Effect of Pedestrians on Capacity of Signalized Intersections

Milazzo, Joseph; Rouphail, Nagui M.; Hummer, Joseph E.; Allen, David

doi:10.3141/1646-05

Cited by 66 publications

(41 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Centralized techniques such as continuum dynamics [8,56], regression [31], route choice models [21] and markov chains [29] model the macroscopic phenomena exhibited in crowds (e.g. stadium evacuation scenarios, urban simulations etc).…”

Section: Related Workmentioning

confidence: 99%

Parallelized egocentric fields for autonomous navigation

et al. 2012

View full text Add to dashboard Cite

In this paper, we propose a general framework for local path-planning and steering that can be easily extended to perform high-level behaviors. Our framework is based on the concept of affordances: the possible ways an agent can interact with its environment. Each agent perceives the environment through a set of vector and scalar fields that are represented in the agent's local space. This egocentric property allows us to efficiently compute a local space-time plan and has better parallel scalability than a global fields approach. We then use these perception fields to compute a fitness measure for every possible action, defined as an affordance field. The action that has the optimal value in the affordance field is the agent's steering decision. We propose an extension to a linear space-time prediction model for dynamic collision avoidance and present our parallelization results on multicore systems. We analyze and evaluate our framework using a comprehensive suite of test cases provided in SteerBench and demonstrate autonomous virtual pedestrians that perform steering and path planning in unknown environments along with the emergence of high-level responses to never seen before situations. Abstract In this paper we propose a general framework for local path-planning and steering that can be easily extended to perform high-level behaviors. Our framework is based on the concept of affordances: the possible ways an agent can interact with its environment. Each agent perceives the environment through a set of vector and scalar fields that are represented in the agent's local space. This egocentric property allows us to efficiently compute a local space-time plan and has better parallel scalability than a global fields approach. We then use these perception fields to compute a fitness measure for every possible action, defined as an affordance field. The action that has the optimal value in the affordance field is the agent's steering decision. We propose an extension to a linear space-time prediction model for dynamic collision avoidance and present our parallelization results on multi-core systems. We analyze and evaluate our framework using a comprehensive suite of test cases provided in SteerBench and demonstrate autonomous virtual pedestrians that perform steering and path planning in unknown environments along with the emergence of high-level responses to never seen before situations.

show abstract

Section: Related Workmentioning

confidence: 99%

Parallelized egocentric fields for autonomous navigation

et al. 2012

View full text Add to dashboard Cite

show abstract

“…Centralized techniques [MRHA98,Lov94,Hen71] focus on the system as a whole, modeling the characteristics of the flow rather than individual pedestrians. Centralized approaches usually model a broader view of crowd behaviors as flows rather than focusing on individual specialized agent behaviors.…”

Section: Steering Approachesmentioning

confidence: 99%

Scenario Space: Characterizing Coverage, Quality, and Failure of Steering Algorithms

2016

Simulating Heterogeneous Crowd With Interactive Behaviors

View full text Add to dashboard Cite

Navigation and steering in complex dynamically changing environments is a challenging research problem, and a fundamental aspect of immersive virtual worlds. While there exist a wide variety of approaches for navigation and steering, there is no definitive solution for evaluating and analyzing steering algorithms. Evaluating a steering algorithm involves two major challenges: (a) characterizing and generating the space of possible scenarios that the algorithm must solve, and (b) defining evaluation criteria (metrics) and applying them to the solution. In this paper, we address both of these challenges. First, we characterize and analyze the complete space of steering scenarios that an agent may encounter in dynamic situations. Then, we propose the representative scenario space and a sampling method that can generate subsets of the representative space with good statistical properties. We also propose a new set of metrics and a statistically robust approach to determining the coverage and the quality of a steering algorithm in this space. We demonstrate the effectiveness of our approach on three state of the art techniques. Our results show that these methods can only solve 60% of the scenarios in the representative scenario space. Disciplines Computer Sciences | Engineering | Graphics and Human Computer InterfacesThis conference paper is available at ScholarlyCommons: http://repository.upenn.edu/hms/170

show abstract

“…This depends on the statistically established relations of the flow variables. In [20], the flow features are based on the environment components such as stairs and doors, etc.…”

Section: Route Choice and Regression Modelsmentioning

confidence: 99%

Robot-Assisted Crowd Evacuation under Emergency Situations: A Survey

Sakour

2017

Robotics

View full text Add to dashboard Cite

In the case of emergency situations, robotic systems can play a key role and save human lives in recovery and evacuation operations. To realize such a potential, we have to address many scientific and technical challenges encountered during robotic search and rescue missions. This paper reviews current state-of-the-art robotic technologies that have been deployed in the simulation of crowd evacuation, including both macroscopic and microscopic models used in simulating a crowd. Existing work on crowd simulation is analyzed and the robots used in crowd evacuation are introduced. Finally, the paper demonstrates how autonomous robots could be effectively deployed in disaster evacuation, as well as search and rescue missions.

show abstract

Effect of Pedestrians on Capacity of Signalized Intersections

Cited by 66 publications

References 0 publications

Parallelized egocentric fields for autonomous navigation

Parallelized egocentric fields for autonomous navigation

Scenario Space: Characterizing Coverage, Quality, and Failure of Steering Algorithms

Robot-Assisted Crowd Evacuation under Emergency Situations: A Survey

Contact Info

Product

Resources

About