Evaluation of Deformation of Adjacent Buildings With Different Foundation Types Caused by Foundation Pit Excavation Under Combined Support of Isolation Pile and Diaphragm Wall

The process of excavation and unloading of a deep subway foundation pit will cause deformation of the surrounding buildings. There are significant differences in building deformation due to different methods of supporting the foundation pit and building foundation forms. This study takes the deep foundation pit project of the station as an example to investigate this difference. A three-dimensional numerical finite element model of a deep foundation pit has been established that considers different types of building foundations (independent foundation, box foundation, and pile foundation). The sensitivity of the two supporting methods of the diaphragm wall and the combined support of isolation pile and diaphragm wall regarding the maximum settlement value of the building, the horizontal inclination value, the slope angle, and the foundation angular distortions were analyzed. Finally, the sensitivity of the length of the isolated pile to the maximum settlement value and the horizontal displacement value of different types of building foundations are discussed. The results show that the combined support method of isolation piles and diaphragm walls has the highest supporting efficiency (93.5% of independent foundations and 42.3% of box foundations) for angular distortions of shallow foundation buildings. The efficiency of pile foundation support is the lowest (31.4%). For the combined support method of isolation piles and diaphragm walls, the maximum settlement value, and the value of horizontal displacement of the building will decrease with increasing the length of isolation pile. When the depth of isolation pile is greater than 24 m, the settlement deceleration rate of the independent foundation and the pile foundation slows down; when the depth of isolation pile is greater than 27 m, the settlement deceleration rate of the box foundation will slow down, and the deceleration rate of the horizontal displacement of the independent foundation and box foundation will slow down.

A distributed simulation study to investigate pedestrians’ head-turning behaviour when crossing in response to automated and human-driven vehicles displaying different braking patterns

This distributed simulator study investigated pedestrians’ head-turning behaviour during a series of road crossings in a CAVE-based pedestrian simulator. Pedestrians were required to cross the road in front of an approaching vehicle, the kinematic behaviour of which was either programmed by the simulation to depict an automated vehicle (AV) or controlled by a human driver (HD), via a connected (hidden) desktop driving simulator. A within-participant experimental design was used with twenty-five pairs of participants (a pedestrian and a driver). For each trial, pedestrians had to decide whether to cross in front of the HD/AV, which was instructed (or programmed) to yield (or not) to the pedestrian. For the AV trials, two braking patterns were included: a hard-braking AV (AVHB, deceleration rate = 3.2 m/s2, stopping distance = 12 m from pedestrian) and soft-braking (AVSB, deceleration rate = 2.5 m/s2, stopping distance = 4 m from pedestrian). Pedestrians’ head-turning frequency and the change in head-turning angle, were calculated for each condition, both before a crossing was initiated, and during the actual road crossing. Results showed a significant increase in head-turning behaviour in the last 2 seconds before a crossing initiation in the yielding trials, in line with a ‘last-second check’ reported in observations of real-world crossings (Hassan, Geruschat, & Turano, 2005). The vehicle’s braking behaviour and stopping distance were the most important factors affecting pedestrians’ head-turning patterns during the crossing, with the least head-turning behaviour seen in the AVSB condition, compared with AVHB and HDB trials. This suggests that a closer stopping distance for the AV was associated with less confusion for the pedestrian, although this condition was also associated with the longest crossing initiation time. In contrast, the highest number of head-turnings were seen for the human-driven vehicle, which, on average, yielded about 40 m away from the participants, enabling a much faster crossing initiation. Overall, the shortest crossing initiation time (~ 1 sec) and highest head-turning behaviour were seen in the non-braking conditions, where participants crossed as quickly as the circumstances allowed. These results provide new insights about the use of VR simulators for understanding pedestrians’ crossing behaviour in response to different vehicle kinematics. They also extend our knowledge of pedestrian cues for the development of suitable sensors in future automated vehicles, which should help with providing a more seamless interaction between AVs and other road users in mixed traffic settings.

AN ANALYSIS OF TYPE I DILEMMA ZONE AT SIGNALISED INTERSECTIONS

Intersections, for vehicles coming from different directions, are conflict points in road networks. When a driver approaching a signalised intersection encounters the yellow light, he/she is in a dilemma either to safely stop or to pass through the intersection during clearance time. The decision to stop or to pass may change depending on some factors such as duration of yellow light, deceleration and acceleration rate, width of intersection, speed and length of vehicle, etc. This study aims to put forth the effects of some related factors affecting the length of the Type I dilemma zone. To perform this study, five factors including vehicle speed, maximum deceleration rate, perception-reaction time, clearance time, the total intersection width-vehicle length were considered and a total of 648 different traffic cases were investigated. The study results showed that the Type I dilemma zone length increased with the increase of speed, total intersection width-vehicle length and perception-reaction time, but decreased with the increase of clearance time and deceleration rate.

Scaling of subcellular actin structures with cell length through decelerated growth

How cells tune the size of their subcellular parts to scale with cell size is a fundamental question in cell biology. Until now, most studies on the size control of organelles and other subcellular structures have focused on scaling relationships with cell volume, which can be explained by limiting pool mechanisms. Here, we uncover a distinct scaling relationship with cell length rather than volume, revealed by mathematical modeling and quantitative imaging of yeast actin cables. The extension rate of cables decelerates as they approach the rear of the cell, until cable length matches cell length. Further, the deceleration rate scales with cell length. These observations are quantitatively explained by a 'balance-point' model, which stands in contrast to the limiting pool mechanisms and that senses the linear dimensions of the cell.

Development Of Framework For In-Vehicle Rear-End Collision Warning System Considering Driver Characteristics

Rear-end collisions are one of the serious traffic safety problems. These collisions occur when the following vehicle driver is inattentive or could not judge a potential rear-end collision situation. The use of rear-end collision warning systems (RECWS) may help drivers to avoid rear-end collision. The existing systems assumed constant driver reaction time for all driver population in their design and evaluation. They also ignore variations in driver characteristics, such as age and gender. The objectives of this thesis research are: (1) to develop reaction-time models that incorporate driver characteristics, (2) to develop a car-following simulation model that represents driver behaviour, and (3) to develop a rear-end collision warning system that accounts for driver characteristics and produces reliable collision warnings. In the human-factors study, four driver reaction-time models are developed for four different car-following scenarios: lead vehicle decelerating with normal deceleration rate, lead vehicle decelerating with emergency deceleration rate, lead vehicle stationary, and car-following acceleration regime. These models describe how the driver and situational factors affect reaction-time. The driver factors include age and gender, and the situational factors include speed and spacing between the following and lead vechiles. The developed car-following model assumes that drivers adjust their speeds based on information of both the lead and the back vehicles. The model also assumes that the driver reaction-time varies based on driver characteristics and kinematics. The proposed model represents driver behaviour in acceleration, deceleration, and steady state regimes of the car-following scenarios. Another unique feature of the model is that it explicitly considers information on the back vehicle. The model is calibrated and validated using vehicle tracking database. The driver reaction-time models and other kinematics constraints were integrated to develop a driver-sensitive rear-end collision warning system algorithm (RECWA). The developed car-following model is used to evaluate and validate the performance of the proposed RECWA. The results show that the proposed RECWA is functioning and producing reliable results. With further research and development, the proposed algorithm can be integrated into driving simulators or real vehicles to further evaluate and examine its benefits.

Development Of Framework For In-Vehicle Rear-End Collision Warning System Considering Driver Characteristics

Rear-end collisions are one of the serious traffic safety problems. These collisions occur when the following vehicle driver is inattentive or could not judge a potential rear-end collision situation. The use of rear-end collision warning systems (RECWS) may help drivers to avoid rear-end collision. The existing systems assumed constant driver reaction time for all driver population in their design and evaluation. They also ignore variations in driver characteristics, such as age and gender. The objectives of this thesis research are: (1) to develop reaction-time models that incorporate driver characteristics, (2) to develop a car-following simulation model that represents driver behaviour, and (3) to develop a rear-end collision warning system that accounts for driver characteristics and produces reliable collision warnings. In the human-factors study, four driver reaction-time models are developed for four different car-following scenarios: lead vehicle decelerating with normal deceleration rate, lead vehicle decelerating with emergency deceleration rate, lead vehicle stationary, and car-following acceleration regime. These models describe how the driver and situational factors affect reaction-time. The driver factors include age and gender, and the situational factors include speed and spacing between the following and lead vechiles. The developed car-following model assumes that drivers adjust their speeds based on information of both the lead and the back vehicles. The model also assumes that the driver reaction-time varies based on driver characteristics and kinematics. The proposed model represents driver behaviour in acceleration, deceleration, and steady state regimes of the car-following scenarios. Another unique feature of the model is that it explicitly considers information on the back vehicle. The model is calibrated and validated using vehicle tracking database. The driver reaction-time models and other kinematics constraints were integrated to develop a driver-sensitive rear-end collision warning system algorithm (RECWA). The developed car-following model is used to evaluate and validate the performance of the proposed RECWA. The results show that the proposed RECWA is functioning and producing reliable results. With further research and development, the proposed algorithm can be integrated into driving simulators or real vehicles to further evaluate and examine its benefits.

Modeling Acceleration and Deceleration Rates for Two-Lane Rural Highways Using Global Positioning System Data

Several past studies developed acceleration/deceleration rate models as a function of a single explanatory variable. Most of them were spot speed studies with speeds measured at specific locations on curves (usually midpoint of the curve) and tangents to determine acceleration and deceleration rates. Fewer studies adopted an estimated value of 0.85 m/s2 for both deceleration and acceleration rates while approaching and departing curves, respectively. In this study, instrumented vehicles with a high-end GPS (global positioning system) device were used to collect the continuous speed profile data for two-lane rural highways. The speed profiles were used to locate the speeds at the beginning and end of deceleration/acceleration on the successive road geometric elements to calculate the deceleration/acceleration rate. The influence of different geometric design variables on the acceleration/deceleration rate was analysed to develop regression models. This study also inspeced the assumption of constant operating speed on the horizontal curve. The study results indicated that mean operating speeds measured at the point of curvature (PC) or point of tangency (PT), the midpoint of curve (MC), and the end of deceleration in curve were statistically different. Acceleration/deceleration rates as a function of different geometric variables improved the accuracy of models. This was evident from model validation and comparison with existing models in the literature. The results of this study highlight the significance of using continuous speed profile data to locate the beginning and end of deceleration/acceleration and considering different geometric variables to calibrate acceleration/deceleration rate models.

Evaluating Relationships between Perception-Reaction Times, Emergency Deceleration Rates, and Crash Outcomes using Naturalistic Driving Data

Perception-reaction time (PRT) and deceleration rate are two key components in geometric design of highways and streets. Combined with a design speed, they determine the minimum required stopping sight distance (SSD). Current American Association of Highway Transportation Officials (AASHTO) SSD guidance is based on 90th percentile PRT and 10th percentile deceleration rate values from experiments completed in the mid-1990s. These experiments lacked real-world distractions, and so forth. Thus, the values from these experiments may not be applicable in real-world scenarios. This research evaluated (1) differences in PRTs and deceleration rates between crash and near-crash events and (2) developed predictive models for PRT and deceleration rate that could be used for roadway design. This was accomplished using (1) genetic matching (with Rosenbaum’s sensitivity analysis) and (2) quantile regression. These methods were applied to the Strategic Highway Research Program 2 (SHRP2) Naturalistic Driving Study (NDS) data. The analysis results indicated that there were differences in PRT and deceleration rates for crash and near-crash events. The specific estimates were that, on average, drivers involved in crash events took 0.487 s longer to react and decelerated at 0.018 g’s (0.58 ft/s2) slower than drivers in equivalent near-crashes. Prediction models were developed for use in roadway design. These models were used to develop tables comparing existing SSD design criteria with SSD criteria based on the results of the predictive models. These predicted values indicated that minimum design SSD values would increase by 10.5–129.2 ft, dependent on the design speed and SSD model used.