The Experts below are selected from a list of 34848 Experts worldwide ranked by ideXlab platform
Teruo Higashino - One of the best experts on this subject based on the ideXlab platform.
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crowdmeter gauging Congestion Level in railway stations using smartphones
Pervasive and Mobile Computing, 2019Co-Authors: Moustafa Elhamshary, Moustafa Youssef, Akira Uchiyama, Hirozumi Yamaguchi, Akihito Hiromori, Teruo HigashinoAbstract:Abstract We present CrowdMeter: a participatory system that leverages the sensed data collected from users’ phones during their daily train commutes to gauge the real-time Congestion Level in railway stations. CrowdMeter tracks the passenger’s position in the station as well as identifies her/his context (e.g., waiting for a train, buying a ticket) along with her trajectory from the station’s entrance to the train. Therefrom, CrowdMeter extracts novel features, based on the user’s location and context, from the phone sensors. These features capture the passenger’s behavior (e.g., the walking pattern) and the ambient environment characteristics (e.g., the ambient sound) that can indicate the surrounding Congestion Level along the passenger’s route in a railway station. CrowdMeter utilizes the passengers’ contexts to show the Congestion Level for each area such as crowd density in passageways and the queue length of ticketing machines. Both passengers and operators can easily recognize the more and less congested areas, which helps to support proper decision making in their trips and smarter guidance of crowds. Evaluation of CrowdMeter through a field experiment in 29 different train stations in Japan shows that it can infer the Congestion Levels accurately, highlighting its promise as a ubiquitous travel-support service.
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CrowdMeter: Congestion Level Estimation in Railway Stations Using Smartphones
2018 IEEE International Conference on Pervasive Computing and Communications (PerCom), 2018Co-Authors: Moustafa Elhamshary, Moustafa Youssef, Akira Uchiyama, Hirozumi Yamaguchi, Teruo HigashinoAbstract:We present CrowdMeter: a participatory system that leverages the sensed data collected from users' phones during their daily train commutes to gauge the real-time Congestion Level in railway stations. CrowdMeter tracks the passenger's position in the station as well as identifies her context (e.g., waiting for a train, buying a ticket) along her trajectory from the station's entrance to the train. Therefrom, CrowdMeter extracts novel features, based on the user's location and context, from the phone sensors. These features capture the passenger's behavior (e.g., the walking pattern) and the ambient environment characteristics (e.g., the ambient sound) that can indicate the surrounding Congestion Level along the passenger's route in a railway station. Finally, the system highlights each area of the station with a specific color (green, amber, red) that corresponds to one of a three Congestion Levels (low, medium, high).Evaluation of CrowdMeter through a field experiment in 10 different train stations in Japan shows that it can infer the Congestion Levels accurately, highlighting its promise as a ubiquitous travel-support service.
Badri Nath - One of the best experts on this subject based on the ideXlab platform.
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accurate and energy efficient Congestion Level measurement in ad hoc networks
Wireless Communications and Networking Conference, 2005Co-Authors: Jaewon Kang, Yanyong Zhang, Badri NathAbstract:Congestion in ad hoc networks not only degrades throughput, but also wastes scarce energy due to a large number of retransmissions and packet drops. For efficient Congestion control, an accurate and timely estimation of resource demands by measuring the network Congestion Level is necessary. Congestion Level measurement in ad hoc networks is more difficult than in wired networks due to time-variant channel capacity, contention among neighboring nodes, and non-deterministic node scheduling. We propose a new Congestion detection mechanism that quantifies the Congestion Level accurately and energy-efficiently at both a node-Level (implemented at the MAC layer) and a flow-Level (implemented at the routing layer) in ad hoc networks. For accurate Congestion measurement, a set of metrics that decouple the measurement from various MAC protocol characteristics is defined. For energy-efficient Congestion measurement, an asynchronous channel loading measurement scheme, called lazy measurement, which emulates synchronous measurement by using virtual channel sampling, is incorporated into the proposed scheme. Simulation results show that the proposed mechanism significantly cuts down the energy needed to measure Congestion accurately, while maintaining the high Level of accuracy needed for timely Congestion control.
Stephen F Smith - One of the best experts on this subject based on the ideXlab platform.
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using bi directional information exchange to improve decentralized schedule driven traffic control
International Conference on Automated Planning and Scheduling, 2019Co-Authors: Stephen F SmithAbstract:Recent work in decentralized, schedule-driven traffic control has demonstrated the ability to improve the efficiency of traffic flow in complex urban road networks. In this approach, a scheduling agent is associated with each intersection. Each agent senses the traffic approaching its intersection and in real-time constructs a schedule that minimizes the cumulative wait time of vehicles approaching the intersection over the current look-ahead horizon. In order to achieve network Level coordination in a scalable manner, scheduling agents communicate only with their direct neighbors. Each time an agent generates a new intersection schedule it communicates its expected outflows to its downstream neighbors as a prediction of future demand and these outflows are appended to the downstream agent’s locally perceived demand. In this paper, we extend this basic coordination algorithm to additionally incorporate the complementary flow of information reflective of an intersection’s current Congestion Level to its upstream neighbors. We present an asynchronous decentralized algorithm for updating intersection schedules and Congestion Level estimates based on these bi-directional information flows. By relating this algorithm to the self-optimized decision making of the basic operation, we are able to approach network-wide optimality and reduce inefficiency due to strictly self-interested intersection control decisions.
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bi directional information exchange in decentralized schedule driven traffic control
Adaptive Agents and Multi-Agents Systems, 2018Co-Authors: Stephen F SmithAbstract:Recent work in decentralized, schedule-driven traffic control has demonstrated the ability to improve the efficiency of traffic flow in complex urban road networks. In this approach, each time an agent generates a new intersection schedule it communicates its expected outflows to its downstream neighbors as a prediction of future demand and these outflows are appended to the downstream agent's locally perceived demand. In this paper, we extend this basic coordination protocol to additionally incorporate the complementary flow of information reflective of an intersection's current Congestion Level to its upstream neighbors. We present an asynchronous decentralized algorithm for updating intersection schedules and Congestion Level estimates based on these bi-directional information flows. By relating this algorithm to the self-optimized decision making of the basic protocol, we are able to approach network-wide optimality and reduce inefficiency due to myopic intersection control decisions.
Moustafa Elhamshary - One of the best experts on this subject based on the ideXlab platform.
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crowdmeter gauging Congestion Level in railway stations using smartphones
Pervasive and Mobile Computing, 2019Co-Authors: Moustafa Elhamshary, Moustafa Youssef, Akira Uchiyama, Hirozumi Yamaguchi, Akihito Hiromori, Teruo HigashinoAbstract:Abstract We present CrowdMeter: a participatory system that leverages the sensed data collected from users’ phones during their daily train commutes to gauge the real-time Congestion Level in railway stations. CrowdMeter tracks the passenger’s position in the station as well as identifies her/his context (e.g., waiting for a train, buying a ticket) along with her trajectory from the station’s entrance to the train. Therefrom, CrowdMeter extracts novel features, based on the user’s location and context, from the phone sensors. These features capture the passenger’s behavior (e.g., the walking pattern) and the ambient environment characteristics (e.g., the ambient sound) that can indicate the surrounding Congestion Level along the passenger’s route in a railway station. CrowdMeter utilizes the passengers’ contexts to show the Congestion Level for each area such as crowd density in passageways and the queue length of ticketing machines. Both passengers and operators can easily recognize the more and less congested areas, which helps to support proper decision making in their trips and smarter guidance of crowds. Evaluation of CrowdMeter through a field experiment in 29 different train stations in Japan shows that it can infer the Congestion Levels accurately, highlighting its promise as a ubiquitous travel-support service.
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CrowdMeter: Congestion Level Estimation in Railway Stations Using Smartphones
2018 IEEE International Conference on Pervasive Computing and Communications (PerCom), 2018Co-Authors: Moustafa Elhamshary, Moustafa Youssef, Akira Uchiyama, Hirozumi Yamaguchi, Teruo HigashinoAbstract:We present CrowdMeter: a participatory system that leverages the sensed data collected from users' phones during their daily train commutes to gauge the real-time Congestion Level in railway stations. CrowdMeter tracks the passenger's position in the station as well as identifies her context (e.g., waiting for a train, buying a ticket) along her trajectory from the station's entrance to the train. Therefrom, CrowdMeter extracts novel features, based on the user's location and context, from the phone sensors. These features capture the passenger's behavior (e.g., the walking pattern) and the ambient environment characteristics (e.g., the ambient sound) that can indicate the surrounding Congestion Level along the passenger's route in a railway station. Finally, the system highlights each area of the station with a specific color (green, amber, red) that corresponds to one of a three Congestion Levels (low, medium, high).Evaluation of CrowdMeter through a field experiment in 10 different train stations in Japan shows that it can infer the Congestion Levels accurately, highlighting its promise as a ubiquitous travel-support service.
Katsuhiro Nishinari - One of the best experts on this subject based on the ideXlab platform.
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some considerations on crowd Congestion Level
arXiv: Physics and Society, 2020Co-Authors: Francesco Zanlungo, Katsuhiro Nishinari, Claudio Feliciani, Zeynep Yucel, Takayuki KandaAbstract:The concept of (crowd) Congestion Level ($CL$) was introduced in Feliciani et al (Transportation Research, 2018) and presented at the PED 2018 conference by C. Feliciani. Following the PED presentation, along with appreciation for the novel contribution, a few interesting questions were raised, concerning the integral/differential nature of the definition of $CL$, and the possibility of defining a related pure number. In these short notes we are going, although with no attempt at rigour or formality, to present some considerations suggesting that the two problems are related, and providing a possible solution. Furthermore, using both theoretical arguments and analysis of simulated data in complex scenarios, we will try to shed further light on the meaning and applications of this concept. Finally, we analyse some results of an experiment performed with human participants in a ``crossing-flows'' scenario.
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investigation of pedestrian evacuation scenarios through Congestion Level and crowd dang
Collective Dynamics, 2020Co-Authors: Claudio Feliciani, Katsuhiro NishinariAbstract:In this paper, we present two quantities aimed at numerically describing the Level of Congestion and the intrinsic risk of pedestrian crowds. The Congestion Level allows to assess the smoothness of pedestrian streams and recognize regions where self-organization is difficult or not possible. This measure differs from previous attempts to quantify Congestion in pedestrian crowds by employing velocities as vector entities (thus not only focusing on the absolute value). The crowd danger contains elements related to Congestion, but also includes the effect of density, consequently allowing to asses the risks intrinsically created by the dynamics of crowds. Details on the computational methods related to both quantities are described in the paper and their properties are discussed. As a practical application, both measures are used to investigate supervised experiments where evacuation (or similar conditions) are considered. Results for small room sizes and limited number of pedestrians show that the crowd danger distribution over the space in front of the exit door has similar patterns to typical quantities used in the frame of pedestrian dynamics (density and flow) and symmetrical shapes are obtained. However, when larger scenarios are considered, then Congestion map and crowd danger become unrelated from density and/or flow, showing that both quantities express different aspects of pedestrian motion.
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taming macroscopic jamming in transportation networks
Journal of Statistical Mechanics: Theory and Experiment, 2015Co-Authors: Takahiro Ezaki, Ryosuke Nishi, Katsuhiro NishinariAbstract:In transportation networks, a spontaneous jamming transition is often observed, e.g. in urban road networks and airport networks. Because of this instability, flow distribution is significantly imbalanced on a macroscopic Level. To mitigate the Congestion, we consider a simple control method, in which congested nodes are closed temporarily, and investigate how it influences the overall system. Depending on the timing of the node closure and opening, and Congestion Level of a network, the system displays three different phases: free-flow phase, controlled phase, and deadlock phase. We show that when the system is in the controlled phase, the average flow is significantly improved, whereas when in the deadlock phase, the flow drops to zero. We study how the control method increases the network flow and obtain their transition boundary analytically.