Driving Behavior at Signalized Intersections Operating under Disordered Traffic Conditions

2021 ◽  
pp. 036119812110338
Author(s):  
Ritvik Chauhan ◽  
Ashish Dhamaniya ◽  
Shriniwas Arkatkar
Keyword(s):  
Traffic Flow ◽  
Flow Analysis ◽  
Driving Behavior ◽  
Signalized Intersections ◽  
Lateral Movement ◽  
Trajectory Data ◽  
Lateral Velocity ◽  
Data Set ◽  
Vehicle Class ◽  
Traffic Conditions

A higher degree of heterogeneity in vehicle class and drivers, coupled with non-lane-based driving habits, creates several challenges in traffic flow analysis. This study investigates vehicles’ microscopic driving behavior at signalized intersections operating under weak lane discipline with mixed traffic (disordered) conditions. For this purpose, a comprehensive vehicular trajectory data set is developed from field-recorded video footage using a semi-automated tool for data extraction. Microscopic parameters such as relative velocity, spacing between vehicles, following time, lane preference, longitudinal and lateral speed profile, hysteresis evidence, and lateral movement of different vehicle classes during different traffic phases are presented in the study. The data is then segregated into three flow conditions: stopped flow, saturated flow, and unaffected flow. It is found that smaller vehicles prefer near-side lanes over far-side lanes. Motorized three-wheeler (3W) and motorized two-wheeler (2W) vehicle classes exhibit the greatest lateral velocity, lateral movement, and aggressiveness. This results in several interactions between vehicles as a function of different leader–follower vehicle pairs. Signalized intersections with more heterogeneity in traffic composition, especially higher composition of 2W and 3W vehicle classes, exhibit higher levels of aggressive driving behavior that might lower safety standards. As a practical application, ranges of various driving behavior parameter values for different leader–follower combinations and traffic conditions are quantified in the study. The observations and results are expected to help better understand prevailing driving behavior in disordered traffic and contribute toward robust calibration of microscopic traffic flow models for better replicating disordered traffic conditions at signalized intersections.

scholarly journals A Crash Prediction Method Based on Artificial Intelligence Techniques and Driving Behavior Event Data

2021 ◽  
Vol 13 (11) ◽  
pp. 6102
Author(s):  
Yunjong Kim ◽  
Juneyoung Park ◽  
Cheol Oh
Keyword(s):  
Traffic Flow ◽  
Traffic Accidents ◽  
Prediction Method ◽  
Driving Behavior ◽  
Crash Risk ◽  
Gradient Boosting ◽  
Event Data ◽  
Trajectory Data ◽  
Data Set ◽  
Ensemble Technique

Various studies on how to prevent and deal with traffic accidents are ongoing. In the past, the key research emphasis was on passive accident response measures that analyzed roadway-based historical data to identify road sections with high crash risk. Through assessing crash risks by analyzing simulation data and actual vehicle driving trajectory data, this study suggests a method of effectively preventing accidents before they happen. In this analysis, using digital tachograph (DTG) data, which is the vehicle trajectory data for commercial vehicles running on Korean highways, hazardous and normal traffic flows were identified and extracted. Driving behavior event data for both types of traffic flow was processed by measuring safety indicators through the extracted data. Safety indicators with a high impact on traffic flow classification were then extracted using gradient boosting, a representative ensemble technique. A neural network analysis was performed using the extracted safety indicators as independent variables to create a traffic flow classifier, which had a high accuracy of 94.59%. The DTG data set was also classified based on the severity of each accident that occurred in the studied roadway, the time of the accident, and the weather; the results were compiled to enable comprehensive accident prediction. It is expected that proactive crash prevention will be possible in the future by evaluating real-time accident risks using the findings and ensemble-based methodologies of this paper.

Application of Trajectory Data for Investigating Vehicle Behavior in Mixed Traffic Environment

2018 ◽  
Vol 2672 (43) ◽  
pp. 122-133 ◽  
Author(s):  
Narayana Raju ◽  
Pallav Kumar ◽  
Aayush Jain ◽  
Shriniwas S. Arkatkar ◽  
Gaurang Joshi
Keyword(s):  
Traffic Flow ◽  
Research Work ◽  
Driving Behavior ◽  
Video Data ◽  
Hysteresis Phenomenon ◽  
Mixed Traffic ◽  
Trajectory Data ◽  
Traffic Conditions ◽  
Micro Level ◽  
Flow States

The research work reported here investigates driving behavior under mixed traffic conditions on high-speed, multilane highways. With the involvement of multiple vehicle classes, high-resolution trajectory data is necessary for exploring vehicle-following, lateral movement, and seeping behavior under varying traffic flow states. An access-controlled, mid-block road section was selected for video data collection under varying traffic flow conditions. Using a semi-automated image processing tool, vehicular trajectory data was developed for three different traffic states. Micro-level behavior such as lateral placement of vehicles as a function of speed, instant responses, vehicle-following behavior, and hysteresis phenomenon were evaluated under different traffic flow states. It was found that lane-wise behavior degraded with increase in traffic volume and vehicles showed a propensity to move towards the median at low flow and towards the curb-side at moderate and heavy flows. Further, vehicle-following behavior was also investigated and it was found that with increase in flow level, vehicles are more inclined to mimic the leader vehicle’s behavior. In addition to following time, perceiving time of subject vehicle for different leading vehicles was also evaluated for different vehicle classes. From the analysis, it was inferred that smaller vehicles are switching their leader vehicles more often to escape from delay, resulting in less following and perceiving time and aggressive gap acceptance. The present research work reveals the need for high-quality, micro-level data for calibrating driving behavior models under mixed traffic conditions.

scholarly journals Traffic Inequality and Relations in Maritime Silk Road: A Network Flow Analysis

2021 ◽  
Vol 10 (1) ◽  
pp. 40
Author(s):  
Naixia Mou ◽  
Haonan Ren ◽  
Yunhao Zheng ◽  
Jinhai Chen ◽  
Jiqiang Niu ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Network Flow ◽  
Road Traffic ◽  
Flow Analysis ◽  
Silk Road ◽  
Identification System ◽  
Traffic Network ◽  
Trajectory Data ◽  
Maritime Traffic ◽  
Maritime Silk Road

Maritime traffic can reflect the diverse and complex relations between countries and regions, such as economic trade and geopolitics. Based on the AIS (Automatic Identification System) trajectory data of ships, this study constructs the Maritime Silk Road traffic network. In this study, we used a complex network theory along with social network analysis and network flow analysis to analyze the spatial distribution characteristics of maritime traffic flow of the Maritime Silk Road; further, we empirically demonstrate the traffic inequality in the route. On this basis, we explore the role of the country in the maritime traffic system and the resulting traffic relations. There are three main results of this study. (1) The inequality in the maritime traffic of the Maritime Silk Road has led to obvious regional differences. Europe, west Asia, northeast Asia, and southeast Asia are the dominant regions of the Maritime Silk Road. (2) Different countries play different maritime traffic roles. Italy, Singapore, and China are the core countries in the maritime traffic network of the Maritime Silk Road; Greece, Turkey, Cyprus, Lebanon, and Israel have built a structure of maritime traffic flow in the eastern Mediterranean Sea, and Saudi Arabia serves as a bridge for maritime trade between Asia and Europe. (3) The maritime traffic relations show the characteristics of regionalization; countries in west Asia and the European Mediterranean region are clearly polarized, and competition–synergy relations have become the main form of maritime traffic relations among the countries in the dominant regions. Our results can provide a scientific reference for the coordinated development of regional shipping, improvement of maritime competition, cooperation strategies for countries, and adjustments in the organizational structure of ports along the Maritime Silk Road.

scholarly journals Estimating Lane Change Duration for Overtaking in Nonlane-Based Driving Behavior by Local Linear Model Trees (LOLIMOT)

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Ehsan Ramezani-Khansari ◽  
Masoud Tabibi ◽  
Fereidoon Moghadas Nejad
Keyword(s):  
Linear Model ◽  
Traffic Flow ◽  
Driving Behavior ◽  
Lane Change ◽  
Lateral Movement ◽  
Relative Speed ◽  
Time To Collision ◽  
Local Linear ◽  
Lateral Distance ◽  
Transverse Distance

Lane change (LC) is one of the main maneuvers in traffic flow. Many studies have estimated LC duration directly by using lane-based data. The current research presents an estimate of LC duration for overtaking maneuver in nonlane-based traffic flow. In this paper, the LC duration is estimated implicitly by modeling lateral speed and applying the length of required lateral movement to complete the LC maneuver. In lateral speed modeling, the local linear model tree is applied which consists of three variables: the initial lateral distance, longitudinal speed, and time to collision (TTC), which itself is a function of the relative speed of follower and the distance between the two vehicles. The initial lateral distance is the relative transverse distance from which the following vehicle initializes the LC. The range of lateral speed was estimated between 0.5 and 5 km/h, which resulted in the LC duration between 2.5 and 24 sec. The results indicate that the lateral and longitudinal speed would be inversely related, while the lateral speed and the initial transverse distance as well as TTC would be directly related. The findings also indicate that TTC can be assumed as the most important factor affecting lateral speed. TTC at 8 sec can be considered as the threshold for its effect on the LC duration since at longer TTCs, and the lateral speed has remained almost constant. When TTC is longer than 8 sec, it would not affect the LC duration.

scholarly journals Modelling vehicular behaviour using trajectory data under non-lane based heterogeneous traffic conditions

2019 ◽  
Vol 52 (4) ◽  
pp. 95-108 ◽  
Author(s):  
Hari Krishna Gaddam ◽  
K. Ramachandra Rao
Keyword(s):  
Traffic Flow ◽  
Fast Moving ◽  
Frictional Resistance ◽  
Separation Distance ◽  
Heterogeneous Traffic ◽  
Trajectory Data ◽  
Traffic Conditions ◽  
Moving Vehicles ◽  
Slow Moving ◽  
Lane Based

The present study aims to understand the interaction between different vehicle classes using various vehicle attributes and thereby obtain useful parameters for modelling traffic flow under non-lane based heterogeneous traffic conditions. To achieve this, a separate coordinate system has been developed to extract relevant data from vehicle trajectories. Statistical analysis results show that bi-modal and multi-modal distributions are accurate in representing vehicle lateral placement behaviour. These distributions help in improving the accuracy of microscopic simulation models in predicting vehicle lateral placement on carriageway. Vehicles off-centeredness behaviour with their leaders have significant impact on safe longitudinal headways which results in increasing vehicular density and capacity of roadway. Another interesting finding is that frictional clearance distance between vehicles influence their passing speed. Analysis revealed that the passing speeds of the fast moving vehicles such as cars are greatly affected by the presence of slow moving vehicles. However, slow moving vehicles does not reduce their speeds in the presence of fast moving vehicles. It is also found that gap sizes accepted by different vehicle classes are distributed according to Weibull, lognormal and 3 parameter log logistic distributions. Based on empirical observations, the study proposed a modified lateral separation distance factor and frictional resistance factor to model the non-lane heterogeneous traffic flow at macro level. It is anticipated that the outcomes of this study would help in developing a new methodology for modelling non-lane based heterogeneous traffic.

A Development of a Conceptual Framework of the Driving Task

1964 ◽  
Vol 6 (4) ◽  
pp. 375-382 ◽  
Author(s):  
Charles W. Algea
Keyword(s):  
Conceptual Framework ◽  
Traffic Flow ◽  
Driving Behavior ◽  
Minor Role ◽  
Traffic Conditions ◽  
Driving Task ◽  
A Minor

A conceptual framework is developed based on observations of driving behavior under actual traffic conditions. After the framework has been established, the assumptions and implications are contrasted with those in the theories of traffic flow that relegate the driver's contribution to a minor role. It would appear that theorists are mainly concerned with traffic flow and safety while each driver is more concerned with independence and constancy of his vehicle's movement.

scholarly journals Modeling Left-Turn Driving Behavior at Signalized Intersections with Mixed Traffic Conditions

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Hong Li ◽  
Sujian Li ◽  
Haijian Li ◽  
Lingqiao Qin ◽  
Shi Li ◽  
...  
Keyword(s):  
Traffic Safety ◽  
Traffic Engineering ◽  
Driving Behavior ◽  
Signalized Intersections ◽  
Mixed Traffic ◽  
Left Turn ◽  
Security Issues ◽  
Traffic Conditions ◽  
Traffic Characteristics ◽  
Potential Applications

In many developing countries, mixed traffic is the most common type of urban transportation; traffic of this type faces many major problems in traffic engineering, such as conflicts, inefficiency, and security issues. This paper focuses on the traffic engineering concerns on the driving behavior of left-turning vehicles caused by different degrees of pedestrian violations. The traffic characteristics of left-turning vehicles and pedestrians in the affected region at a signalized intersection were analyzed and a cellular-automata-based “following-conflict” driving behavior model that mainly addresses four basic behavior modes was proposed to study the conflict and behavior mechanisms of left-turning vehicles by mathematic methodologies. Four basic driving behavior modes were reproduced in computer simulations, and a logit model of the behavior mode choice was also developed to analyze the relative share of each behavior mode. Finally, the microscopic characteristics of driving behaviors and the macroscopic parameters of traffic flow in the affected region were all determined. These data are important reference for geometry and capacity design for signalized intersections. The simulation results show that the proposed models are valid and can be used to represent the behavior of left-turning vehicles in the case of conflicts with illegally crossing pedestrians. These results will have potential applications on improving traffic safety and traffic capacity at signalized intersections with mixed traffic conditions.

Traffic Flow Analysis at Manual Tollbooth Operation under Mixed Traffic Conditions

2019 ◽  
Vol 145 (6) ◽  
pp. 04019023 ◽  
Author(s):  
Yogeshwar V. Navandar ◽  
Ashish Dhamaniya ◽  
D. A. Patel ◽  
Satish Chandra
Keyword(s):  
Traffic Flow ◽  
Flow Analysis ◽  
Mixed Traffic ◽  
Traffic Conditions ◽  
Traffic Flow Analysis
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