The TTI Center for Transportation Safety is home to a Realtime Technologies, Inc. (RTI) driving simulator that provides measurements of drivers’ responses to roadway situations, in-vehicle technologies, and driving-related tasks. RTI’s
SimCreator® and SimVista® software tools provide a library of different roadway cross-sections and interchanges, as well as a variety of roadway objects, buildings, and ambient traffic. In addition, custom roadway tiles can be programmed to match a specific roadway segment. This allows for in-house development of a wide range of rural and urban roadway scenarios, making it possible to inexpensively test multiple variations and placements of roadway devices or in-vehicle
signals and displays. Using the driving simulator, researchers can test a wider variety of roadway geometries and traffic conditions than are typically possible in a test-track study or fiscally practical in a field study.
This was a "just for fun" project as part of the US DOT + Uber Hackathon. Feb, 2016.
The goal was to use Open Data from the government, as well as Uber's API, to help solve potential policy issues.
http://celebratingcities.github.io/
Note that we had 24 hrs to form a team, create a product, and present.
How to avoid train collisions - a proposal for a GPS-based anti-collision systemAndreas Brepohl
One can try to avoid head-on train collisions via an independent location-based app system for train drivers which monitors users location, speed and travel direction via GPS and sends this data to a central information processing unit which monitors all user (=train) movements on high risk tracks and looks out for “same track / opposed direction”-movements and alerts train drivers when a collision is possible or probable.
The TTI Center for Transportation Safety is home to a Realtime Technologies, Inc. (RTI) driving simulator that provides measurements of drivers’ responses to roadway situations, in-vehicle technologies, and driving-related tasks. RTI’s
SimCreator® and SimVista® software tools provide a library of different roadway cross-sections and interchanges, as well as a variety of roadway objects, buildings, and ambient traffic. In addition, custom roadway tiles can be programmed to match a specific roadway segment. This allows for in-house development of a wide range of rural and urban roadway scenarios, making it possible to inexpensively test multiple variations and placements of roadway devices or in-vehicle
signals and displays. Using the driving simulator, researchers can test a wider variety of roadway geometries and traffic conditions than are typically possible in a test-track study or fiscally practical in a field study.
This was a "just for fun" project as part of the US DOT + Uber Hackathon. Feb, 2016.
The goal was to use Open Data from the government, as well as Uber's API, to help solve potential policy issues.
http://celebratingcities.github.io/
Note that we had 24 hrs to form a team, create a product, and present.
How to avoid train collisions - a proposal for a GPS-based anti-collision systemAndreas Brepohl
One can try to avoid head-on train collisions via an independent location-based app system for train drivers which monitors users location, speed and travel direction via GPS and sends this data to a central information processing unit which monitors all user (=train) movements on high risk tracks and looks out for “same track / opposed direction”-movements and alerts train drivers when a collision is possible or probable.
TC SMART MAPS - A 10 Years Journey with FMESafe Software
Transport Canada (TC) SMART MAPS is a comprehensive collection of over 300 GB of vector Kml data ranging from Safety & Security, Marine, Air and Rail Transportation (SMART). With over 100 unique map layers, this one-stop geospatial portal is used by various TC Departments including Marine Group, Aviation Group, Programs/Policy Group, and Surface Group (MAPS), enabling Policy Makers, Regulators, Inspectors and Enforcement Officers alike to collaborate, work smarter and make better business decisions.
Online/Offline Lane Change Events Detection AlgorithmsFeras Tanan
Abstract—in this paper, We are presenting two algorithms
for lane change detection. The first one is used
for online detection (real-time detection) with accuracy of
85% and the other one is used for offline detection with
accuracy of 95%. The main purpose of the offline detection
algorithm is to find at which GPS locations the number
of happened left/right lane changes.
For the purpose of these algorithms we used the
”crowd-sensing” approach which means that the sensors of
different mobile devices that were fixed in different cars
are the sources of input data for the above mentioned
algorithms. Specifically speaking, we used Accelerometer
and Gyroscope sensors. We also presented an algorithm
for blinker pattern extraction using the microphone sensor.
Keywords: Pattern Extraction, Lane Change detection,
Accelerometer, Gyroscope and Crowd Sensing
Toward a resilient prediction system for non-uniform traffic data Osamu Masutani
We developed a traffic prediction system which enhances a traffic information service. The prediction method is based on time series analysis and is applicable to short to long term prediction. Traffic information system are real-time and real-world system therefore it suffers various kind of disturbance from environment. To preserve traffic prediction quality, we need fundamental treatment on overall system so that the prediction engine be tolerant toward incomplete traffic data feed or non-stationary traffic data. A solution for incomplete data feed is a combination of data for multiple links. A solution for non-stationary traffic is a traffic simulation dedicated to traffic accidents. With these enhancements toward cyber disturbance and physical disturbance, the system resiliency can be higher.
Cloudsim t-drive enhancing driving directions with taxi drivers’ intelligenceecway
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TC SMART MAPS - A 10 Years Journey with FMESafe Software
Transport Canada (TC) SMART MAPS is a comprehensive collection of over 300 GB of vector Kml data ranging from Safety & Security, Marine, Air and Rail Transportation (SMART). With over 100 unique map layers, this one-stop geospatial portal is used by various TC Departments including Marine Group, Aviation Group, Programs/Policy Group, and Surface Group (MAPS), enabling Policy Makers, Regulators, Inspectors and Enforcement Officers alike to collaborate, work smarter and make better business decisions.
Online/Offline Lane Change Events Detection AlgorithmsFeras Tanan
Abstract—in this paper, We are presenting two algorithms
for lane change detection. The first one is used
for online detection (real-time detection) with accuracy of
85% and the other one is used for offline detection with
accuracy of 95%. The main purpose of the offline detection
algorithm is to find at which GPS locations the number
of happened left/right lane changes.
For the purpose of these algorithms we used the
”crowd-sensing” approach which means that the sensors of
different mobile devices that were fixed in different cars
are the sources of input data for the above mentioned
algorithms. Specifically speaking, we used Accelerometer
and Gyroscope sensors. We also presented an algorithm
for blinker pattern extraction using the microphone sensor.
Keywords: Pattern Extraction, Lane Change detection,
Accelerometer, Gyroscope and Crowd Sensing
Toward a resilient prediction system for non-uniform traffic data Osamu Masutani
We developed a traffic prediction system which enhances a traffic information service. The prediction method is based on time series analysis and is applicable to short to long term prediction. Traffic information system are real-time and real-world system therefore it suffers various kind of disturbance from environment. To preserve traffic prediction quality, we need fundamental treatment on overall system so that the prediction engine be tolerant toward incomplete traffic data feed or non-stationary traffic data. A solution for incomplete data feed is a combination of data for multiple links. A solution for non-stationary traffic is a traffic simulation dedicated to traffic accidents. With these enhancements toward cyber disturbance and physical disturbance, the system resiliency can be higher.
Cloudsim t-drive enhancing driving directions with taxi drivers’ intelligenceecway
Final Year IEEE Projects, Final Year Projects, Academic Final Year Projects, Academic Final Year IEEE Projects, Academic Final Year IEEE Projects 2013, Academic Final Year IEEE Projects 2014, IEEE JAVA, .NET Projects, 2013 IEEE JAVA, .NET Projects, 2013 IEEE JAVA, .NET Projects in Chennai, 2013 IEEE JAVA, .NET Projects in Trichy, 2013 IEEE JAVA, .NET Projects in Karur, 2013 IEEE JAVA, .NET Projects in Erode, 2013 IEEE JAVA, .NET Projects in Madurai, 2013 IEEE JAVA, .NET Projects in Salem, 2013 IEEE JAVA, .NET Projects in Coimbatore, 2013 IEEE JAVA, .NET Projects in Tirupur, 2013 IEEE JAVA, .NET Projects in Bangalore, 2013 IEEE JAVA, .NET Projects in Hydrabad, 2013 IEEE JAVA, .NET Projects in Kerala, 2013 IEEE JAVA, .NET Projects in Namakkal, IEEE JAVA, .NET Image Processing, IEEE JAVA, .NET Face Recognition, IEEE JAVA, .NET Face Detection, IEEE JAVA, .NET Brain Tumour, IEEE JAVA, .NET Iris Recognition, IEEE JAVA, .NET Image Segmentation, Final Year JAVA, .NET Projects in Pondichery, Final Year JAVA, .NET Projects in Tamilnadu, Final Year JAVA, .NET Projects in Chennai, Final Year JAVA, .NET Projects in Trichy, Final Year JAVA, .NET Projects in Erode, Final Year JAVA, .NET Projects in Karur, Final Year JAVA, .NET Projects in Coimbatore, Final Year JAVA, .NET Projects in Tirunelveli, Final Year JAVA, .NET Projects in Madurai, Final Year JAVA, .NET Projects in Salem, Final Year JAVA, .NET Projects in Tirupur, Final Year JAVA, .NET Projects in Namakkal, Final Year JAVA, .NET Projects in Tanjore, Final Year JAVA, .NET Projects in Coimbatore, Final Year JAVA, .NET Projects in Bangalore, Final Year JAVA, .NET Projects in Hydrabad, Final Year JAVA, .NET Projects in Kerala, Final Year JAVA, .NET IEEE Projects in Pondichery, Final Year JAVA, .NET IEEE Projects in Tamilnadu, Final Year JAVA, .NET IEEE Projects in Chennai, Final Year JAVA, .NET IEEE Projects in Trichy, Final Year JAVA, .NET IEEE Projects in Erode, Final Year JAVA, .NET IEEE Projects in Karur, Final Year JAVA, .NET IEEE Projects in Coimbatore, Final Year JAVA, .NET IEEE Projects in Tirunelveli, Final Year JAVA, .NET IEEE Projects in Madurai, Final Year JAVA, .NET IEEE Projects in Salem, Final Year JAVA, .NET IEEE Projects in Tirupur, Final Year JAVA, .NET IEEE Projects in Namakkal, Final Year JAVA, .NET IEEE Projects in Tanjore, Final Year JAVA, .NET IEEE Projects in Coimbatore, Final Year JAVA, .NET IEEE Projects in Bangalore, Final Year JAVA, .NET IEEE Projects in Hydrabad, Final Year JAVA, .NET IEEE Projects in Kerala, Final Year IEEE MATLAB Projects, Final Year Projects, Academic Final Year Projects, Academic Final Year IEEE MATLAB Projects, Academic Final Year IEEE MATLAB Projects 2013, Academic Final Year IEEE MATLAB Projects 2014, IEEE MATLAB Projects, 2013 IEEE MATLAB Projects, 2013 IEEE MATLAB Projects in Chennai, 2013 IEEE MATLAB Projects in Trichy, 2013 IEEE MATLAB Projects in Karur, 2013 IEEE MATLAB Projects in Erode, 2013 IEEE MATLAB Projects in Madurai, 2013 IEEE MATLAB
Android t-drive enhancing driving directions with taxi drivers’ intelligenceecway
Final Year IEEE Projects, Final Year Projects, Academic Final Year Projects, Academic Final Year IEEE Projects, Academic Final Year IEEE Projects 2013, Academic Final Year IEEE Projects 2014, IEEE JAVA, .NET Projects, 2013 IEEE JAVA, .NET Projects, 2013 IEEE JAVA, .NET Projects in Chennai, 2013 IEEE JAVA, .NET Projects in Trichy, 2013 IEEE JAVA, .NET Projects in Karur, 2013 IEEE JAVA, .NET Projects in Erode, 2013 IEEE JAVA, .NET Projects in Madurai, 2013 IEEE JAVA, .NET Projects in Salem, 2013 IEEE JAVA, .NET Projects in Coimbatore, 2013 IEEE JAVA, .NET Projects in Tirupur, 2013 IEEE JAVA, .NET Projects in Bangalore, 2013 IEEE JAVA, .NET Projects in Hydrabad, 2013 IEEE JAVA, .NET Projects in Kerala, 2013 IEEE JAVA, .NET Projects in Namakkal, IEEE JAVA, .NET Image Processing, IEEE JAVA, .NET Face Recognition, IEEE JAVA, .NET Face Detection, IEEE JAVA, .NET Brain Tumour, IEEE JAVA, .NET Iris Recognition, IEEE JAVA, .NET Image Segmentation, Final Year JAVA, .NET Projects in Pondichery, Final Year JAVA, .NET Projects in Tamilnadu, Final Year JAVA, .NET Projects in Chennai, Final Year JAVA, .NET Projects in Trichy, Final Year JAVA, .NET Projects in Erode, Final Year JAVA, .NET Projects in Karur, Final Year JAVA, .NET Projects in Coimbatore, Final Year JAVA, .NET Projects in Tirunelveli, Final Year JAVA, .NET Projects in Madurai, Final Year JAVA, .NET Projects in Salem, Final Year JAVA, .NET Projects in Tirupur, Final Year JAVA, .NET Projects in Namakkal, Final Year JAVA, .NET Projects in Tanjore, Final Year JAVA, .NET Projects in Coimbatore, Final Year JAVA, .NET Projects in Bangalore, Final Year JAVA, .NET Projects in Hydrabad, Final Year JAVA, .NET Projects in Kerala, Final Year JAVA, .NET IEEE Projects in Pondichery, Final Year JAVA, .NET IEEE Projects in Tamilnadu, Final Year JAVA, .NET IEEE Projects in Chennai, Final Year JAVA, .NET IEEE Projects in Trichy, Final Year JAVA, .NET IEEE Projects in Erode, Final Year JAVA, .NET IEEE Projects in Karur, Final Year JAVA, .NET IEEE Projects in Coimbatore, Final Year JAVA, .NET IEEE Projects in Tirunelveli, Final Year JAVA, .NET IEEE Projects in Madurai, Final Year JAVA, .NET IEEE Projects in Salem, Final Year JAVA, .NET IEEE Projects in Tirupur, Final Year JAVA, .NET IEEE Projects in Namakkal, Final Year JAVA, .NET IEEE Projects in Tanjore, Final Year JAVA, .NET IEEE Projects in Coimbatore, Final Year JAVA, .NET IEEE Projects in Bangalore, Final Year JAVA, .NET IEEE Projects in Hydrabad, Final Year JAVA, .NET IEEE Projects in Kerala, Final Year IEEE MATLAB Projects, Final Year Projects, Academic Final Year Projects, Academic Final Year IEEE MATLAB Projects, Academic Final Year IEEE MATLAB Projects 2013, Academic Final Year IEEE MATLAB Projects 2014, IEEE MATLAB Projects, 2013 IEEE MATLAB Projects, 2013 IEEE MATLAB Projects in Chennai, 2013 IEEE MATLAB Projects in Trichy, 2013 IEEE MATLAB Projects in Karur, 2013 IEEE MATLAB Projects in Erode, 2013 IEEE MATLAB Projects in Madurai, 2013 IEEE MATLAB
JAVA 2013 IEEE DATAMINING PROJECT T drive enhancing driving directions with t...IEEEGLOBALSOFTTECHNOLOGIES
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
T drive enhancing driving directions with taxi drivers’ intelligenceIEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
DYNAMIC RESOURCE ALLOCATION IN ROAD TRANSPORT SECTOR USING MOBILE CLOUD COMPU...IAEME Publication
Literature review revealed application of various techniques for efficient use of existing resources in road transport sector vehicles, operators and related facilities. This issue assumes bigger dimensions in situations where there are multiple routes and the demand in the routes is highly fluctuating over the day. The application of the existing techniques as reported in literature addresses above issues to a considerable extent. However the main draw back in existing techniques is lack of
proper uninterrupted information about vehicles and demand available at a central place for allocation of vehicles in different roads and huge computational times required for processing. Cloud computing is a recently developed processing tool that is used in effective utilization of resources in transport sector under dynamic resource allocation.
Simulation Based Analysis of Bee Swarm Inspired Hybrid Routing Protocol Param...Editor IJCATR
Vehicular Ad-hoc Networks (VANET's) are basically emanated from Mobile Ad hoc networks (MANET's) in which
vehicles act as the mobile nodes, the nodes are vehicles on the road and mobility of these vehicles are very high. The main objective of
VANET is to enhance the safety and amenity of road users. It provides intelligent transportation services in vehicles with the
automobile equipment to communicate and co-ordinates with other vehicles in the same network that informs the driver’s about the
road status, unseen obstacles, internet access and other necessary travel service information’s. The evaluation of vehicular ad hoc
networks applications in based on the simulations. A Realistic Mobility model is a basic component for VANET simulation that
ensures that conclusion drawn from simulation experiments will carry through to real deployments. This paper attempts to evaluate the
performance of a Bee swarm inspired Hybrid routing protocol for vehicular ad hoc network, that protocol should be tested under a
realistic condition including, representative data traffic models, and the realistic movement of the mobile nodes which are the vehicles.
In VANET the simulation of Realistic mobility model has been generated using SUMO and MOVE software and network simulation
has been performed using NS2 simulator, we conducted performance evaluation based on certain metric parameters such as packet
delivery ratio, end-to-end delay and normalized overhead ratio.
Neural Network Based Parking via Google Map GuidanceIJERA Editor
Intelligent transportation systems (ITS) focus to generate and spread creative services related to different transport modes for traffic management and hence enables the passenger informed about the traffic and to use the transport networks in a better way. Intelligent Trip Modeling System (ITMS) uses machine learning to forecast the traveling speed profile for a selected route based on the traffic information available at the trip starting time. The intelligent Parking Information Guidance System provides an eminent Neural Network based intelligence system which provides automatic allocate ion of parking's through the Global Information system across the path of the users travel. In this project using efficient lookup table searches and a Lagrange-multiplier bisection search, Computational Optimized Allocation Algorithm converges faster to the optimal solution than existing techniques. The purpose of this project is to simulate and implement a real parking environment that allocates vacant parking slots using Allocation algorithm.
Driving cycle tracking device development and analysis on route-to-work for K...TELKOMNIKA JOURNAL
Driving cycle is a series of speed versus time profile used to represent driving patterns of a vehicle. research in this field guides vehicle manufacturers and environmentalists to investigate air quality through emissions. Study on driving cycle also aids manufacturers to manage vehicle emissions and to save energy released through exhaust. Also, driving cycles can provide information on road condition and driving behaviour of an individual. For that, a proper data collection method is crucial as it is solely based on real world driving. This research is an initiative to construct a prototype of driving cycle tracking device (DC-TRAD) in which it was implemented with internet-of-things (IoT) to manage big number of collected data. U-Blox global positioning system (GPS) neo 7 M sensor was used to increase the accuracy of data capturing and it was used on route-to-work for Kuala Terengganu city (RTW DC for KT city) for analysis.
Bed side patients monitoring system with emergency alert
Cloudsim t-drive enhancing driving directions with taxi drivers’ intelligence
1. ECWAY TECHNOLOGIES
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VISIT: www.ecwayprojects.com MAIL TO: ecwaytechnologies@gmail.com
T-Drive Enhancing Driving Directions with Taxi Drivers’ Intelligence
ABSTRACT:
This paper presents a smart driving direction system leveraging the intelligence of experienced
drivers. In this system, GPS-equipped taxis are employed as mobile sensors probing the traffic
rhythm of a city and taxi drivers’ intelligence in choosing driving directions in the physical
world.
We propose a time-dependent landmark graph to model the dynamic traffic pattern as well as the
intelligence of experienced drivers so as to provide a user with the practically fastest route to a
given destination at a given departure time. Then, a Variance-Entropy-Based Clustering
approach is devised to estimate the distribution of travel time between two landmarks in different
time slots. Based on this graph, we design a two-stage routing algorithm to compute the
practically fastest and customized route for end users.
We build our system based on a real-world trajectory data set generated by over 33,000 taxis in a
period of three months, and evaluate the system by conducting both synthetic experiments and
in-the-field evaluations. As a result, 60- 70 percent of the routes suggested by our method are
faster than the competing methods, and 20 percent of the routes share the same results. On
average, 50 percent of our routes are at least 20 percent faster than the competing approaches.
