The effect of a lane change on a car-following manoeuvre: anticipation and relaxation behaviour

Ghaffari, Ali; Khodayari, Alireza; Hosseinkhani, Niloofar; Salehinia, Saeed

doi:10.1177/0954407014547930

Cited by 20 publications

(16 citation statements)

References 20 publications

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“…For instance, Zheng et al (2013) found that a follower undergoes three stages during a lane-changing manoeuvre: (a) anticipation (the change in driving behaviour of the follower when s/he notices the lane-changing request), (b) relaxation (where the follower and its leader are willing to accept a shorter spacing and then relax to normal spacing), and (c) change in the follower's behaviour (an aggressive (timid) driver becomes less aggressive (timid)). Similar observations have been reported by other researchers (Ghaffari et al, 2015, Li et al, 2018. These studies only consider the case where the follower is assumed to yield (or show courtesy) to a successful lane-changing manoeuvre, however, past research suggests that followers may try to avoid a lane-changing request by accelerating and closing down the lag gap (Hidas, 2002, Talebpour et al, 2015, Kang and Rakha, 2017, Ali et al, 2019b.…”

Section: Drivers' Responses To Lane-changingsupporting

confidence: 78%

Cooperate or not? Exploring drivers’ interactions and response times to a lane-changing request in a connected environment

Ali

Bliemer

Zheng

et al. 2020

Transportation Research Part C: Emerging Technologies

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Lane-changing is one of the complex driving tasks that depends on the number of vehicles, objectives, and lanes. A driver often needs to respond to a lane-changing request of a lane-changer, which is a function of their personality traits and the current driving conditions. A connected environment is expected to assist during the lane-changing decision-making process by increasing situational awareness of surrounding traffic through vehicle-to-vehicle communication and vehicle-to-infrastructure communication. Although lane changing decision making process in a traditional environment (an environment without driving aids) has been frequently investigated, our understanding of drivers' interactions during the lane-changing decisionmaking process in a connected environment remains elusive due to the novelty of the connected environment and the scarcity of relevant data. As such, this study examines drivers' responses to lane-changing requests in a connected environment using the CARRS-Q Advanced Driving Simulator. Seventy-eight participants responded to the lane-changing request of a lanechanger under two randomised driving conditions: baseline (traditional environment without driving aids) and connected environment (with driving aids). A segmentation-based approach is employed to extract drivers' responses to the lanechanging request and subsequently estimate their response time from trajectory data. Additionally, drivers' response times are modelled using a random parameter accelerated failure time (AFT) hazard-based duration model. Results reveal that drivers tend to be more cooperative in response to a lane-changing request in the connected environment compared with the baseline condition whereby they tend to accelerate to avoid the lane-changing request. The AFT model suggests that on average drivers' response times are shorter in the connected environment, implying that drivers respond to the lane-changing request faster in the presence of driving aids. However, at the individual level, connected environment's impact on drivers' response times is mixed as drivers' response times may increase or decrease in the connected environment compared to the baseline condition, for instance, we find that female drivers have lower response times in the connected environment than that of male drivers. Overall, this study finds that drivers in connected environment, on average, take less time to respond and appear to be more cooperative, and thus, are less likely to be engaged in safety-critical events.

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Section: Drivers' Responses To Lane-changingsupporting

confidence: 78%

Cooperate or not? Exploring drivers’ interactions and response times to a lane-changing request in a connected environment

Ali

Bliemer

Zheng

et al. 2020

Transportation Research Part C: Emerging Technologies

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“…One of these methods is to employ Moving Average Filter. 27,28 This method calculates the average of data before and after a point in time. Moving Average Filter (window size 9) is utilized in the present research.…”

Section: Hypotheses and A New Idea In The Determination Of Initial Anmentioning

confidence: 99%

A predictive model of discretionary lane change behavior considering human factors in the framework of time parameters

Pourmahmoudi

Ghaffari

Javadi

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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Lane changing is regarded as one of the most challenging behaviors of drivers. The lane-changing behaviors are divided into mandatory and discretionary. This study proposes an adaptive neuro-fuzzy model of discretionary lane-changing behavior in real traffic flow. Similar to other behaviors of drivers, lane changing is influenced by human factors, including age, gender, level of driving experience, hastiness, cautiousness, and alertness as well as environmental factors such as road and weather conditions. Identifying and measuring the said factors seem to be difficult or, in some cases, impossible. This study sorts out the lane-changing behavior into moments and two time intervals. In these time intervals, distance and relative speed, affected by the said factors, are accounted for in terms of time parameters and fed as inputs to the proposed predictive model. This is the innovative and distinguishing feature of the present study when compared to other researches. Finally, simulation and comparison based on real data indicate that when time parameters are considered and fed as inputs to model the error between the driver’s behavior and the proposed predictive model is less than when time parameter is not accounted for.

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“…The model of LC was classified as a mandatory lane change (MLC) or a discretionary lane change (DLC) [12]. MLC, it occurs when a driver is obliged to leave the lane or change the lane in order to reach his/her destination, for example, to use an off-ramp [13].…”

Section: Lane Changing Modelsmentioning

confidence: 99%

Assessment Lane Changing Behavior in Local Rural Roads

Kadhim¹,

Highways²,

Al-Jameel³

2019

JEASD

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The lane changing model is one of the most important cornerstone models to build a traffic microscopic simulation model. To build such lane changing model, there is a need to investigate the drivers' characteristics from field data. Accordingly, this study has mainly focused on lane changing behavior of Iraqi drivers. A huge amount of data has been collected from different sites in Iraq using specific type of cameras; these five sites (roads) are: Baghdad-Hilla, Baghdad-Mahmudiyah, Baghdad-Kut, Baghdad-Diyala and Najaf-Karbala roads. In addition, gun-speed has been also used to collect data of desired speeds. Different data have also been collected such as flow, headway, and speeds. The results show that as the flow increases, the frequency of lane changing also increases up to specific range of flow then as flow increases the frequency of lane changing decreases. In addition, this study indicates higher number of lane changing over all observed sites comparing with other countries. Finally, this study also indicates that the violated number of lane change varies with different levels of flow.Space with a font size 6 9 of the leading, as well as traffic density and time headway [21]. Some examples of three lanes and two lanes to the process of changing lanes are described below:A-Three lanes: From Site No.1 and Site No.2, data were collected for different hours of the day, analyzed by a time interval of 5min [18] and a distance (D v ) of 250m (This distance to calculate the number of times during which change the lane), Figure 9 shows the relationship between FLC and the total traffic flow. For three lanes, the peak-hour of traffic flow is 4272veh/hr from Site No.2 as indicated in Figure 10. According to Figure 9, it was found that as flow increases the FLC increases, too, up to 3000veh/hr, then approximately as flow increases the FLC decrease which corresponds to the relations between flow and FLC. However, the FLC (about 1700LC/hr/km) is higher than other countries (see Figure 3) which is about 1100LC/hr/km. This is due to absence pavement marking and violated LC.

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The effect of a lane change on a car-following manoeuvre: anticipation and relaxation behaviour

Cited by 20 publications

References 20 publications

Cooperate or not? Exploring drivers’ interactions and response times to a lane-changing request in a connected environment

Cooperate or not? Exploring drivers’ interactions and response times to a lane-changing request in a connected environment

A predictive model of discretionary lane change behavior considering human factors in the framework of time parameters

Assessment Lane Changing Behavior in Local Rural Roads

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