Jumping from A Moving Train: Risk, Migration and Rights at NAFTA's Southern Border

2008 ◽  
Vol 30 (2) ◽  
pp. 32-36 ◽  
Author(s):  
Christine Kovic
Keyword(s):  
United States ◽  
The United States ◽  
The Other ◽  
Central American ◽  
Severe Injury ◽  
Freight Train ◽  
Railway Tracks ◽  
Southern Border ◽  
Train Tracks ◽  
Moving Train

July 2007. Hundreds of Central American migrants were camped along the railway tracks in Arriaga, Chiapas waiting to for the freight train to leave. Some were eating, perhaps their last food for days, others had bottles of water tied across their shoulders, some attempted to rest under the train cars to escape the hot sun. One young man brushed his teeth under the trees, using the water he carried in a recycled coca-cola bottle, to prepare himself for the journey ahead. Arriaga, a town of 25,000 people, is split in half by the train tracks. The town's tiny plaza, with a small playground, fondas (eateries), and a railway museum, sits on one side of the tracks. The town's church and market lie on the other. These Central American migrants in Arriaga, some 150 miles from Mexico's southern border with Guatemala, were eager to jump the freight train to continue their journey north to the United States. The train had not left Arriaga for a full week and many were desperate as they felt trapped. Their preparations underscored the dangers and harshness of the trip. They would have to hold on to the train for hours and days at a time, riding on ladders and the roofs of tank cars. Those who fall asleep and lose their grip risk death or severe injury, such as dismemberment.

Train tracks and zipping sequences for pseudo-Anosov braids

1998 ◽  
Vol 9 (4-5) ◽  
pp. 793-809 ◽  
Author(s):  
Robert D. Kleinberg ◽  
William W. Menasco
Keyword(s):  
Train Tracks

scholarly journals Intuitive Archeology: Detecting social transmission in the design of artifacts

2018 ◽  
Author(s):  
Adena Schachner ◽  
Timothy F. Brady ◽  
Kiani Oro ◽  
Michelle Lee
Keyword(s):  
Bayesian Model ◽  
Social Information ◽  
Social Transmission ◽  
Efficient Design ◽  
The People ◽  
Trial Basis ◽  
Train Tracks

Human-made objects (artifacts) often provide rich social information about the people who created them. We explore how people reason about others from the objects they create, characterizing inferences about when social transmission of ideas (copying) has occurred. We test whether judgments are driven by perceptual heuristics, or structured explanation- based reasoning. We develop a Bayesian model of explanation-based inference from artifacts and a simpler model of perceptual heuristics, and ask which better predicts people’s judgments. Our artifact-building task involved two characters who built toy train tracks. Participants viewed pairs of tracks, and judged whether copying had occurred. Our explanation-based model accurately predicted on a trial-by- trial basis when participants inferred copying; the perceptual heuristics model was significantly less accurate. Efficient design ‘explained away’ similarity, making similarity weaker evidence of copying for efficient tracks. Overall, data show that like intuitive archeologists, people make rich explanation-based inferences about others from the objects they create.

scholarly journals Bicycling crashes on streetcar (tram) or train tracks: mixed methods to identify prevention measures

2021 ◽  
Author(s):  
Kay Teschke ◽  
Jessica Dennis ◽  
Conor C. O. Reynolds ◽  
Meghan Winters ◽  
M. Anne Harris
Keyword(s):  
Mixed Methods ◽  
Urban Areas ◽  
Prevention Measures ◽  
The Road ◽  
Rail Systems ◽  
Route Design ◽  
Individual Knowledge ◽  
Engineering Information ◽  
Train Tracks ◽  
Width Measurements

Background Streetcar or train tracks in urban areas are difficult for bicyclists to negotiate and are a cause of crashes and injuries. This study used mixed methods to identify measures to prevent such crashes, by examining track-related crashes that resulted in injuries to cyclists, and obtaining information from the local transit agency and bike shops. Methods We compared personal, trip, and route infrastructure characteristics of 87 crashes directly involving streetcar or train tracks to 189 crashes in other circumstances in Toronto, Canada. We complemented this with engineering information about the rail systems, interviews of personnel at seven bike shops about advice they provide to customers, and width measurements of tires on commonly sold bikes. Results In our study, 32 % of injured cyclists had crashes that directly involved tracks. The vast majority resulted from the bike tire being caught in the rail flangeway (gap in the road surface alongside rails), often when cyclists made unplanned maneuvers to avoid a collision. Track crashes were more common on major city streets with parked cars and no bike infrastructure, with left turns at intersections, with hybrid, racing and city bikes, among less experienced and less frequent bicyclists, and among women. Commonly sold bikes typically had tire widths narrower than the smallest track flangeways. There were no track crashes in route sections where streetcars and trains had dedicated rights of way. Conclusions Given our results, prevention efforts might be directed at individual knowledge, bicycle tires, or route design, but their potential for success is likely to differ. Although it may be possible to reach a broader audience with continued advice about how to avoid track crashes, the persistence and frequency of these crashes and their unpredictable circumstances indicates that other solutions are needed. Using tires wider than streetcar or train flangeways could prevent some crashes, though there are other considerations that lead many cyclists to have narrower tires. To prevent the majority of track-involved injuries, route design measures including dedicated rail rights of way, cycle tracks (physically separated bike lanes), and protected intersections would be the best strategy.

Classifying C1+ structures on dynamical fractals: 1. The moduli space of solenoid functions for Markov maps on train tracks

1995 ◽  
Vol 15 (4) ◽  
pp. 697-734 ◽  
Author(s):  
A. A. Pinto ◽  
D. A. Rand
Keyword(s):  
Moduli Space ◽  
Scaling Function ◽  
Matching Condition ◽  
Continuous Functions ◽  
Conjugacy Classes ◽  
Train Track ◽  
Hölder Continuous ◽  
Expanding Circle ◽  
Train Tracks ◽  
Smooth Conjugacy

AbstractSullivan's scaling function provides a complete description of the smooth conjugacy classes of cookie-cutters. However, for smooth conjugacy classes of Markov maps on a train track, such as expanding circle maps and train track mappings induced by pseudo-Anosov systems, the generalisation of the scaling function suffers from a deficiency. It is difficult to characterise the structure of the set of those scaling functions which correspond to smooth mappings. We introduce a new invariant for Markov maps called the solenoid function. We prove that for any prescribed topological structure, there is a one-to-one correspondence between smooth conjugacy classes of smooth Markov maps and pseudo-Hölder solenoid functions. This gives a characterisation of the moduli space for smooth conjugacy classes of smooth Markov maps. For smooth expanding maps of the circle with degree d this moduli space is the space of Hölder continuous functions on the space {0,…, d − 1}ℕ satisfying the matching condition.

Train Tracks and Automorphisms of Free Groups

1992 ◽  
Vol 135 (1) ◽  
pp. 1 ◽  
Author(s):  
Mladen Bestvina ◽  
Michael Handel
Keyword(s):  
Free Groups ◽  
Automorphisms Of Free Groups ◽  
Train Tracks

GNSS/IMU/Odometer based Train Positioning

2020 ◽  
Author(s):  
Qing Li ◽  
Robert Weber
Keyword(s):  
Kalman Filter ◽  
Urban Areas ◽  
Moving Average ◽  
Accurate Determination ◽  
Integrated System ◽  
Single Frequency ◽  
Integration Algorithm ◽  
Speed Sensor ◽  
Train Positioning ◽  
Train Tracks

<p>Usually train positioning is realized via counting wheel rotations (Odometer), and correcting at fixed locations known as balises. A balise is an electronic beacon or transponder placed between the rails of a railway as part of an automatic train protection (ATP) system. Balises constitute an integral part of the European Train Control System, where they serve as “beacons” giving the exact location of a train. Unfortunately, balises are expensive sensors which need to be placed over about 250 000 km of train tracks in Europe.</p><p>Therefore, recently tremendous efforts aim on the development of satellite-based techniques in combination with further sensors to ensure precise train positioning. A fusion of GNSS receiver and Inertial Navigation Unit (IMU) observations processed within a Kalman Filter proved to be one of potential optimal solutions for train traction vehicles positioning.</p><p>Today several hundreds of trains in Austria are equipped with a single-frequency GPS/GLONASS unit. However, when the GNSS signal fails (e.g. tunnels and urban areas), we expect an outage or at least a limited positioning quality. To yet ensure availability of a reliable trajectory in these areas, the GNSS sensor is complemented by a strapdown IMU platform and a wheel speed sensor (odometer).</p><p>In this study a filtering algorithm based on the fusion of three sensors GPS, IMU and odometer is presented, which enables a reliable train positioning performance in post-processing. Odometer data are counts of impulses, which relate the wheel’s circumference to the velocity and the distance traveled by the train. This odometer data provides non-holonomic constraints as one-dimensional velocity updates and complements the basic IMU/GPS navigation system. These updates improve the velocity and attitude estimates of the train at high update rates while GPS data is used to provide accurate determination in position with low rates. In case of GNSS outages, the integrated system can switch to IMU/odometer mode. Using the exponentially weighted moving average method to estimate of measurement noise for odometer velocity helps to construct measurement covariance matrices. In the presented examples an IMU device, a GPS receiver and an Odometer provide the data input for the loosely coupled Kalman Filter integration algorithm. The quality of our solution was tested against trajectories obtained with the software iXCOM-CMD (iMAR) as reference.</p>

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