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Current – Intelligent Transportation System

Current – Intelligent Transportation System . Where do you need to go?. Outline. 3 Team Introduction 4 Problem Statement 5-10 Background Research 11 Process Flows (Pre Solution) 12 Solution 13 Process Flows (Post Solution) 14 Objectives

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Current – Intelligent Transportation System

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  1. Current – Intelligent Transportation System CS410 Red Team Where do you need to go?

  2. Outline • 3 Team Introduction • 4 Problem Statement • 5-10 Background Research • 11 Process Flows (Pre Solution) • 12 Solution • 13 Process Flows (Post Solution) • 14 Objectives • 15-18 Market Analysis • 19 What’sIn The Box • 20 What’sNot In The Box • 21 Major Functional Component • 22-27 Hardware Overview • 28-31 Hardware Milestones • 32-39 Software Overview • 40-42 User Interface Overview • 43-49 Software Milestones • 50-52 Database Schemas • 53-56 Gantt Charts • 57-59 Project Budget & Cost • 60-64 Project Risks • 65 Conclusion • 67 References CS410 Red Team

  3. Introduction: Our Team Nathan Lutz - Project Manager - Hardware Specialist Chris Coykendall - Web Developer - Software Specialist Akeem Edwards - Financial Specialist - Software Specialist Brian Dunn - Marketing Specialist - Web Developer Dean Maye - Documentation - Database Admin CS410 Red Team Kevin Studevant - Database Admin - Software Specialist CJ Deaver - Risk Analyst - Hardware Specialist Domain Expert Kamlesh Chowdary ITS Engineer at HRT Domain Expert Dr. Tamer Nadeem Mobile Apps at ODU Mentor Dave Farrell Systems Engineer at MITRE Corp.

  4. Introduction: The Problem Lack of complete information prevents transit organizations and local businesses from maximizing the potential benefits of light rail systems. CS410 Red Team

  5. Background: Economy • Studies show that light rail systems have a history of directly boosting local economies in three key ways: • Increased retail sales • New jobs and development • Higher property values CS410 Red Team

  6. Background: Increased Sales Due to increased accessibility and an influx of new customers, local businesses in light rail service areas see increased sales: • A study in Dallas showed a 33% increase in retail sales of businesses near the DART starter line.1 • Near Norfolk’s Tide light rail station on Newtown Road, a 7-Eleven owner reported a 13-14% increase in sales.2 • In Salt Lake City, a restaurant owner reported annual increases of 25-30% due to their proximity to the TRAX light rail.3 • In Phoenix, one business owner reported a 30% increase in revenue since the local light rails opening.4 However, these systems do not maximize this potential by working with local businesses and providing information to riders. CS410 Red Team • http://www.detroittransit.org/cms.php?pageid=26 • http://hamptonroads.com/2012/02/some-stores-near-norfolk-light-rail-stations-see-boost • http://www.gulfcoastinstitute.org/university/LightRail_BusinessImpact.pdf • http://www.friendsoftransit.org/The-Businesses-of-Light-Rail.pdf

  7. Background: Jobs & Development Over the past five years, studies have shown light rail systems as an effective stimulant for new development and jobs: • In Charlotte, over $291 million in new development was seen along their new 10-mile line with another $1.6 billion expected.1 • The Maryland Transit Administration estimated 27,000 new jobs per year over the next 30 years attributed to their new Purple Line.2 If light rail usage is maximized, then the potential for further expansion can boost these numbers even further. CS410 Red Team Dallas LRT Projected Spending vs. Impact3 • http://www.detroittransit.org/cms.php?pageid=26 • http://washingtonexaminer.com/local/maryland/2011/11/purple-line-expected-be-major-economic-engine-md-officials-say • http://www.dart.org/about/WeinsteinClowerTODNov07.pdf

  8. Background: Tide Case Study A survey of over 1000 Norfolk residents was taken and although 90% were aware of new light rail, many lacked other information: • About 70% of downtown workers did not know the stop locations. • About 55% of other respondents did not know the stop locations. • 69% of respondents ranked information about stops as an important problem. • 75% of respondents ranked schedule information as an important problem. CS410 Red Team http://www.gohrt.com/publications/reports/sir-light-rail-summary.pdf

  9. The Tide ridership started strong, breaking the first-year 2,900 daily rider estimate in its opening months, but has been in decline since.1 Background: Tide Ridership CS410 Red Team http://www.gohrt.com/public-records/Commission-Documents/Commission-Meetings/FY2012/January-2012.pdf

  10. Process Flow pre-Current ITS Static ridership data Set schedule, stops/stations and fare for light rail, and determine new service areas Need to evaluate & expand Tide light rail services Light rail normal operation Receive user feedback about service through traditional means Local Business Owners -Visit website -Get schedule information -Get fare info -Get stop info -Purchase e-ticket Ride to next stop Go to stop/station Need to go somewhere Embark CS410 Red Team Disembark Tide Rider Traditional advertising media (print, radio, TV) Want to attract Light Rail customers No big returns on tax payer investment in light rail Inefficient marketing

  11. The Solution CurrentIntelligent Transportation System (ITS) Current will provide accessible, real-time, and accurate information to transit authorities for maximizing adoption and expansion of emerging light rail public transportation systems. CS410 Red Team

  12. Process Flow with Current ITS Historical data & event data Quickly & accurately set schedule, stops/stations and fare for light rail Need to evaluate & expand Tide light rail services Real-time ridership + GPS data Efficient light rail operation Send alerts & receive user feedback about service through Current ITS Current ITS provides all info needed by rider Ride to next stop Need to go somewhere Go to stop/station CS410 Red Team Embark Tide Rider Disembark Advertising with Current ITS Realize returns on tax payer investment in light rail Want to attract light rail customers Effectively target market Local Business Owners

  13. Objectives • Cooperation with local businesses through targeted advertising and listing will directly contribute to local economic growth. • Direct, two-way communication with riders will allow operators to deliver important information and collect feedback from riders. • Provide transit authorities and local businesses with analysis and reports showing detailed information about riders and their habits. • Provide real-time updates on train locations, seat availability, service interruptions, local events, and important announcements. • Provide easily accessible static information to riders regarding schedules, stop locations, and local businesses. • Multiple mediums (mobile apps, station kiosks, and websites) will be used for information and communication to ensure easy access. CS410 Red Team

  14. Current Trend Analysis • Current ITS provides detailed information regarding light rail usage. This data can be sorted to highlight different stops, special events, and time of day trending. • Current ITS will not provide automatic rerouting or boost capacity in itself, but will provide operators the necessary information to make these decisions. • As an example, Norfolk’s Grand Illumination Parade generated 3x the normal average daily ridership, but HRT provided no additional capacity.1 CS410 Red Team Average Daily Boarding 2 http://www.gohrt.com/public-records/Operations-Documents/Rail/Monthly-Ridership/Rail-Ridership-Current.pdf Debbie Messina, “The Tide.” The Virginian-Pilot. February 18th, 2012.

  15. Local Businesses • Previous research showed how much impact light rail stops can have on local businesses, but riders still lack information about them. • Through a GUI allowing users to easily find local businesses and attractions, riders will be more likely to explore and rely on the system for recreational usage. • In addition, the business owner backend will allow local businesses to advertise companies through Current ITS. CS410 Red Team

  16. Target Market • As traffic, gas prices, and pollution rise, light rails are quickly catching on as a more efficient means of transportation.1 • As the result of Obama investing $8 Billion in stimulus funding for rail transit, even more projects are now under development and expansion.1 • New light rail development and expansion costs millions to taxpayers who demand quick results for their money.2 CS410 Red Team Light Rail Project Costs http://www.cbsnews.com/8301-503544_162-4949672-503544.html http://www.lightrail.com/projects.htm

  17. Our Competition CS410 Red Team

  18. In The Box A service to set up and maintain: • Web Application Engine • Prediction Server/ Decision Engine • Embedded Linux Transmission Application • Android Application • Real-Time Train Tracking (GPS) • Real-Time Passenger Counting (APC) Algorithms • To provide customized reports and forecast data • Backend to provide location based business advertisements CS410 Red Team

  19. Not In The Box • Trains • Tracking System for Buses • Real-time Rerouting • Text message alerts (future feature) • QR Code Ticketing (future feature) • Social media integration (future feature) • Total transit management integration (future feature) CS410 Red Team

  20. Real World Product (RWP) Major Functional Component Diagram Decision Engine Web App Server DB GTFS CS410 Red Team GPS Transponder Onboard Unit Infrared Counters

  21. Prototype Major Functional Component Diagram CS Dept Virtual Machine Trending Algorithms Web App Server DB GTFS CS410 Red Team Simulated GPS Data Simulated APC Data

  22. RWP vs. Prototype CS410 Red Team

  23. RWP vs. Prototype CS410 Red Team

  24. RWP vs. Prototype CS410 Red Team

  25. In The Prototype A service to set up and maintain: • Web Application Engine • Decision Engine for Forecasting • Android Application • Test Driver Algorithms • To provide forecast data • Backend to provide location based business advertisements CS410 Red Team

  26. Prototype Software Overview LEVEL I LEVEL II LEVEL III LEVEL IV (ASYNCHRONOUS) Simulated APC Data Mobile Application Web Application Engine Decision Engine Internet DB CS410 Red Team Browser Interface Simulated GPS Data

  27. Level I – Embedded System • In actual product deployment, vehicles will have an embedded Linux-based PC module running a transmission application to send GPS and Automatic Passenger Counter (APC) information back the database via GSM network. • For prototyping purposes a test driver will be used to simulate modifiable static ridership and train position data. CS410 Red Team

  28. Level II - Prediction • Ridership counts and GPS coordinates of the vehicles will be retrieved from database, along with historical ridership data. • This data will be analyzed based upon various features of time, riders, waypoints and other trends. • The Decision Engine will generate and save a training data set for forecasting. MySQL Database Server CS410 Red Team Decision Engine

  29. Decision Engine (DE) Request Algorithms WAE Request Received SQL Database Poll Interval Reached Predictiontype? Request new historical data Capacity Delay Associate ridership/time/locations with actual reported incidents Retrieve ridership forecast table Retrieve delay forecast table CS410 Red Team Generate new training sets and save to forecast tables Apply batch gradient descent learning algorithm w/ client position vector Reset poll clock Return forecast result to WAE

  30. Level III - Reporting • The Web Application Engine (WAE) publishes a public, accessible feed compliant with General Transit Feed Specification (GTFS). • The WAE also checks with the Google API to update its record of local destinations at the station waypoints from Google Places. CS410 Red Team Internet Decision Engine Web ApplicationEngine

  31. Level IV - Presentation • With the WAE in place and an extensible interface to it, any web-enabled device can retrieve the information using our API. • Rider feedback from end-users (website , Android app, etc.) will be collected to the database. • Transit authorities and businesses can view the trend data via a back-end monitoring interface. CS410 Red Team Web ApplicationEngine Internet

  32. Mobile App GUI Sitemap Splash Screen User Login Feedback Submission Form Main Menu& Alerts App Settings (Menu) Local Events Browse Attractions Trip Planning CS410 Red Team Starred Events Google Maps Overlay Upcoming Event Calendar Plan Trip w/ Destination Rail Vehicle Vacancy & Delays Rail Stop List Map

  33. HRT GUI Mockup CS410 Red Team

  34. Business GUI Mockups CS410 Red Team

  35. Milestone Overview Software Server Software Mobile Application Test Driver Simulated APC Data CS410 Red Team Simulated GPS Data

  36. Milestone Overview Software Server Software Mobile Application Test Driver Database CS410 Red Team Decision Engine Web Application Engine

  37. Mobile App Milestone Mobile Application Local Database GUI Processes GUI Setting Shared Preferences Schedule Delays UI Event Handler CS410 Red Team GPS/Triangulation Checker Rail Capacity & Delay Forecast Rider Feedback Module WAE Requester (Interface) Ridership Counts Rider Feedback Submission Local Places Local Event Calendar

  38. DB Server Milestone Database Server Configure Server Configure DBMS Access Control Install OS Disk Layout Design Schemas CS410 Red Team Networking Tables Fields Firewall Keys Backups Constraints Install DBMS

  39. Decision Engine Milestone Decision Engine Database I/O Request Handler Gradient Descent / Supervised LearningAlgorithm CS410 Red Team Delay Forecast Forecast Tables Rider Features Ridership Forecast Historical Features Location Features

  40. WAE Milestone Web Application Engine Database I/O Web GUI General Request Handler Syndication Process Administrative Interface Capacity Check Google Places API Checker Rider Feedback CS410 Red Team Schedule Delays GTFS/AJAX/Etc Publication Retrieve Schedule Rail Capacity & Delay Forecast Accept Feedback Rider Feedback Module Local Destinations Ridership Counts Retrieve Forecast Local Event Calendar

  41. User Database Schemas CS410 Red Team

  42. Other Database Schemas Events and Attractions will be stored in reference to the stop closest to them. CS410 Red Team

  43. Database Schema ERD Interface User Profile Relays Stops provides Events Info CS410 Red Team alerts Lists within radius Attractions Info Trains

  44. Risk Matrix 5 High Impact CS410 Red Team Low 5 0 High Low Probability

  45. Technical Risks T1: Data latency/accuracy 2/4 • Risk: Data provided to the end user has exceeded time of use. • Risk Strategy: Determine acceptable latency periods and provide user warning if data is time deficient. • Risk: Data is incorrect or not updating. • Risk Strategy: Provide system diagnostic capability to run during maintenance periods T2: Realistic Representation of Sensor Data 1/3 • Risk: Sensor simulations are not accurate enough to predict actual values. • Prototype Risk Strategy:Conduct data collection to form an accurate model for simulation. CS410 Red Team

  46. Customer Risks C1: Lack of interest by transit authorities 2/4 • Risk: Transit authorities feel current systems are efficient • Risk Strategy: Spur interest by providing granular riding data to aid in faster service changes to maximize efficiency and predict growth. C2: Low rider acceptance 1/2 • Risk: Riders and prospective are averse to utilizing products. • Risk Strategy: Develop application to operate on multiple platforms to address customer preference range. C3: No local business buy-in 3/2 • Risk: Local businesses choose to not support with advertising dollars. • Risk Strategy: Provide local businesses with adequate resources to update and inform prospective customers to drive up business. CS410 Red Team

  47. Prototype Risk Mitigations T1: Data latency/accuracy 2/4 • Test and display actual latency times and accuracy factors C1: Lack of interest by transit authorities 2/4 • Better decision making from real-time data • Improvement of customer satisfaction C2: Low rider acceptance 1/2 • Ease of use for rider • Multiple access platforms C3: No local business buy-in 3/2 • Targeted advertising capability • Increase customer awareness CS410 Red Team

  48. Conclusion • Right Now: Inefficient or nonexistent communication, resulting in non-optimal Tide utilization. • Current ITS will solve these issues in a flexible manner. • The prototype will be developed to show the completeness of our design. CS410 Red Team

  49. Questions? CS410 Red Team

  50. References • http://www.gohrt.com/publications/reports/sir-light-rail-summary.pdf • http://www.gohrt.com/public-records/Commission-Documents/Commission-Meetings/FY2012/January-2012.pdf • http://hamptonroads.com/2011/11/poll-public-board-expanding-lightrail-route • http://www.metro-magazine.com/News/Story/2011/08/INIT-employees-to-serve-as-Tide-Guides-.aspx • http://hamptonroads.com/2011/07/control-room-nsu-serves-brains-light-rail • http://www.serpefirm.com/responsibilities-the-tide-light-rail-controller-operator.aspx • http://www.gohrt.com/public-records/Operations-Documents/Rail/Monthly-Ridership/Rail-Ridership-Current.pdf • http://www.metro-magazine.com/News/Story/2011/08/Va-s-The-Tide-opens-hits-30K-boardings.aspx • http://www.cbsnews.com/8301-503544_162-4949672-503544.html • http://www.lightrail.com/projects.htm • http://www.realtor.org/wps/wcm/connect/212699004205f031b404fcc7ba2f3d20/cpa_transport_090.pdf • http://hamptonroads.com/2012/02/some-stores-near-norfolk-light-rail-stations-see-boost • Debbie Messina, “The Tide.” The Virginian-Pilot. February 18th, 2012. • http://apta.com/resources/statistics/Documents/Ridership/2011-q3-ridership-APTA.pdf • http://www.lightrailnow.org/success2.htm • http://www.prweb.com/releases/light_rail/light_rail_transit/prweb4253534.htm • http://www.itscosts.its.dot.gov/its/benecost.nsf/images/Reports/$File/Ben_Cost_Less_Depl_2011%20Update.pdf • http://www.detroittransit.org/cms.php?pageid=26 • http://www.dart.org/about/economicimpact.asp • http://reason.org/news/show/126773.html • http://mobility.tamu.edu/files/2011/09/congestion-cost.pdf • http://www.vtpi.org/railben.pdf CS410 Red Team

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