Driver Behavior on Freeway Entrance Ramp Terminals

Alyamani, Mohannad; Hassan, Yasser

doi:10.1061/jtepbs.0000606

Cited by 8 publications

(12 citation statements)

References 14 publications

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“…(Jouzy et al, 1963;Polus et al, 1985;Torbic et al, 2012a) Most observed drivers tended to merge soon and at low speeds at ramps with parallel-type design. (Alyamani et al, 2021) Most observed drivers tended to merge onto freeway though lane on the taper after the SCL had ended at ramps with taper-type design. (Jouzy et al, 1963;Torbic et al, 2012a) Most observed drivers tended to use the full length of the taper-type lane (where the SCL width is around 3.60 m) before merging.…”

Section: Referencementioning

confidence: 95%

“…More recently, Alyamani et al (2021) examined speeds and behaviour of merging vehicles as they moved along the on-ramp and acceleration SCL using SHRP-2 Naturalistic Driving Study (NDS) database from two different US states. Alyamani et al (2021) noted that many observed drivers tended to merge onto the freeway right lane on the taper section after the SCL had ended, particularly at ramp terminals with a taper-type SCL design. It was also noted that drivers tended to continue their acceleration for some distance on the freeway mainline lanes.…”

Section: Driver Behaviour At Freeway Entrance Ramp Areasmentioning

confidence: 99%

“…(Jouzy et al, 1963;Torbic et al, 2012a) Most observed drivers tended to use the full length of the taper-type lane (where the SCL width is around 3.60 m) before merging. (Alyamani et al, 2021;Dabbour et al, 2021) A portion of merging drivers tended to continue their acceleration on the freeway mainline after merging. (Hunter et al, 2001) Observed drivers tended to start the acceleration and merging process only after having a clear view of the freeway right-lane traffic.…”

Section: Referencementioning

confidence: 99%

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Examining Driver Behaviour at Freeway Ramp Terminals Based on Trajectory Data Collected Using Unmanned Aerial Vehicles and Video Image Processing

Alamry¹

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This research utilizes Unmanned Aerial Vehicles (UAVs) to examine real-life speeds and behaviour of drivers at freeway entrance and exit ramp terminals. To achieve this goal, a total of 1,127 minutes of high-resolution aerial video data were collected for traffic movements at thirteen single-lane ramp terminals (seven entrances and six exits) using single and multiple UAVs. A complete space-time trajectory was extracted for each vehicle as it moved on the freeway right lane (FRL) or speed-change lane (SCL) and ramp using a combination of computer vision and deep learning tools. The trajectories were processed to extract relevant driver-vehicle behaviour measures (e.g., merging/diverging location, merging/diverging speed, acceleration/deceleration distances, SCL utilization rates, and accepted merging gaps). A descriptive analysis was performed for better understanding of driver behaviour over the entire stretch of the freeway ramp terminal segment, including FRL, SCL, and ramp. The trends of driver behaviour measures and their relationships with the SCL and ramp geometric characteristics were investigated under free-flow and platoon conditions. Results of the descriptive analysis highlighted differences between taper and parallel SCLs in terms of merging/diverging location, merging/diverging speed, and SCL utilization. Observations of data also confirmed the importance of accounting for the effects of ramp controlling features on the behaviour and vehicle acceleration needs, especially at exit ramps.Several statistical models were developed using regression analysis to model drivers' behaviour measures on SCLs and ramps. Moreover, a set of the observed merging accepted gap data were fitted to the models proposed in the literature to check which models provide the best fit. Results revealed that the models developed using trip data from SHRP-2 Naturalistic Driving Study (NDS) database relatively fitted the data better than other models in the literature. This finding is significant in validating the transferability of models developed using the NDS to other study areas iii in North America. Finally, the research concluded with a demonstration of the practical application of the developed regression models in reliability analysis, considering actual drivers' behaviour and speeds on SCLs and ramps.Professor Yasser Hassan for his advice, support, and guidance during my research and study at Carleton University. His priceless advice guided me at various stages of my academic life. Without his continued assistance, support, and encouragement, this work would not have been possible.Thirdly, I would like to express my sincere gratitude to Milton Carrasco, co-founder and CEO of Transoft Solutions, for his unstinting help and support and for providing access to Transoft Solutions platform to process the UAV videos. I would also like to extend my sincere gratitude to Transoft Solutions team for their help in processing the UAV videos. Finally, I would like to acknowledge the financial support given for this thesis by Taibah...

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Section: Referencementioning

confidence: 95%

Section: Driver Behaviour At Freeway Entrance Ramp Areasmentioning

confidence: 99%

Section: Referencementioning

confidence: 99%

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Examining Driver Behaviour at Freeway Ramp Terminals Based on Trajectory Data Collected Using Unmanned Aerial Vehicles and Video Image Processing

Alamry¹

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“…Alyamani and Hassan (2021) developed distinct sets of models to predict lead, lag, and total (distance between lead and lag vehicles) accepted gaps for merging manoeuvres using data from various SCLs. This research used equations (6.1), (6.2), and (6.3) by Alyamani and Hassan (2021) to model merging gap acceptance because they were made with data from the same study area. vehicles generated in each model run.…”

Section: Gap Acceptance Modelmentioning

confidence: 99%

Reliability Analysis of Freeway Speed Change Lanes Using Naturalistic Driving Study Data

Alyamani¹

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This research investigates and models drivers' behaviour on freeway entrance and exit ramp terminals using SHRP-2 Naturalistic Driving Study (NDS) data from 24 sites located in three different US states. The research included first a qualitative assessment of the driver speed behaviour as they navigate the ramp and acceleration/deceleration speed change lane (SCL). A trend of continuous vehicle acceleration was evident from the beginning of the ramp controlling curve that continued after merging onto the freeway right lane (FRL). A similar finding was noted at exit ramps but with deceleration behaviour from the beginning of taper-type SCL that continued after diverging onto the ramp controlling curve. It was also found that at entrance ramps, a portion of the drivers tends to merge onto the FRL on the taper after the SCL had ended on acceleration.At exit ramps, a portion of the observed drivers tends to diverge from the FRL on the taper before the beginning of the deceleration SCL, with this behaviour being dominant on the taper-type SCL.Statistical tests showed that the driver behaviour measures in general follow a normal distribution at each site. Tests of hypothesis examined the differences of different driver behaviour measures on the study sites, and it was shown that the speed and acceleration/deceleration behaviour of the drivers differs on the different sites indicating that it depends on the complete set of geometric characteristics at the site. Linear-mixed models were developed for driver behaviour parameters such as the speed, acceleration, and deceleration, to account for the repeated measures caused by repeated trips by the same drivers in the dataset. Also, the research involved conducting a reliability analysis to evaluate the performance of acceleration SCLs, taking into consideration essential factors such as driver speeds and behavior, SCL and ramp geometric design, and traffic conditions. 𝑉𝑉 𝐵𝐵𝐵𝐵𝐵𝐵𝑉𝑉 𝑀𝑀𝐵𝐵𝐵𝐵 𝑉𝑉 𝐸𝐸𝐵𝐵𝐵𝐵 𝑉𝑉 𝑀𝑀 𝐿𝐿 𝐴𝐴𝐷𝐷𝐴𝐴,𝑁𝑁 𝑎𝑎 All Data 0.000 a 0.000 a 0.000 a 0.000 a 0.000 a 0.000 a Off-Peak 0.000 a 0.000 a 0.000 a 0.008 0.000 a 0.000 a On-Peak 0.360 b 0.024 0.000 a 0.008 0.000 a 0.000 a 88.50 speed limit 0.000 a 0.000 a 0.000 a 0.000 a 0.000 a 0.000 a Taper-type SCL 0.015 0.108 b 0.001 a 0.784 b 0.000 a 0.000 a Parallel-type SCL 0.000 a 0.000 a 0.000 a 0.000 a 0.000 a 0.000 a Equality of MeanAll Data 0.000 a 0.000 a 0.000 a 0.000 a 0.000 a 0.000 a Off-Peak 0.000 a 0.000 a 0.000 a 0.000 a 0.000 a 0.000 a On-Peak 0.000 a 0.000 a 0.000 a 0.000 a 0.000 a 0.000 a 88.50 speed limit 0.000 a 0.000 a 0.000 a 0.000 a 0.000 a 0.000 a Taper-type SCL 0.011 0.000 a 0.000 a 0.967 b 0.000 a 0.000 a Parallel-type SCL 0.000 a 0.000 a 0.000 a 0.000 a 0.000 a 0.000 a

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“…Models are already available in the literature to estimate the minimum acceptable gap (𝐺 𝐴,𝑚𝑖𝑛 ) for DVs. Based on data collected from different SCLs, Fatema et al (2015) and Alyamani and Hassan (2021) provided different sets of models to predict lead, lag, and total (distance between lead and lag vehicles) accepted gaps for merging manoeuvres. In this chapter, Equations (4.2) and ( 4.3) by Fatema et al (2015) were selected to model merging gap acceptance as they were developed using data at the same study area and were used in this simulation to estimate the minimum lag and lead acceptable gaps for DVs:…”

Section: Gap Acceptance Modelmentioning

confidence: 99%

Accommodation of Freeway Merging in Environment of Mixed Vehicle Technologies

Pakzadnia¹

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The connected autonomous vehicle (CAV) is the most significant breakthrough in the automobile industry since the wide adoption of automobiles as a mode of transportation. These vehicles can respond faster, drive more precisely within their lane, and keep gaps shorter than driver-operated vehicles (DVs). Therefore, they can improve traffic operations and reduce collisions resulting from human errors. However, shorter gaps on the freeway right lane (FRL) can create difficulty for on-ramp vehicles to merge onto the freeway. In addition, there is a transition period from all DVs to all CAVs. As a result, there would be a mixed traffic environment including DVs and CAVs, which might necessitate different merging management strategies.This study proposes eight possible solutions to address the merging problems by providing acceptable gaps or resolving the conflicts between merging and mainline vehicles in a mixed traffic environment. These strategies are then evaluated based on different measures to determine which merging solution is most effective for each traffic condition. Average travel times and the capacity drop are used as traffic performance measures, while safety measures include the percentage of vehicles with low merging speed (VLMS) and probability of non-compliance (PNC) of merging manoeuvre.The behaviour of CAVs and performance merging strategies are modelled using Vissim v2020, internal programing, and a MATLAB program. The simulation results indicate that when the CAV penetration rate is between 0% and 100%, most proposed strategies outperform the base condition of do-nothing. The traffic performance changes depending on the CAV penetration rate and the type of traffic management strategy.iiiThe results of the safety measure for most strategies based on the VLMS index indicate that at low traffic volumes, increasing CAV penetration rate reduces VLMS, thereby improving safety. At high traffic volume, some strategies such as dissolving platoons and ramp metering show better performance. For the PNC measure, most strategies have lower PNC and better expected safety performance than the base strategy.Furthermore, increasing the CAV penetration rate reduces PNC. Finally, a regression analysis between PNC and the existing collision database demonstrated a correlation between PNC and collision frequency.

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Driver Behavior on Freeway Entrance Ramp Terminals

Cited by 8 publications

References 14 publications

Examining Driver Behaviour at Freeway Ramp Terminals Based on Trajectory Data Collected Using Unmanned Aerial Vehicles and Video Image Processing

Examining Driver Behaviour at Freeway Ramp Terminals Based on Trajectory Data Collected Using Unmanned Aerial Vehicles and Video Image Processing

Reliability Analysis of Freeway Speed Change Lanes Using Naturalistic Driving Study Data

Accommodation of Freeway Merging in Environment of Mixed Vehicle Technologies

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