Unsignalized Intersections Research Articles

Effects of penetration rates on the application of in-vehicle traffic lights at unsignalized intersections

Objective In-vehicle traffic lights have been demonstrated as an effective method to assist drivers in crossing unsignalized intersections, by displaying virtual traffic lights inside vehicles based on vehicle-to-vehicle communications. However, previous studies assumed that all the vehicles were equipped with vehicular communications, of which the deployment might last for decades. Therefore, it is necessary to consider the application of the system in partial deployment scenarios. This study aimed to analyze the influences of in-vehicle traffic light system on driving safety at unsignalized intersections within different penetration rates. Methods Driving simulator experiments involving 18 participants were performed. Evaluation indexes including post-encroachment time, maximum acceleration stroke of ego vehicle, and times of near miss incidents were investigated for different penetration rates of vehicular communications. Results Significant differences were observed between the conditions when no in-vehicle traffic light was used and when the penetration rate was 100% for the post-encroachment time (p = 0.02), and the maximum acceleration stroke of ego vehicle (p = 0.05). Meanwhile, it was found that the post-encroachment time slightly decreased, and the times of near miss incidents slightly increased when the penetration rate increased from 0% to 50%. However, the post-encroachment time was still longer for different penetration rates, compared to the condition when no in-vehicle traffic light was applied. Conclusions The results indicated that the in-vehicle traffic light system could significantly improve driving safety at unsignalized intersections when the penetration rate was 100%. For partial deployment conditions, it is essential to speed up the deployment of vehicular communication-based driver assistance systems within early deployment stage, especially when the penetration rate was within 50%, to ensure driving safety.

EVALUASI KINERJA RUAS JALAN DAN SIMPANG TIDAK BERSINYAL DENGAN FASILITAS PUTAR BALIK (U-TURN) STUDI KASUS JALAN SRIWIJAYA DI KOTA MATARAM

Along with the increasing population and increasing development in all sectors in Mataram City every year, resulting in an increase in the volume of vehicles which often causes potential traffic conflicts. This condition results in disturbances for motorists in the flow of vehicles on the u-turn facility so that it affects performance on the road. One of the roads in the city of Mataram that is the choice in the travel route for the community is Jalan Sriwijaya. It is necessary to review the performance of roads with median openings, especially on unsignalized roads and intersections.The method used in this study is the calculation of urban road performance using MKJI 1997. Data from the location of median openings on unsignaled roads and intersections were collected through a field survey and then calculated the average traffic flow, average speed, average turning time average, capacity, degree of saturation, and level of road service. The results of the analysis show that the average traffic flow that occurs on the Sriwijaya road is 864.73 pcu/hour with a capacity of 2737.15 pcu/hour and the degree of saturation (DS) is 0.316 so that it is classified as a service level "B", while the average traffic flow at the unsignalized intersection is 2441.77 pcu/hour with a capacity of 4199.91 pcu/hour and a degree of saturation (DS) value of 0.581 so that it is classified in the "C" service level. The average turning time on roads is 8.04 seconds and at unsignalized intersections 6.94 seconds.

A Multi-Intersection Vehicular Cooperative Control Based on End-Edge-Cloud Computing

Cooperative Intelligent Transportation Systems (C-ITS) will change the modes of road safety and traffic management, especially at intersections without traffic lights, namely unsignalized intersections. Existing researches focus on vehicle control within a small area around an unsignalized intersection. This paper expands the control domain to a large area with multiple intersections. In particular, a Multi-intersection Vehicular Cooperative Control (MiVeCC) is proposed to enable cooperation among vehicles in a large area with multiple unsignalized intersections. Firstly, a vehicular end-edge-cloud computing framework is proposed to facilitate end-edge-cloud vertical cooperation and horizontal cooperation among vehicles. Under the framework, a two-stage reinforcement learning is implemented to obtain the optimal policy for vehicle control. To support RL in the proposed framework, a multi-vehicle state representation method and a safety-oriented value representation method are designed. The former structures the collected vehicle information, and the latter evaluates the traffic condition. A multi-intersection simulation platform is developed to evaluate the proposed scheme. Simulation results show that the proposed MiVeCC can improve travel efficiency at multiple intersections by up to 4.59 times without collision compared with benchmark methods.

Development of Road Diet Segment and Intersection Crash Modification Factors

Road diets can offer potential safety improvements for both pedestrians and vehicles. The additional space provided by reducing the number of vehicular through lanes can be reallocated to other uses such as bicycle lanes, parking, sidewalks, transit use, turn lanes, curb extensions, parklets, or pedestrian refuge islands. This study evaluated the safety effectiveness of road diets in Virginia using the empirical Bayes method, focusing on the common road diet conversion from a four-lane roadway to a three-lane roadway with added bike lanes. A total of 36 segment sites and 39 intersections were identified in Virginia where road diet installations were implemented between the years 2009 to 2018. The analysis showed segment crash modification factors (CMFs) of 0.62 for total crashes and 0.36 for fatal and injury (FI) crashes. Across all intersection types, the CMFs were 0.65 for total crashes and 0.54 for FI crashes. All CMFs were found to be statistically significant at a 95% confidence level. When intersections were separated into signalized and unsignalized intersections, no significant safety benefit was found for unsignalized intersections. Based on the results, it was concluded that road diets can potentially reduce crashes, and public agencies should consider the safety benefits of road diets when justifying roadway improvements.

Investigating Differences in Behavior and Brain in Human-Human and Human-Autonomous Vehicle Interactions in Time-Critical Situations.

Some studies provide evidence that humans could actively exploit the alleged technological advantages of autonomous vehicles (AVs). This implies that humans may tend to interact differently with AVs as compared to human driven vehicles (HVs) with the knowledge that AVs are programmed to be risk-averse. Hence, it is important to investigate how humans interact with AVs in complex traffic situations. Here, we investigated whether participants would value interactions with AVs differently compared to HVs, and if these differences can be characterized on the behavioral and brain-level. We presented participants with a cover story while recording whole-head brain activity using fNIRS that they were driving under time pressure through urban traffic in the presence of other HVs and AVs. Moreover, the AVs were programmed defensively to avoid collisions and had faster braking reaction times than HVs. Participants would receive a monetary reward if they managed to finish the driving block within a given time-limit without risky driving maneuvers. During the drive, participants were repeatedly confronted with left-lane turning situations at unsignalized intersections. They had to stop and find a gap to turn in front of an oncoming stream of vehicles consisting of HVs and AVs. While the behavioral results did not show any significant difference between the safety margin used during the turning maneuvers with respect to AVs or HVs, participants tended to be more certain in their decision-making process while turning in front of AVs as reflected by the smaller variance in the gap size acceptance as compared to HVs. Importantly, using a multivariate logistic regression approach, we were able to predict whether the participants decided to turn in front of HVs or AVs from whole-head fNIRS in the decision-making phase for every participant (mean accuracy = 67.2%, SD = 5%). Channel-wise univariate fNIRS analysis revealed increased brain activation differences for turning in front of AVs compared to HVs in brain areas that represent the valuation of actions taken during decision-making. The insights provided here may be useful for the development of control systems to assess interactions in future mixed traffic environments involving AVs and HVs.

Exploring the stop sign running at all-way stop-controlled intersections with the SHRP2 naturalistic driving data

IntroductionAll-way stop control (AWSC) has been widely used at unsignalized intersections in the United States for its safety effects. However, many drivers do not make a complete stop before stop signs in practice (i.e., stop sign running), which presents safety concerns. MethodThis study explores driver behaviors at AWSC intersections with the SHRP2 naturalistic driving data. ResultsFirst, it is found that the full-stop rate is only 20.2% at AWSC intersections. Then, the study quantitatively analyzes what factors might influence the stop sign running decisions at AWSC intersections, where driver, vehicle, intersection geometry, maneuver, and environmental features are taken into account. In addition, considering the possible unobserved heterogeneities across drivers and intersections, a logistic regression model with both driver and intersection random effects is adopted. The results show that young and older drivers are less likely to fully stop, but there is no gender difference found. SUVs and vans are less likely to fully stop, drivers are less likely to fully stop at 3-leg intersections, and drivers are more likely to fully stop in daytime and weekdays. In terms of maneuvers, left-turn traversals are more likely to make a complete stop. In addition, both the driver and intersection random effects are found to be significant, vary greatly by individuals, and can be used to identify the few but critical high-risk drivers/intersections. Practical applicationsThe findings are expected to provide new insights for transportation agencies to formulate effective measures to deter stop sign running.

Assessment of Risk-Taking Behaviour of Young Motorcyclists at Un-Signalised Intersections – A Partial Least Square Structural Equation Modelling Approach

At un-signalised at-grade intersections or roundabouts, motorcyclists have to make a quick decision to manoeuvre and avoid crash. Many studies show that risk-taking behaviour is the major cause of accidents in young motorcyclists. In this study, we analysed various factors that are involved in the risk-taking behaviour of young motorcyclists at un-signalised intersections. Online questionnaires were distributed among university and college students in Islamabad and Rawalpindi. The data of 490 respondents were collected to test the research model. Partial least square structural equation modelling approach was used to evaluate the measurement model, structural model and importance-performance map analysis. In this study, we assumed that risk-taking behaviour of young motorcyclists at un-signalised intersections could be influenced by several factors, i.e., demographic, past crash involvement, and peer influence. The results revealed that past crash involvement, confidence level, and peer influence were the significant factors that affect the risk-taking behaviour. Peer influence has the highest effect on the risk-taking behaviour. The person whose friends encourage them to take risk and accept challenges is more likely to exhibit the risk-taking behaviour. Those people who are more confident while riding a motorcycle are more likely to take risks.

Injury severity analysis of two-vehicle crashes at unsignalized intersections using mixed logit models

The severity of the two-vehicle crash is closely related to the characteristics of both the struck and striking vehicles. Ignoring vehicle roles may lead to biased results. Thus, this study used mixed logit models to determine the factors that influence injury severity in the two-vehicle crash, taking into account the vehicle characteristics of the different crash roles. The data used is collected from Pennsylvania Department of Transportation (PennDOT) Open Data Portal. First, the synthetic minority oversampling technique and nearest neighbors (SMOTE-ENN) strategy was selected to address the class imbalance problem of crash data. Then, two separated mixed logit models were developed for four- and three-legged unsignalized intersections. The results suggest that the type and movement of vehicles have significant effects on crash severity. For example, right-turn vehicles being struck can lead to more serious crashes than striking other vehicles. Large trucks striking other vehicles are found to increase crash severity, but being struck is found to decrease crash severity. Additionally, several factors were also identified to affect crash severity in both models and effective countermeasures suggestions were proposed to mitigate crash severity. Supplemental data for this article is available online at at .

MENINGKATKAN KINERJA BUNDARAN KELAPA GADING DENGAN SIMPANG BERSINYAL MENGGUNAKAN SOFTWARE PTV VISSIM STUDENT VERSION

The Kelapa Gading roundabout junction, North Jakarta is one of the unsignalized intersections with the roundabout. Traffic conditions that have a density in every arm, trigger traffic jams during rush hours. In this study, an analysis of the performance of the Kelapa Gading roundabout will be presented.Primary data includes geometric, environmental conditions, traffic flow and location maps. While secondary data includes population and vehicle growth in DKI Jakarta Province. The research method uses the formula contained in the 1997 MKJI and modeling using the Vissim Student Version softwareAfter evaluating the average roundabout performance, it was found that DS < 0.69, capacity of 7927 pcu/hour, intersection current 5286 emp/hour at the average intersection at the peak hour of the roundabout, in this case the roundabout is still feasible to maintain. However, in this study, forecasting in the next 5 years was obtained in the 3rd year (three) the degree of saturation reached DS > 0.75 on one or several parts of the road so it could change to a signalized intersection, in this study we will use the PTV Vissim Stundent Version software to perform analysis of existing conditions and plans. Key words: Signalized intersection, MKJI, vissim.

Statistical aspects of gap-acceptance theory for unsignalized intersection capacity

We partially correct and significantly deepen the Siegloch’s method (1973), which is currently used to determine the capacity of unsignalized intersections. Taking into account current knowledge about microstructure of vehicular traffic flows we suggest Generalized Inverse Gaussian distribution as a theoretically and empirically substantiated alternative to the exponential distribution of priority-stream clearances, considered in Siegloch’s original methodology. Furthermore, we formulate a statistical model for gap-acceptance theory and present a series of validated theoretical calculations leading to general formulas for proportion and statistical distribution of priority-stream clearances that exactly k minor-stream vehicles have utilized for their inclusion maneuver (accepted-clearance distribution of order k). Using up-to-date empirical data-sets we test hypotheses of priority-stream clearance-distribution and analyze sample acceptance-ratios and empirical distribution of accepted clearances. By means of an original concept we finally estimate an implicit acceptance-rule, with the help of which a minor-street driver is deciding on acceptance/rejection of an offered priority-clearance.

Human-like driving decision at unsignalized intersections based on game theory

Unsignalized intersection driving is challenging for automated vehicles. For safe and efficient performances, the diverse and dynamic behaviors of interacting vehicles should be considered. Based on a game-theoretic framework, a human-like payoff design methodology is proposed for the automated decision at unsignalized intersections. Prospect Theory is introduced to map the objective collision risk to the subjective driver payoffs, and the driving style can be quantified as a tradeoff between safety and speed. To account for the dynamics of interaction, a probabilistic model is further introduced to describe the acceleration tendency of drivers. Simulation results show that the proposed decision algorithm can describe the dynamic process of two-vehicle interaction in limit cases. Statistics of uniformly-sampled cases simulation indicate that the success rate of safe interaction reaches 98%, while the speed efficiency can also be guaranteed. The proposed approach is further applied and validated in four-vehicle interaction scenarios at a four-arm intersection.

ANALISIS KINERJA SIMPANG EMPAT TAK BERSINYAL PERSIMPANGAN TANJUNG PATI KABUPATEN 50 KOTA

Congestion often occurs at several crossroads in the district. Therefore, efficient and optimal handling is needed, as well as planning for Traffic Lights. In this thesis research examines the Performance Analysis of Unsignaled Intersection. The research location is on the West Sumatra-Riau Cross Road, precisely at the Tanjung Pati Intersection, Fifty Cities Regency with 4 intersection arms, namely Tanjung Pati Road, West Sumatra-Riau Road, Lubuak Batingkok Road, and Batu Balang Road. The purpose of this study is to determine the traffic volume and whether it is necessary to reactivate the Traffic Light (Red Light) at the Tanjung Pati Simpang Empat. Data collection was obtained through field surveys. From the results of research and discussion at the Tanjung Pati Intersection, it was found that the degree of saturation (DS) was 1.25 with an average delay of 141 seconds/pcu. So from these calculations we can conclude that at Simpang Empat Tanjung Pati it is necessary to reactivate the Traffic Light to reduce traffic congestion that occurs at certain peak hours. Keywords: Traffic Light, unsignalized intersection, degree of saturation, time delay.

Evaluasi Kinerja Simpang Tiga Tak Bersinyal (Studi Kasus : Jl. Cemara – Jl. Cemara Asri Booulevard Raya)

Along with population growth and the magnitude of development as well as increasing transportation, the number of vehicles or traffic volume on roads is increasing, including one intersection in Deli Serdang Regency which has traffic density every day with concomitant congestion and a decrease in speed in certain road segments. This study aims to analyze the performance of the unsignalized intersection of Cemara Road – Cemara Asri Boulevard Raya Road and find alternatives to solve the problems that exist at the intersection of Cemara Road – Cemara Asri Boulevard Raya Road. The data collection method is carried out within a certain time by studying primary and secondary data. In this study resulted in the conclution that the intersection of Cemara Road – Cemara Asri Boulevard Raya Road experienced the peak of traffic flow on Sunday at 04.00 – 05.00 PM with a traffic volume of 2,782 smp/hour. The actual capacity is 2,451.6 smp/hour. The degree of saturation is 1.134. Based on value of the degree of saturation, the level of service at the intersection into category F exceeds the saturation limit suggested by the Indonesia Road Capacity Manual, which is > 1.00. It is necessary to revisit the three-arm intersection on Cemara Road – Cemara Asri Boulevard Raya Road. An alternative solution that can be given to obstacles found is the need for widening the geometric of the road in order to increase the capacity of the intersection and based on the degree saturation value, it is necessary to install the tools provided (lights traffic).

A Control Method with Reinforcement Learning for Urban Un-Signalized Intersection in Hybrid Traffic Environment.

To control autonomous vehicles (AVs) in urban unsignalized intersections is a challenging problem, especially in a hybrid traffic environment where self-driving vehicles coexist with human driving vehicles. In this study, a coordinated control method with proximal policy optimization (PPO) in Vehicle-Road-Cloud Integration System (VRCIS) is proposed, where this control problem is formulated as a reinforcement learning (RL) problem. In this system, vehicles and everything (V2X) was used to keep communication between vehicles, and vehicle wireless technology can detect vehicles that use vehicles and infrastructure (V2I) wireless communication, thereby achieving a cost-efficient method. Then, the connected and autonomous vehicle (CAV) defined in the VRCIS learned a policy to adapt to human driving vehicles (HDVs) across the intersection safely by reinforcement learning (RL). We have developed a valid, scalable RL framework, which can communicate topologies that may be dynamic traffic. Then, state, action and reward of RL are designed according to urban unsignalized intersection problem. Finally, how to deploy within the RL framework was described, and several experiments with this framework were undertaken to verify the effectiveness of the proposed method.

Decision making at unsignalized inner city intersections using discrete events systems

AbstractAutonomous driving is a promising technology to, among many aspects, improve road safety. There are however several scenarios that are challenging for autonomous vehicles. One of these are unsignalized junctions. There exist scenarios in which there is no clear regulation as to is allowed to drive first. Instead, communication and cooperation are necessary to solve such scenarios. This is especially challenging when interacting with human drivers. In this work we focus on unsignalized T-intersections. For that scenario we propose a discrete event system (DES) that is able to solve the cooperation with human drivers at a T-intersection with limited visibility and no direct communication. The algorithm is validated in a simulation environment, and the parameters for the algorithm are based on an analysis of typical human behavior at intersections using real-world data.

Performance Analysis of Road Section and Unsignalized Intersections in Order to Prevent Traffic Jams on Jl H. Djole – Jl. Pasar Lama

The unsignalized intersection of Jalan H Djole – Jalan Pasar Lama is located in Bekasi City is a commercial area with densely populated settlements and dense economic activity every day. Lack of facilities and infrastructure led to traffic jams on the intersection. A analysis is needed that aims to determine the volume of the vehicle, the performance of road sections and intersections which are measured based on capacity, degree of saturation, and level of service. And then alternative solutions to improve the performance of these roads and intersections.
 The analytical method used is the Indonesian Road Capacity Manual 1997. The data required in this study are primary data in the form of road geometric data, traffic volume, vehicle speed data and side friction data. As well as secondary data such as location maps, land use, and population data. Based on the results of the analysis that has been carried out, the highest traffic volume at the intersection occurs on Friday, 17.00 - 18.00 WIB, with a DS value of 1.06 and LOS F. Alternative solutions that can be given at the intersection are vehicles on minor roads A and C You can only turn left, with the alternative solution, the DS values ​​are 0.73 and LOS C. Whereas on the roads it has DS values ​​of 0.71 and LOS C. An alternative solution that can be given to roads is to reduce the level of side friction around the segment. Street. With this alternative solution, the DS value is 0.65 and LOS B.

Investigating the Impact of Connected and Automated Vehicles on Signalized and Unsignalized Intersections Safety in Mixed Traffic

Road traffic crashes are a major safety problem, with one of the leading factors in crashes being human error. Automated and connected vehicles (CAVs) that are equipped with Advanced Driver Assistance Systems (ADAS) are expected to reduce human error. In this paper, the Simulation of Urban MObility (SUMO) traffic simulator is used to investigate how CAVs impact road safety. In order to define the longitudinal behavior of Human Drive Vehicles (HDVs) and CAVs, car-following models, including the Krauss, the Intelligent Driver Model (IDM), and Cooperative Adaptive Cruise Control (CACC) car-following models were used to simulate CAVs. Surrogate safety measures were utilized to analyze CAVs’ safety impact using time-to-collision. Two case studies were evaluated: a signalized grid network that included nine intersections, and a second network consisting of an unsignalized intersection. The results demonstrate that CAVs could potentially reduce the number of conflicts based on each of the car following model simulations and the two case studies. A secondary finding of the research identified additional safety benefits of vehicles equipped with collision avoidance control, through the reduction in rear-end conflicts observed for the CACC car-following model.

Courteous Behavior of Automated Vehicles at Unsignalized Intersections Via Reinforcement Learning

The transition from today's mostly human-driven traffic to a purely automated one will be a gradual evolution, with the effect that we will likely experience mixed traffic in the near future. Connected and automated vehicles can benefit human-driven ones and the whole traffic system in different ways, for example by improving collision avoidance and reducing traffic waves. Many studies have been carried out to improve intersection management, a significant bottleneck in traffic, with intelligent traffic signals or exclusively automated vehicles. However, the problem of how to improve mixed traffic at unsignalized intersections has received less attention. In this letter, we propose a novel approach to optimizing traffic flow at intersections in mixed traffic situations using deep reinforcement learning. Our reinforcement learning agent learns a policy for a centralized controller to let connected autonomous vehicles at unsignalized intersections give up their right of way and yield to other vehicles to optimize traffic flow. We implemented our approach and tested it in the traffic simulator SUMO based on simulated and real traffic data. The experimental evaluation demonstrates that our method significantly improves traffic flow through unsignalized intersections in mixed traffic settings and also provides better performance in a wide range of traffic situations compared to the state of the art.

Driving Tasks Transfer Using Deep Reinforcement Learning for Decision-Making of Autonomous Vehicles in Unsignalized Intersection

Knowledge transfer is a promising concept to achieve real-time decision-making for autonomous vehicles. This paper constructs a transfer deep reinforcement learning (RL) framework to transform the driving tasks in the intersection environments. The driving missions at the unsignalized intersection are cast into a left turn, right turn, and running straight for automated vehicles. The goal of the autonomous ego vehicle (AEV) is to drive through the intersection situation efficiently and safely. This objective promotes the studied vehicle to increase its speed and avoid crashing other vehicles. The decision-making policy learned from one driving task is transferred and evaluated in another driving mission. Simulation results reveal that the decision-making strategies related to similar tasks are transferable. It indicates that the presented control framework could reduce time consumption and realize online implementation.

Cooperative Control in Eco-Driving of Electric Connected and Autonomous Vehicles in an Un-Signalized Urban Intersection

This paper addresses the problem of finding the optimal Eco-Driving (ED) speed profile of an electric Connected and Automated Vehicle (CAV) in an isolated urban un-signalized intersection. The problem is formulated as a single-level optimization and solved using Pontryagin's Minimum Principle (PMP). Analytical solutions are presented for various conflicts that occur at an intersection. Cooperation is introduced amongst CAVs as the ability to share intentions. Two levels of cooperation, namely the Cooperative ED (C-ED) and Non-Cooperative (NC-ED) algorithms are evaluated, in a simulation environment, for energy efficiency with Intelligent Driver Model (IDM) as the baseline.