Transit Performance Using Big Data DIDIER M. VALDS EDGARDO ROMN August 13, 2015 State University of New York at Buffalo Buffalo, New York The First Annual Symposium on Transportation Informatics Big Data Analytics Transforming Operations, Management and Safety Introduction Transit Systems play a very important role in terms of mobility, energy savings and the environment Transit use for the trip to work reached a low point in the mid-1990s since its high peak after WWII. Personal vehicles provide 84% to 88% of the journeys to work since 1980 Transit had not passed the 5% mark The transit market share may vary up to 20% in the most dense parts of big cities like Manhattan, New York Annual Transit Ridership Trend by Bus, 1922 to 2010 Data Source: American Public Transportation Association Public Transportation Fact Book, Appendix A: Historical Tables. We must measure Transit Performance The TCRP Report 88: 1. Because we are required; 2. Because it is useful to the agency, and 3. Because others outside the agency need to know what is going on. Performance measurement involves: collection, evaluation, and reporting of data They tell us how well an organization is performing its functions and meeting its goals and objectives. Evaluate the system Improve the system Increase of ridershipContinuous Monitoring Our purpose of using performance metrics: What to measure? During the past decades many authors have discussed which aspects of a transit system should be measured and reported. Most research had focused on the supply side To increase ridership (as one of the purposes established before) its essential to measure and also understand demand Several frameworks have been established to explain this phenomenon A high level classic perspective presented by Manheim differentiate the Activity System from the Transportation System that interact to generate the Flows (trips). A transit operators perspective presented by Bhuiyan (2015) divides the factors with a simple criteria: Does the operator has control over it?. Therefore considers Internal and External Factors What to measure? Example of External and Internal Factors according to Bhuiyan et al. What to measure? Transportation System Modes Elements People, commodities, vehicles and facilities Movement Origin Destination Relationships 1.Flow are determined by both systems 2.Flow may cause change in activity system 3.Flow may cause change in transportation system Activity Systems Pattern of social and economic activities. Consists of many subsystems, overlapping and interrelated Social Structures, political institutions, all kind of markets (housing, labor, etc.), and so on. Example of the classic Manheim system of variables and their relations What to measure? Manheim stated that The internal dynamics of the activity system are very complex, and our understanding of it is very incomplete. Compact, high-density neighborhoods gather people together in sufficient numbers that local stores can find customers and bus lines can find passengers within easy walking distance. Several recent studies found strong correlations between ridership, income and employment, real GDP, and other economic factors (Taylor et al. 2002; Yoh et al. 2003; Taylor et al. 2009). Challenges and Opportunities to apply the Big Data Framework What to measure? Researchers like Alam (2009) have used geographic information systems (GIS), to align household or Census tract-level socioeconomic data Street maps and transit system coverage Developing more precise mapping of sociodemographic, employment, and transit access data There is a need to examine transit travel demand functions that include both geographic-unit-related variables and transit system-specific variables Our goal is to find new metrics using the combination of several data sources, to obtain suitable performance metrics for real-time decision making integrating headways, running times, and passenger loads What are we doing? In the past demand modeling and performance metrics had been greatly biased toward the transportation system relying on small samples of data Our objectives: Better understand the transit performance to improve operations Measure aspects of both transportation and activity systems (or internal and external factors) To study the whole phenomenon with detailed data from multiple sources What are we doing? Establish a cross-disciplinary team that includes: Transportation Engineers Computer Engineers Electrical Engineers Start from the basics Establish a Data Management Framework that allows for the integration of Big Data in real time with historical data, and dynamic data from several sources. Recently Puerto Ricos government made some substantial changes on its transit system on the SJMA (San Juan Metropolitan Area) Transportation System Automatic Vehicles Locators or GPS systems can provide with the coordinates of any vehicle with intervals as short as 5 seconds Algorithms have been developed to use location data to calculate the main operational variables for the main routes of the Metropolitan Bus Authority (AMA for its acronym in Spanish) Automatic Passenger Counters can provide demand data for 25% of the buses Users perceptions and satisfaction will be obtained using social networks to distribute surveys Spatial Information have been digitalized using GIS: Route Stops and Terminals locations Distance between stops Accessibility, Walk side conditions What are we doing? Raw Data and Processing Example Initial Processing Raw Data Example Data Analysis For Each Route Cycle Time Data Analysis For Each Route Cycle Time Activity System GIS and Census Tracks (Users Characteristics and needs) Population Income Household information Race Age Government Agencies on GIS databases Spatial Distributions of Activities Such as Schools, Hospitals, Colleges, mall centers, etc. All these variables are static, they wont change within a day The next challenge is to measure the temporal distribution of these activities (Conversion to dynamic data). The Flows Traffic patterns monitored with video cameras will provide check points for dynamic data Transit Usage will be monitored using passenger counters Combination of other sources of information Proposed graphical representation Effective data visualizations can quickly communicate key aspects of data analysis and reveal new patterns to decision makers and the public The information output style, should be accessible, communicative and compelling. The graphical tools will include the following capabilities: Geo-spatial: plotting data on customizable maps with additional geographical information. Time resolution: observing hourly, daily, weekly etc. patterns by easily switching between different time resolutions. 3D: data depicted as 3D objects on a 3D globe for an immersive experience. Animation: free navigation to different periods of time in the data and comparison capabilities. Interaction: ability to pan or zoom to particular points and interact with them to display additional information. Conclusions With the new information available from automatic sensors and other sources, we propose a new framework that could: Measures both Transportation and Activity Systems effectively Change scale of analysis on temporal or spatial terms easily Predict Demand accurately on actual or alternative conditions Results in suggestion of changes of both Transportation and Activity Systems Some Challenges Ahead Development of the framework for processing Big Data with the budget constraints of AMA Sample data may be required on the Activity System near the stops. None of this information is digital. Integration of various sources of information Measure the dynamic aspects of the Activity System References TR Circular E-C192: Data and Statistics for Valuing Transportation Infrastructure and Transportations Contribution to the Economy. Transportation Research Board, Washington, DC: 2015 TCRP Report 88: A Guidebook for Developing Transit Performance-Measurement Systems. Transportation Research Board, Washington, DC: 2003 TCRP Report 141: A Methodology for Performance Measurement and Peer Comparison in the Public Transportation Industry. Transportation Research Board, Washington, DC: 2010 Transportation Statistics Annual Report Bureau of Transportation Statistics. Washington, DC: 2014 CPB Report: BIG DATA AND TRANSPORT: UNDERSTANDING AND ASSESSING OPTIONS. International Transport Forum. Paris: 2015 Manheim, M.L. (1979). Fundamentals of Transportation Systems Analysis, Vol. 1: Basic Concepts. Cambridge, MA: MIT Press References MTI Report 12-30: INVESTIGATING THE DETERMINING FACTORS FOR TRANSIT TRAVEL DEMAND BY BUS MODE IN US METROPOLITAN STATISTICAL AREAS. Mineta Transportation Institute: San Jos, Ca: 2015 Metcalf, G. (2002). The path to a livable city. Transportation for a Livable City. San Francisco, Ca. Taylor, Brian D., et al Increasing Transit Ridership: Lessons from the Most Successful Transit Systems in the 1990s. The Mineta Transportation Institute, MTI Report Taylor, Brian D., Douglas Miller, Hiroyuki Iseki, and Camille Fink Nature and/or Nurture? Analyzing the Determinants of Transit Ridership across US Urbanized Areas. Transportation Research Part A: Policy and Practice 43 (1): 6077 Alam, Bhuiyan M Transit Accessibility to Jobs and Employment Prospects of Welfare Recipients Without Cars. Transportation Research Record: Journal of the Transportation Research Board 2110: 7886. THANKS. Didier M. Valds, Ph.D. University of Puerto Rico at Mayaguez