Discretionary Lane Changing Research Articles

Development of a Microsimulation Model for Motorway Roadworks With Narrow Lanes

This paper presents a newly developed microsimulation model for motorway roadwork sections to evaluate the efficiency of different temporary traffic management schemes (TTMSs) such as the use of narrow lanes, offside and inside lane closures. The effect on traffic performance of various parameters (e.g. flow rates, percentage of heavy goods vehicles (HGVs), roadwork zone lengths and speed limits) has been tested. The model was built using the FORTRAN programing language. It was developed based on car-following, discretionary lane changing, mandatory lane changing, gap acceptance and narrow lanes rules. Data from four sources (including data taken from different UK motorway sites) were collected and analyzed. The data were used in the verification, calibration and validation processes of the model. Observations from motorway roadwork sites with the narrow lanes scheme show certain prominent drivers’ behaviors, namely avoiding passing HGVs traveling on adjacent lanes and lane repositioning before passing a HGV. Such behaviors were included in the modeling process which suggested that the presence of HGVs had a noticeable impact on reducing section capacity.

Impact of curbside bus stop locations on mixed traffic dynamics: a bus route perspective

Intermittent lane blockage at curb-side bus stops, characterized by aggressive lane changes, undue traffic delay and drop of road capacity, creates the major bottleneck in mixed urban traffic. This paper develops an extended link-node structured two-lane cell transmission model (CTM) that is capable of dynamically capturing the effect of lane blockage at bus stops and comprehensively modelling mandatory and discretionary lane changing (MLC & DLC) manoeuvers along a bus route under various traffic demand levels. Such modelling features, with proper calibration, have been validated to outperform the traditional CTM in terms of replicating the impact of curbside bus stops on mixed traffic dynamics. Application of the proposed model at a route level indicates existence of the optimal distribution of bus stop locations that minimizes the total traffic delay regardless of link lengths and stop densities on the route. Microscopic traffic flow characteristics such as time-varying DLC, MLC and cell densities along the bus route further demonstrate the capability of the proposed route-level model to capture the traffic phenomena (e.g. turning traffic and spill-over exacerbated by stops very close to upstream intersections) that are underrated in traditional single link or intersection based studies. The proposed model, embedded with a mathematical optimization framework, may provide insights in better planning, design, and relocation of curbside bus stops along urban streets to mitigate traffic congestion and to improve bus service.

Modeling discretionary lane-changing behavior on urban streets considering drivers’ heterogeneity

ABSTRACTLane changes are particularly frequent on urban streets, which not only impact traffic operations, but also cause negative effects on safety. This study uses vehicle trajectory data collected from Southwest Road in Dalian, China, to investigate the influential factors of discretionary lane changing in an urban road environment. Both the standard logit model and mixed logit model were fitted to the data, which evidently identified several key factors. The mixed logit model outperformed the standard logit model in terms of model fit. The results suggest that driver heterogeneity is present on the speed differentials of the subject vehicle and the leading vehicles, and the distance gap in the target lane. A sensitivity analysis was further conducted to quantify the degree of influence of the statistically significant variables. The findings support the notion and purpose of discretionary lane changing, i.e. seeking a speed advantage and/or a more satisfactory driving environment.

Discretionary lane-changing behavior: empirical validation for one realistic rule-based model

ABSTRACTIn this paper, we discuss the mechanisms for discretionary lane-changing behavior in traffic flow. NGSIM video data are used to check the validity of different lane-changing rules, and 373 lane changes at 4 locations in US-101 highway are analyzed. We find that the classical lane-changing rules of rule-based model cannot explain many cases in the empirical dataset. Therefore, we propose one new decision rule, comparing the position after a time horizon of several seconds without a lane-change. This rule can be described as “to have a further position within 9 seconds”. The tests on NGSIM data show that this rule can explain most (76%) of the lane-changing cases. Besides, some data when lane changes do not occur are also studied. We find that most (81%) of non-lane-changing vehicles do not fulfill the new rule. Thus, it can be considered as one sufficient and necessary condition for discretionary lane-changing.

Comparisons of mandatory and discretionary lane changing behavior on freeways

This research performs comparative analyses on drivers’ behavior during mandatory and discretionary lane changes. We do this by examining the statistical properties of four lane changing decision variables that describe the gaps between the subject vehicle and the surrounding vehicles. Mandatory and discretionary lane changes in NGSIM’s I-80 Freeway and U.S. Highway 101 data collection sites were identified. First, for each variable at the same site, descriptive statistics for the two types of lane changes were compared, and hypothesis tests on the difference between two means were conducted. Then, for each decision variable at the same site, the observed cumulative distributions between the mandatory and discretionary lane changes were compared by means of the Kolmogorov–Smirnov test. This test was repeated for the fitted distributions of the same decision variable at the same site. The results show that, for the three decision variables associated with gaps in the target lane, the means and distributions between the two types of lane changes are not significantly different. The only variable found to have significant differences in means and distributions is the gap between the subject vehicle and the preceding vehicle in the original lane. This may be because this variable is not an important input in mandatory lane change decisions. This finding provides statistical justification for researchers to develop models with different inputs for mandatory and discretionary lane changes in driver assist systems, in autonomous vehicles, and in microscopic traffic simulation tools.

Retrieving Common Discretionary Lane Changing Characteristics From Trajectories

Conventional lane change methods directly collected steering angle data via onboard sensors to accurately capture the actions of individual drivers. We can hardly use such methods to collect massive data from examinees, because of time and financial costs. In order to retrieve common steering behaviors for lots of drivers, we propose a method to retrieve common discretionary lane change (DLC) steering characteristics from trajectory data. The key technique of this new method is solving an inverse problem that converts the measured trajectory into the unmeasured steering maneuvers under the assumed vehicle movement dynamics. We find that most normal DLC trajectories in the next generation simulation datasets could be well reproduced by a simple target heading angle preview control model. This finding sheds important light into driver behavior study and better explains how human control vehicles. Based on these findings, we can nonintrusively evaluate driving performance or physiological states of drivers based on online roadside monitoring data (e.g., the data collected from roadside video cameras). This opens a promising field of applications for enhancing driving safety.

An entropy-based analysis of lane changing behavior: An interactive approach

ABSTRACTObjectives: As a novelty, this article proposes the nonadditive entropy framework for the description of driver behaviors during lane changing. The authors also state that this entropy framework governs the lane changing behavior in traffic flow in accordance with the long-range vehicular interactions and traffic safety.Methods: The nonadditive entropy framework is the new generalized theory of thermostatistical mechanics. Vehicular interactions during lane changing are considered within this framework. The interactive approach for the lane changing behavior of the drivers is presented in the traffic flow scenarios presented in the article. According to the traffic flow scenarios, 4 categories of traffic flow and driver behaviors are obtained. Through the scenarios, comparative analyses of nonadditive and additive entropy domains are also provided.Results: Two quadrants of the categories belong to the nonadditive entropy; the rest are involved in the additive entropy domain. Driving behaviors are extracted and the scenarios depict that nonadditivity matches safe driving well, whereas additivity corresponds to unsafe driving. Furthermore, the cooperative traffic system is considered in nonadditivity where the long-range interactions are present. However, the uncooperative traffic system falls into the additivity domain. The analyses also state that there would be possible traffic flow transitions among the quadrants. This article shows that lane changing behavior could be generalized as nonadditive, with additivity as a special case, based on the given traffic conditions.Conclusions: The nearest and close neighbor models are well within the conventional additive entropy framework. In this article, both the long-range vehicular interactions and safe driving behavior in traffic are handled in the nonadditive entropy domain. It is also inferred that the Tsallis entropy region would correspond to mandatory lane changing behavior, whereas additive and either the extensive or nonextensive entropy region would match discretionary lane changing behavior. This article states that driver behaviors would be in the nonadditive entropy domain to provide a safe traffic stream and hence with vehicle accident prevention in mind.

A binary decision model for discretionary lane changing move based on fuzzy inference system

This paper presents a Fuzzy Inference System (FIS) which models a driver’s binary decision to or not to execute a discretionary lane changing move on freeways. It answers the following question “Is it time to begin to move into the target lane?” after the driver has decided to change lane and have selected the target lane. The system uses four input variables: the gap between the subject vehicle and the preceding vehicle in the original lane, the gap between the subject vehicle and the preceding vehicle in the target lane, the gap between the subject vehicle and the following vehicle in the target lane, and the distance between the preceding and following vehicles in the target lanes. The input variables were selected based on the outcomes of a drivers survey, and can be measured by sensors instrumented in the subject vehicle. The FIS was trained with Next Generation SIMulation (NGSIM) vehicle trajectory data collected at the I-80 Freeway in Emeryville, California, and then tested with data collected at the U.S. Highway 101 in Los Angeles, California. The results of the test have shown that the system made lane change recommendations of “yes, change lane” with 82.2% accuracy, and “no, do not change lane” with 99.5% accuracy. These accuracies are better than the same performance measures given by the TRANSMODELER’s gap acceptance model for discretionary lane change on freeways, which is also calibrated with NGSIM data. The developed FIS has a potential to be implemented in lane change advisory systems, in autonomous vehicles, as well as microscopic traffic simulation tools.

Logistic regression model for discretionary lane changing under congested traffic

Assessing the probability of lane-changing (LC) is essential to traffic simulation for more realistic representation of complicated traffic phenomena in congested traffic. Discretionary lane changes (DLC), which are not required for reaching a destination, are decided by drivers for the purpose of faster travel. The probability of DLC is related to the speed difference and density difference between adjacent lanes. To reveal the characteristics of DLC, we aggregated Next Generation Simulation trajectory data into a mesoscopic scale and analysed them to obtain detailed information on traffic conditions near DLC actions. We first constructed a joint probability distribution of LC in the speed difference and density difference domains. The distribution shows that speed difference and density difference significantly influence the LC rate. The constructed distribution indicates that drivers tend to change their lanes more frequently to travel faster and to have more space in the target lane. To quantify these relations, logistic regression was applied to the collected data. The outcomes of logistic regression show that the relations are statistically significant and non-linear.

Analysis of Discretionary Lane Changing Parameters on Freeways

A lane changing event, at the instant when a vehicle crosses the lane marker, involves up to five vehicles: the subject vehicle, preceding and following vehicles in the original lane, and the preceding and following vehicles in the target lane. Understanding the interactions of the subject vehicle with the surrounding vehicles is fundamental to the study of the safety and modeling of lane changing behavior. This research studies the statistical properties of 10 lane changing parameters. These parameters describe the gaps, times to collision between vehicles and the subject vehicle's speed. The parameter values were extracted from the vehicle trajectory data in the Next Generation Simulation data sets. The results show that (i) all the parameters are positively correlated with each other; (ii) the gaps and distance are best described by the log-normal distribution; (ii) the times to collision are best described by the Laplace distribution; (iii) the speed may be described by the log-logistic distribution and the normal distribution. The results suggest that using one or few selected parameters may be sufficient to quantify the risk of a lane changing event.

Oversaturated Freeway Flow Algorithm for Use in Next Generation Simulation

Existing simulation models have difficulty in accurately modeling oversaturated traffic conditions on freeways. A new behavioral algorithm for oversaturated freeway flow can be used in microscopic simulation models; it was developed as part of the Next Generation Simulation (NGSIM) project, sponsored by FHWA. The proposed algorithm is an integrated car-following and lane-changing modeling framework that is consistent with the kinematic wave theory. The algorithm can explicitly model mandatory and discretionary lane changing, including cooperation during lane changing. As an extension of the algorithm, a new on-ramp merging model was developed and incorporated into the model. The proposed algorithm also accounts for the relaxation process following lane changing. The proposed model includes a small number of parameters that can be readily measured in the field. The proposed model was implemented into a microscopic simulator, and it was validated with both vehicle trajectory data and macroscopic detector data from an NGSIM test site. The results show good agreement with real-world traffic behavior. The study products, including an algorithm description, analysis results, and computer code, are documented and made available to developers and users of traffic simulation tools.