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COORDINATED INTELLIGENT TRANSPORTATION

SYSTEMS DEPLOYMENT IN NEW YORK CITY (CIDNY)University Transportation

Research Center - Region 2

FINAL REPORT

TASK 8

DEVELOP DATA STORAGE AND ACCESS PLATFORM

FOR MTA BUS TIME DATA

Performed by: New York University

The FHWA, through its New York Division/New York City urban Intelligent Transportation Systems (ITS) in the region. The NYCDOT and NYSDOT-Region 11 Planning have taken the initiative in working with FHWA to take advantage of this FHWA program. NYCDOT and NYSDOT have developed the Training Courses and Research and Development Programs for the NYCDOT and NYSDOT Coordinated Intelligent Transportation Systems Deployment in New York City (CIDNY) which is a set objectives of these programs. The 2013 studies are being performed by institutions of the Region 2 University Transportation Research Center (UTRC). The studies focused on the following program areas: Construction and Detection Technologies, Strategic ITS Deployment Plan, Pedestrians and Cyclists Safety, Data Storage and Access Platform for MTA Bus Time Data. The following tasks have been completed under this program. Task 2 - Develop a multi-agency/multi modal construc- tion management tool to enhance coordination of con- struction projects citywide during planning and operation phases to improve highway mobility and drivers experience •Task 5 - Develop a Comprehensive Guide to Signal

Timing, New Detection and Advanced Signal

•Task 6 - Strategic ITS Deployment Plan For New York City •Task 7 - Research on Pedestrians and Cyclists Safety

Using ITS Technology in NYC

Task 8 - Develop Data Storage and Access Platform

for MTA Bus Time Data.

ABOUT THE PROGRAM

TASK 8 FINAL REPORTUTRC-RF Project No:

Project's Completion Date:

January 2017

Project Title: Develop Data Storage

and Access Platform for MTA Bus

Time Data

Project's Website:

http://www.utrc2.org/research/proj- ects

Principal Investigator(s): Kaan Ozbay, Ph.D.

Professor

Department of Civil and Urban Engi-

neering & Center for Urban Science and Progress (CUSP)

Tandon School of Engineering, NYUClaudio SilvaProfessorComputer Science & EngineeringTandon School of Engineering, NYU

Performing Institution(s):

New York University (NYU)

TECHNICAL REPORT STANDARD TITLE PAGE

1. Report No.2.Government Accession No.

4. Title and Subtitle5. Report Date

DEVELOP DATA STORAGE AND ACCESS

PLATFORM FOR MTA BUSTIME DATA January 2017

6. Performing Organization Code

7. Author(s)8. Performing Organization Report No.

Claudio Silva

Kaan Ozbay

9. Performing Organization Name and Address10. Work Unit No.

NYU Tandon School of Engineering

NYCDOT

6 Metro Tech Center

Brooklyn, NY 11201 11. Contract or Grant No.

57315-01-26

12. Sponsoring Agency Name and Address13. Type of Report and Period Covered

Final, 4/24/15-10/31/16

14. Sponsoring Agency Code

15. Supplementary Notes

16. Abstract

Travel times can be collected from a large number of potential sources. Conventionally, fixed detectors such as inductive loops embedded in the roadway have been

used to measure vehicle flows and estimate speeds. Recent technological advances and the widespread deployment of Global Positioning Systems (GPS) in

consumer devices make mobile data sources a promising and potentially cost-effective way to monitor the congestion in a transportation system.

New York City Department of Transportation (NYCDOT) along with many other DOTs in the region and around the country have been using probe vehicle data for

monitoring time-dependent traffic conditions and conducting before and after studies of various transportation projects. Specifically, NYCDOT has been using probe

vehicle data from yellow taxis and other vehicles equipped with GPS as well as the TRANSMIT system. In this project, NYCDOT wants to automate and enhance

their use of Metropolitan Transportation Authority (MTA) bus data that they are already acquiring under a protocol developed between the two agencies.

The overall goal of improving the current MTA bus data acquisition, processing, storage and querying procedures for NYCDOT comprised of 3 main tasks: The first

task is to develop efficient data acquisition, storage, maintenance, querying, and visualization procedures to automate and improve the overall process of using MTA

bus data. The second task is to create a web--going in- house data development efforts as well New York

University (NYU) C task is to

provide recommendations to enhance the developed tool based on the experience obtained throughout this project and to incorporate this developed app and its

functionalities into NYCDOT operations in a more routine manner.

In this project, we showed that it is possible to develop a simple yet powerful web based tool to acquire, store, process and visualize bus time data. This tool has an

intuitive mapping user interface that can be improved by incorporating functions that can improve the robustness of the tasks at hand. The fact that the tool is web

based makes it easy for the end users to access stored data and to query it without any delay or external help. Moreover, the tool enables the users to conduct a series

of data visualization and analysis operations demonstrating the potential of such a web based tool for future applications.

17. Key Words18. Distribution Statement

Bus time data, data storage and querying

19. Security Classif (of this report)20. Security Classif. (of this page)21. No of Pages22. Price

UnclassifiedUnclassified32

Form DOT F 1700.7 (8-69) NYCDOT

34-02 Queens Blvd. 2nd floor

Long Island City, NY 11101

Disclaimer

The contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the information presented herein. The contents do not necessarily reflect the official views or policies of the UTRC or the Federal Highway Administration. This report does not constitute a standard, specification or regulation. This document is disseminated under the sponsorship of the Department of Transportation, University Transportation Centers Program, in the interest of information exchange. The U.S. Government assume no liability for the contents or use thereof.

1 CIDNY TASK 8:

DEVELOP DATA STORAGE AND ACCESS

PLATFORM FOR MTA BUSTIME DATA

Final Report Submitted to:

New York City Department of Transportation

January 2017

Department of Computer Science and Engineering &

Department of Civil and Urban Engineering

School of Engineering

New York University (NYU)

2 Table of Contents

EXECUTIVE SUMMARY ................................................................................................ 4

GENERAL INFORMATION ............................................................................................ 5

1.1 Purpose............................................................................................................. 5

AGENCY INTERVIEWS .................................................................................................. 6

2.1 MTA .................................................................................................................. 6

2.1.1 Current Practice at MTA ............................................................................ 6

2.1.2 Improvements to Existing Capabilities ..................................................

6

2.2 NYCDOT ........................................................................................................... 6

2.2.1 Current Practice at NYCDOT ..................................................................... 6

2.2.2 Improvement Suggestions ......................................................................... 7

FUNCTIONAL REQUIREMENTS

................................................................................... 9

3.1 Functional Requirements for Current Bus Data ................................................

9

3.2 Functional Requirements for Historical Bus Data ............................................ 10

3.3 Functional Requirements for Additional Data Integration ................................ 11

3.4 Functional Requirements for Every Day Data Analytics .................................. 11

3.5 Functional Requirements for Scenario-Driven Data Analytics......................... 11

3.6 Functional Requirements for Visualization ...................................................... 12

3.7 Functional Requirements for Output and Reporting ........................................ 12

IMPLEMENTED METHODS, PROCEDURES, AND CHALLENGES ........................... 13

4.1 Summary of Implemented Features and Potential Improvements .................. 13

4.1.1 Functionalities of the Tool ........................................................................ 13

4.1.2 Short Term Improvements ....................................................................... 14

4.1.3 Long Term Improvements ........................................................................ 14

4.2 Data Irregularities ............................................................................................ 16

USER MANUAL ............................................................................................................ 19

5.1 Architectural Representation ........................................................................... 19

5.2 Graphical User Interface ................................................................................. 20

5.2.1. User Case View ........................................................................................... 20

5.2.2. General Query ............................................................................................. 20

5.2.3. Spatial Selection Tool .................................................................................. 21

5.2.4. Results ......................................................................................................... 24

CONCLUSION & RECOMMENDATIONS .................................................................... 27

6.1 Recommendations Based on the Interviews ........................................................ 27

6.2 Recommendations Based on the Data ................................................................ 28

6.3 Recommendations Based on the Software Development.................................... 28

6.4 Conclusions ......................................................................................................... 29

APPENDIX A ................................................................................................................ 30

Query Example .......................................................................................................... 30

APPENDIX B ................................................................................................................ 32

Summary of the Interviews ........................................................................................ 32

3

List of Tables

Table 1: Differences between DOT flat file and Bus Time API data (2015) ................... 16

Table 2: Summary of Requested Functionalities ........................................................... 27

Table 3: Not Completed Functionalities......................................................................... 27

Table 4: Additional Functionality Requests ................................................................... 28

List of Figures

Figure 1: Visualization of Records throughout 2015 (Zhou, et al., 2016)....................... 17 Figure 2: The Frequency of Actual Intervals from Bus Time API (Zhou, et al., 2016) ... 18

Figure 3: The System Architecture ................................................................................ 19

Figure 4: User Case View ............................................................................................. 20

Figure 5: Data Filters ..................................................................................................... 21

Figure 6: Node Selection Tool. The light blue segments show the loaded LION layer. The green segment shows the corridor hovered by the user when hovering the mouse.

The red circles show the selected nodes. ..................................................................... 22

Figure 7: Segment Selection Tool. The green segment shows the current corridor highlighted by the user; the red segment shows selected segments. ........................... 22 Figure 8: The Difference between Segment and Node Selection .................................. 23 Figure 9: Bus Stop Selection. The orange circles show the location of bus stops. The

red circles show the selected stops. .............................................................................. 23

Figure 10: Exported CSV File ........................................................................................ 24

Figure 11: Segment Selection Results. The segments are color coded according to the color scale on the top right of the screen. When a user clicks a segment, more

information about that particular segment is shown in a pop-up. .................................. 25

Figure 12: Node Selection Results. The circles are color coded according to the color scale on the top right of the screen. When a user clicks a node, more information about

that particular node is shown in a pop-up. ..................................................................... 25

Figure 13: Scenario Comparison Tool. After loading two CSV files, the user can compare the two scenarios. The bars in the bar chart are ordered according to the clicks made by the user when creating the segment selection. If the user hovers a bar, then

the corresponding segment will be highlighted on the map. .......................................... 26

4

EXECUTIVE SUMMARY

Travel times can be collected from a large number of potential sources. Conventionally, fixed detectors such as inductive loops embedded in the roadway have been used to measure vehicle flows and estimate speeds. Recent technological advances and the widespread deployment of Global Positioning Systems (GPS) in consumer devices make mobile data sources a promising and potentially cost-effective way to monitor the congestion in a transportation system. New York City Department of Transportation (NYCDOT) along with many other DOTs in the region and around the country have been using probe vehicle data for monitoring time-dependent traffic conditions and conducting before and after studies of various transportation projects. Specifically, NYCDOT has been using probe vehicle data from yellow taxis and other vehicles equipped with GPS as well as the TRANSMIT system. In this project, NYCDOT wants to automate and enhance their use of Metropolitan Transportation Authority (MTA) bus data that they are already acquiring under a protocol developed between the two agencies. The overall goal of improving the current MTA bus data acquisition, processing, storage and querying procedures for NYCDOT comprised of 3 main tasks: The first task is to develop efficient data acquisition, storage, maintenance, querying, and visualization procedures to automate and improve the overall process of using MTA bus data. The second task is to create a web-- going in- house data development efforts as well New York University (NYU) Center for Urban Science and Progress (CUSP) extensive resources and expertise in the area of big data management. The third task is to provide recommendations to enhance the developed tool based on the experience obtained throughout this project and to incorporate this developed app and its functionalities into NYCDOT operations in a more routine manner. In this project, we showed that it is possible to develop a simple yet powerful web based tool to acquire, store, process and visualize bus time data. This tool has an intuitive mapping user interface that can be improved by incorporating functions that can improve the robustness of the tasks at hand. The fact that the tool is web based makes it easy for the end users to access stored data and to query it without any delay or external help. Moreover, the tool enables the users to conduct a series of data visualization and analysis operations demonstrating the potential of such a web based tool for future applications. 5

GENERAL INFORMATION

NYCDOT along with many other DOTs in the region and around the country have been using probe vehicle data for monitoring time-dependent traffic conditions and conducting before and after studies of various transportation projects. Specifically, NYCDOT has been using probe vehicle data from yellow taxis and other vehicles equipped with GPS as well as the TRANSMIT system. In this project, NYCDOT wants to automate and enhance their use of MTA bus data that they are already acquiring under a protocol developed between the two agencies. The MTA is also one of CUSP partnering institutions, and over the last year, the two institutions have worked towards developing a close partnership. During these interactions, it has become clea and store historic Bus Time data, while processing and validating the data against scheduled service The effort in this project includes the development of a software tool to provide NYCDOT with seamless access to MTA Bus Time data. Some of the team members have been working with MTA and NYCDOT for several years on various data related projects. For example, MTA and CUSP staff members and researchers have organized multiple meetings and workshops covering different parts of the MTA system and its data. The research team has taken advantage of the existing partnerships and familiarity with MTA bus data to obtain the best outcome in this project.

1.1 Purpose

The overall goal of improving the current MTA bus data acquisition, processing, storage and querying procedures for NYCDOT will be achieved by completing the necessary tasks needed to achieve the following objectives: Objective 1. Develop efficient data acquisition, storage, maintenance, querying, and visualization procedures to automate and improve the overall process of using MTA bus data.

Objective 2. Create a web-on-

going in- house data development efforts as well NYU expertise in the area of big data management. This web-based application would allow users access to the MTA data and include functionalities to create customized reports that can be used for planning and eventually real-time or near real-time travel time estimation or congestion management projects. To achieve this objective we took advantage of the various features and components of a powerful visualization tool developed by Professor Silva and his students for NY City taxi data. Objective 3. Provide recommendations to enhance the developed tool based on the experience obtained throughout this project and to incorporate this developed app and its functionalities into NYCDOT operations in a more routine manner. 6

AGENCY INTERVIEWS

2.1 MTA

2.1.1 Current Practice at MTA

MTA BusTime data is used mainly for planning purposes. A web-based interface based on in-house data including BusTime data is used for analyses and generating reports. Data is not processed in real-time. Reports are generated for the next day using filtered daily records. Point-to-point travel time and speed information can be gathered from the tool for project impact assessment. MTA mainly uses BusTime data for planning purposes within the agency. Web-based ORGA system is an in-house developed system that uses only own data such as garage and vehicle operations, personnel data. BusTime data is a part of the tool. The system is not designed to generate reports in real-time using live feed. Historical BusTime data are usually analyzed one day after the data are collected. The results are compiled and reported using previous day records around 11 am every morning after filtering and processing of the data is completed. Several performance measures can be extracted from the tool. These include headway analysis, speed analysis, personnel timekeeping, identifying other service problems, etc. Additional bus data collected in MTA buses, such as Intelligent Vehicle Network (IVN) data, holds rich information that can support performance analysis however these data is found to be difficult to process and is not included in in-house tools in MTA.

2.1.2 Improvements to Existing Capabilities

Desirable features by MTA include work zone location display and resulting traffic delays information. Projects such as Transit Signal Priority could be evaluated using MTA BusTime data, with speed and bus volume information. Since entire bus fleet is equipped with GPS devices that transmit real-time location information with 30 seconds frequency, travel time analysis could be undertaken along bus routes.

2.2 NYCDOT

2.2.1 Current Practice at NYCDOT

NYCDOT does not have an accessible, easy to use web based tool utilizing MTA BusTime data feed. NYCDOT uses archived Bus Time data for project planning and evaluation by employing PostgreSQL database and Python functions to clean, store, 7 read/write and analyze data. This requires a certain level of familiarity with aforementioned tools to extract data. existing in-house MTA BusTime data usage capabilities and their future needs. The summary of the meeting is below. MTA mainly uses BusTime data for planning purposes. o Web-based ORGA system is an in-house developed system that relies o The system is not designed to use and report using real-time data. Historical BusTime data is analyzed usually the day after the data collected. The results are finalized and reported using previous day records around 11 am every morning after filtering and processing of the data is completed. o Several performance measures can be extracted from the tool. These include headway analysis, speed analysis, timekeeping, identifying other service problems etc. o Other bus data, such as IVN data, holds rich information that can support performance analysis however these data is found to be difficult to process.

2.2.2 Improvement Suggestions

Data Analytics Group:

o Point-to-point travel time and average speed information. o Segment-based travel times. o Work zone and related traffic delay information. o Project assessment for large projects such as Transit Signal Priority (TSP).

Signals Group:

o The tool can be used to calibrate modeling efforts of existing conditions. o Currently, the group uses snapshot data, i.e. manual and automated traffic counts, for model calibration and validation purposes. o There is a need for reliable travel time information to supplement modeling data, not necessarily in real-time. o Average travel time and speed information are desired to be aggregated in 15-min or any other adjustable intervals. o Turn movements and turn counts for buses should be extracted from the tool. o Project a queried to identify buses with TSP equipment. SBS routes and TSP implementation areas should be provided by Signals group as a layer template. o Buses should be filtered according to their service status: in service, deadheading, etc. Also on revenue buses should be identified. 8 o For user privileges for tool access NYCDOT system, TIMS, can be taken as an example.

TMC Group:

o Since they work with real-time data, they are not interested in using the tool unless real-time accuracy of the travel time information is verified. 9

FUNCTIONAL REQUIREMENTS

The research team held interviews with transit decision-makers to discuss desired functionalities for the web-based application. There were four main topics interested by the users which included the corridor definition and travel time queries; day, date range and time definition; output with different levels of aggregation, and the map output. The functionalities requested by frond-end users are scored depending on their plausibility on a 1-5 scale, with 5 being most likely and 1 being least likely. After this evaluation, 41 potential functionalities are identified. Out of these 41 features, 25 are scored higher than 3 labeled as critical. The list of scored requirements can be seen in the appendix.

3.1 Functional Requirements for Current Bus Data

i. Bus GPS data will be acquired from MTA API developer sources in real-time and will be stored in a web-based server. a. The system should acquire latitude, longitude information from BusTime feed as primary input. b. Web-based mapping tools such as CartoDB and MapBox should be used as a primary tool for showing data points graphically on a map. c. quotesdbs_dbs17.pdfusesText_23