Urban Road Intersection Research Articles

An Automatic Conflict Detection Framework for Urban Intersections Based on an Improved Time Difference to Collision Indicator

Urban road intersections are one of the key components of road networks. Due to complex and diverse traffic conditions, traffic conflicts occur frequently. Accurate traffic conflict detection allows improvement of the traffic conditions and decreases the probability of traffic accidents. Many time-based conflict indicators have been widely studied, but the sizes of the vehicles are ignored. This is a very important factor for conflict detection at urban intersections. Therefore, in this paper we propose a novel time difference conflict indicator by incorporating vehicle sizes instead of viewing vehicles as particles. Specially, we designed an automatic conflict recognition framework between vehicles at the urban intersections. The vehicle sizes are automatically extracted with the sparse recurrent convolutional neural network, and the vehicle trajectories are obtained with a fast-tracking algorithm based on the intersection-to-union ratio. Given tracking vehicles, we improved the time difference to the conflict metric by incorporating vehicle size information. We have conducted extensive experiments and demonstrated that the proposed framework can effectively recognize vehicle conflict accurately.

Study of the ways to increase the urban street and road network capacity

During the existing situation assessment on the urban road network and intersections design, it is necessary to establish the traffic capacity value of streets and its elements. When forecasting the capacity value it is necessary to turn traffic flow presented by different vehicles to uniformity expressed in the equivalent number of passenger cars by using passenger car equivalent coefficients (PCE). PCE currently used in the Russian Federation are formed on the capacity studies of the street-road network, carried out in the 70s-80s of the last century on rural roads, where the traffic flow was mostly presented by heavy vehicles. Nowadays the major percentage of traffic flow is presented by passenger cars. At the same time, the dynamic performances of trucks and buses have changed significantly. In this regard, the question of clarifying the traffic flow composition and revising the passenger car equivalent coefficients becomes urgent. High level of streets congestion and intersections with a small heavy vehicle percentage in the traffic flow is most often observed in large cities. The studies were carried out on the the multi-lane highway in Moscow in the sections free from the intersections and junctions influence. The purpose of the study was to clarify the traffic flow composition and determine the passenger car equivalent coefficients in modern traffic conditions.

Multi-Task Fusion Deep Learning Model for Short-Term Intersection Operation Performance Forecasting

Urban road intersection bottleneck has become an important factor in causing traffic delay and restricting traffic efficiency. It is essential to explore the prediction of the operating performance at intersections in real-time and formulate corresponding strategies to alleviate intersection delay. However, because of the sophisticated intersection traffic condition, it is difficult to capture the intersection traffic Spatio-temporal features by the traditional data and prediction methods. The development of big data technology and the deep learning model provides us a good chance to address this challenge. Therefore, this paper proposes a multi-task fusion deep learning (MFDL) model based on massive floating car data to effectively predict the passing time and speed at intersections over different estimation time granularity. Moreover, the grid model and the fuzzy C-means (FCM) clustering method are developed to identify the intersection area and derive a set of key Spatio-temporal traffic parameters from floating car data. In order to validate the effectiveness of the proposed model, the floating car data from ten intersections of Beijing with a sampling rate of 3s are adopted for the training and test process. The experiment result shows that the MFDL model enables us to capture the Spatio-temporal and topology feature of the traffic state efficiently. Compared with the traditional prediction method, the proposed model has the best prediction performance. The interplay between these two targeted prediction variables can significantly improve prediction accuracy and efficiency. Thereby, this method predicts the intersection operation performance in real-time and can provide valuable insights for traffic managers to improve the intersection’s operation efficiency.

An Analysis on Scheduling of Traffic Light at Urban Traffic Intersection Using Fuzzy Control Algorithm

In modern society, private cars have become the first choice for many families because of their convenience and versatility. The volume of vehicles on the road is the basis of traffic accident and traffic congestion. In urban sector the traffic congestion is normally high due to the green light time interval at four road intersections. The traffic light control and time setting are basically timer control operation at current traffic control management system, this shows that, the current system is not intelligent so that there is still heavy traffic congestion. It is vital to implement routinely adjusted schedule as per the real-time position of vehicles at urban cross road intersection. Now, there are various detector systems for traffic monitoring, like Inductive Loop microwave radar, laser, infrared, ultrasonic, magnetometer and video image processing. But they have relevant weakness, such as high cost and complex technology. As a more and more widely used technology, image processing plays an important role in the management and control of intelligent transportation system. Image processing systems are based on motion detection of vehicles, wherein computer vision algorithms extract vehicles from traffic video data for traffic density estimations. This paper is an analysis on scheduling of traffic light of traffic management system using Fuzzy Control Algorithm. With the increase of the number of vehicles and population, it will also improve the traffic jam and the mood of people because of the cause of jam. Rather than previous technology, it will be low cost and simple, which can be adopted in every place as far as possible. MATLAB tool was used to figure out the variables impact on scheduling of traffic light at urban traffic intersection. The vehicle number, vehicle speed, lane length and vehicle type variables are identified and tested against vehicle driving for conclusion on traffic management performance. From findings the results were identified as the vehicle number, vehicle speed, and vehicle type have significant positive relationship with vehicle driving. However, the lane length did not significantly affect the vehicle driving. This indicates that the lane length is less important in scheduling of traffic light at urban traffic intersection.

A simulation of traffic noise emissions at a roundabout based on a cellular automaton model

The calculation and evaluation of traffic noise is an important task in urban road design. Roundabouts are a common form of urban road intersection. The complexity of traffic operations makes the calculation of traffic noise near a roundabout challenging. To explore traffic noise at roundabouts, a cellular automaton traffic flow model for a two-lane roundabout is established. Based on this model, a dynamic simulation method for traffic noise at roundabouts is proposed. The traffic operation and noise emissions at a roundabout are simulated. The vehicle speed distribution and traffic noise distribution at the roundabout are analysed, and the relationship between the traffic volume and sound power level of the cells is discussed. Finally, the proposed method is compared with existing traffic noise models, and the accuracy and efficiency of the proposed method are verified. The results of this paper show that the speed distribution and noise emission distribution at the roundabout are not uniform. When the traffic volume increases to saturation, the noise emission on the ring road will not keep increasing, and the sound power level of the cells on the inner ring is approximately 2 dBA higher than that of the outer ring. The methods and results in this paper may be valuable for road traffic design and noise control.

Analytical Method of Traffic Conflict at Urban Road Intersections Based on Risk Region

A two-stage method of traffic conflict analysis was proposed based on risk region. By using the inD dataset, traffic conflicts were recognized with the calculation of time to risk region(tTTR), and then the degree of traffic conflict risk was judged by risk region duration. Compared with the method based on time-to-collision(tTTC), the proposed method can not only identify rear-end conflicts and crossing conflicts more effectively, but also characterize the degree of traffic conflict risk at different times.

Optimal signal timing design for the intersection of Dazhi Street and Hexing Road

The phenomenon of intersection congestion has become a major problem in the city. Based on the intersection of Hexing road and Dazhi street, the delay phenomenon of Urban Road intersection is optimized. The intersection of Hexing road and Dazhi street is seriously delayed. In order to improve the road capacity of the intersection and reduce the delay of the intersection, the traffic characteristics parameters of the intersection during peak period are obtained, and the traffic capacity and intersection service level of the signalized intersection are analyzed and calculated. Based on the study of the basic content and research methods of signal timing at signalized intersections, the microscopic simulation software VISSIM was used to improve the traffic control scheme with serious delays at intersections and low urban road capacity, the simulation optimization is carried out by changing the signal timing and resetting the channelization scheme of intersection. Meanwhile, the signal timing of intersection is optimized. The results show that the traffic capacity of the intersection has been greatly improved by VISSIM simulation.

On the Potential Impacts of Smart Traffic Control for Delay, Fuel Energy Consumption, and Emissions: An NSGA-II-Based Optimization Case Study from Dhahran, Saudi Arabia

Intelligent traffic control at urban intersections is vital to ensure efficient and sustainable traffic operations. Urban road intersections are hotspots of congestion and traffic accidents. Poor traffic management at these locations could cause numerous issues, such as longer travel time, low travel speed, long vehicle queues, delays, increased fuel consumption, and environmental emissions, and so forth. Previous studies have shown that the mentioned traffic performance measures or measures of effectiveness (MOEs) could be significantly improved by adopting intelligent traffic control protocols. The majority of studies in this regard have focused on mono or bi-objective optimization with homogenous and lane-based traffic conditions. However, decision-makers often have to deal with multiple conflicting objectives to find an optimal solution under heterogeneous stochastic traffic conditions. Therefore, it is essential to determine the optimum decision plan that offers the least conflict among several objectives. Hence, the current study aimed to develop a multi-objective intelligent traffic control protocol based on the non-dominated sorting genetic algorithm II (NSGA-II) at isolated signalized intersections in the city of Dhahran, Kingdom of Saudi Arabia. The MOEs (optimization objectives) that were considered included average vehicle delay, the total number of vehicle stops, average fuel consumption, and vehicular emissions. NSGA-II simulations were run with different initial populations. The study results showed that the proposed method was effective in optimizing considered performance measures along the optimal Pareto front. MOEs were improved in the range of 16% to 23% compared to existing conditions. To assess the efficacy of the proposed approach, an optimization analysis was performed using a Synchro traffic light simulation and optimization tool. Although the Synchro optimization resulted in a relatively lower signal timing plan than NSGA-II, the proposed algorithm outperformed the Synchro optimization results in terms of percentage reduction in MOE values.

Operating Times and Users’ Behavior at Urban Road Intersections

The safety of at grade road intersections is a relevant issue with social, economic, and environmental implications. It is related to the behavior of a driver approaching an intersection that, in its turn, is affected by kinematic and physiological variables. This study proposes a model to calculate the intersection operation time (IOT) for typical non-signalized 4-leg and 3-leg (or T-leg) urban intersections. Data available in the literature have been considered in order to identify the points of interest and assess the number and the time of a driver’s eye fixation on them. When approaching an intersection, the probability of glancing in a particular area changes with the distance to the yield or stop line; for this reason, a probabilistic approach was used to model the phenomenon. All possible maneuvers have been considered: left turning, right turning, and through-movement. The proposed model allowed an objective comparison between time spent by drivers for various maneuvers and layout conditions, and identification of the critical conditions. Indeed, significant differences in terms of IOT were found: they could lead to modification of the traffic management considering different needs of road users, traffic demand, and geometrical and functional constraints.

Dangerous Behavior Recognition of Autonomous Vehicles at Intersection Based on Gaussian Mixture Model

Urban road intersections are a typical complex traffic scene, and they are also prone to road traffic accidents. Aimed at the safety problems of road intersections, this paper proposes a model of vehicle dangerous behavior recognition based on Gaussian mixture model method, which provides an empirical basis for autonomous vehicle behavior decision at intersections. By analyzing the characteristics and rules of driving behavior at intersections, we select parameters like PET as input parameters of the model, and then construct a simulation model based on Beijing’s Weigongcun intersection, which is used to obtain the dataset in intersection scene. The obtained data was subject to Kalman filtering and normalized as data input of a Gaussian mixture model. Dangerous scenes and general scenes at the intersection scene were screened out. The screening results were analyzed and evaluated, and proved to be reliable. The model could provide an empirical basis for the decision-making design of autonomous vehicles at intersection.

Clustering algorithm based on analytic hierarchy process in LTE-V2V

As a hot area of urban transportation, the development of internet of vehicles and related technologies has received widespread attention. This paper proposes an analytic hierarchy process clustering algorithm (AHP-CA) based on the analytic hierarchy process in the LTE vehicle self-organizing network in the scenario of urban two-direction road intersections. The AHP-CA algorithm introduces the average distance between vehicles, normalizes the average relative speed, and uses the average relative motion direction as the vehicle eigenvalue for the clustering index. The analytic hierarchy process (Analytic Hierarchy Process, AHP) is used to determine the weights of different vehicle feature values, and the appropriate nodes are selected as cluster heads by combining the weights. The simulation results verify the stability of the cluster head under different vehicle speeds and communication distances.

Research on Intersection Frequent Overflow Control Strategy Based on Wide-Area Radar Data

Overflow identification and control at urban road intersection is a derivative problem of oversaturation control. The existing cross-section detector uses the road cross-section occupation of vehicles to identify the overflow state; then, the downstream road condition is often ignored. At present, more and more advanced traffic detection technologies, such as vehicle-to-infrastructure and wide-area radar, can provide reliable data for accurate overflow identification. In this paper, a new method of overflow identification and control at intersections is proposed by using advanced wide-area radar detection data. As a detector for specific segment of road, the wide-area radar can detect the traffic flow data in a certain range of road and provide more data types. Therefore, the average speed and space occupancy of the effective detection road segment are selected as subindicators to establish the comprehensive identification index of overflow identification. Then, the overflow control strategy is developed considering the traffic demand of the overflow phase and the nonoverflow phase. It is proved that the method is more accurate and effective in overflow identification and control by using simulation experiment of field data.

DESIGN OF TRAFFIC SIGNAL CONTROL SYSTEMS BASED ON FUZZY CONTROL

With a gradual increase in urban road traffic volume and traffic congestion degree, how to improve and solve the current traffic pressures has been a problem requiring urgent solution. To improve the traffic congestion status in urban road intersections and heighten the road traffic efficiency, the principle of fuzzy control is employed in this paper. Moreover, the multi-phase signal traffic control of intersections is performed together with vehicle queue lengths in lanes corresponding to the key traffic flow, and a traffic signal fuzzy control system is designed. Finally, this paper compares the simulation results between the fuzzy control system designed herein and the existing fixed traffic signal control methods. The comparative test results have shown that the fuzzy control method can well better actual congestion and traffic efficiency at intersections to a greater degree than the fixed timing method does.

Crash Prediction for Prioritization of Intersections for Safety Improvement: Case Study of Kathmandu Valley

Every year globally 1.3 million people lose their lives from road traffic crashes (RTAs). Similarly, increasing rate of RTAs has been observed in Nepal including Kathmandu valley. This study is focused on the analysis of crash trends and respective site specific geometric features of urban road intersections in Kathmandu valley. Seventeen major intersections based on the data availability and traffic volume, are considered for the analysis of crash type. Previous crash data and traffic volume records of one year have been analysed. Common types of three and four legged intersections were taken for the study. Classified traffic volume at those intersections were converted into the Annual Average Daily Traffic. Evaluation factors for the crash analysis were determined by using predictive method. Crash frequency, crash rate, critical crash rate and crash prediction methods were used for ranking of the intersection. Priority for the safety improvement is prepared based on the results of this study.

Influence of Selected Vehicle Maneuvers on Reduction of the Urban Roads Capacity

Capacity assessment of urban roads according to Slovak standard does not sufficiently take into account the influence of vehicle maneuvers on road section performance between the two intersections. This research analyzed turning relations on selected urban road intersections and the influence of right turn and left turn on major stream speed changes. For the purpose of determining the influence of the right turn on decrease in capacity, measurements were carried out at 7 uncontrolled intersections with curve radius ranging from 6 to 30m. To determine the influence of the left turn on the capacity of urban road a critical gap was evaluated, using the Raff’s, method, based on the analysis of accepted and rejected gaps in the major traffic stream, which was one of the main inputs for simulations. The findings were used to determine the influence of selected maneuvers on the road capacity, which led to a proposal for reduction coefficients based on computational analysis performed using simulations in PTV VISSIM.

Theoretical Approach to the Flow Patterns in a Four-Branch Intersection

In this paper, the hydraulic structure of flood flow on a four-branch urban road intersection with two adjacent input streets and two output streets was investigated theoretically. Not only were previous flow patterns investigated theoretically, but also two new flow patterns were introduced and discussed in this study: Flow Pattern Type 1, when the flows approaching the junction in the street were in subcritical condition and return to subcritical in output streets downstream of the junction; Flow Pattern Type 1 Sub-regime 1, when the flows approaching in the input streets were in supercritical condition and return to supercritical in output streets downstream of the junction. The deflection of supercritical flows was formulated and solved numerically through writing MATLAB codes and was investigated graphically. Nomo-graphs were generated for the case that two oblique hydraulic jumps formed in the junction, which can be used to predict the characteristics of flow downstream of the junction. For example, the jumps angle can be predicted from the known parameters of incoming flows by applying the Angle Model formulas.

The application of Raman spectroscopy combined with multivariable analysis on source apportionment of atmospheric black carbon aerosols

Source apportionment studies become increasingly crucial for black carbon (BC) in atmospheric particulate matters given its linkage with adverse public health and climate impacts. In this work, a facile and rapid method using Raman spectra combined with stepwise discriminant analysis (SDA) was proposed to identify and quantify the contributions of atmospheric BC sources. Four BC samples from biomass burning, coal combustion, gasoline and diesel vehicle emission were characterized by Raman spectra. The SDA model was established based on 10 parameters with significant differences (p < 0.05), giving an accuracy of 83% with a cross-validation rate of 80%. Utilizing four suggested discriminant variables from SDA model, vehicle emission was predicted as the dominant contributor to ambient BC particles, among which gasoline contributed much higher than diesel at an urban road intersection in Shanghai, China. This new method shows great potential to classify and investigate the sources of atmospheric BC aerosols and provide more effective information on air pollution control measures.

Evaluating and Diagnosing Road Intersection Operation Performance Using Floating Car Data.

Urban road intersections play an important role in deciding the total travel time and the overall travel efficiency. In this paper, an innovative traffic grid model has been proposed, which evaluates and diagnoses the traffic status and the time delay at intersections across whole urban road networks. This method is grounded on a massive amount of floating car data sampled at a rate of 3 s, and it is composed of three major parts. (1) A grid model is built to transform intersections into discrete cells, and the floating car data are matched to the grids through a simple assignment process. (2) Based on the grid model, a set of key traffic parameters (e.g., the total time delay of all the directions of the intersection and the average speed of each direction) is derived. (3) Using these parameters, intersections are evaluated and the ones with the longest traffic delays are identified. The obtained intersections are further examined in terms of the traffic flow ratio and the green time ratio as well as the difference between these two variables. Using the central area of Beijing as the case study, the potential and feasibility of the proposed method are demonstrated and the unreasonable signal timing phases are detected. The developed method can be easily transferred to other cities, making it a useful and practical tool for traffic managers to evaluate and diagnose urban signal intersections as well as to design optimal measures for reducing traffic delay and increase operation efficiency at the intersections.

Multi-objective optimization of urban road intersection signal timing based on particle swarm optimization algorithm

Currently, signal control mode is the main control method of urban road intersections. Given that the traffic efficiency of road intersections is mainly affected by signal timing schemes, it is imp...

An Integrated Design Framework for Safety Interventions on Existing Urban Roads—Development and Case Study Application

The need for improving urban road safety, livability, and sustainability is evident. Quantitative estimates and qualitative methods/strategies can be used by road safety practitioners to design safety interventions. This study proposes a flexible integrated design framework for safety interventions on existing urban road segments and intersections that integrates quantitative and qualitative methods. The proposed design framework is divided into four stages of the safety management process: End of Network Screening, Diagnosis, Selection of Countermeasures, and Economic Assessment. Pilot applications of the proposed method were performed on existing roads of the urban road network of the Municipality of Bari, Italy. Results from the application were useful to highlight some possible problems in the different stages of the design process. In particular, the discussed problems include a lack of crash and traffic data, difficulties with defining the road functional classifications, including rural-to-urban transitions, a lack of local inspection procedures, the recurrent problems from diagnosis, difficulties regarding the safety assessment of cycling infrastructures and sight distances, the criteria for grouping countermeasures into sets, and the choice of appropriate predictive methods. In response, appropriate solutions to the highlighted problems were presented. The usefulness of the proposed method for both practitioners and researchers was shown.