Urban Intersections Research Articles

Investigating Autonomous Vehicle Accidents at Urban Intersections based on Road Geometry Data

Investigating Autonomous Vehicle Accidents at Urban Intersections based on Road Geometry Data

Unveiling pre-crash driving behavior common features based upon behavior entropy

Driving behavior is considered as the primary crash influencing factor, whereas studies claimed that over 90% crashes were attributed by behavior features. Therefore, unveil pre-crash driving behavior features is of great importance for crash prevention. Previous studies have established the correlations between features such as vehicle speed, speed variability, and the probability of crash occurrences, but these analyses have concluded inconsistent results. This is due to the varying operating characteristics among roadway facilities, where given the same driving behavior statistical features, the corresponding traffic states are not identical. In this study, a behavioral entropy index was proposed to address the abovementioned issue. First, through comparing the individual driving behavior with the group distribution, behavioral entropy index was calculated to quantify the abnormality of driving behavior. Then, crash classification models were established by comparing the behavioral entropy prior to crash events and normal driving conditions. The empirical analyses have been conducted based on 1,634,770 naturalistic driving trajectories and 1027 crash events. And models have been carried out for urban roadway sections, urban intersections, and highway sections separately. The results showed that utilizing the behavior entropy instead of the statistical features could enhance the crash classification accuracy by 11.3%. And common pre-crash features of increased behavioral entropy were identified. Moreover, the speed coefficient of variation (QCV) entropy was concluded as the most influencing factor, which can be used for real-time driving risk monitoring and enables individual-level hazard mitigation.

Performance Analysis of One-Level Signalized Urban Intersections with Exclusive Pedestrian Phases and Diagonal Crossings

The exclusive pedestrian (hereinafter P) phase with a diagonal crossing is routinely introduced to improve P safety at high-volume intersections. The article analyses and evaluates the feasibility of the exclusive P phase and diagonal crossing at single-level smaller intersections, identifying the advantages and disadvantages of the exclusive P phase and diagonal crossing from the point of view of time losses. In the experimental part, traffic flow modelling is carried out. The traffic flow simulations show that an exclusive P phase is most beneficial in terms of time losses at an intersection with 2+2 lane intersecting streets and ≥ 900 P/hour with ≥ 1600 vehicles/hour (hereinafter V). In addition, an exclusive P phase can be implemented at the small intersections analysed in this paper, where the volume of V is low or medium, regardless of the number of P at the intersection.

Application of machine learning models to predict driver left turn destination lane choice behavior at urban intersections

When there are multiple lanes to choose from downstream of a turning movement, drivers should choose the innermost lane so that drivers at other approaches of the intersection may make concurrent turning movements in the outermost lane(s). However, human drivers do not always choose the innermost lane, which could lead to crashes with other vehicles. Therefore, predicting human driver behaviors is vital in reducing crashes, as the need to share the roadways with automated vehicles (AVs) continues to grow. In this research, various machine learning models have been used to predict the left turn destination lane choice of human-driven vehicles (HDVs) at urban intersections based on several quantifiable parameters. A total of 174 subject vehicles were extracted and analyzed in Los Angeles, California, and Atlanta, Georgia, using HDV trajectory data from the Next Generation SIMulation (NGSIM) database. Five machine learning techniques, namely binary logistic regression, k nearest neighbors, support vector machines, random forest, and adaptive neuro-fuzzy inference system, were applied to the extracted data to predict the lane choice behavior of drivers. The k nearest neighbors model showed the most promising results for the evaluated data with a correct decision score of over 93% for the unseen test data. This model may be programmed into: (i) AVs, in conjunction with sensors, to predict if an HDV is about to turn into the incorrect destination lane; and (ii) microscopic traffic simulation tools so that modelers can identify potential conflicts when HDVs do not select the appropriate destination lane.

Performance Analysis of One-Level Signalized Urban Intersections with Exclusive Pedestrian Phases and Diagonal Crossings

The exclusive pedestrian (hereinafter P) phase with a diagonal crossing is routinely introduced to improve P safety at high-volume intersections. The article analyses and evaluates the feasibility of the exclusive P phase and diagonal crossing at single-level smaller intersections, identifying the advantages and disadvantages of the exclusive P phase and diagonal crossing from the point of view of time losses. In the experimental part, traffic flow modelling is carried out. The traffic flow simulations show that an exclusive P phase is most beneficial in terms of time losses at an intersection with 2+2 lane intersecting streets and ≥ 900 P/hour with ≥ 1600 vehicles/hour (hereinafter V). In addition, an exclusive P phase can be implemented at the small intersections analysed in this paper, where the volume of V is low or medium, regardless of the number of P at the intersection.

Risk assessment of pedestrian red-light violation behavior using surrogate safety measures: Influence of human, road, vehicle, and environmental factors

Pedestrian red-light violation is one of the crucial causes of pedestrian crashes at urban intersections, which cause considerable injuries and casualties to this vulnerable road group of road users. The objective of this study is to evaluate the risk of pedestrian-vehicle collisions by clustering the pedestrians' red-light violations using surrogate safety measures. The present study utilized surveillance camera footage to collect data on pedestrians' red-light violations at two urban intersections in Babol City. Based on critical thresholds of post-encroachment time (PET), Time to Collision (TTC), and Gap Time (GT), three different risk levels of red-light violations were identified through the use of a K-means algorithm. Moreover, structural equation models were developed for each of the risk levels considering variables that are associated with four major components: human, environment, road, and vehicle. Lastly, policy insights into amending pedestrian behavior and promoting traffic safety culture were proposed, with an overarching emphasis on the human factor, due to its identified greater influence on the propensity for red-light violations.

Analyzing and Optimizing the Emission Impact of Intersection Signal Control in Mixed Traffic

Signalized intersections are one of the typical bottlenecks in urban transport systems that have reduced speeds and which have substantial vehicle emissions. This study aims to analyze and optimize the impacts of signal control on the emissions of mixed traffic flow (CO, HC, and NOx) containing both heavy- and light-duty vehicles at urban intersections, leveraging high-resolution field emission data. An OBEAS-3000 (Manufacturer: Xiamen Tongchuang Inspection Technology Co., Ltd., Xiamen, China.) vehicle emission testing device was used to collect microscopic operating characteristics and instantaneous emission data of different vehicle types (light- and heavy-duty vehicles) under different operating conditions. Based on the collected data, the VSP (Vehicle Specific Power) model combined with the VISSIM traffic simulation platform was used to quantitatively analyze the impact of signal control on traffic emissions. Heavy-duty vehicles contribute to most of the emissions regardless of the low proportion in the traffic flows. Afterward, a model is proposed for determining the optimal signal control at an intersection for a specific percentage of heavy-duty vehicles based on the conversion of emission factors of different types of vehicles. Signal control is also optimized based on conventional signal timing, and vehicle emissions are calculated. In the empirical analysis, the changes in CO, HC, and NOx emissions of light- and heavy-duty vehicles before and after conventional signal control optimization are quantified and compared. After the signal control optimization, the CO, HC, and NOx emissions of heavy-duty vehicles were reduced. The CO and HC emissions of light-duty vehicles were reduced, but the NOx emissions of light-duty vehicles remained unchanged. The emissions of vehicles after optimized signal control based on vehicle conversion factors are reduced more significantly than those after conventional optimized signal control. This study provides a scientific basis for developing traffic management measures for energy saving and emission reduction in transport systems with mixed traffic.

The Influence of Construction Methods on the Stability of Tunnels and Ground Structures in the Construction of Urban Intersection Tunnels

The construction of intersection tunnels in urban induces multiple stress redistribution in the surrounding rock, leading to engineering disasters such as instability in rock strata during excavation, disturbance of supporting structures in existing tunnels, and subsidence of ground adjacent buildings. Employing an appropriate construction method is crucial in circumventing excessive stress concentrations and large-scale rock strata subsidence, making it a key aspect of urban intersection tunnel engineering. In this paper, a numerical model for an urban intersection tunnel is developed based on an underground circular road project in a central business district. We conduct numerical simulations of the excavation processes using the full-section method, step method, and center cross diagram (CRD) method, respectively. The findings indicate that while different construction methods do not change the variation trends of surrounding rock stress and displacement, adjacent ground building deformation, and existing tunnel convergence, they affect the variation degrees. The maximum compressive and tensile stresses in the surrounding rock caused by the CRD method are the smallest, which are 3.56 MPa and 0.76 MPa, respectively. The maximum arch subsidence affected the amount, and horizontal convergence affected the amount of branch tunnel #1 caused by the CRD method are the smallest too, which respectively are 1.428 mm and 0.931 mm. The foundation subsidence and overall inclination of the ground building resulting from the three methods are identical. Then, we discuss the construction safety of the three methods and obtain the influence order on construction stability, which is as follows: full-section method > step method > CRD method. It is concluded that the CRD method is the most suitable for urban intersection tunnel engineering in terms of safety. This study could offer valuable insights for selecting construction methods in urban intersection tunnel engineering and provide a foundation for evaluating the safety and stability of tunnel construction.

Dynamic Straight-Right Lane Design and Signal Control at Urban Intersections

Dynamic Straight-Right Lane Design and Signal Control at Urban Intersections

Roadside Infrastructure Support for Urban Automated Driving

Automated driving offers excellent opportunities for ecology, economy as well as society. Especially in urban intersections, there is a considerable margin for benefits in these sectors. This work takes a structured simulation approach to find answers on the benefit of additional information about surrounding objects of automated vehicles provided by roadside ITS stations to utilize collective perception. We are using advanced sub-microscopic 3D hardware in the loop simulation framework to generate data based on which we can achieve reliable conclusions. Our simulation data set, consisting of 400 simulation iterations, suggests that automated vehicles greatly benefit from collective perception. A lack of roadside ITS station support might lead to a disruptive impact of automated vehicles on the macroscopic traffic flow of urban road networks. If roadside collective perception is introduced to the problem, maneuver time is reduced, and traffic efficiency is increased by 19.8% on average.

The Impact of Parallel U-Turns on Urban Intersection: Evidence from Chinese Cities

As the number of motor vehicles in China’s cities continues to increase, the imbalance between the capacity that existing urban roads have for construction and the demand for motor vehicles is becoming increasingly evident. Indeed, the design of the intersection U-turn scheme has garnered significant attention from researchers. However, as the number of vehicles requiring U-turns increases, the traditional U-turn in the median or U-turn at the intersection fails to meet the timely demand for U-turns. In such cases, vehicles needing to make U-turns are required to queue first. As the queue length grows, it ultimately impacts the operational efficiency of the intersection. To optimize the imbalance between supply and demand at these intersections and promote the sustainable development of intersections, an innovative form of U-turn organization called the Parallel U-turn has been developed. In the engineering practice of reconstructing existing intersections or constructing new ones, it is crucial to investigate the compatibility between various U-turn design forms and traffic volumes. This exploration helps ensure that the chosen U-turn design effectively accommodates the specific traffic demands at the intersection. Therefore, in this paper, a typical intersection in Xi’an was chosen as the study intersection to investigate traffic data. The researchers calibrated and simulated four U-turn organization schemes using VISSIM microsimulation software. The four schemes included a traditional U-turn at the intersection, a Parallel U-turn at the intersection, a traditional U-turn in the median, and a Parallel U-turn in the median. Then, the researchers used the entropy-weighted TOPSIS method (EWTM) to evaluate the compatibility of each U-turn organization scheme for different traffic combinations. This assessment was conducted based on three criteria: operational efficiency, environmental protection, and safety performance. The results of this study indicate that the Parallel U-turn design is advantageous for the XiaoZhai intersection in Xi’an, China, under specific traffic conditions. When the traffic volume at the intersection exceeds 5940 vehicles per hour but remains below the intersection’s maximum capacity, implementing the Parallel U-turn design could yield positive outcomes in terms of operational efficiency, safety performance, and a reduction in intersection pollution. In summary, by enhancing operational efficiency, safety, and environmental impact, the Parallel U-turn design promotes the overall performance and sustainability of the XiaoZhai intersection and the transportation system in Xi’an, China.

Research on right-turn warning and control technology for urban intersection bus

This paper proposes a right-turn warning and control technology for urban intersection buses to address the safety issues associated with right turns. Firstly, the safety of bus-vehicle, non-motorized vehicle, and pedestrian conflicts is studied, and conflict levels are defined using the K-means clustering algorithm, trajectory smoothing, and the 85th percentile cumulative frequency curve method. Secondly, a measurement system based on the OpenCV laser triangulation method and velocity sensors, combined with YOLO v7 for target classification and recognition, is used to calculate the Time to Collision (TTC) value. A microcontroller receives the TTC signal to generate signals for vehicle deceleration or turning, thereby avoiding collisions. SUMO scene simulation is conducted for validation. The results show that the proposed system enables buses to predict traffic conflicts in advance, issue early warnings, and take evasive actions, thus reducing accident rates. This research provides inspiration for related studies on the safety of right turns for urban intersection buses both domestically and internationally.

Dynamic-learning spatial-temporal Transformer network for vehicular trajectory prediction at urban intersections

Forecasting vehicles' future motion is crucial for real-world applications such as the navigation of autonomous vehicles and feasibility of safety systems based on the Internet of Vehicles (IoV). Vehicular trajectory prediction at urban intersections remains challenging due to the difficulty in modeling temporal dependencies and spatial interactions among traffic agents. This paper proposes a dynamic-learning spatial-temporal Transformer network (DSTTN) with domain adaptation training methods based on two modules for two-dimensional vehicular trajectory prediction at urban intersections. The first module is trajectory maneuver characterization (TMC) which captures latent driving maneuver features and divides trajectory data into different categories with different distributions, which can be regarded as different domains in transfer learning (TL). The second module is trajectory distribution matching (TDM) which adopts a novel spatial-temporal Transformer network with distribution matching loss to dynamically learn domain-invariant maneuvers and achieve accurate trajectory prediction. Experiments and ablation studies collected at two unsignalized urban intersections of the inD dataset first validate the interpretability, transferability, and high prediction accuracy of the proposed DSTTN. The results show that the TMC module has maneuver-representational ability. The proposed STTN achieves good and stable prediction accuracy compared with other baselines, including the state-of-the-art deep learning model, while DSTTN with the TMC and TDM modules further improves accuracy. Additional experiments have been conducted under 70 randomly-selected urban intersection scenarios of the Waymo dataset to validate the good prediction accuracy of the proposed method. The formulation of DSTTN offers new ideas for dividing trajectory data into different domains and general domain adaptation to vehicle trajectory prediction under urban road environments.

Traffic arrival pattern estimation at urban intersection using license plate recognition data

Traffic arrival pattern is an indispensable part of the traffic state analysis and optimization of signal control schemes at the urban intersection. In some previous research relevant to the traffic parameters at the intersection, such as the queue length estimation, delay estimation, and vehicle trajectory reconstruction, the vehicle arrival patterns are assumed to obey a Normal or Poisson distribution. However, the actual traffic arrival patterns at the intersection are dynamic and more complex. License plate recognition (LPR) data containing rich vehicle trajectory information are promising in data-driven applications of traffic control and management. We propose a framework based on the LPR data, to infer the dynamic traffic arrival pattern at the intersection. The proposed integrative data-driven framework consists of three main parts, a method for inferring traffic signal cycle parameters, deriving the cycle start time and cycle length from time headway sequences; a link travel time estimation method, extracting the valid travel time at the selected time interval, and an arrival pattern estimation model based on the above modules to perform the second-level estimation at the downstream intersection. The unique characteristics of LPR data are exploited and utilized in this study. To validate the proposed method, the LPR data from two adjacent intersections in the city of Changsha in China are used. Numerical results reveal that the proposed framework can achieve satisfactory estimation under different traffic scenarios. The findings in this study can be extended for supporting efficient traffic control applications.

Reservation-Based Cooperative Ecodriving Model for Mixed Autonomous and Manual Vehicles at Intersections

Oversaturation has become a serious issue for urban intersections worldwide due to the rapid increase in population and traffic demands. The emergence of connected and automated vehicle (CAV) technologies demonstrates the potential to improve oversaturated arterial traffic. Integrating vehicular control and intersection controller optimization into a single process based on CAV technologies can optimize the performance of mixed traffic flow scenarios with various levels of CAV market penetration. This paper proposes an efficient reservation-based cooperative ecodriving model (RCEM) for an isolated intersection under partial and complete CAV market penetration, which can simultaneously optimize the CAV trajectories and intersection controller. CAVs are utilized to precluster manual vehicles into a platoon to improve vehicle passage efficiency. Then, a heuristic-based algorithm is developed to effectively obtain an optimal solution. The proposed RCEM scheme is compared with fixed signal control and actuated signal control in a Simulation of Urban MObility (SUMO)-based platform. Experimental results prove that the RCEM scheme outperforms the fixed signal control and actuated signal control in terms of stop delay, fuel consumption, and emissions under the condition of low levels of CAV penetration. Sensitivity analysis indicates that the system performance further improves as the CAV penetration rate increases, and the stop delay is almost eliminated when the CAV market penetration reaches 100%. Furthermore, the vehicle delay fluctuation under left-turning rates ranging from 5%-75% is 4.4 sec, which is far better than the vehicle delay fluctuation of the fixed signal control (176 sec) and actuated control (65.6 sec).

Analyzing Takeaway E-Bikers’ Risky Riding Behaviors and Formation Mechanism at Urban Intersections with the Structural Equation Model

To study the internal formation mechanisms of risky riding behaviors of takeaway e-bikers at urban intersections, we designed a takeaway riding risky behavior questionnaire and obtained 605 valid samples. An exploratory factor analysis was then conducted to extract five scales: individual characteristics, safety attitude, riding confidence, risk perception, and risky riding behavior. On this basis, a structural equation model was constructed to explore the intrinsic causal relationships among the variables that affect the risky riding behaviors of takeaway e-bikers. The results show that the influence of incentive compensation driven by the takeaway platform was the greatest one. Takeaway riders tend to fight against time to improve punctuality and income by red-light running and speeding. They usually need to pay attention to order information and the delivery routes and communicate with customers to pick up meals in real-time, which inevitably lead to the use of cell phone while riding. Road factors such as “no turnaround at the intersection” and “no non-isolation facilities between on-motorized and motorized lane” lead riders to riding against the traffic, riding on the motor lane, and parking outside the stop line. In addition, lax traffic regulations lead to frequent loopholes for takeaway riders. It means that improving the takeaway platform system, strengthening traffic safety education, and adopting mandatory restraint measures are extremely important. The empirical results provide theoretical support for the benign and healthy development of the takeaway industry, which is significant for preventing and reducing risky behaviors of takeaway riders and improving safety at urban intersections.

Inferring vehicle spacing in urban traffic from trajectory data

This study presents a new method to infer the average two-dimensional (2D) spacing between interacting vehicles in urban traffic from trajectory data. In this context, 2D spacing reflects the amount of road space consumed by pairs of interacting vehicles, and is related to 2D density at the macroscopic level. Due to complex interaction and conflicts in urban traffic, the inherent assumptions in traditional traffic flow models, such as unidirectional flow and homogeneity, are often violated. Such violation challenges direct measurement of urban vehicle spacing. The proposed method addresses this challenge by focusing on the relative movement between interacting vehicles and aggregating the accumulated presence of vehicles in similar scenarios. We apply the method to a large-scale urban trajectory dataset called pNEUMA, and validate the consistency of the method through bootstrapping. By applying the method we obtain a new empirical relation between the average 2D spacing and the relative speeds between interacting vehicles. There are similarities between this empirical relation with the classical Fundamental Diagram of traffic flow in terms of shape and interpretation, and so we term it the “interaction Fundamental Diagram” (iFD). However, there are also key differences. The iFD does not represent steady-state (homogeneous and stationary) longitudinal behaviour; it describes the average amount of road space needed for vehicle interactions at different relative speeds. We believe these iFD relations contribute to understanding vehicle interaction in urban traffic, and can offer new insights for designing safer and more efficient urban intersections.

Identifying critical urban intersections from a fine-grained spatio-temporal perspective

With the rising travel demand of crowds, urban road networks are under increasing pressure, which makes them progressively more fragile. Accurate identification of critical intersections is essential for urban management, resource allocation and traffic supervision. However, existing intersection evaluation methods fail to fully exploit transportation infrastructure, travel behavior and their synergistic relationships, resulting in coarse-grained and low-dynamic evaluation results. In this study, we develop an urban Intersection Evaluation framework from a Fine-grained Spatio-Temporal perspective (IE-FST). The framework is divided into three parts: fine-grained extraction of intersection information, multi-perspective assessment of node importance, and dynamic perception of spatio-temporal patterns in importance ranking. Specifically, IE-FST upgrades the intersection evaluation scale to turn-level, proposes the concept of Turning Sub-Node (TSN) and constructs a refined TSN topology network. Then, by finely extracting the network properties and mobility patterns of TSNs and mining the association patterns among TSNs, IE-FST proposes a Critical Fine-grained node Identification (CFI) algorithm to achieve a refined and dynamic evaluation of urban intersections. Finally, IE-FST analyzes the temporal characteristics and spatial distribution of TSN importance rankings, and provides traffic management measures for different types of TSNs to improve urban transport efficiency. Experiments are performed in Wuhan, China, and the results indicate that the proposed IE-FST framework achieves an accurate perception of urban intersections with high spatial and temporal resolution. This study promotes a better understanding of the synergistic relationships between crowd travel movements and urban transportation infrastructure, and provides theoretical support for infrastructure siting, refined traffic supervision, and rational resource allocation, thus contributing to the resilient and sustainable development of transportation systems and society.

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.

Traffic Signal Optimization Based on Fuzzy Control and Differential Evolution Algorithm

Urban traffic congestion is often concentrated at urban intersections. An urban road traffic signal control system is needed to prevent problems such as driving delays caused by frequent traffic congestions on trunk lines, exhaust emissions owing to frequent start and stop of vehicles, and fuel wastage due to long idling times. Maximizing the traffic capacity of an intersection and reducing the delay rate of vehicles has always been a problem for traffic control research. The coordinated control of urban traffic signals is regarded as a multi-objective optimization problem. A mathematical model for urban trunk traffic is studied herein. An average delay model, average queue length model, total delay calculation model for vehicles at intersections, and vehicle exhaust emission model are established to obtain an optimization model for a new traffic trunk coordinated control system. In addition, our study combines the fuzzy control theory with the adaptive sequencing mutation multi-objective differential evolution algorithm (FASM-MDEA). This new optimization method for traffic signal control at urban intersections is proposed as a solution for the traffic flow optimization model to solve the problem of traffic signal coordination and control of urban trunk lines. The simulation results demonstrate the effectiveness of the model optimization algorithm proposed in this study.