Recent developments and research needs in modeling lane changing

Zheng, Zuduo

doi:10.1016/j.trb.2013.11.009

Cited by 415 publications

(149 citation statements)

References 74 publications

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“…As a result of the importance of the role of microscopic traffic simulation models, how to improve the accuracy of the lane-changing model has attracted wide attention among scholars over the past decades. A large amount of work has been conducted to collect trajectory data [4][5][6][7][8][9] and formulate models of lane changing [10].…”

Section: Introductionmentioning

confidence: 99%

Crash Risk Prediction Model of Lane-Change Behavior on Approaching Intersections

Lü

2017

Discrete Dynamics in Nature and Society

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The driving tendency of drivers is one of the most important factors in lane-changing maneuvers. However, the heterogeneity of the characteristics of drivers' lane-changing behaviors has not been adequately considered. The primary objective of the present study is to explore the risk level of the lane-changing implementation process under different driving tendencies upon approaching signalized intersections in an urban area. This paper defines the Integrated Conflict Risk Index (ICRI), which takes into account the probability and severity of risk. Using the index as the dependent variable, the risk prediction model of implementing the lanechange process is established. A series of experiments, which included a questionnaire, a number of tests, and on-road experiments, was conducted to identify the driving tendencies of the participants. A combination of video recording and instrumented vehicles was used to collect lane-changing trajectory data of different driving tendencies. The parameters of the model were calibrated, and the results indicate that driving tendency has a significant effect on the risk level of lane-changing execution. More specifically, the more aggressive the driving tendency, the higher the risk level. The quantitative results of the study can provide the basis for conflict risk assessment in the existing lane-changing models.

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Section: Introductionmentioning

confidence: 99%

Crash Risk Prediction Model of Lane-Change Behavior on Approaching Intersections

Lü

2017

Discrete Dynamics in Nature and Society

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“…This observation implies the necessity to adequately accommodate human factors in the microscopic modeling of driving behaviors (primarily, their lane-changing maneuvers and car-following behavior), as advocated in the recent literature (e.g., [5,42,43]). However, the explicit incorporation of risk perceptions in the modeling of the merging process was beyond the scope of this study and is a topic for future research.…”

Section: Conclusion and Further Workmentioning

confidence: 99%

“…Numerous studies have shown that merging from the acceleration lane has a significant impact on traffic operations at freeway on-ramp bottlenecks [1,2] and can also trigger traffic breakdown [3][4][5]. Merging behavior is a complex task, because, typically, the driver has to focus on three or more vehicles in their current and target lanes in a limited timeframe [5].…”

Section: Introductionmentioning

confidence: 99%

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Modeling and Predicting Stochastic Merging Behaviors at Freeway On-Ramp Bottlenecks

Sun

Zuo

Jiang

et al. 2018

Journal of Advanced Transportation

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Merging behavior is inevitable at on-ramp bottlenecks and is a significant factor in triggering traffic breakdown. In modeling merging behaviors, the gap acceptance theory is generally used. Gap acceptance theory holds that when a gap is larger than the critical gap, the vehicle will merge into the mainline. In this study, however, analyses not only focus on the accepted gaps, but also take the rejected gaps into account, and the impact on merging behavior with multi-rejected (more than once rejecting behavior) gaps was investigated; it shows that the multi-rejected gaps have a great influence on the estimation of critical gap and merging prediction. Two empirical trajectory data sets were collected and analyzed: one at Yan'an Expressway in Shanghai, China, and the other at Highway 101 in Los Angeles, USA. The study made three main contributions. First, it gives the quantitative measurement of the rejected gap which is also a detailed description of non-merging event and investigated the characteristics of the multi-rejected gaps; second, taking the multi-rejected gaps into consideration, it further expanded the concept of the "critical gap" which can be a statistic one and the distribution function of merging probability with respect to such gaps was analyzed by means of survival analysis. This way could make the full use of multi-rejected gaps and accepted gaps and reduce the sample bias, thus estimating the critical gap accurately; finally, considering multi-rejected gaps, it created logistic regression models to predict merging behavior. These models were tested using field data, and satisfactory performances were obtained.

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“…Further, they found that even a small proportion of timid drivers significantly reduce the road capacity while it needs much more aggressive drivers to increase road capacity. Zheng [19] exhaustively reviewed the lane changing models available for microscopic traffic flow modelling. This study suggested that a comprehensive model that captures lane changing decisions needs to be developed and that this model should be able to predict vehicle trajectories close to that observed in field at microscopic level.…”

Section: Introductionmentioning

confidence: 99%

Modelling vehicular interactions for heterogeneous traffic flow using cellular automata with position preference

2017

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This paper proposes and validates a modified cellular automata model for determining interaction rate (i.e. number of car-following/overtaking instances) using traffic flow data measured in the field. The proposed model considers lateral position preference by each vehicle type and introduces a position preference parameter b in the model which facilitates gradual drifting towards preferred position on road, even if the gap in front is sufficient. Additionally, the model also improves upon the conventional model by calculating safe front and back gap dynamically based on speed and deceleration properties of leader and follower vehicles. Sensitivity analysis was carried out to determine the effect of b on vehicular interactions and the model was calibrated and validated using interaction rates observed in the field. Paired tests were conducted to determine the validity of the model in determining interaction rates. Results of the simulations show that there is a parabolic relationship between area occupancy and interaction rate of different vehicle types. The model performed satisfactorily as the simulated interaction rate between different vehicle types were found to be statistically similar to those observed in field. Also, as expected, the interaction rate between light motor vehicles (LMVs) and heavy motor vehicles (HMVs) were found to be higher than that between LMVs and three wheelers because LMVs and HMVs share the same lane. This could not be done using conventional CA models as lateral movement rules were dictated by only speeds and gaps. So, in conventional models, the vehicles would end up in positions which are not realistic. The position preference parameter introduced in this model motivates vehicles to stay in their preferred positions. This study demonstrates the use of interaction rate as a measure to validate microscopic traffic flow models.

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Recent developments and research needs in modeling lane changing

Cited by 415 publications

References 74 publications

Crash Risk Prediction Model of Lane-Change Behavior on Approaching Intersections

Crash Risk Prediction Model of Lane-Change Behavior on Approaching Intersections

Modeling and Predicting Stochastic Merging Behaviors at Freeway On-Ramp Bottlenecks

Modelling vehicular interactions for heterogeneous traffic flow using cellular automata with position preference

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