Subway train stop detection using magnetometer sensing data
01 Oct 2014-pp 766-769
TL;DR: The method detects the arrival of a train based on the magnetometer sensing data, identifies the arrived stop and computes the time differences between the schedule and the actual, and shows over 90% accurate results.
Abstract: The schedule of subway provides information on the arrival and departure of a train at stations. The subway passengers often refer to the subway schedule to make an appointment or to make their own schedule. Many apps on subway schedule are available for smartphone users. For example, in Korea, there are more than 10 apps that users can download to get the schedule of subway. However, most of the apps provide the schedule of subway based on fixed time table. As a result, there is no way to inform the differences between the time on the time table and the actual arrival time of a subway train. In this paper, we propose a method to provide correct information on the schedule of subway. Detecting the arrival of a train is required, and the difference between the time on time table and the actual arrival time of the train should be known. The method detects the arrival of a train based on the magnetometer sensing data. Then it identifies the arrived stop and computes the time differences between the schedule and the actual. The identified time differences are reflected to the time table to inform more correct schedule to the rest of the users. When we tested the detection accuracy of a subway train using the proposed technique at Seoul subway line, it showed over 90% accurate results. This indicates that the proposed method can be widely accepted to the subway schedule app developers.
Citations
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07 Sep 2015
TL;DR: It is shown that StationSense can identify periods of train stops with accuracy of 81%, which is almost 2 times higher than the existing accelerometer-based solutions.
Abstract: In this paper we develop StationSense, a novel mobile sensing solution for precisely tracking temporal stop-and-go patterns of railway passengers. While such motion context serves as a promising enabler of various traveler support systems, we found through experiments in a major railway network in Japan that existing accelerometer-based passenger tracking systems can poorly work in modern trains, where jolts during motion have been dramatically reduced. Towards robust motion tracking, StationSense harnesses characteristic features in ambient magnetic fields in trains to find candidates of stationary periods, and subsequently filters out false positive detections by a tailored acceleration fusion mechanism. Then it finds optimal boundaries between adjacent moving/stationary periods, employing unique signatures in accelerometer readings. Through field experiments around 16 railway lines, we show that StationSense can identify periods of train stops with accuracy of 81%, which is almost 2 times higher than the existing accelerometer-based solutions.
17 citations
Additional excerpts
...[4] suggest possibility of employing magnetometers for train stop detection....
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TL;DR: A smartphone-based interchange time measuring method from the passengers’ perspective that leverages low-power sensors embedded in modern smartphones to record ambient contextual features, and utilizes a two-tier classifier to infer interchange states during a metro trip, and further distinguishes 10 fine-grained cases during interchanges.
Abstract: High variability interchange times often significantly affect the reliability of metro travels. Fine-grained measurements of interchange times during metro transfers can provide valuable insights on the crowdedness of stations, usage of station facilities and efficiency of metro lines. Measuring interchange times in metro systems is challenging since agent-operated systems like automatic fare collection systems only provide coarse-grained trip information and popular localization services like GPS are often inaccessible underground. In this paper, we propose a smartphone-based interchange time measuring method from the passengers’ perspective. It leverages low-power sensors embedded in modern smartphones to record ambient contextual features, and utilizes a two-tier classifier to infer interchange states during a metro trip, and further distinguishes 10 fine-grained cases during interchanges. Experimental results within 6 months across over 14 subway lines in 3 major cities demonstrate that our approach yields an overall interchange state inference F1-measurement of 91.0% and an average time error of less than 2 min at an inference interval of 20 s, and an average accuracy of 89.3% to distinguish the 10 fine-grained interchange cases. We also conducted a series of case studies using measurements collected from crowdsourced users during 3 months, which reveals findings previously unattainable without fine-grained interchange time measurements, such as portions of waiting time during interchange, interchange directions, usage of facilities (stairs/escalators/lifts), and the root causes of long interchange times.
14 citations
Cites background from "Subway train stop detection using m..."
...Unlike previous works (Yu et al., 2014; Lee and Han, 2014; Higuchi et al., 2015; Thiagarajan et al., 2010; Stockx et al., 2014) that track the Stop and Running of a single metro, we focus on detecting the Interchange of passengers during a metro trip....
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...These low-cost sensors are also utilized to robustly track stops and runs of metros (Yu et al., 2014; Lee and Han, 2014; Higuchi et al., 2015; Stockx et al., 2014)....
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...During operation, significant fluctuations of the current generate strong magnetic fields, which in turn create a considerable torque to push the metro forward (Lee and Han, 2014)....
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...Lee and Han (2014) exploited ambient magnetic fields in metros to differentiate their motion states....
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28 Nov 2016
TL;DR: This work proposes MetroEye, an intelligent smartphone-based tracking system for metro passengers underground that leverages low-power sensors embedded in modern smartphones to record ambient contextual features, and infers the state of passengers during an entire metro trip using a Conditional Random Field (CRF) model.
Abstract: Metro has become the first choice of traveling for tourists and citizens in metropolis due to its efficiency and convenience. Yet passengers have to rely on metro broadcasts to know their locations because popular localization services (e.g. GPS and wireless localization technologies) are often inaccessible underground. To this end, we propose MetroEye, an intelligent smartphone-based tracking system for metro passengers underground. MetroEye leverages low-power sensors embedded in modern smartphones to record ambient contextual features, and infers the state of passengers (Stop, Running, and Interchange) during an entire metro trip using a Conditional Random Field (CRF) model. MetroEye further provides arrival alarm services based on individual passenger state, and aggregates crowdsourced interchange durations to guide passengers for intelligent metro trip planning. Experimental results within 6 months across over 14 subway trains in 3 major cities demonstrate that MetroEye yields an overall accuracy of 80.5% outperforming the state-of-the-art.
10 citations
Cites background from "Subway train stop detection using m..."
...In MetroEye, we further extend the use of acceleration to recognize passengers during interchanges, where a passenger either walks towards a platform or waits for a coming metro....
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...CCS Concepts •Human-centered computing → Ubiquitous and mobile computing systems and tools; Keywords underground public transport; location-based service; smartphone; crowdsourcing...
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...It helps users to plan routes and guides metro officials for traffic management....
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Patent•
11 Sep 2015TL;DR: In this paper, a sampling device can measure RF signals detected at a train station of a transit system or a route of the transit system, and a location server can filter RF signal measurements using one or more readings from sensors coupled to the sampling device and that are different from RF receivers.
Abstract: Methods, program products, and systems for using multiple sensors to determine a location fingerprint are described. A sampling device can measure RF signals detected at a train station of a transit system or a route of the transit system. The sampling device, or a location server receiving the measurements, can filter RF signal measurements using one or more readings from sensors coupled to the sampling device and that are different from RF receivers. The readings can be taken concurrently with the RF signal measurements. These readings, designated as motion cues, can include motion sensor readings, barometer readings, or magnetometer readings. Using the motion cues, the sampling device or location server can distinguish different platforms of a station of the transit system and different levels of the station, or filter out RF signal measurements that may have been inaccurate, e.g., as caused by disturbances from a train entering or leaving a station.
10 citations
TL;DR: This paper presents a novel yet practical idea to track passengers in realtime using the smartphone accelerometer and a training database of the entire London underground network, arguing that London tubes are self-driving transports with predictable accelerations, decelerations, and travelling time.
Abstract: Passengers travelling on the London underground tubes currently have no means of knowing their whereabouts between stations. The challenge for providing such service is that the London underground tunnels have no GPS, Wi-Fi, Bluetooth, or any kind of terrestrial signals to leverage. This paper presents a novel yet practical idea to track passengers in realtime using the smartphone accelerometer and a training database of the entire London underground network. Our rationales are that London tubes are self-driving transports with predictable accelerations, decelerations, and travelling time and that they always travel on the same fixed rail lines between stations with distinctive bumps and vibrations, which permit us to generate an accelerometer map of the tubes' movements on each line. Given the passenger's accelerometer data, we identify in realtime what line they are travelling on and what station they depart from, using a pattern-matching algorithm, with an accuracy of up to about 90% when the sampling length is equivalent to at least 3 station stops. We incorporate Principal Component Analysis to perform inertial tracking of passengers' positions along the line when trains break away from scheduled movements during rush hours. Our proposal was painstakingly assessed on the entire London underground, covering approximately 940 km of travelling distance, spanning across 381 stations on 11 different lines.
6 citations
References
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28 Jun 2011
TL;DR: An indoor positioning system that measures location using disturbances of the Earth's magnetic field caused by structural steel elements in a building that demonstrates accuracy within 1 meter 88% of the time in experiments in two buildings and across multiple floors within the buildings.
Abstract: We present an indoor positioning system that measures location using disturbances of the Earth's magnetic field caused by structural steel elements in a building. The presence of these large steel members warps the geomagnetic field in a way that is spatially varying but temporally stable. To localize, we measure the magnetic field using an array of e-compasses and compare the measurement with a previously obtained magnetic map. We demonstrate accuracy within 1 meter 88% of the time in experiments in two buildings and across multiple floors within the buildings. We discuss several constraint techniques that can maintain accuracy as the sample space increases.
464 citations
"Subway train stop detection using m..." refers background in this paper
...By using these geomagnetic features various researches are performed such as indoor positioning [5][6][7]....
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TL;DR: An analytical formulation is provided to calculate the optimal speed profile with fixed trip time for each section and the algorithm is fast enough to be used in the automatic train operation (ATO) system for real-time control.
Abstract: Given rising energy prices and environmental concerns, train energy-efficient operation techniques are paid more attention as one of the effective methods to reduce operation costs and energy consumption. Generally speaking, the energy-efficient operation technique includes two levels, which optimize the timetable and the speed profiles among successive stations, respectively. To achieve better performance, this paper proposes to optimize the integrated timetable, which includes both the timetable and the speed profiles. First, we provide an analytical formulation to calculate the optimal speed profile with fixed trip time for each section. Second, we design a numerical algorithm to distribute the total trip time among different sections and prove the optimality of the distribution algorithm. Furthermore, we extend the algorithm to generate the integrated timetable. Finally, we present some numerical examples based on the operation data from the Beijing Yizhuang subway line. The simulation results show that energy reduction for the entire route is 14.5%. The computation time for finding the optimal solution is 0.15 s, which implies that the algorithm is fast enough to be used in the automatic train operation (ATO) system for real-time control.
295 citations
"Subway train stop detection using m..." refers background in this paper
...Train timetable optimization model exploits determine arrival and departure times for trains at stations so that the resources can be effectively operated [2][3]....
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TL;DR: This paper proposes a timetable optimization model to increase the utilization of regenerative energy and, simultaneously, to shorten the passenger waiting time, and formulates a two-objective integer programming model with headway time and dwell time control.
Abstract: The train timetable optimization problem in subway systems is to determine arrival and departure times for trains at stations so that the resources can be effectively utilized and the trains can be efficiently operated. Because the energy saving and the service quality are paid more attention, this paper proposes a timetable optimization model to increase the utilization of regenerative energy and, simultaneously, to shorten the passenger waiting time. First, we formulate a two-objective integer programming model with headway time and dwell time control. Second, we design a genetic algorithm with binary encoding to find the optimal solution. Finally, we conduct numerical examples based on the operation data from the Beijing Yizhuang subway line of China. The results illustrate that the proposed model can save energy by 8.86% and reduce passenger waiting time by 3.22% in comparison with the current timetable.
157 citations
"Subway train stop detection using m..." refers background in this paper
...Train timetable optimization model exploits determine arrival and departure times for trains at stations so that the resources can be effectively operated [2][3]....
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14 Oct 2010
TL;DR: The proposed approach provides a simple, low-cost, and space-efficient solution for solving the SLAM problem present in many domestic and swarm robotics application domains.
Abstract: In this paper we propose a simultaneous localization and mapping (SLAM) method that utilizes local anomalies of the ambient magnetic field present in many indoor environments. We use a Rao-Blackwellized particle filter to estimate the pose distribution of the robot and Gaussian Process regression to model the magnetic field map. The feasibility of the proposed approach is validated by real world experiments, which demonstrate that the approach produces geometrically consistent maps using only odometric data and measurements obtained from the ambient magnetic field. The proposed approach provides a simple, low-cost, and space-efficient solution for solving the SLAM problem present in many domestic and swarm robotics application domains.
79 citations
"Subway train stop detection using m..." refers background in this paper
...By using these geomagnetic features various researches are performed such as indoor positioning [5][6][7]....
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TL;DR: A novel method to reduce the recalibration costs of a radio map by automatically updating the radio map using the appearance frequencies of access points detected from user feedback data is presented.
Abstract: This paper presents a novel method to reduce the recalibration costs of a radio map by automatically updating the radio map. The appearance frequencies of access points (APs) detected from user feedback data are mainly used for the update. The proposed method appeared superior to previous methods, especially in its ability to update newly installed APs in the radio map. According to the experiment conducted for the radio map of 233 Seoul subway stops, the proposed method was effective for updating APs with weak as well as strong signal strengths.
56 citations