Intersection Throughput Research Articles

Analysis of the intersection throughput at changes in the traffic flow structure

The paper addresses the throughput of an urban signalized intersection when the traffic flow structure varies widely. In the course of the study, data were collected from a street CCTV camera tracking traffic flows at a single intersection. At the preliminary step during the analysis of the dynamics of vehicles passing the intersection when leaving the queue that formed in front of the red light, it was found that it significantly changes for different types of vehicles. Therefore, the next step was to develop a statistically significant mathematical model of the intersection throughput as a multiple regression equation including all vehicle types under consideration. This model gave us various predictive estimates with the traffic flow structure varied. To obtain a visual representation of the results, we tested a model of category-based traffic flow at the intersection, built on the basis of fuzzy logic methods. This model allowed us to estimate the intersection throughput depending on some uncertain traffic flow factors in a graphic 3D view. The study was oriented towards the development of an algorithm to analyze the time required for a heterogeneous queue of vehicles to pass the intersection in real time. The algorithm can be used in intelligent transportation systems, in particular, in problems related to uninterrupted traffic of unmanned vehicles. The models considered may serve as a good basis for justified decisions on improving the vehicle-to-infrastructure communication (V2I).

A Dynamic Control Method for Signalized Intersections Using the Flow Fluctuation Amplitude

Intersections are a vital component of the urban road network. Their use is essential for traffic efficiency and operational stability. In this paper, a dynamic control method for two-phase signal control intersection is proposed by using the intercycle flow standard deviation. This technique can dynamically assign weights to different control goals of traffic efficiency and operational stability. For intersections with large flow fluctuation amplitudes, greater weight is given to the control goal of operational stability to reduce the sensitivity of the signal control on flow fluctuation. For intersections with a small flow fluctuation amplitude, greater weight is given to the control goal of traffic efficiency to maximize the intersection throughput. Compared with the plan selection and pretimed control method, the numerical results can validate that this method has better control performance under different flow fluctuations amplitude. The fluctuation range length is significantly optimized, especially in the cases of the low and medium flow saturation conditions.

Signal Timing Optimization Model for Intersections in Traffic Incidents

The intersection control and management can alleviate the traffic congestion caused by traffic incidents. Therefore, it becomes essential to develop a signal optimization method for intersections influenced by traffic incidents, which will be beneficial to prevent congestion spreading. In this paper, the proposed model is capable of maximizing the intersection throughput by comprehensively considering the queue length as the penalty value. The headway of leaving vehicles is assumed to follow the Cowan’s M3 headway distribution, where formulas for queue length can be derived based on gap acceptance theory. To satisfy the conditions for efficiently identifying feasible solutions in a short time, a heuristic algorithm (simulated annealing algorithm) is employed to solve the model. The numerical results can validate that the proposed method can solve the problem more efficiently and alleviate the intersection congestion caused by the incidents more desirably. When the incident occurs away from the intersection stop line, the impacts on intersection throughput will be gradually weakened. The proposed method is capable of improving the signalized intersection throughput while preventing the congestion from spreading to the upstream intersection.

Identification of distinguishing characteristics of intersections based on statistical analysis and data from video cameras

The article discusses the issues of improving the collection of traffic information using video cameras and the statistical processing of collected data. The aim of the article was to identify the main patterns of traffic at intersections in traffic congestion and to develop an analysis technique to improve traffic management at intersections. In modern conditions, there is a sharp increase in the number of vehicles, which leads to negative consequences, such as an increase in travel time, additional fuel consumption, increased risk of traffic accidents and others. To solve the problem of improving traffic control at intersections, it is necessary to have a reliable information collection system and apply modern effective methods of processing the collected information. The purpose of this article is to determine the most important traffic characteristics that affect the throughput of intersections. As a criterion for the cross-pass ability of the intersection, the actual number of passing cars during the permission signal of the torch light is taken. Using multivariate regression analysis, a model was developed to predict intersection throughput taking into account the most important traffic characteristics. Analysis of the throughput of intersections using the fuzzy logic method confirmed the correctness of the developed model. In addition, based on the results of processing information collected at 20 intersections and including 597 observations, a methodology was developed for determining the similarity of traffic intersections. This allows us to identify homogeneous types of intersections and to develop typical traffic management techniques in the city, instead of individually managing each node of the city’s transport network individually. The results obtained lead to a significant reduction in costs for the organization of rational traffic flows.

Management of Delay Optimization at Intersections

Congestion extends the time of the journey for both people and goods. Therefore, transport solutions should be optimized. Management scientists and technical scientists worked together in order to develop a proprietary solution to increase efficiency in terms of productivity improvements for intelligent transport systems. The most fundamental functions of management have been paired with a detailed analysis of city traffic. The authors developed a method for determining the order of vehicles at traffic lights and connected it with vehicle-to-vehicle communication and GPS signals. As a result, a novel method to increase the throughput of intersections is presented. This solution generates a sound signal in order to inform the driver that the preceding car has started moving forward. The proposed solution leads to the shortening of the reaction time of the drivers waiting in a queue. This situation is most common at red lights. Consequently, the traffic simulation shows that the discharge of queues at traffic lights may be quicker by up to 13.5%. Notably, that proposed solution does not require any modification of the infrastructure as well as any additional devices for vehicle-to-infrastructure communication at the road intersections. To conclude, proper implementation of the proposed solution will certainly contribute to efficiency improvements within intelligent transport systems, with the potential to reduce traffic jams.

INFORMATION APPROACH TO DETERMINING THE TRAFFIC ROUTE BY VEHICLES DRIVERS IN CITIES

Purpose. Modeling the transport network, streamlining the development and planning of traffic flows, which is possible through the introduction of information technology, leads to better control of the transport system and transport operations. The study is aimed at gaining new knowledge on determining traffic routes for vehicle drivers in cities. Currently discovered patterns of drivers choosing traffic routes are offered for all drivers of vehicles, regardless of their individual characteristics. Methodology. When determining the patterns of drivers choosing the routes of vehicles in cities, it is proposed to conduct differentially depending on the individual characteristics of drivers, which are determined by the type of nervous system. Based on the analysis, factors were identified that affect the choice of traffic route for drivers. In order to fix the identified factors, a survey was conducted using a specially designed questionnaire. To take into account the individual characteristics of drivers using a typological questionnaire, the type of nervous system was determined. To determine the patterns of choice of traffic routes for drivers of vehicles, methods of regression analysis were used. Findings. Based on the data obtained from a survey of vehicle drivers, a model was developed for changing the correspondence share implemented by the drivers with the sanguine type of nervous system on the alternative traffic routes. A statistical evaluation of the resulting model indicates the admissibility of its use for predicting the parameters of traffic flows in urban sustainable development projects. Originality. For the first time, the authors conducted a study of the patterns of choice of traffic routes for drivers of vehicles, taking into account the individual characteristics of drivers, which are determined by the type of nervous system. Practical value. Based on the results obtained, it is possible to model the transport network, rationalize the development and plan traffic flows, which is possible through the introduction of information technologies. Modeling the traffic routes of vehicles makes it possible to analyze the throughput of highways and intersections. The introduction of information technology leads to better control of the transport system and transport operations.

The Capacity of the Road Network: Data Collection and Statistical Analysis of Traffic Characteristics

The possibilities of collecting the necessary information using multi-touch cameras and ways to improve road traffic data collection are considered. An increase in the number of vehicles leads to traffic jams, which in turn leads to an increase in travel time, additional fuel consumption and other negative consequences. To solve this problem, it is necessary to have a reliable information collection system and apply modern effective methods of processing the collected information. The technology considered in the article allows taking into account pedestrians crossing the intersection. The purpose of this article is to determine the most important traffic characteristics that affect the traffic capacity of the intersection, in other words, the actual number of passing cars. Throughput is taken as a dependent variable. Based on the results of the regression analysis, a model was developed to predict the intersection throughput taking into account the most important traffic characteristics. Besides, this model is based on the fuzzy logic method and using the Fuzzy TECH 5.81d Professional Edition computer program.

Assessment of the impact of capital construction on the adjacent street-road network with traffic at unregulated intersections

The article discusses the problems of assessing the influence of centers of gravity on the adjacent street-road networks in the process of their functioning, expansion or reprofiling. The paper shows the main models for estimating the throughput of unregulated intersections proposed in the domestic and foreign literature. The author gives sequence of calculating the transport demand transforming into the intensity of the traffic flow on the road network. Moreover, the study proposes recommendations on assessing the level of traffic service and load factor based on transport demand and throughput, adjacent street-road network.

A CAV Platoon Control Method for Isolated Intersections: Guaranteed Feasible Multi-Objective Approach with Priority

This paper proposed a multi-objective guaranteed feasible connected and autonomous vehicle (CAV) platoon control method for signalized isolated intersections with priorities. Specifically, we prioritized the intersection throughput and traffic efficiency under a pre-defined signal cycle, based on which we minimized fuel consumption and emissions for CAV platoons. Longitudinal safety was also considered as a necessary condition. To handle the aforementioned targets, we firstly designed a vehicular sub-platoon splitting algorithm based on Farkas lemma to accommodate a maximum number of vehicles for each signal green time phase. Secondly, the CAV optimal trajectories control algorithm was designed as a centralized cooperative model predictive control (MPC). Moreover, the optimal control problem was formulated as discrete linear quadratic control problems with constraints with receding predictive horizons, which can be efficiently solved by quadratic programming after reformulation. For rigor, the proofs of the recursive feasibility and asymptotic stability of our proposed predictive control model were provided. For evaluation, the performance of the control algorithm was compared against a non-cooperative distributed CAV control through simulation. It was found that the proposed method can significantly enhance both traffic efficiency and energy efficiency with ensured safety for CAV platoons at urban signalized intersections.

Design and evaluation of a new intersection model to minimize congestions using VISSIM software

AbstractTraffic modelling and simulation is one of the frequent tools used in road infrastructure design. Software tools designed for traffic simulations are an important supportive tool in decision-making and in choosing the optimal solution. The aim of this paper is to introduce the application of the VISSIM program, in the design and testing a model of the traffic-light-controlled intersection. The traffic on the selected congested intersection is modelled and simulated first for the current state, then for two models with modifications that are to increase the throughput of the intersection. The monitored criterion of the intersection throughput is the length of queues. Both adjustments have led to a significant reduction of the number of vehicles waiting in direction of the greatest congestions. In the first model, the average line length was reduced by 75%, and in the second model, the modifications lead to a fluent passage of right-turn vehicles and a significant reduction in vehicle lines for other directions.

Time Difference Penalized Traffic Signal Timing by LSTM Q-Network to Balance Safety and Capacity at Intersections

The conflict between limited road resources and rapid car ownership makes the traffic signal timing become a pivotal challenge. Emerging studies have been carried out on adaptive signal timing, but most of them still focus on the throughput of intersections, leaving safety and travel experience unconsidered. This paper proposes a time difference penalized traffic signal timing method by reinforcement learning technique to balance safety and throughput capacity in traffic control system. Firstly, a microcosmic state representation is proposed to integrate the dynamics of both traffic lights and road vehicles, including driver behaviors of lane changing, car-following, previous phase of traffic light and its duration. Secondly, an action space, including 8 signal phases, and a behavior-aware reward function are designed to resist the red-light overflow. Finally, a partial long short term memory (LSTM) network is trained to balance traffic efficiency and traveling experience. In the network training, a parallel sampling method is adopted to obtain experience from multiple environments to accelerate the training convergence in practical application. Experimental results show that the proposed method improves the intersection efficiency up to 14.28% compared to the fixed signal timing and 5.26% compared to DQN while getting rid of red-light overflow time.

Преобразование обычного кольцевого пересечения автомобильной дороги в турбокольцевое: сравнение пропускной способности

In large cities, a situation often arises when roundabout intersections of highways no longer cope with the passage of traffic flows. The main reasons for this problem are the inconsistency of the potential traffic capacity of the roundabout with real traffic flows, as well as the lack of knowledge and skills of drivers on the roundabout carriageway. To reduce the influence of the human factor, various solutions are proposed, but, as practice has shown, ducted roundabouts or «turbo rings» are the most effective. A feature of this configuration of the carriageway is the predictability of driver behavior due to the physical separation of lanes for traffic. In a number of countries, turbo ring intersections have already been put into operation, which made it possible to confirm the alleged advantages of this configuration: the absence of changeover maneuvers on the ring carriageway, the need to give way to a maximum of two traffic flows, and a decrease in the average travel speed along the ring intersection. The aim of this study was to compare the performance of the existing threelane intersection, characterized by a predominance of lefthand turn flow during rush hours, and the developed design of the turbo ring interchange. Empirical data on traffic flows and length of queues, as well as a matrix of correspondences, were used to calibrate the computer micromodel. Micromodeling was carried out by means of: specifying the matrix of correspondences, taking into account the distribution of the traffic flow in time; setting the rules of priority, speed of approach to the intersection and speed reduction zones; development of scenarios for the analysis of morning and evening rush hours, fluctuations in traffic flows in time. Performance comparisons were made for the node as a whole, as well as for the selected busiest routes in terms of average and maximum congestion lengths, average latency and downtime. To assess the performance of the intersections, it was proposed to use foreign methods for assessing the capacity based on the mathematical models of Bowie, Hagring and Fortine. It was revealed that the transformation of the existing circular intersection into a turbo ring made it possible not only to increase the throughput of the node, but also to reduce congestion at the entrances and exits, which determines the practical significance of the study. To study the possibility of integrating the methodology for assessing the throughput, considered in the article, into the domestic method for choosing the type of intersections, which is the basis of regulatory and technical documents. The ultimate goal of the study is to create a new methodology for assessing the throughput of circular intersections of various types, including taking into account the peculiarities of the functioning of circular intersections on the territory of the Russian Federation.

Measuring Impact of Traffic Parameters in Adaptive Signal Control through Microscopic Simulation

This paper aims to exploit the traffic parameters setting in adaptive traffic control. In this study, is known as Dynamic Timing Optimiser (DTO). DTO is an online algorithm, uses real-time optimisation in estimating cycle length according to fluctuations arrival flow registered from the detector. DTO cycle time estimation is also incorporated with preset parameters including saturation flow rate (s) and lost time (L). However, these traffic flow parameters commonly inputted as one deterministic value which adopted for the whole day. For example, presumed constant of saturation flow rate (s) do not accurately represent an actual oversaturated condition. The effects of employing inaccurate saturation flow rate (s) lead to the underestimation of cycle length. Therefore, a set of parameters value is applied and tested encompass of default value and adjusted value that implied a heaviest traffic condition through microscopic simulation. This resulted in outcomes of intersection performance in terms of intersection delay, travel time and throughput. According to simulation result, saturation flow rate (s) parameters show a great influence in cycle length optimisation compared to lost time (L) parameter. Employing a realistic saturation flow rate (s) while inputting parameters in DTO according to real traffic conditions contribute to a less intersection delay. In addition, the study revealed that a longer lost time (L) configured in the signal system, a longer cycle length generated by DTO algorithm. As predicted, high delay occurs during long cycle length yet benefited in allowing a higher throughput.

Space Distribution Method for Autonomous Vehicles at a Signalized Multi-Lane Intersection

Under the connected vehicle environment, autonomous vehicles (AVs) could bring numerous advantages including: improving the traffic flow, enhancing safety and alleviating air pollution. However, optimally operating AVs at signalized multi-lane intersections is a challenging problem due to the complex interaction of vehicles between lanes. It is thus a desire to manage and control the dynamics of AVs at signalized multi-lane intersections. To this end, this paper puts forward a bi-level control framework to optimize the intersection throughput. In our proposed method, the upper level (i.e. the intersection controller) is used to optimize the lane usages of each approach and the AVs' positions. In contrast, the lower level (i.e. the vehicle controllers) receives information from the upper level to control the AVs to get the maximum speed. More specifically, in the upper level, we apply a novel Space Distribution Method (SDM) for the AVs to maximize the throughput (i.e. a number of AVs) of the (multi-lane) intersection where signal timings are predefined. The SDM is divided into three steps: i) platoon formulation; ii) lane-mode optimization; and iii) AVs' position distribution. To maximize the throughput, the intersection controller receives information about the states of the AVs (e.g. the trajectories), then optimizes the lane usages for each approach, the desired speed, and the gap of the AVs as well as the AV's position along the approach. After that, each AV which is allowed to cross the intersection will determine its own trajectory and travel with the scheduled time without crash. Numerical simulations are set up to show that the throughput increases significantly, even more than twice of the throughput obtained from other methods in some circumstances.

CSIP

Intersection management is one of the main challenging issues in road safety because intersections are a leading cause of traffic congestion and accidents. In fact, more than 44% of all reported crashes in the U.S. occur around intersection areas, which, in turn, has led to 8,500 fatalities and approximately 1 million injuries every year. With vehicles expected to become self-driving, the question is whether high throughput can be obtained through intersections while keeping them safe. A spatio-temporal intersection protocol named the Ballroom Intersection Protocol (BRIP) [8] was recently proposed in the literature to address this situation. Under this protocol, automated and connected vehicles arrive at and go through an intersection in a cooperative fashion with no vehicle needing to stop, while maximizing the intersection throughput. Though no vehicles run into one another under ideal environments with BRIP, vehicle accidents can occur when the self-driving vehicles have location errors and/or control system failure. In this article, we present a safe and practical intersection protocol named the Configurable Synchronous Intersection Protocol (CSIP). CSIP is a more general and resilient version of BRIP. CSIP utilizes a certain inter-vehicular distance to meet safety requirements in the presence of GPS inaccuracies and control failure. The inter-vehicular distances under CSIP are much more acceptable and comfortable to human passengers due to longer inter-vehicular distances that do not cause fear. With CSIP, the inter-vehicular distances can also be changed at each intersection to account for different traffic volumes, GPS accuracy levels, and geographical layout of intersections. Our simulation results show that CSIP never leads to traffic accidents even when the system has typical location errors, and that CSIP increases the traffic throughput of the intersections compared to common signalized intersections.

Composite Platoon Trajectory Planning Strategy for Intersection Throughput Maximization

Recently, the ever-increasing vehicle population has become a severe challenge to traffic efficiency and air quality in modern city life. Signal-controlled intersection is a bottleneck of the urban traffic system. Platooning has the potential to improve the intersection throughput, and a typical V2X application, green light optimal speed advisory (GLOSA), can also reduce vehicles’ stopping at intersections to enhance traffic efficiency. However, there is scarcely any literature regarding GLOSA system for platoon to increase intersection throughput. In this paper, the composite platoon trajectory planning strategy (CPTPS) is proposed to maximize the intersection throughput. Since the intersection throughput maximization problem can be transformed into minimizing vehicles’ intersection arrival time in a green phase by optimizing vehicles’ trajectories, three portions are designed in CPTPS to solve the problem, which are a GLOSA-based trajectory planning method for platoon leaders, a reinforcement learning-based trajectory planning method for platoon followers, and a flexible platoon management protocol to ensure the smooth operations in a platoon. Simulation results show that, compared to the non-optimized cases, CPTPS can effectively increase intersection throughput while reduce stoppings at intersections and gas emissions. Especially in saturation cases, CPTPS can achieve a near-ideal throughput performance. And the communication delay within 100 ms scarcely affects CPTPS's headway maintenance performance. Moreover, CPTPS outperforms the existing works in headway maintenance when vehicles drive in a platoon. These results validate the effectiveness, robustness, and practical feasibility of CPTPS, for solving the signal-controlled intersection traffic efficiency problem.

Pedestrian's risk-based negotiation model for self-driving vehicles to get the right of way

Negotiations among drivers and pedestrians are common on roads, but it is still challenging for a self-driving vehicle to negotiate for its right of way with other human road users, especially pedestrians. Currently, the self-driving vehicles are programmed for conservative behavior, yielding to approaching pedestrians. Consequently, the future urban traffic will slow down significantly. In this paper, a conceptual model of vehicle–pedestrian negotiation is proposed. This model allows individual decision making of multiple vehicles and pedestrians, extending a prior negotiation model for a single vehicle and a single pedestrian. The possible negotiation opportunities for vehicles are modeled considering different risk-taking behaviors of pedestrians. Simulation results show an overall improvement in the waiting time of vehicles and thus in the intersection throughput, compared to conservative vehicle behavior. The simulation results show also that the benefit of reduced waiting times for vehicles comes at the cost of some waiting time for pedestrians. However, the observed pedestrian waiting times in this model are not more than the generally accepted waiting times reported in empirical studies.

Development of a signal-head-free intersection control logic in a fully connected and autonomous vehicle environment

Establishment of effective cooperation between vehicles and transportation infrastructure improves travel reliability in urban transportation networks. Lack of collaboration, however, exacerbates congestion due mainly to frequent stops at signalized intersections. It is beneficial to develop a control logic that collects basic safety message from approaching connected and autonomous vehicles and guarantees efficient intersection operations with safe and incident free vehicle maneuvers. In this paper, a signal-head-free intersection control logic is formulated into a dynamic programming model that aims to maximize the intersection throughput. A stochastic look-ahead technique is proposed based on Monte Carlo tree search algorithm to determine the near-optimal actions (i.e., acceleration rates) over time to prevent movement conflicts. Our numerical results confirm that the proposed technique can solve the problem efficiently and addresses the consequences of existing traffic signals. The proposed approach, while completely avoids incidents at intersections, significantly reduces travel time (ranging between 59.4% and 83.7% when compared to fixed-time and fully-actuated control strategies) at intersections under various demand patterns.

Cooperative Intersection Control Based on Virtual Platooning

This paper proposes a cooperative intersection control strategy, which aims to decrease the number of accidents and to increase the traffic flow at intersections. Existing high-level automation methodologies mainly focus on the determination of a safe crossing sequence of the involved vehicles, typically ignoring realistic vehicle dynamics aspects. The solution proposed in this paper, referred to as cooperative intersection control (CIC), takes into account the dynamics of the vehicles and is based on the novel concept of virtual platooning. Virtual platooning allows to form platoons of vehicles that are in different lanes of the intersection and have different directional intentions. Herewith, both safe passage of the vehicles through the intersection and a high intersection throughput (due to close “virtual” vehicle following) can be achieved. The performance of the proposed strategy is assessed, and a comparison between the CIC and an intersection controlled with traffic lights is presented.

A Cell‐Based Distributed‐Coordinated Approach for Network‐Level Signal Timing Optimization

AbstractThis article develops an efficient methodology to optimize the timing of signalized intersections in urban street networks. Our approach distributes a network‐level mixed‐integer linear program (MILP) to intersection level. This distribution significantly reduces the complexity of the MILP and makes it real‐time and scalable. We create coordination between MILPs to reduce the probability of finding locally optimal solutions. The formulation accounts for oversaturated conditions by using an appropriate objective function and explicit constraints on queue length. We develop a rolling‐horizon solution algorithm and apply it to several case‐study networks under various demand patterns. The objective function of the optimization program is to maximize intersection throughput. The comparison of the obtained solutions to an optimal solution found by a central optimization approach (whenever possible) shows a maximum of 1% gap on a number of performance measures over different conditions.