Impact of Driver and Surrounding Traffic on Vehicle Deceleration Behavior at Onset of Yellow Indication

El-Shawarby, Ihab; Rakha, Hesham A.; Amer, Ahmed; McGhee, Catherine C

doi:10.3141/2248-02

Cited by 18 publications

(8 citation statements)

References 6 publications

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“…At VTTI, Amer et al (11) and Li et al (12) conducted controlled field data collection efforts to model passenger car driver PRT and deceleration behavior at the onset of yellow indications as a function of various driver, vehicle, and traffic stream characteristics under different weather and roadway conditions. A comparison of driver PRT, deceleration levels, and driver stop-go behavior demonstrated that the results obtained from the controlled test environment were consistent with other empirical field observations and thus were valid for model development (14)(15)(16).…”

Section: Experimental Designsupporting

confidence: 79%

Design of Traffic Signal Clearance Intervals to Consider Impacts of Buses

Ong

Rakha

El-Shawarby

2015

Transportation Research Record: Journal of the Transportation R

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Traffic signal design procedures use only light-duty vehicles in the design of yellow and clearance intervals. For quantification of the impact of buses on yellow and clearance times, a controlled field experiment was conducted. In the study, 864 stop and run records were collected for a 56 km/h (35 mph) approach speed; participant bus drivers encountered a yellow indication initiation at different distances from the intersection. Participant drivers were selected randomly from three age groups (i.e., younger than 40 years old, 40 to 64 years old, and 65 years or older), and an equal number of male and female participants was included in each age group. Data collected were used to generate two linear mixed models: one for bus driver perception–reaction times and one for deceleration levels. Both models considered roadway surface and environmental parameters, driver attributes (i.e., age and gender), roadway grade, approaching speed, and time and distance to the intersection at the onset of yellow. The proposed models were used to perform a Monte Carlo simulation to generate yellow timing and all-red durations. Lookup tables were generated for the proposed yellow timing duration for different approach speeds, roadway grades, confidence intervals, precipitation, and percentage of buses in the traffic stream to provide practical guidelines for the design of traffic signal clearance times.

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Section: Experimental Designsupporting

confidence: 79%

Design of Traffic Signal Clearance Intervals to Consider Impacts of Buses

Ong

Rakha

El-Shawarby

2015

Transportation Research Record: Journal of the Transportation R

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“…The second test was executed at a single instructed speed of 72 km/h with no leading or following vehicles because the previous study did not find the lead or following vehicle to have a statistically significant impact on driver decisions to stop or go decisions, PRTs, and deceleration levels. A comparison of driver PRT, deceleration levels, and driver stop or go behavior demonstrated that the results obtained from the controlled test environment were consistent with other empirical field observations and thus are valid for model development (13)(14)(15)(16).…”

Section: Light-duty Vehicle Field Studysupporting

confidence: 79%

Designing Traffic Signal Yellow and Change Intervals considering Truck Impacts

Rakha

Baird

El-Shawarby

2014

Transportation Research Record: Journal of the Transportation R

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This research quantifies the effect of heavy vehicles on the design of yellow and all-red intervals. With empirical data from previous studies and truck performance characteristics, models are developed to simulate driver perception–reaction times and truck deceleration levels at signalized intersections. These models consider roadway surface and weather conditions (dry, fog, or rain); driver attributes (age and gender); roadway grade; approach speed; and time and distance to the intersection at the onset of the yellow indication. An agent-based approach is used to develop driver- and vehicle-specific yellow times. The yellow timings for light-duty and heavy-duty vehicles are then combined and consider different percentages of heavy vehicles in the traffic stream. Lookup tables are developed for grades, precipitation types and levels, and percentages of trucks to provide practical guidelines for the design of yellow and all-red signal timings. In general, the research demonstrates that adding trucks to the traffic stream increases yellow times by 2% to 20%, depending on approach speed, grade, and weather conditions, with the most significant increases occurring on dry roadways with high positive gradients at lower speeds. In absolute terms, the yellow time is increased from approximately 0.1 to 0.6 s when trucks constitute 5% of the traffic stream to 0.6 to 1.4 s when trucks are 30% of the traffic stream. The study also demonstrates a need to increase the all-red time by up to 0.5 s when trucks comprise 30% of the traffic stream.

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“…For example, in a controlled field test setting, El-Shawarby et al (2011) introduced other road users in the scene. Occasionally, a lead or a following vehicle appeared.…”

Section: Introductionmentioning

confidence: 99%

Driving behaviour at traffic light intersections

Rittger¹,

Schmidt²,

Maag

et al. 2015

Cogn Tech Work

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To develop a driver assistance system with the goal to increase driving efficiency, we aimed at understanding unassisted driving behaviour. With this knowledge, we will then be able to estimate the potential of the assistance system to support drivers in avoiding unnecessary deceleration and acceleration when approaching traffic lights and to estimate the amount of influence the driver assistance system could have on normal driving. Efficient driving was defined as driving behaviour that leads to reduced fuel consumption and emissions. In a driving simulator experiment with twelve participants and a within-subjects design, drivers approached intersections while the traffic light was either solid green or solid red, or changed from red to green or from green to red during the approach. In addition, we varied whether there was a lead vehicle present and manipulated visibility through the presence or absence of fog. Driving speed, acceleration and pedal usage were analysed and interpreted due to their relation with fuel consumptions and emissions, which is well known from the literature. Participants avoided strong accelerations and decelerations when approaching a solid green traffic light compared to a changing red to green traffic light. Speed was reduced earlier, when the traffic light was solid red compared to when the traffic light changed from green to red. Higher visibility in the non-fog conditions compared to the fog condition was only an advantage in terms of more efficient driving behaviour when the traffic light phase did not change during the approach. The potential for improvements in driving efficiency was higher when drivers were in free driving compared to when following a lead vehicle. We propose that approaching traffic light intersections takes place in three phases: an orientation, a preparation and a realisation phase. A driver assistance system is expected to improve drivers' anticipation of the driving scene and could recommend efficient driving behaviour in all three phases.

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Impact of Driver and Surrounding Traffic on Vehicle Deceleration Behavior at Onset of Yellow Indication

Cited by 18 publications

References 6 publications

Design of Traffic Signal Clearance Intervals to Consider Impacts of Buses

Design of Traffic Signal Clearance Intervals to Consider Impacts of Buses

Designing Traffic Signal Yellow and Change Intervals considering Truck Impacts

Driving behaviour at traffic light intersections

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