Analysis of driving behavior at expressway toll plazas

Saad, Moatz; Abdel-Aty, Mohamed; Lee, Jae Young

doi:10.1016/j.trf.2017.12.008

Cited by 36 publications

(12 citation statements)

References 15 publications

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“…Few studies have analysed adaptations in driver behaviour capturing the impact of several explanatory factors and interdependencies between repeated observations over time for the same subject. For this purpose, recent studies have proposed linear mixed-effects models for repeated measures, which can accommodate both fixed and random effects capturing complex error structures (Peng, Boyle, and Lee 2014;Peng and Boyle 2015;Oviedo-Trespalacios et al 2017;Wang et al 2017;Geden, Staicu, and Feng 2018;Saad, Abdel-Aty, and Lee 2018;Albert 2019). Linear mixed-effects models allow to define explicitly a hierarchical structure (e.g.…”

Section: Statistical Analysis Methods For Adaptations In Driver Behavmentioning

confidence: 99%

Adaptations in driver behaviour characteristics during control transitions from full-range Adaptive Cruise Control to manual driving: an on-road study

Varotto

Farah

Bogenberger

et al. 2020

Transportmetrica A: Transport Science

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Adaptive Cruise Control (ACC) can reduce traffic congestion and accidents. In dense traffic flow conditions and when changing lanes, drivers prefer to deactivate the ACC. These control transitions between automation and manual driving could impact driver behaviour characteristics. However, few studies have analysed the magnitude and duration of these adaptations. This research aims at quantifying the adaptations in speed, acceleration, distance headway and relative speed when drivers resume manual control. We collected driver behaviour data in an on-road experiment with fullrange ACC during peak hours in Munich. We analysed these data using linear mixed-effects models to identify statistically significant changes in driver behaviour characteristics after drivers resumed manual control (transition period). The results reveal that the speed decreased significantly after the system was deactivated and it increased significantly after the system was overruled by pressing the gas pedal. These adaptations might have a substantial impact on traffic efficiency and safety.

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Section: Statistical Analysis Methods For Adaptations In Driver Behavmentioning

confidence: 99%

Adaptations in driver behaviour characteristics during control transitions from full-range Adaptive Cruise Control to manual driving: an on-road study

Varotto

Farah

Bogenberger

et al. 2020

Transportmetrica A: Transport Science

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“…Crash prediction models have been proven as a prominent tool for predicting crash frequencies and identifying the factors that affect crashes. Previous studies utilized the NB framework as a flexible approach for developing the crash prediction models (38)(39)(40)(41)(42). In this study, 54% of intersections had a zero bicycle crash count.…”

Section: Crash Prediction Modelsmentioning

confidence: 99%

“…It is also recommended to include the identified geometric factors (e.g., bike lanes, sidewalk width) in simulation studies. Traffic simulation is a vital tool for improving roadway geometry and infrastructure ( 53 – 60 ). The simulation study could help further explain the reason that the identified factors have significant effects on the occurrence of bicycle crashes at intersections.…”

Section: Conclusion and Recommendationsmentioning

confidence: 99%

Bicycle Safety Analysis at Intersections from Crowdsourced Data

Saad

Abdel-Aty

Lee

et al. 2019

Transportation Research Record

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Cycling is encouraged in countries around the world as an economic, energy efficient, and sustainable mode of transportation. Although there are many studies focusing on analyzing bicycle safety, they have limitations because of the shortage of bicycle exposure data. This study represents a major step forward in estimating safety performance functions for bicycle crashes at intersections by using crowdsourced data from STRAVA. Several adjustments in respect of the population distribution and field observations were made to overcome the disproportionate representation of the STRAVA data. The adjusted STRAVA data which include bicycle exposure information were used as input to develop safety performance functions. The functions are negative binomial models aimed at predicting frequencies of bicycle crashes at intersections. The developed model was compared with three counterparts: the model using the unadjusted STRAVA data, the model using the STRAVA data with field observation data adjustments only, and the model using the STRAVA data with adjusted population. The results revealed that the case of STRAVA data with both population and field observation data adjustments had the best performance in bicycle crash modeling. The results also addressed several key factors (e.g., signal control system, intersection size, bike lanes) which are associated with bicycle safety at intersections. Additionally, the safety-in-numbers effect was acknowledged when bicycle crash rates decreased as bicycle activities increased. The study concluded that crowdsourced data are a reliable source for exploring bicycle safety after the appropriate adjustments.

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“…e most common driver behavior is the shortest path selection, which can be applied by using spanning tree in graph theory. Some literatures use polynomial logit model [1], mixed model of random effects [2], and XCS learning algorithm [3] to study the driver's lane selection [4] and the influence of road signs [5]. It is found that both dynamic information signs and road signs can reduce unsafe driving behaviors.…”

Section: Research On Driver Behaviormentioning

confidence: 99%

Multiobjective Optimization Design of Toll Plaza

Wang

Chen

et al. 2020

Mathematical Problems in Engineering

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This paper mainly studies the optimization design of toll plaza, including the determination of the number of tollbooths and the design of the shape and size of the toll plaza. The optimization objectives are the construction cost of toll plaza, the throughput, and the accident rate. Through the analysis on the structure of toll plaza and the traffic flow, we determine the impact factors for optimization targets and select the number of tollbooths, the length of entrance and exit queue area, and the ingress and egress angles as decision variables and then build the function relationship between construction cost, accident rate, throughput, and decision variables. Then based on those functions to build the mathematical programming model, so as to get the optimal design plan.

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Analysis of driving behavior at expressway toll plazas

Cited by 36 publications

References 15 publications

Adaptations in driver behaviour characteristics during control transitions from full-range Adaptive Cruise Control to manual driving: an on-road study

Adaptations in driver behaviour characteristics during control transitions from full-range Adaptive Cruise Control to manual driving: an on-road study

Bicycle Safety Analysis at Intersections from Crowdsourced Data

Multiobjective Optimization Design of Toll Plaza

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