Urban Intersections Research Articles

Behaviour Analysis of Left-Turning Mopeds at Signal Controlled Intersections – A Case Study in Yancheng City

Mopeds (electric bicycles and light motorcycles) are commonly used as a personal transportation mode in China. However, the understanding of characteristics of left-turning mopeds at signal-controlled intersections has been relatively limited. To bridge this gap, firstly, this paper proposed a video conversion method of moped movement data acquisition. Then, a method of data accuracy verification was introduced by comparing the results between the field experiment and the video conversion method. Secondly, the ideal traffic space for left-turn mopeds from different entrances was defined to analyse the characteristics of the left-turning mopeds at intersections. Further, three indicators, namely, transverse distance, the proportion of left-turning mopeds with crossing behaviour, and the average number of avoidance behaviour, were proposed and used to analyse the asymmetrical characteristics behaviour, crossing behaviour, and avoidance behaviour. Finally, based on empirical data collected from five signal-controlled intersections, the proposed methods and behaviours were analysed. This paper provides both a valid method of obtaining the position data of mopeds and a reliable basis for improving the safety of left-turning moped riders at urban signal-controlled intersections.

Traffic Light Optimization Based on Modified Webster Function

Intersection traffic lights are a basic means of ensuring the normal operation of road traffic. A good signal timing scheme is essential for improving traffic congestion. To obtain the signal timing scheme of the designated intersection, the method proposed in this article is based on a modified Webster function. The method uses the signal cycle and proportion of green light duration as independent variables to establish the corresponding intersection vehicle delay function. This function is converted from a multiobjective optimization to a single-objective optimization formulation; a modified genetic algorithm is then used to find the optimal solution to this function. The experimental results show that the timing scheme optimized by the improved genetic algorithm can reduce the intersection delay by nearly 15.64%. The proposed traffic signal timing based on the modified Webster function will be of value as an important reference for the optimization of traffic lights at urban intersections.

Millimeter-Wave Radar Scheme With Passive Reflector for Uncontrolled Blind Urban Intersection

Modern millimeter-wave automotive radars are employed to keep a safe distance between vehicles and reduce the collision risk when driving. Meanwhile, an on-board radar module is supposed to operate in the line-of-sight condition, which limits its sensing capabilities in intersections with obstructed visibility. Therefore, this paper investigates the scheme with passive reflector, enabling the automotive radar to detect an approaching vehicle in the non-line-of-sight (blind) urban intersection. First, extensive radar measurements of the backscattering power are carried out with the in-house assembled millimeter-wave radar equipment. Next, the measured data is employed to calibrate an accurate analytical model, deduced and described in this paper. Finally, the analytical models are deployed to define the optimal parameters of the radar scheme in the particular geometry of the selected intersection scenario. Specifically, it is found that the optimal angular orientation of the reflector is 43.5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> , while the 20 m curvature radius shows better performance compared to a flat reflector. Specifically, the curved convex shape increases scattering power by 20 dB in the shadow region and, thus, improves the detection probability of the vehicle, approaching the blind intersection.

Independent Reinforcement Learning for Weakly Cooperative Multiagent Traffic Control Problem

The adaptive traffic signal control (ATSC) problem can be modeled as a multiagent cooperative game among urban intersections, where intersections cooperate to counter the city's traffic conditions. Recently, reinforcement learning (RL) has achieved marked successes in managing sequential decision making problems, which motivates us to apply RL in the ATSC problem. One of the largest challenges of this problem is that the observation of intersection is typically partially observable, which limits the learning performance of RL algorithms. Considering the large scale of intersections in an urban traffic environment, we use independent RL to solve ATSC problem in this study. We model ATSC problem as a partially observable weak cooperative traffic model (PO-WCTM). Different from a traditional IRL task that averages the returns of all agents in fully cooperative games, the learning goal of each intersection in PO-WCTM is to reduce the cooperative difficulty of learning, which is also consistent with the traffic environment hypothesis. To achieve the optimal cooperative strategy of PO-WCTM, we propose an IRL algorithm called Cooperative Important Lenient Double DQN (CIL-DDQN), which extends Double DQN (DDQN) algorithm using two mechanisms: the forgetful experience mechanism and the lenient weight training mechanism. The former mechanism decreases the importance of experiences stored in the experience reply buffers, while the latter mechanism increases the weight experiences with high estimation and `leniently' trains the DDQN neural network. Experiments in two real traffic scenarios and one simulated traffic scenarios show that, CIL-DDQN outperforms other methods in almost all performance indicators of ATSC.

Impact of Traffic Flow on Pollution at Urban Intersections

Intersections are mainly considered as critical elements of road networks in terms of air quality impact, and their control type and geometric configuration can affect significantly vehicular emissions. This study aims mainly to investigate the effect of traffic flow on the level of pollution at a selected intersection in Kerbela city. Hence, the video camera has been installed for three normal days to investigate the traffic flow fluctuation for 24h. Then, Gasmet has been used for investigating the amount of emission for both CO2 and CO within the peak hour. A well-known SIDRA 8.0 has been used for analyzing the intersection. The results indicated that the CO2 is exceeded the acceptable limit with worse traffic operations for both selected intersections.

A Holistic Intersection Rating System (HIRS)—A Novel Methodology to Measure the Holistic Operational Performance of Signalized Urban Intersections

Signalized urban intersections are key components of urban transportation networks. They are traditionally viewed and designed as primarily motorized traffic facilities, and thus their physical and operational designs have traditionally aimed at maximizing traffic throughput subject to constraints dictated by vehicular safety requirements and pedestrian crossing needs. Seen from a holistic viewpoint, urban intersections are hubs or effective centers of community activities of which traffic flow is only one. Those hubs have direct and indirect impacts on the overlapping traffic functionalities, the environment, public health, community wellbeing, and the local economy. This study proposes a new rating system, the Holistic Intersection Rating System (HIRS), aimed at appraising signalized intersections from a more inclusive viewpoint. This appraisal covers traffic functionality, sustainability, and public health and community wellbeing. This rating system can be used as a guide to conceive, plan, or design new intersections or revamp existing ones. HIRS rates signalized urban intersections based on the level of use of relevant enabling technologies, and the physical and operational designs that allow those intersections to operate holistically, thus leading to a more human-centric and sustainable operational performance. HIRS was validated using a panel of experts in construction, transportation, and public health. The Relative Importance Index (RII) method was used to weigh the HIRS features. The rating system was piloted on a sample of 20 intersections in different cities in the UAE. The results revealed glaring gaps in services to or the consideration of pedestrians, cyclists, and nearby households. The sample intersections scored a mean of 32% on the public health and community wellbeing section, 37% on the pedestrian subsection, and 15% on the cyclist subsection. Such relatively low scores serve as indicators of areas for improvements, and if mapped to their specific features and their relative weights, specific physical and operations designs and technology integration can be identified as actionable items for inclusion in plans and/or designs.

Standard against nonstandard urban intersection nighttime traffic safety evaluation using cross-sectional method

Standard against nonstandard urban intersection nighttime traffic safety evaluation using cross-sectional method

Spatiotemporal analysis of pedestrian exposure to submicron and coarse particulate matter on crosswalk at urban intersection

At the crosswalk of urban traffic intersections, vehicles frequently stop with their engines idling adjacent to it during the red-light period and accelerate with intensified emissions during the green-light period. As a result, pedestrians are inevitably at a higher risk of exposure to near-source traffic-related air pollution while walking on the crosswalk or waiting on the roadside. Therefore, this study focuses on pedestrian exposure to particulate matter on the crosswalk at urban intersections. Firstly, mobile measurements at varied locations of the crosswalk were carried out to reveal the spatial difference of particles on the whole crosswalk. The results show that the concentrations of submicron and coarse particles are higher on the parking crosswalk than that on the non-parking crosswalk. Secondly, mobile measurements at varied heights of crosswalk were carried out to discover the vertical difference of particles on the crosswalk. Particulate concentration is higher at the height of 1.5 m than that at 0.5 m. Thirdly, fixed-site measurements were conducted on the roadside to make a comparison with the crosswalk. Particulate concentration is found to be nearly 6% higher on the crosswalk than that on the roadside. Finally, pedestrian exposure to particulate matter in varied trips on the crosswalk is investigated and the exposure in trips with a 10s–20s delay after the onset of a walk signal is lower than that in other situations. The results can help fully recognize the particulate pollution at intersections and better understand the particulate exposure of pedestrians on crosswalk at urban intersections.

High-integrity based cooperative file transmission at urban intersections using pure V2V communication

Vehicular Ad Hoc Networks (VANETs) provide safety management and entertainment services in smart city scenarios. However, due to the highly time-varying network topology caused by vehicles’ movement and non-line-of-sight (NLOS) areas in complex urban environment, the connection time between vehicles might be very short, leading to limited transmission capacity between vehicles. As a result, large file transmission such as online video sharing might be unavailable. To alleviate this issue, we study high-integrity based file transmission using pure vehicle-to-vehicle (V2V) communication at urban intersections. First, we develop the analytical models to evaluate file transmission at intersections, including vehicle mobility model, connection time prediction model, and V2V communication model. Then, we derive the relationship between transmission capacity and vehicles’ mobility. Based upon this analysis, we propose a cooperative file transmission strategy for the transmitting vehicle, which helps transmit the target file to the receiving vehicle. The proposed strategy includes three phases: transmission capacity evaluation, cluster establishment, and cooperative file transmission. By taking advantage of the cluster characteristics, the strategy can achieve file transmission with high integrity. Through extensive simulations, we demonstrate the accuracy of the proposed system model which matches our analysis well, and show the effectiveness of the proposed strategy by comparing with other file transmission strategies in aspects of transmission success rate and data transmission volume.

Simulation of non-motor vehicle and pedestrian mixed crossing behavior

In order to study the mixed traffic of non-motor vehicles and pedestrians at urban intersections, a two-dimensional cellular automata model was established by analyzing the movement characteristics of non-motor vehicles and pedestrians, which included the field, cellular space, attributes, and forward decision-making of pedestrians and non-motor vehicles. This model can reflect the interweaving and interference behavior of non-motor vehicles and pedestrians in the time window of the intersection, and describe the behavior characteristics of non-motor vehicles and pedestrians in different stages of crossing the intersection.

Traffic flow interaction mechanism and Facility design at signalized intersections

In China’s urban traffic, mixed traffic is a basic feature. Especially at urban intersections, the phenomenon of non-mixed traffic is more serious. At many intersections in our country, due to the different characteristics of traffic flow, there are various interactions between motor vehicle traffic flow and non-motor vehicle traffic flow. This paper analyzes the interaction principle between motor vehicles and non-motor vehicles at intersections. It is found that the expansion characteristics of non-motor vehicles at intersections have a certain influence on the straight-going non-motor vehicles, and the interference degree is defined as an index to measure the influence of non-motor vehicle on motor vehicle. Finally the paper proposes corresponding solutions to improve the safety and operation efficiency of intersections.

Design of On-line Simulation System for Signal Control of Urban Intersections Based on Visual Sensing Technology

Intersection area is the area with the most serious conflicts and accidents in the whole urban traffic system. Therefore, it is of great significance to combine modern computer technology, sensing technology and communication technology to intelligently control intersections, improve the efficiency of traffic signal control and reduce the delay time in conflict areas. Starting with video detection and using deep learning in the detection module, a video detection mechanism based on restricted Boltzmann machine network is proposed. Based on C # programming language, the intersection traffic signal simulation module is designed through COM interface provided by traffic simulation software VISSIM, which realizes road network import, parameter setting, signal timing change, simulation operation and simulation result output. Numerical experiments show that fuzzy control method with self-correcting factor is superior to timing control method and classical fuzzy control method in average delay time of vehicles.

TaxiInt: Predicting the Taxi Flow at Urban Traffic Hotspots Using Graph Convolutional Networks and the Trajectory Data

Taxi flow is an important part of the urban intelligent transportation system. The accurate prediction of taxi flow provides an attractive way to find the potential traffic hotspots in the city, which helps to avoid serious traffic congestions by taking effective measures in advance. The current prediction of taxi flow and its impact on urban transportation are closely related to the passenger origin-destination (OD) information. However, high-quality OD information is not always available. To address this problem, a prediction model, named as TaxiInt, is proposed in this study. Different from other density-clustering-based approaches, neural network, or OD information based models, TaxiInt predicted the taxi flow using the trajectory data of taxis. The spatial features and temporal features of each road were extracted using a graph convolutional network, which was trained with the road network information and the trajectory data. The experiments carried on a real taxi dataset showed the validity of our model. It can predict the taxi flow at a given urban intersection with high accuracy.

Research on Traffic Condition Discriminant Technology of Urban Intersections Under Intelligent Lighting System Based on Fuzzy Comprehensive Evaluation

Firstly, the basic traffic data collection methods based on intelligent lighting system are introduced, including RTMS data collection and video data collection. Then, based on the fuzzy comprehensive evaluation, a traffic condition discrimination model of urban intersections under the intelligent lighting system is established. The average speed, stop delay and queue length detected by the intelligent lamp post collection equipment are selected as evaluation factors, and the traffic status of signalized intersections is defined as smooth traffic, normal congestion and severe congestion. The research results have realized the fine discrimination of urban intersection traffic operating condition under the intelligent lighting system.

Solution Concepts in Hierarchical Games Under Bounded Rationality With Applications to Autonomous Driving

With autonomous vehicles (AV) set to integrate further into regular human traffic, there is an increasing consensus of treating AV motion planning as a multi-agent problem. However, the traditional game theoretic assumption of complete rationality is too strong for the purpose of human driving, and there is a need for understanding human driving as a bounded rational activity through a behavioral game theoretic lens. To that end, we adapt three metamodels of bounded rational behavior; two based on Quantal level-k and one based on Nash equilibria with quantal errors. We formalize the different solution concepts that can be applied in the context of hierarchical games, a framework used in multi-agent motion planning, for the purpose of creating game theoretic models of driving behavior. Furthermore, based on a contributed dataset of human driving at a busy urban intersection with a total of ~4k agents and ~44k decision points, we evaluate the behavior models on the basis of model fit to naturalistic data, as well as their predictive capacity. Our results suggest that among the behavior models evaluated, modeling driving behavior as pure strategy Nash equilibria with quantal errors at the level of maneuvers with bounds sampling of actions at the level of trajectories provides the best fit to naturalistic driving behavior, and there is a significant impact of situational factors on the performance of behavior models.

The influence of car passengers’ sitting postures in intersection crashes

Car passengers are frequently sitting in non-nominal postures and are able to perform a wide range of activities since they are not limited by tasks related to vehicle control, contrary to drivers. The anticipated introduction of Autonomous Driven vehicles could allow “drivers” to adopt similar postures and being involved in the same activities as passengers, allowing them a similar set of non-nominal postures. Therefore, the need to investigate the effects of non-nominal occupant sitting postures during relevant car crash events is becoming increasingly important.This study aims to investigate the effect of different postures of passengers in the front seat of a car on kinematic and kinetic responses during intersection crashes. A Human Body Model (HBM) was positioned in a numerical model of the front passenger seat of a midsize Sports Utility Vehicle (SUV) in a total of 35 postures, including variations to the lower and upper extremities, torso, and head postures. Three crash configurations, representative of predicted urban intersection crashes, were assessed in a simulation study; two side impacts, a near-side and a far-side, respectively, and a frontal impact. The occupant kinematics and internal loads were analyzed, and any deviation between the nominal and altered posture responses were quantified using cross-correlation of signals to highlight the most notable variations.Posture changes to the lower extremities had the largest overall influence on the lower extremities, pelvis, and whole-body responses for all crash configurations. In the frontal impact, crossing the legs allowed for the highest pelvis excursions and rotations, which affected the whole-body response the most. In the two side-impacts, leaning the torso in the coronal plane affected the torso and head kinematics by changing the interaction with the vehicle’s interior. Additionally, in far-side impacts supporting the upper extremity on the center console resulted in increased torso excursions. Moreover, the response of the upper extremities was consistently sensitive to posture variations of all body regions.

Simulation and assessment of traffic pollutant dispersion at an urban signalized intersection using multiple platforms

Evaluating the performance of air quality models for roadside traffic exhaust dispersion is critical to sustainable environmental development and urban pollution management. This study introduces three models, namely CAL3QHC, ENVI-met and ANSYS Fluent, to model the pollutant dispersion at an isolated busy urban signalized intersection, by taking fine particulate matter (PM2.5) as the subject matter. A dynamic emission factor model was established based on Cell Transmission Model (CTM) and Portable Emission Measurement System (PEMS) experiment, which was further implemented with the User Defined Function (UDF) of ANSYS Fluent. Results from an empirical study based on the Jianchuan and Humin Road intersection, in suburb Shanghai, demonstrate that ANSYS Fluent performs better for PM2.5 concentration prediction with carefully calibrated parameter settings. Simulation results from both CAL3QHC and ANSYS Fluent are within an acceptable scale, while ENVI-met performs better in assessing the correlation relationships between PM concentrations and the intersectional and meteorological factors. The concentration of PM2.5 was found to increase significantly during the idle phase, which tends to be accumulated in front of the intersection buildings simultaneously. With the increasing of the altitude, the overall average PM2.5 concentration of the intersection decreases gradually. Street canyons with high buildings were found with comparable lower wind speeds, hindering the pollutant diffusion. Finds of this study may assist urban intersection design from a variety of perspectives, e.g., lane channelization, signal timing, and even architecture styles of the surrounding buildings.

An observational study on the risk behaviors of electric bicycle riders performing meal delivery at urban intersections in China

Along with the increase of online catering, the number of accidents involved meal delivery riders increases dramatically in China, and their riding behavior might contribute to fatal accidents. We conducted a field observation study to investigate the risk behavior characteristics of meal delivery couriers riding electric bicycles at urban intersections in China. We selected 4 typical signalized urban intersections in Beijing, and video recorded traffic behaviors of meal delivery riders for eight hours. A total of 1891 couriers crossing intersections on electric bicycles were observed, categorized, and analyzed. The results showed that red-light-running and riding-on-the-motor-lane were the most frequent risky traffic behaviors. The ratios of risk behaviors from delivery couriers were almost all significantly higher than those of normal riders (p < 0.05). There was no significant difference in these risk behaviors for riders across different platforms. The findings provide valuable insights for understanding couriers’ risk behaviors at intersections and make useful suggestions for improving traffic safety.

Assessment of intersection conflicts between riders and pedestrians using a GIS-based framework and portable LiDAR

ABSTRACT Adequate sight distances are crucial parameters ruling the design of roads and riding facilities. LiDAR-retrieved data have proved to serve many transportation applications by creating updated and accurate representations of the road environment. As a result, many sight distance evaluation methodologies use LiDAR delivered point clouds to carry out visibility analyses. However, current approaches are mainly focused on motorized users along highways and streets, and most of those methodologies are carried out directly on the point cloud, which does not allow the evaluation or relocation of visual obstructions. This study presents a visibility-based procedure intended to assess the circulation of pedestrians and riders at urban intersections. For this purpose, first, the required sight distances of all users along the intersection are obtained. Second, the conflicts between vulnerable road users are evaluated, considering the type of control present at the intersection. The available sight distances are then obtained using a GIS-based method that employs 3D road models derived from the LiDAR retrieved data. Finally, after a comparison between the required and the available visibility, an evaluation determines which user sees a larger percentage of the intersection. This approach facilitates evaluating the impact that vegetation, traffic, and/or furniture elements could have on visibility and serve as an urban design aid tool. Results show that the proposed method offers valuable insights into the distributions of visibility among intersecting users and into the quantifications of the effect of specific adjacent traffic. Moreover, this procedure stands as a useful and flexible approach that allows the evaluation of both cyclists and e-scooter riders in different road facilities and positionings.

Comparison of safety and kinematic patterns of automated vehicles turning left in interaction with oncoming manually driven vehicles

Highly and fully automated driving has been under development for the past two decades in order to increase comfort, efficiency, and traffic safety. Particularly in the latter domain, experts agree on automated driving, especially in case of automated vehicles (AV) with SAE level 4 or higher, having the most promising effects. Automated driving is expected to decrease the number of seriously injured or even killed road users to zero (Vision Zero). However, automated driving is still in an early stage of development and many AV tend to drive very carefully to avoid crashes. So, the goal is to make driving more efficient while maintaining the highest level of safety. In the project "Digitaler Knoten 4.0" cooperative automated driving was assessed regarding efficiency and safety aspects. One of the use cases investigated was turning left with oncoming traffic at an urban intersection as this situation represents one of the most complex situations in urban areas yielding to crashes with-in many cases-serious consequences for the involved road users. At the Application Platform Intelligent Mobility (AIM) Research Intersection in Braunschweig, Germany, an SAE level 3 AV was turning left interacting with oncoming manually driven vehicles (MV). The performance of the AV was compared to MV executing the same manoeuvre. The recorded video-based trajectories of the respective AV as well as MV were analysed regarding the influence of situational factors (e.g. position of the vehicle in the queue and gap acceptance) and kinematic factors (e.g. speed and acceleration) on traffic safety. The similarities and differences between this specific AV and MV were identified yielding insight for further developing algorithms for more efficient driving while maintaining the same traffic safety level. For instance, it appears that the AV shows a very conservative left turning behaviour leading to very safe PET distributions in comparison to left turning MV.