Pedestrian flow characteristics through bends: Effects of angle and desired speed

This study quantitatively described how the desired speed, which may reflect the emergency level, and the angle of bend affect the pedestrian flow by comparing fundamental diagrams derived from trajectory data collected through laboratory experiments. Results showed that the slow running (≈ 2.8 m/s speed) can increase the maximum flow through a bend by around 60 % compared to normal walking (≈ 1.4 m/s speed) regardless of the turning angle. Further, it was found that the turning angle of the bend has a stronger negative impact on the moving speed of crowds under running conditions. Compared to the turning angle, congestion level seemed to have a minor impact on the average moving speed through the bends. On the other hand, for 90° and 180° bends, the variations of the speed were observed to decrease with the increase of density which indicated that although congestion level deteriorated the flow conditions at bends, it homogenized the collective moving speed of pedestrians

Modeling Trajectories and Trajectory Variation of Turning Vehicles at Signalized Intersections

© 2013 IEEE. Information on the trajectories of turning vehicles at signalized intersections can be used in numerous applications, such as movement planning of autonomous vehicles, realistic representation of surrounding vehicle movements in driving simulator and virtual reality applications, and in microscopic simulation tools. However, no proper framework is currently available to realistically model and estimate trajectories of turning vehicles reflecting the intersection geometries, which is critical for the reliability of simulation models. This study explores the applicability of the minimum-jerk principle, which has been initially applied in neuroscience and robotics domains, to model and simulate free-flow trajectories of turning vehicles. The modeling method is validated by comparing model outputs with empirical trajectories collected at several signalized intersections in Nagoya, Japan. The capability of the model in realistically capturing the variations in turning trajectories based on intersection geometry (e.g., intersection angle and turning radius) is also explained. Further, the applicability of the modeling framework at intersections with different geometric features under different speeds and accelerations are also discussed.This work was supported in part by the Japan Society for the Promotion of Science (JSPS) under Grant 19H02261

Analysis of Pedestrian Clearance Time at Signalized Crosswalks in Japan

AbstractAt signalized crosswalks, pedestrian clearance time is a key design parameter for ensuring safe pedestrian crossing. It is generally defined as the time required by pedestrians who enter crosswalks at the end of the green indication to complete crossing before conflicting vehicular traffic movements are released. In Japan, pedestrian green indications are followed by pedestrian flashing green (PFG) indications during which time pedestrians are not allowed to start crossing and those in the crosswalk have to finish crossing to either side of the crosswalk; as such, some pedestrians are expected to return to the side they came from. Therefore, PFG intervals are designed to be shorter than the necessary clearance time. Instead, relatively longer red buffer intervals (BI) are provided between the end of the PFG and the succeeding vehicle green indication. This study clarifies the differences between signal setting concepts in various countries and analyses pedestrian clearing behaviors under the Japanese signal control system. Empirical analyses show that the current PFG and BI settings in Japan are shorter than the necessary clearance time and the settings in the US and Germany. As a result, most observed pedestrians who started crossing after the onset of PFG cannot finish crossing before its end and cannot even finish before the succeeding vehicle green indication

Modeling Traffic Flows on Urban Arterials Considering the Downstream Influence

© National Academy of Sciences: Transportation Research Board 2020. Arterials are important transportation facilities, undertaking the two functions of mobility and accessibility. In the urban area, signalized intersections along arterials are usually closely spaced and bear heavy traffic pressure. Capacities of intersections can be reduced by downstream intersections even without having spillback. This effect will be accumulated and amplified back along the traffic direction and may lead to severe congestion in the upstream intersection which can be frequently observed, especially during peak hours. However, existing traffic simulators cannot capture this phenomenon accurately because they ignore the capacity drop before spillback happens. In this study, downstream influence is quantified by a virtual optimal speed (vop). vop is the speed by which the upstream platoon reaches the endpoint of the queue ahead when the last vehicle in the downstream queue just starts. Based on that, two piecewise regression models, for start-up lost time and saturation flow rate, are formulated to estimate the capacity reduction. These regression models are further introduced to improve the modified cell transmission model (CTM). The result of the simulation experiment shows that the proposed CTM model has better performance in simulating traffic flow on signalized arterials than the existing CTM, especially in reproducing the traffic congestion in upstream. The analysis emphasized the importance of considering the downstream influence when simulating the traffic on signalized arterials. Finally, a sensitivity analysis is designed to further reveal the causes of upstream congestion

Defensive or competitive Autonomous Vehicles: Which one interacts safely and efficiently with pedestrians?

The emergence of Autonomous Vehicles (AVs) could provoke unexpected challenges in urban traffic environments. One such crucial challenge is the conflicts between pedestrians and AVs, particularly on unsignalized mid-block crosswalks (UMC), where pedestrians are exposed to the AV flow. This study investigates the efficiency and safety performance of a UMC in the presence of both AVs and pedestrians considering the diversities in their behaviors. Through empirical analyses, two pedestrians’ crossing decision models are built and four groups of speed profiles are classified. Meanwhile, based on previous literature, defensive and competitive driving strategies are assumed for AVs. The simulation is implemented on an agent-based framework that can dynamically reproduce the kinematic interactions between pedestrians and vehicles. Results indicated that with a reasonable safety margin (2.5 s), percentages of low post encroachment time events for competitive AVs with different pedestrian types are smaller than defensive AVs with differences of 0.2% to 2.9%. The average delays of competitive AVs for all pedestrian types are smaller than defensive AVs with a maximum estimated difference of 39 s. Moreover, the analysis showed that lowering the speed limit may reduce the crash rate of competitive AV up to 0%. It is also found that the pedestrians who make reckless crossing decisions and change their speed drastically during the crossing process are more likely to incur crashes with competitive AVs. Therefore, if pedestrian behaviors can be regulated reasonably, competitive AVs with appropriate parameter settings are most suitable for UMC in the future.This publication was made possible by the Qatar–Japan Research Collaboration Application Award [M-QJRC-2020-8] from Qatar University. The statements made herein are solely the responsibility of the authors. This research is supported by The Kurata Grants No. 1397 of Hitachi Global Foundation

Can automated driving prevent crashes with distracted Pedestrians? An exploration of motion planning at unsignalized Mid-block crosswalks

Pedestrian distraction may provoke severe difficulties in automated vehicle (AV) control, which may significantly affect the safety performance of AVs, especially at unsignalized mid-block crosswalks (UMCs). However, there is no available motion-planning model for AVs that considers the effect of pedestrian distraction on UMCs. This study aims to explore innovative approaches for safe and reasonable automated driving in response to distracted pedestrians with various speed profiles at UMCs. Based on two common model design concepts, two new models are established for AVs: a rule-based model that solves motion plans through a fixed calculation procedure incorporating several optimization models, and a learning-based model that replaces the deterministic optimization process with policy-gradient reinforcement learning. The developed models were assessed through simulation experiments in which pedestrian speed profiles were defined using empirical data from field surveys. The results reveal that the learning-based model has outstanding safety performance, whereas the rule-based model leads to remarkable safety problems. For distracted pedestrians with significant crossing-speed changes, rule-based AVs lead to a 5.1% probability of serious conflict and a 1.4% crash probability. The learning-based model is oversensitive to risk and always induces high braking rates, which results in unnecessary efficiency loss. To overcome this, a hybrid model based on the learning-based model was developed, which introduces a rule-based acceleration value to regularize the action space of the proposed learning-based model. The results indicate that the hybrid approach outperforms the other two models in preventing crash hazards from distracted pedestrians by employing appropriate braking behaviors. The high safety performance of the hybrid models can be attributed to the spontaneous slowing down of the vehicle that initiates before detecting pedestrians on UMCs. Although such a cautious driving pattern leads to extra delay, the time cost of the hybrid model is acceptable considering the significant improvements in ensuring pedestrian safety.This publication was made possible by the Qatar–Japan Research Collaboration Application Award [M-QJRC-2020-8] from Qatar University. The statements made herein are the sole responsibility of the authors. This research was also partially supported by Kurata Grants No. 1397 of the Hitachi Global Foundation

Interactions between autonomous vehicles and pedestrians at unsignalized mid-block crosswalks considering occlusions by opposing vehicles

Visibility can be identified as one of the critical determinants for the safety performance of autonomous vehicles (AVs) on unsignalized mid-block crosswalks (UMC), which may be significantly influenced by build-up environment and surrounding vehicles. This study investigates the safety performance when AVs interact with pedestrians approaching from far-side sidewalks to UMCs considering the visual occlusion of opposing vehicles. A mathematical model is proposed for judging the visibilities of objects from observers’ location under the impact of visual obstacles and is embedded into an agent-based pedestrian-vehicle interaction framework. Two yielding decision modules is assumed for AVs: The normal decision module implements the pedestrian priority rule simply based on the current detectable information, whereas the memory aid decision module extends AVs’ detection abilities by incorporating the memory data. Through simulation experiments, it is found that the percentages of short post encroachment time (%SPET) between AVs and far-side pedestrians reach peaks when the pedestrian flow rate is 300–400 ped/h. When opposing vehicles are in stationary queue conditions, %SPETs are only sensitive to the net distance between the last opposing vehicles in the queue and crosswalks (Dqueue). As the Dqueue decreases to lower than 15 m, %SPETs start to increase drastically. However, when opposing vehicles are in free flow conditions, %SPETs are influenced by multiple factors such as pedestrians’ crossing decisions, sizes and flow rates of opposing vehicles. Furthermore, only when opposing vehicles are in free flow conditions, memory aid AVs can significantly eliminate the impacts of opposite vehicles. Finally, several countermeasures are developed to enhance the visibility and safety at UMCs based on the findings of this study.The Qatar–Japan Research Collaboration Application Award [M-QJRC-2020-8] from Qatar University. This research is supported by The Kurata Grants No. 1397 of Hitachi Global Foundation

Human-like motion planning model for driving in signalized intersections

AbstractHighly automated and fully autonomous vehicles are much more likely to be accepted if they react in the same way as human drivers do, especially in a hybrid traffic situation, which allows autonomous vehicles and human-driven vehicles to share the same road. This paper proposes a human-like motion planning model to represent how human drivers assess environments and operate vehicles in signalized intersections. The developed model consists of a pedestrian intention detection model, gap detection model, and vehicle control model. These three submodels are individually responsible for situation assessment, decision making, and action, and also depend on each other in the process of motion planning. In addition, these submodels are constructed and learned on the basis of human drivers' data collected from real traffic environments. To verify the effectiveness of the proposed motion planning model, we compared the proposed model with actual human driver and pedestrian data. The experimental results showed that our proposed model and actual human driver behaviors are highly similar with respect to gap acceptance in intersections