Urban Road Intersection Research Articles

Multidimensional graph transformer networks for trajectory prediction in urban road intersections

Multidimensional graph transformer networks for trajectory prediction in urban road intersections

Semantic Segmentation Method for Road Intersection Point Clouds Based on Lightweight LiDAR

Lightweight LiDAR, characterized by its ease of use and cost-effectiveness, offers advantages in road intersection information acquisition. This study used lightweight LiDAR to collect 3D point cloud data from an urban road intersection and propose a semantic segmentation model based on the improved RandLA-Net. Initially, raw data from multiple positions and perspectives were obtained, and complete road intersection point clouds were stitched together using the iterative closest point algorithm for sequential registration. Subsequently, a semantic segmentation method for point clouds based on the improved RandLA-Net was proposed. This method included a spatial information encoding module based on feature similarities and a feature enhancement module based on multi-pooling fusion. This model optimized the feature aggregation capabilities during downsampling with the weighted cross-entropy loss function applied to reduce the impact of input sample scale imbalances. In comparisons of the improved RandLA-Net with PointNet++ and RandLA-Net on the same dataset, our method showed improved segmentation accuracy for various categories. The overall prediction accuracy on two road intersection point cloud test sets was 87.68% and 89.61%, with average F1 scores of 82.76% and 80.61%, respectively. Most notably, the prediction accuracy for road surface areas reached 94.48% and 94.79%. The results show that our model can enrich the spatial feature expression of input data and enhance semantic segmentation performance in road intersection scenarios.

A multiple directions turning vehicle counting method at intersections based on arbitrary-oriented detection and stack Long Short-Term Memory

The traffic flow parameters of turning vehicles at urban road intersections play an important role in the intelligent traffic system. Traffic road monitoring is difficult to cover large intersection scenes. With the application of unmanned aerial vehicles in traffic, drone-based intersection monitoring has great potential due to the large perspective. But there is rarely research on multiple turning vehicle counting at intersections based on drones. A few existing multiple turning vehicle counting methods are based on vehicle detection and tracking methods. However, the vehicles at the intersection have various orientations, the accurate information of the vehicle cannot be obtained by detection, and the tracking process is complicated and redundant for the counting task. Based on the traffic video at intersections taken by unmanned aerial vehicles, we propose a eight turnings vehicle counting model without complex tracking. Firstly, a spatial attention and channel adaptive attention model net is proposed for arbitrary-oriented vehicle detection to get the orientation and position of vehicles. Secondly, a turning spatio-temporal counting feature for different turning vehicles and its extraction method are proposed. Finally, the stack Long Short-Term Memory net based counting model is designed to process the turning spatio-temporal counting feature and counting eight different turnings vehicles at intersection. The experiments show that the proposed method can realize counting eight different turnings vehicles simultaneously in drone-based traffic video at the intersection without relying on complex multi-target tracking. The average counting accuracy has reached 98.18%.

The Association between Rainfall and Taxi Travel Activities: A Case Study from Wuhan, China

Rainfall has a significant impact on urban population mobility, posing great challenges to traffic management and urban planning. An understanding of this influence from multiple perspectives is urgently needed. In this study, we devised a multiscale comparative research framework to explore the spatiotemporal effects of rainfall on taxi travel patterns, aiming to provide a new perspective on the investigation of rainfall’s impact on urban human mobility. More specifically, at the macroscopic scale, we computed taxi travel indicators across the entire study area and used kernel density estimates to observe the spatiotemporal distribution patterns influenced by rainfall. Subsequently, complex traffic networks were constructed by considering urban road intersections as nodes and combined with visualization methods to understand changes in taxi travel patterns visually at the microscopic level. We selected Wuhan City, a typical urban area in southern China with frequent rainfall, as the study area and used meteorological data along with a large volume of taxi spatiotemporal trajectory data for investigation. Results indicated a 4.16% decrease in weekly travel volume due to rainfall, with a 3.96% decrease on workdays and a 4.64% decrease on weekends. However, nighttime rainfall between 19:00 and 22:00 on weekdays increased the demand for taxi travel. Furthermore, the impact of rainfall on weekends exceeded that on workdays, restricting people’s mobility and leisure activities, resulting in reduced travel to recreational tourist spots and commercial pedestrian streets. Rainfall altered residents’ travel preferences to some extent, with more residents choosing taxis during rainy weather, which led to decreased transportation efficiency and increased traffic congestion. These findings contribute to a deeper understanding of the complex relationship between population mobility patterns and the urban ecological environment, providing valuable insights for planning resident travel and taxi dispatching under adverse weather conditions.

Analyzing Delay and CO Emissions: A Simulation Study of the Median U-Turn Method at Intersections

To improve traffic efficiency and reduce pollutant emissions at urban road intersections, VISSIM software was used to simulate traffic states to compare the median U-turn method with the direct left-turn method under various traffic volumes and left-turn ratios. Based on the average delay and CO emissions, suitable conditions were identified for using the median U-turn method at intersections. The results show that there are three critical left-turn ratio boundary curves named Rdcri(1), Rdcri(2), and Rdcri(3) based on the relatively smaller average delay and there is a critical left-turn ratio boundary curve RCOcri based on the lower average CO emissions at the intersection when the through traffic volume is in the range of 0–3000 veh/h and the left-turn ratio is in the range of 0–4. The median U-turn method is considered applicable at the intersection when the through traffic volumes are in the range of 0–87 veh/h, 87–400 veh/h, 400–416 veh/h, 416–934 veh/h, and 934–3000 veh/h, respectively, and, accordingly, the left-turn ratios are in the range of 0–Rdcri(2), 0–Rdcri(2) or Rdcri(1)–4, 0–4, 0–Rdcri(3), and RCOcri–Rdcri(3). These findings can provide a reference for traffic managers to organize the left-turn traffic at an intersection reasonably.

A Multi-Objective Optimization Method for Single Intersection Signals Considering Low Emissions

The exponential growth of urban centers has exacerbated the prevalence of traffic-related issues. This surge has amplified the conflict between the escalating need for travel among individuals and the constricted availability of road infrastructure. Consequently, the escalation of traffic accidents and the exacerbation of environmental pollution have emerged as increasingly pressing concerns. Urban road intersections, serving as pivotal junctures for vehicle convergence and dispersal, have remained a focal point for scholarly inquiry regarding enhanced operational efficacy and safety. Concurrently, vehicles navigating intersections are subject to external influences, such as pedestrian crossings and signal controls, causing frequent fluctuations in their operational dynamics. These fluctuations contribute to heightened exhaust emissions, exacerbating air pollution and posing health risks to pedestrians frequenting these intersections. A reasonable signal timing scheme can enable more vehicles to pass through the intersection safely and smoothly and reduce the pollutants generated by transportation. Therefore, optimizing signal timing schemes at intersections to alleviate traffic problems is a topic that needs to be studied urgently. In this paper, the emission model based on specific power is analyzed. Through an analysis of the correlation between specific power distribution intervals and the emission rates of individual pollutants, it has been observed that vehicle emission rates are at their lowest during idle speed, progressively increasing with rising vehicle speeds. Investigation into specific power distribution based on variables, such as vehicle type, frequency of stops, and varying delays, has led to the deduction that the peak specific power of vehicles at intersections consistently occurs within the (0, 1) interval. Furthermore, it has been established that high-saturation intersections exhibit higher peak specific power compared to low-saturation intersections.

Safety analysis of pedestrians distracted by mobile phones at street crossings: Field study in Nanjing

The growing public concern over traffic safety hazards caused by pedestrians’ distracted behavior, particularly related to mobile phone usage at pedestrian crossings. Through video recording of pedestrians’ street-crossing behaviors on 12 sidewalks across 9 urban road intersections in Nanjing city, 1778 valid pedestrian samples were collected. The study categorizes mobile phone use during the crossing into five distinct types: no use, voice call, screen gaze, screen gaze with operation, and listening to music with headphones. Then, the effects of gender, age and companion conditions on the mobile phone during the crossing were examined by chi-square tests. Utilizing binomial logistic and Relogit regression models, the study analyzed the impact and safety risks of distraction on crossing behavior. Additionally, a random parameters (RP) logit model with heterogeneity in means was used to investigate the determinants affecting mobile phone usage. Notably, individuals aged 18 to 30 years were identified as the random parameter, while factors such as being under 18 years old, having a companion without interaction, having a companion with interaction, weekdays, sidewalk length, and the total number of other pedestrians were identified as fixed parameters. The study also presented significant variables affecting the probability of mobile phone usage through marginal effects, highlighting the potential safety risks associated with mobile phone usage during street crossing. These findings emphasize the need for heightened pedestrian safety awareness and a reduction in distracted behaviors to enhance overall traffic safety.

Traffic light control with reinforcement learning

Urban traffic signal optimization is important for alleviating congestion in urban transportation systems. This study proposes a real-time traffic light control algorithm based on deep Q learning with a reward function that accounts for queue lengths, delays, travel times, and throughput. The model dynamically decides phase changes based on current traffic conditions. The training of the deep Q network involves an offline stage from pre-generated data with fixed signal timing and an online stage using real-time traffic data. A deep Q network structure with aphase gate component is used to simplify the model's learning task under different phases. Amemory palace" mechanism is used to address sample imbalance during the training process. Both synthetic and real-world traffic flow data are used to validate our approach under an urban road intersection scenario in Hangzhou, China. Results demonstrate significant performance improvements of the proposed method in reducing vehicle waiting time (57.1% to 100%), queue lengths (40.9% to 100%), and total travel time (16.8% to 68.0%) compared to traditional fixed signal timing plans.

Assessment of Traffic Characteristic at Major Urban Road Intersection of Kathmandu Valley: A Case Study of Khanivivag, Kesarmahal, and Narayanhiti Intersections

This study addresses the prevalent issue of traffic congestion at critical intersections in urban areas, leading to slowed traffic and increased queue lengths. The centralization of the Kathmandu Valley has contributed to a gradual rise in population thus impacting the efficiency of the traffic management system. This study focuses on three of the busiest intersections of the valley i.e. Kesharmahal, Narayanhiti, and Khanibhivag intersections and aims to provide insights into current traffic conditions and propose measures for enhancing traffic flow. Data collected is analyzed to determine current traffic flow, saturation flow, existing capacity, Level of Service (LOS), and intersection delay using the HCM Manual. The analysis involves assessing present conditions and making comparisons with standardized data. Three days of traffic volume data during peak hours and geometric features of the intersection were gathered. Results indicate that the intersections are experiencing over-saturation, exceeding their capacity based on existing geometry and signalization. Additionally, the vehicle replacement strategy showed limited effectiveness in reducing saturation flow, with the flow persisting at an over-saturated level. These findings highlight the need for targeted measures to address the challenges posed by congestion and enhance the efficiency of traffic management at these intersections for smooth traffic flow.

Road Intersection Extraction Based on Low-Frequency Vehicle Trajectory Data

Global navigation satellite system (GNSS) vehicle trajectory data play an important role in obtaining timely urban road information. However, most models cannot effectively extract road information from low-frequency trajectory data. In this study, we aimed to accurately extract urban road network intersections and central locations from low-frequency GNSS trajectory data, and we developed a method for accurate road intersection identification based on filtered trajectory sequences and multiple clustering algorithms. Our approach was founded on the following principles. (1) We put in place a rigorous filtering rule to account for the offset characteristics of low-frequency trajectory data. (2) To overcome the low density and weak connection features of vehicle turning points, we adopted the CDC clustering algorithm. (3) By combining the projection features of orientation values in 2D coordinates, a mean solving method based on the DBSCAN algorithm was devised to obtain intersection center coordinates with greater accuracy. Our method could effectively identify urban road intersections and determine the center position and more effectively apply low-frequency trajectory data. Compared with remote sensing images, the intersection identification accuracy was 96.4%, the recall rate was 89.6%, and the F-value was 92.88% for our method; the intersection center position’s root mean square error (RMSE) was 10.39 m, which was 14.9% higher than that of the mean value method.

Multisource fusion of exogenous inputs based NARXs neural network for vehicle speed prediction between urban road intersections

The economy and safety of passages through the urban road intersection environment is an important research topic in the field of intelligent transportation systems, but vehicle speed prediction as its subtopic is still under-researched, and its prediction accuracy is unsatisfactory. Therefore, a model for vehicle speed prediction based on the nonlinear autoregressive model with multisource exogenous inputs (NARXs) neural network is proposed. The model combines the human-vehicle-road model with the NARXs neural network to perform speed prediction between urban road intersections. First, multisource features, including the variables of driving behavior characteristics, vehicle responses, and road conditions, are extracted to construct the human-vehicle-road model. Then, the model is introduced into the NARXs neural network. Finally, the advantages of the proposed model are verified from two perspectives by evaluation indices such as mean absolute error ( MAE), mean absolute percentage error ( MAPE), root mean square error ( RMSE), Theil index ( Theil ic), and goodness-of-fit ( R2) compared with several other models. On the one hand, the analysis results show that the proposed model has higher prediction accuracy than the other comparative models for different prediction durations and has the best performance in 30 s duration backward prediction. On the other hand, the curves of each evaluation index of the proposed model are horizontal, which indicates that the prediction performance of the model hardly varies with the length of the training dataset. These positive results demonstrate the higher accuracy and outstanding characteristics of the proposed model in the subject of vehicle speed prediction.

A novel approach for fine-grained traffic risk characterization and evaluation of urban road intersections

An urban road intersection environment with low traffic risk is an essential component of sustainable cities. Various risk analysis and simulation techniques have been developed to measure and evaluate traffic risks. However, flexible assessment of intersection risk at a fine resolution remains a great challenge. This study proposes a novel approach for characterizing the field distribution of traffic risks at an urban road intersection and assessing traffic risks in multiple dimensions to increase the flexibility of risk assessment. First, we define a new traffic risk metric to quantify the risk of intersection traffic conflict points by combining their frequency and severity. Second, the plume diffusion model is introduced to model the non-linear relationship between traffic conflict points by considering the propagation of the traffic risk in the intersection. In this way, the entire intersection risk distribution map is derived for real-time risk analysis. Finally, a multidimensional hierarchical evaluation system of traffic risk at intersections is designed, including the traffic risk indicators at the intersection, path, and turn mode levels. The results of a case study in Wuhan, China, demonstrate the continuous characterization results of the risk field and provide multidimensional risk assessment index results, which are highly consistent with the cognitive results of manual risk scoring (up to 90% similarity). It also allows for risk assessment in a single path and turn modes through the risk field characterization and reveals the spatiotemporal dynamic risk distribution, thus providing an alternative tool for risk decision-making at urban road intersections.

Analysis of early rutting disease at urban road intersection

Because of the special stress characteristics and location of Urban Road intersection, the proportion of early rutting disease is larger than that of ordinary road section. In order to solve the early rutting problem of intersection, this paper analyzes the causes of rutting road conditions based on the actual project, through the verification of raw materials and mix proportion.

Modelling of traffic noise in the vicinity of urban road intersections

Traffic noise is continuously rising alongside roadways, especially at intersections, due to rapid urbanization, eventually affecting acoustical climate and quality of life. This present study develops a traffic noise model for intersections with minimal evidence of interrelationships among influential traffic noise factors. An integrated Bayesian networks and Partial least squares structural equation modelling approach has been employed on 342-hour field measurement data collected from nineteen intersections in Kanpur, India. The integrated approach developed the traffic noise prediction model with 62.4% explanatory power and identified direct and indirect effects of five influential factors on traffic noise. For instance, Traffic flow attributes, i.e., traffic volume and honking, are the most crucial ones to degrade acoustical climate at intersections. Besides, Built environment and Climate conditions induce only indirect effects on traffic noise. Thus, this study provides a useful basis for planners to understand traffic noise relationships and deploy noise mitigation strategies accordingly.

Inferring Intersection Traffic Patterns With Sparse Video Surveillance Information: An ST-GAN Method

Traffic patterns of urban road intersections are important in traffic monitoring and accident prediction, thus play crucial roles in urban traffic management. Although real-time traffic information is consistently provided by surveillance cameras equipped at road intersections, the sparsity of surveillance distribution poses great challenges in performing a complete real-time traffic pattern analysis. To tackle that, existing works either assume that the traffic patterns are static, or assume a multi-variant distribution model for intersection traffic volumes. The former assumption neglects the temporal features of traffic patterns, and the latter is limited in capturing fine-grained spatiotemporal dependencies. To tackle the problem, we propose a novel framework, SpatioTemporal-Generative Adversarial Network (ST-GAN), that exploits deep spatiotemporal features of urban networks and offers accurate traffic pattern inferences with incomplete surveillance information. The ST-GAN framework incorporates a modified GCN network wired with the encoder-decoder mechanism and an LSTM network, which are further boosted by an iterative adversarial training process. Comprehensive experiments on real datasets show that ST-GAN achieves better inference accuracies than state-of-the-art solutions.

Measuring, Mapping, and Evaluating Daytime Traffic Noise Levels at Urban Road Intersections in Doha, Qatar

In this study, equivalent hourly traffic noise levels at different intersections in the city of Doha, Qatar were measured and compared to the local and World Health Organization (WHO) thresholds. As part of the study, equivalent sound pressure levels, ambient temperature, humidity, and wind speed were recorded during the morning, afternoon, and evening hours on weekdays and weekends. The results showed that regardless of the day (weekday or weekend), the mean 16-h daytime traffic noise levels at all sites exceeded the local and the WHO’s recommended thresholds. The values of the mean weekday noise levels at the sites ranged between 67.6 dB(A) and 77.5 dB(A), whereas the weekend values ranged between 68.8 dB(A) and 76.9 dB(A). The measured noise levels were also compared with traffic noise levels reported in other countries. Finally, some recommendations to reduce excessive traffic noise levels were suggested. The results of the study could be used as a benchmark of traffic noise levels in the country after the implementation of any countermeasures in the future.

Assessing the Traffic Capacity of Urban Road Intersections

Assessing the Traffic Capacity of Urban Road Intersections

Stochastic analysis of urban V2X communications: Orthogonality versus non-orthogonality

In this paper, we study the broadcasting performance of urban Vehicle-to-everything (V2X) communications at road intersections. In such environments, dense buildings obstruct the radio channel between vehicles in adjacent road segments and Non-Line-Of-Sight (NLOS) signal propagation reduces the received power level. Therefore, messages that originate from adjacent roads traveling in a non-parallel direction usually have weaker signals than those from the road segment traveling in parallel opposite direction. Besides, the high vehicular density leads to insufficient orthogonal channel resources and high level of interference. We propose to apply Non-Orthogonal Multiple Access (NOMA) to V2X communications at urban road intersections to enhance the spectrum efficiency and improve the Package Delivery Ratio (PDR) performance. With NOMA, signals from multiple transmitters are decoded together and the interference from undesired users can be canceled if these messages are retrieved. Specifically, we study two NOMA-based V2X communication schemes, namely, NOMA-V2X decoded by Successive Interference Cancellation (SIC-V2X) and NOMA-V2X decoded by Joint Decoding (JD-V2X). Based on the tools developed in stochastic geometry, we derive and compare the PDR expressions for both NOMA schemes and the Orthogonal Multiple Access (OMA) scheme. Our results indicate that 1) both NOMA schemes outperform the conventional OMA scheme and the PDR of LOS/NLOS communications with two-user access increases by 51%/369%; 2) for four-user access, the proposed NOMA scheme shows 375% goodput enhancement as compared with the OMA scheme; and 3) JD-V2X provides significant PDR enhancement compared with SIC-V2X in the high data rate regime. Finally, we also demonstrate that the random access based NOMA schemes can achieve lower transmission latency than collision-free scheduling schemes when the road is over 56% jammed.

Multivariate analysis of traffic flow using copula-based model at an isolated road intersection

Investigating the correlation relationship among traffic flow parameters (speed, time headway, volume) and accurately estimating their distribution function at urban road intersections are critical to provide theoretical support for traffic simulation in traffic management and control. Previous studies usually investigated distributions of traffic flow parameters separately, and they did not take into account the correlation between these parameters. In this study, a copula-based approach is proposed to analyze the dependence structure and model the multivariate distribution of traffic flow parameters during peak and off-peak hours in three directions at the intersections. First, the correlation between the traffic flow parameters/variables is examined by Kendall’s tau and Spearman’s rho to measure the correlation relationship. Then, fitting marginal distributions for traffic variables is conducted. The distribution function with the best fitting performance of each traffic variable is selected based on AIC and K–S tests. Moreover, copula-based models that employ Gaussian, FGM, Gumbel, Clayton, Frank and AMH copula functions are used to construct the bivariate joint distribution. The optimal copula model is selected by using the log-likelihood and AIC to evaluate fitting results. Finally, by comparing the observed data with the same size simulated data generated from the best fitting copula model, we find that the copula-based model could accurately construct the bivariate joint distribution and reproduce the dependence structure between the variables. Overall, the findings of this study provide an in-depth perspective on the dependence structure of traffic variables at urban road intersections.

Signal Control Strategy for Supersaturated Traffic at Urban Road Intersections with a Large Technical Grade Gap

To reduce the risk of queuing overflow on the urban minor road at the intersection under supersaturation where the capacity of the arterial and minor roads shows extreme disparity, reduce the adverse effects caused by long queues of vehicles on the minor road, and comprehensively balance the multiobjective requirements such as priority of the main road, queuing restrictions, and delay on the minor road, the minor road queue model at the end of red, a road remaining capacity model, and multiparameter coordinated signal control model were established, and a multiobjective genetic algorithm was used to optimize this solution. As an example, the multiparameter coordinated control strategy decreased the delay per vehicle by approximately 17% and the queue length by approximately 30%–50% on the minor road and slightly increased the delay per vehicle at the intersection and the length on the main road queue. This control strategy can make full use of the capacity of the main road to control the queue length on the minor road, effectively reduce the risk of minor road queue overflow blocking local road network traffic operation involved, and comprehensively balance the traffic demand between arterial and minor roads. It provides a reference control method for coping with the transfer of the main traffic contradiction under the oversaturated state of the road intersection with large disparity.