Browsing by Subject "public transportation"

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    Exploring the Conditional Effects of Bus Stops on Crime
    (2014-03) Stucky, Thomas D.; Smith, Sarah L.
    Public transportation is a major element of social life in most cities, and the most common mode of public transportation is busing. This study examines whether bus stops are a robust predictor of crime, net of other factors, and whether the effect of bus stops on crime is conditioned by socioeconomic and land use factors. We use geocoded Indianapolis crime and bus stop data for 2010 to predict crime counts in 500-feet × 500-feet square grid cells, using negative binomial models. Net of other factors, bus stops are associated with variation in counts of Uniform Crime Reports reported rape, robbery, aggravated assault, burglary and larceny in a cell. In addition, the effect of bus stops on crime was conditioned by land use characteristics. In particular, the effect of bus stops on crime was greater in commercial and industrial areas, but dampened in areas with high-density residential housing.
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    SubTrack: Enabling Real-Time Tracking of Subway Riding on Mobile Devices
    (IEEE, 2017-11) Liu, Guo; Liu, Jian; Li, Fangmin; Ma, Xiaolin; Chen, Yingying; Liu, Hongbo; Computer and Information Science, School of Science
    Real-time tracking of subway riding will provide great convenience to millions of commuters in metropolitan areas. Traditional approaches using timetables need continuous attentions from the subway riders and are limited to the poor accuracy of estimating the travel time. Recent approaches using mobile devices rely on GSM and WiFi, which are not always available underground. In this work, we present SubTrack, utilizing sensors on mobile devices to provide automatic tracking of subway riding in real time. The real-time automatic tracking covers three major aspects of a passenger: detection of entering a station, tracking the passenger's position, and estimating the arrival time of subway stops. In particular, SubTrack employs the cell ID to first detect a passenger entering a station and exploits inertial sensors on the passenger's mobile device to track the train ride. Our algorithm takes the advantages of the unique vibrations in acceleration and typical moving patterns of the train to estimate the train's velocity and the corresponding position, and further predict the arrival time in real time. Our extensive experiments in two cities in China and USA respectively demonstrate that our system can accurately track the position of subway riders, predict the arrival time and push the arrival notification in a timely manner.