Project Platform

Jupyter Notebook

• The Jupyter Notebook App is a server-client application that allows editing and running notebook documents via a web browser. The Jupyter Notebook App can be executed on a local desktop requiring no internet access (as described in this document) or can be installed on a remote server and accessed through the internet

• KERNAL -->

A notebook kernel is a “computational engine” that executes the code contained in a Notebook document. The ipython kernel, referenced in this guide, executes python code. Kernels for many other languages exist (official kernels).

it's support python, R, julia

US Traffic Accident Dataset*

• US-Accidents currently contains data about 2.25 million
  instances of traffic accidents that took place within the contiguous United States, and over the last three years. Each accident record consists of a variety of intrinsic and contextual attributes such as location, time, natural language description, weather, period-of-day,
  and points-of-interest.

INTRODUCTION

• • Reducing traffic accidents is an important public safety challenge
    around the world. A global status report on traffic safety, notes
    that there were 1.25 million traffic deaths in 2013 alone, with deaths
    increasing in 68 countries when compared to 2010. Accident prediction is important for optimizing public transportation, enabling safer routes, and cost-effectively improving the transportation infrastructure, all in order to make the roads safer. Given its significance, accident analysis and prediction has been a topic of much
    research in the past few decades. While a large body of research as been focused on small-scaled datasets with limited coverage
    (e.g. a small number of road-segments, or just one city) [4, 5, 11, 26],
    the value and impact of predictive solutions may be better studied
    when using large-scale data.

• • order to mitigate these challenges and to provide a context
    for future research on traffic accident analysis and prediction,

• • US-Accidents offers a wide range of data attributes to describe each accident record including location data, time data, natural language description of event, weather data, period-of-day information, and relevant points-of-interest data (traffic signal, stop sign, etc.).

• • Using US-Accidents, we performed a variety of data analysis and profiling to derive a wide-range of insights. Our analyses demonistrated that about 40% of accidents took place on or near high-speed roadways (highways, interstates, etc.) and about 32% on or near local roads (streets, avenues, etc.). We also derived various insights
    with respect to the correlation of accidents with time, points-of -interest, and weather conditions.
    We summarize the contributions.

• • A variety of insights gleaned through analyses of accident hotspot locations, time, weather, and points-of-interest correlations
    with the accident data

• • that may directly be utilized for applications such as urban planning, exploring flaws in transportation
    infrastructure design, traffic management and prediction, and
    personalized insurance.

• *information such as road geometry, annual average daily traffic,
  and weather data to predict frequency of accidents for a highway
  road using a neural network model.

TERMINOLOGY

In this section we peovided a set of definations.

• Traffic Event -->
  We define a traffic event e by e =
  ⟨lat,lnд,time,type,desc⟩, where lat and lnд are GPS latitude and
  longitude, type is the type of the event, and desc provides a natural
  language description of the event. A traffic event is of one of the
  following types: accident, broken-vehicle2, congestion3, construction4,
  event5, lane-blocked6, or flow-incident7

• Weather Observation Record -->
  ). A weather observation w is defined by w = ⟨lat,lng,time,temperature, humidity,pressure,visibility,wind−speed,precip,rain,snow, fog,hail⟩. Here
  lat and lng represent the GPS coordinates of the weather station
  which reported w; precip is the precipitation amount (if any); and
  rain, snow, fog, and hail
  are binary indicators of these events.

• Point-of-Interest -->
  A point-of-interest p is defined
  by p = ⟨lat,lng,type⟩. Here, lat and lnд show the GPS latitude and
  longitude coordinates, and available types for p are described in

US-ACCIDENTS DATASET

USAccident contains about 2.25 million cases of traffic accidents that
took place within the contiguous United States from February 2016
to March 2019.

• • the daily distribution of traffic accidents, where
    significantly more accidents were observed during the weekdays.

• • it can be observed that the
    hourly distribution during weekdays has two peaks (8am and 5pm),
    while the weekend distribution shows a single peak (1pm).

• • most of the accidents took place near junctions or intersections (crossing, traffic signal, and stop).

• *distribution of road types, extracted from the map-matching results (i.e., street names). Here we note that
  about 32% of accidents happened on or near local roads (e.g., streets,venues, and boulevards), and about 40% took place on or near high speed roads (e.g., highways, interstates, and state roads).
  Finally, the period of day data shows that about 73% of accidents happened
  after sunrise (or during the day).

APPLICATION OF THE DATASET

• • US-Accidents may be used for applications such as real-time acicident prediction; studying accident hotspot locations; casualty
    analysis (extracting cause and effect rules to predict accidents); or
    studying the impact of precipitation or other environmental stimuli on accident occurrence. Given the scale of data, researchers may utilize this dataset to derive a variety of insights which can benefit applications such urban planning and improving transportation insfrastructures.

CONCLUSION AND FUTURE WORK

From this dataset, we were able to derive a variety of insights with
respect to the location, time, weather, and points-of-interest of an
accident. We believe that US-Accidents provides a context for future research on traffic accident analysis and prediction. In terms
of our own future work, we plan to employ this dataset to perform
real-time traffic accident prediction.

US Accident(3 Million Records) Data Analysis EDA