unsignalized intersections) were then used to identify the top 10 hazardous locations for each intersection type.

Optimal Driving Model for Connected and Automated Electric Freight Vehicles in a Wireless Charging Scenario at Signalised Intersections

Electric freight vehicles have become an important means of transportation in connected and automated environments owing to their numerous advantages. However, the generally short driving range of connected and automated electric freight vehicles (CAEFVs) does not satisfy the growing transport demand. In this study, wireless charging technology is employed to construct a complex driving scenario including urban roads and dynamic wireless charging facilities. A combination of variable-scale elements consisting of vehicles, roads, and the environment is analysed hierarchically to develop a wireless charging scheme for urban transport systems. Using passage efficiency, energy consumption, and passenger comfort as the joint optimisation objectives, an optimal driving model for CAEFVs in wireless charging scenarios at signalised intersections combining scenario boundaries and vehicle dynamic constraints is proposed. Considering the differentiated charging needs of vehicles, this model is divided into a time priority strategy (TPS), balance priority strategy (BPS), and charging priority strategy (CPS). The obtained results reveal that the CPS is superior to the TPS in terms of the charging benefits but requires a longer travel time. Meanwhile, the BPS increases the charging benefits and passing efficiency. This study provides guidance for the deployment of wireless charging lanes with a high application value.

Sphingosine Kinases at the Intersection of Pro-Inflammatory LPS and Anti-Inflammatory Endocannabinoid Signaling in BV2 Mouse Microglia Cells.

Microglia, the resident immune cells of the central nervous system, play important roles in brain homeostasis as well as in neuroinflammation, neurodegeneration, neurovascular diseases, and traumatic brain injury. In this context, components of the endocannabinoid (eCB) system have been shown to shift microglia towards an anti-inflammatory activation state. Instead, much less is known about the functional role of the sphingosine kinase (SphK)/sphingosine-1-phosphate (S1P) system in microglia biology. In the present study, we addressed potential crosstalk of the eCB and the S1P systems in BV2 mouse microglia cells challenged with lipopolysaccharide (LPS). We show that URB597, the selective inhibitor of fatty acid amide hydrolase (FAAH)-the main degradative enzyme of the eCB anandamide-prevented LPS-induced production of tumor necrosis factor-α (TNFα) and interleukin-1β (IL-1β), and caused the accumulation of anandamide itself and eCB-like molecules such as oleic acid and cis-vaccenic acid ethanolamide, palmitoylethanolamide, and docosahexaenoyl ethanolamide. Furthermore, treatment with JWH133, a selective agonist of the eCB-binding cannabinoid 2 (CB2) receptor, mimicked the anti-inflammatory effects of URB597. Interestingly, LPS induced transcription of both SphK1 and SphK2, and the selective inhibitors of SphK1 (SLP7111228) and SphK2 (SLM6031434) strongly reduced LPS-induced TNFα and IL-1β production. Thus, the two SphKs were pro-inflammatory in BV2 cells in a non-redundant manner. Most importantly, the inhibition of FAAH by URB597, as well as the activation of CB2 by JWH133, prevented LPS-stimulated transcription of SphK1 and SphK2. These results present SphK1 and SphK2 at the intersection of pro-inflammatory LPS and anti-inflammatory eCB signaling, and suggest the further development of inhibitors of FAAH or SphKs for the treatment of neuroinflammatory diseases.

Signal-control refined dynamic traffic graph model for movement-based arterial network traffic volume prediction

Forecasting the forthcoming intersection movement-based traffic volume enables adaptive traffic control systems to dynamically respond to the fluctuation of traffic demands. In this paper, a deep-learning based Signal-control Refined Dynamic Traffic Graph (ScR-DTG) Model is proposed for advancing the network-level movement-based traffic volume prediction task. The proposed model attempts to further improve the-state-of-art and practice algorithms in traffic prediction for arterial network adaptive signal control utilizing tradition traffic flow theory boosted deep-learning methodology. For precisely inferencing the movement-based demand at cycle-to-cycle level, the proposed model incorporates spatial graph convolution inferencing layer and temporal inferencing layer to explore both the intricate spatial temporal dependencies, respectively. A signal control refining module is contrived to deduce the controlled movement saturation flow and introduce the essential control inferences, which is of great significance but frequently neglected in the previous researches. Additionally, according to the real-time movement specified travel time, this paper creatively constructs an adjacent graph with dynamic order for more accurately capturing the ever-changing spatial relevancies. Field experiments with multiple signal schemes were conducted in the downtown area of Zhangzhou (China). The promising results demonstrated the state-of-the-art accuracy than other high-performance volume prediction algorithms. Implementing the proposed model enables to obtain accurate movement-based volume predictions, which would assist the traffic management agencies in adjusting signal timing adaptively and further improve the efficiency of signal intersection.

A hierarchical multinomial logit model to examine the effects of signal strategies on right-turn crash injury severity at signalised intersections

The severity of right-turn crashes (or left-turn crashes for the roads in the US) at signalised intersections tends to be high because of the relatively high conflicting speeds and angle of impact. However, right-turn crash injury severity at signalised intersections was not sufficiently studied. In particular, the effects of signal control strategies on crash injury severity are not known. This study developed crash injury severity models for right-turn crashes at signalised intersections with a novel approach of linking crashes with signal strategies which enabled assessing the effects of signal strategies on crash injury severity. The study provided a comprehensive understanding of the impacts of signal strategies, intersection geometry and traffic factors on crash injury severity of right-turn crashes at signalised intersections. Crash injury severity models were estimated with crash data from 221 signalised intersections in Queensland from 2012 to 2018. To address the hierarchical structure of crash data, two-level hierarchical Multinomial Logit models were applied, hypothesising that the first level includes individual crash characteristics while the second level includes intersection characteristics. The applied hierarchical model accounts for the correlation among crashes within intersections. Results showed that crashes during Lagging right-turn and Diamond overlap turns are likely to be more severe than other signal strategies at intersections, with the Lagging right-turn signal being the most hazardous. The results also illustrate that the probability of severe injuries increases with the number of conflicting lanes, whereas the corresponding probability decreases with the occupancy of the conflicting lane.

A Dueling Deep Q-Network method for low-carbon traffic signal control

Improper traffic signal control will lead to long delays for vehicles and produce massive carbon emissions. The vast vehicle exhaust emissions will pollute the environment and exacerbate the earth’s greenhouse effect. Intersection signal optimization tends to start from the traditional view of improving traffic efficiency but ignores the perspective of reducing vehicle carbon emissions. Under the framework of a deep reinforcement learning strategy, this study proposes a novel signal control method to minimize the carbon emissions of vehicles at the intersection. To associate with carbon emissions and signal control plans, the method employs the negative value of vehicle’s carbon dioxide emissions as the reward and takes the feature vectors at different time points in the two decision action intervals as the state features. The fully connected neural network, convolutional neural network, and long short-term memory network are respectively adopted to extract the state features of the decision-making period and compare their Q-value estimation effects. Through the SUMO simulation platform, the proposed signal control method is comprehensively evaluated and compared with different baseline models. It has been proved that the proposed signal control approach can not only directly reduce vehicle carbon emissions but also improve the operational efficiency of the intersection.

Multimodal Intersection Signal Timing Considerations Framework, Performance Measures, and Case Study

Signal retiming is a cost-effective practice to improve intersection operations through periodic updating of the timing plans. Traditionally, signal retiming has focused on vehicular-based performance (e.g., reducing vehicle delay or number of stops), while often unintentionally ignoring the needs of other users, such as pedestrians, cyclists, and transit vehicle passengers. This unintended omission is further exacerbated by the signal timing tools used in the industry, which tend to primarily report vehicular metrics. To address these challenges, this research developed a multimodal signal timing framework along with easy-to-use performance measures. These performance measures include (1) vehicle delay, (2) crosswalk delay, (3) crossing stress, and (4) bus delay. To assess the approach effectiveness, the proposed framework and the developed tools were applied to 12 intersections along the North Glebe Corridor, in Arlington, VA, for the weekday a.m. peak. Analysis results showed that the proposed framework and tools can incorporate most multimodal considerations and address the limitations of the traditional vehicle-based approaches by quantifying multimodal tradeoffs at signals. With the proposed tools, the alternative timing plan developed for the a.m. peak yielded a 4.5% improvement in performance along the corridor, versus a degradation of 3.7% if only vehicle delay was considered in the analysis. Additional sensitivity analysis conducted along the corridor using various timing plan scenarios showed that the calculated values of the measures are accurately reflecting the anticipated effects of signal timing changes that aim to improve crossing stress for non-motorized users and/or reduce their delay at the intersection crosswalks.

Satellite Network Transmission of Cooperative Relay Superimposed Signal Reconstructed in Spatial Dimension

In order to save frequency resources, a new remote sensing satellite service can gradually adopt relay cooperation transmission to realize the same frequency and common channel transmission of multiple data. To solve the problem of mutual interference between messages, this study proposes a universal model of relay cooperative channel transmission, and the separation mechanism of heterogeneous signals for simultaneous unicast and multicast transmission is also studied. In this study, a signal space is used to reconstruct the degrees of freedom, and an orthogonalized spatial alignment direction is designed to obtain the equivalent parallel transmission channel. We also propose a constellation point remapping scheme under the optimal constraint of spatial separation of transmission signals. Furthermore, we merge constellation points to solve the problem of fuzzy mapping of physical layer network coding. The simulation results show that the co-channel transmission with interference suppression can be realized when the equivalent degrees of freedom of the signal intersection subspace is not less than dpK(K−1)+dcK. The Euclidean distance between constellation points is increased by constructing orthogonal signal spatial alignment directions, which brings an additional BER performance gain of 2 dB. If the signal alignment direction and channel quality are jointly designed, the transmission quality can be further improved.