Lane Change Behavior Of Drivers Research Articles

Context-aware similarity measurement of lane-changing trajectories

Lane change (LC), which is an essential behavior in driving, has an important role in traffic efficiency and safety. Examining the spatial lane change behavior of drivers and determining their movement patterns through investigation of the similarities of lane change trajectories is crucial in transportation systems. The moving objectives’ trajectories are a function of their contexts. Thus, in order to consider these contexts in studies of movements, acquiring a deeper understanding of them is necessary. This paper takes advantage of internal/external contexts and spatial footprints in order to contextualize a measure for lane change trajectory similarities. By employing dynamic time warping (DTW), the similarities of multi-dimensional trajectories were estimated, and through the Next Generation Simulation (NGSIM) dataset, the dynamic time warping approach was scrutinized. The results show that contextual information can limit and increase movements and must be utilized. The vehicles’ lane change trajectories were employed in order to present three novel prediction models for trajectory-grounded position. The historical data underwent similarity evaluations for the prediction of positions. According to the results, the suggested models were applicable in precise anticipation of the positions of LC vehicles. As far as the authors are aware, this research is the first one to employ a similarity-based framework to present a thorough analysis of position simulation of LC vehicles and similarities of lane change trajectories.

Characterizing lane changing behavior and identifying extreme lane changing traits

ABSTRACT This study characterizes lane changing behavior of drivers under differing congestion levels and identifies extreme lane changing traits using high-resolution trajectory data. Total lane change frequency exhibited a reciprocal relationship with congestion level, but the distribution of lane change per vehicle remained unchanged as congestion increased. On average, the speed of trajectories increased by 5.4 ft/s after changing a lane. However, this gain significantly diminished as congestion worsened. Further, the average speed of lane changing vehicles was 3.9 ft/s higher than those that executed no lane changes. Two metrics were employed to identify extreme lane changing behavior: critical time-to-line-crossing (TLCc) and lane changes per unit distance. The lowest 1% TLCc varied between 0.71–1.57 seconds. The highest 1% of lane change rates for all lane changing vehicles was 2.5 lane changes per 1,000 ft traveled. Interestingly, no drivers in thisdataset had both excessive lane changes and lane changes with low TLCc.

Driver Lane-Changing Behavior Prediction Based on Deep Learning

A correct lane-changing plays a crucial role in traffic safety. Predicting the lane-changing behavior of a driver can improve the driving safety significantly. In this paper, a hybrid neural network prediction model based on recurrent neural network (RNN) and fully connected neural network (FC) is proposed to predict lane-changing behavior accurately and improve the prospective time of prediction. The dynamic time window is proposed to extract the lane-changing features which include driver physiological data, vehicle kinematics data, and driver kinematics data. The effectiveness of the proposed model is validated through the experiments in real traffic scenarios. Besides, the proposed model is compared with five prediction models, and the results show that the proposed prediction model can effectively predict the lane-changing behavior more accurate and earlier than the other models. The proposed model achieves the prediction accuracy of 93.5% and improves the prospective time of prediction by about 2.1 s on average.

An Improved IOHMM-Based Stochastic Driver Lane-Changing Model

The prediction and estimation of the lane-changing state of the host car and surrounding cars are important parts of an advanced driving assistant system, which mainly depend on the understanding of the driver lane-changing behavior. To learn driver lane-changing maneuver well, this article provides a novel stochastic driver lane-changing model based on an improved input-output hidden Markov model (IOHMM) framework. First, an improved IOHMM is proposed to address the deficiency that the traditional IOHMM cannot remember previous data and describe continuous output. Then, based on the improved IOHMM framework, a driver lane-changing model is established considering the intention and behavior of the driver in the lane-changing process. The model parameters can be learned from the collected lane-changing data using the maximum likelihood estimation and generalized estimation-maximization methods. Finally, the model is applied to a real driver lane-changing process. It is verified that the proposed model has good performance in predicting the future motion maneuver of the host vehicle and estimating the current motion state of the surrounding cars.

A new method of temporal and spatial risk estimation for lane change considering conventional recognition defects

Risky lane change behavior of drivers normally will pose some negative impacts on traffic safety. To ensure a lane change safe and prevent potential accidents, it is important to recognize some lane-changing conditions with potential risks in advance. Despite the researches on traffic safety assessment have been developed for decades, most of the existing researches are mainly interesting in how to estimate the overall safety of the lane-changing process based on historical data. These methods tend to ignore the interactive impacts between lane-changing vehicle and its surrounding vehicles, and have the disadvantages of the long-term evaluation period and single evaluation index. To address these gaps, this study presents a temporal and spatial risk estimation (TSRE) to recognize lane-changing risk in real-time. However, this study concentrates on the instantaneous risk for a lane change event, considering temporal and spatial dimensions for the current lane change circumstance on the highway. After processing the realistic vehicle trajectory dataset, this study extracted 1444 groups of lane change samples, and then incorporates the temporal risk level (TRL) and spatial risk level (SRL) into a comprehensive risk index by applying fault tree analysis. Furthermore, SRL and comprehensive risk index are both used to determine whether the traffic condition of a lane change is safe, and it can effectively overcome the conventional recognition defects that existed in other methods. To achieve a better evaluation effect, the sensitivity tests of recognition accuracy for various risk threshold combinations were carried out. Ultimately, experimental results showed that the proposed TSRE model achieved 97.45 %, 97.79 %, 84.1 % and 85.05 % accuracy rate in terms of classifying the risky lane change samples, traffic conflicts, risky frames and safe frames, when an appropriate risk threshold combination was selected. This encouraging finding can provide the basis for algorithm design of the lane change warning system for connected vehicles.

Modeling and simulation of vehicle group lane-changing behaviors in upstream segment of ramp areas under a connected vehicle environment

Purpose The purpose of this study is to effectively optimize vehicle lane-changing behavior and alleviate traffic congestion in ramp area through the study of vehicle lane-changing behaviors in upstream segment of ramp areas. Design/methodology/approach In the upstream segment of ramp areas under a connected vehicle environment, different strategies of vehicle group lane-changing behaviors are modeled to obtain the best group lane-changing strategy. The traffic capacity of roads can be improved by controlling group lane-changing behavior and continuously optimizing lane-changing strategy through connected vehicle technologies. This paper constructs vehicle group lane-changing strategies in upstream segment of ramp areas under a connected vehicle environment. The proposed strategies are simulated by VISSIM. Findings The results show that different lane-changing strategies are modeled through vehicle group in the upstream segment of ramp areas, which can greatly reduce the delay of ramp areas. Originality/value The simulation results verify the validity and rationality of the corresponding vehicle group lane-changing behavior model strategies, effectively standardize the driver's lane-changing behavior, and improve road safety and capacity.

Effect of centerline marking on lane changing behavior of drivers

Background: Traffic accidents are in continuous rise on Sri Lankan roads. According to the records available at traffic headquarters, one death is reported at every 3 hour or less on Sri Lankan roads. Similar to the rest of the world, driver misbehavior contribute heavily on those road mishaps among identified reasons for common road traffic accidents in Sri Lanka, careless overtaking has recorded as the number one reason. Therefore, this paper is about a study carried out to study the effect of centerline markings on lane changing behavior of drivers. Methods: Three road segments from the Sri Lankan main roads were selected for this study. All the roads were single carriageways with one lane per direction. Out of three road segments, two were mountainous and winding and one is flat and rolling. No Road type Nature Total length (km) Percentage non lane changeable length 1 A Class Mountainous – Winding 15 74.6 2 AB Class Flat and Rolling 16.6 57.8 3 A Class Mountainous – Rolling 15.3 32.7 For each road segment, non-lane changeable segments were divided into three types. They were Length less than 100 m ( L1), Length between 100 m and 300 m ( L2) and length greater than 300 m ( L3). Vehicle movement behavior were observed using video cameras and number of overtaking were collected after the field surveys of one hour each for each kind of non- changeable lane segments. Video cameras were installed to observe vehicle movement at non- lane changeable as well as legally acceptable lane changes. With respect to non- lane changeable segments, both the beginning of non-lane changeable areas as well as the ending of non – lane changeable segments were observed. Results: Following tables are the summary of observations made based on video recordings. The next table is the summary of overtaking observed at the beginning and the end of non- lane changeable segments; According to the field measurements, it was clear that there is a high tendency for drivers to overtake when the continuous length of non-changeable road segments (single line or double line divided) is longer than 300 m. Further, chances were slightly higher when there is a bunching. Bunching is due to slow moving vehicle controlling the optimum speed of most of the other vehicles in the bunch. Non lane changeable type Road segment – 1 Road segment – 2 Road segment – 3 L1 L2 L3 L1 L2 L3 L1 L2 L3 Nature of lane changing Legally acceptable 20 8 16 12 35 18 31 17 13 Non – lane changeable- without vehicle bunching 6 13 21 5 7 13 6 11 13 Non – lane changeable – with vehicle bunching 3 11 24 2 5 17 4 3 15 Road segment 1 Road Segment 2 Road Segment 3 L1 L2 L3 L1 L2 L3 L1 L2 L3 Begin 3 5 20 2 5 11 5 4 15 End 6 19 25 5 7 19 5 10 13 Conclusion: From the summarized results of this study, it is evident that drivers are forced to change the lane to overtake vehicle in front of them when the length of the non- lane changeable lane length is longer. More frequent when there is a bunching effect due to slow moving vehicles. Further, lane changing to overtake vehicles is more prominent towards the latter part of the non-lane changeable section than the beginning. It may be an indication that all the drivers are not really indiscipline in changing the lane and overtaking but they may be frustrated after driving a long distance without having opportunity to go at their comfortable speed. Therefore, introducing the overtaking lanes at regular intervals or any other such suitable measures can be an answer to such illegal lane changing to overtaking. Though there are no adequate details to prove, such overtaking may lead to a traffic accident.

Slip ramp design for dedicated truck lanes using microscopic traffic simulation

Dedicated Truck Lanes (DTL) are planned in the United States along freight-intensive corridors, namely Corridors of the Future. In rural areas along these corridors, slip ramps will provide access to heavy vehicles between the truck-only lanes (TOL) and the general purpose lanes (GPL) to subsequently enter and exit the facility using an interchange. This paper examines the distance between the slip ramp and an interchange required by heavy vehicles to enter and exit the facility using a microscopic traffic simulation program, VISSIM. This distance consists of a merge, link, and a diverge segment. The procedure for determining this distance included (a) examining the acceleration/deceleration characteristics of vehicles, (b) specifying the parameters in VISSIM based on vehicle characteristics and driver behavior, (c) coding and simulating the traffic on DTL in VISSIM, (d) analyzing the speed–flow relation for passenger and heavy vehicles for the various lengths of merge, link, and diverge segments on the GPL, slip ramps, merge, and diverge segments of TOL, and (e) determining the slip ramp distance to (and from) the interchange for level, upgrade, and downgrade. To determine the different segment lengths, a rotatable central composite design (RCCD) was used to generate a reasonable number of cases required for simulation. Simulation of peak period traffic on level grades indicated that the lengths of segments were directly proportional to the average speed of heavy vehicles. As the slip ramp distance from and to the interchange depended on the lane change behavior of drivers, a sensitivity analysis was performed for the safety distance reduction factor (SDRF), the main lane change parameter in VISSIM. This paper provides state departments of transportation with design recommendations for slip ramp design for the Corridors of the Future project. The paper also provides a unique application of microscopic simulation to highway facility design.