Title: Ready or Not, Big Data is Coming to a City (Transportation Agency) Near You

Transcription

1 Title: Ready or Not, Big Data is Coming to a City (Transportation Agency) Near You Submission date: August 1, 01 Response to call: Transportation Issues in Major U.S. Cities (ABE0), Applications of "Big Data" in Addressing Urban Transportation Issues Word counts Abstract: 0 Text:, References: 1 Number of figures and tables: x 0 = 0 Total word count:, Corresponding Author Stephen Buckley City of Toronto, Transportation Services, General Manager 0 Queen Street West, E Toronto, Ontario MH N Canada Phone: sbuckle@toronto.ca Fax: Other Authors Deborah Lightman MMM Group Limited, Planner Hunt Club Road, Suite 00 Ottawa, Ontario K1V 0Y Canada Phone: lightmand@mmm.ca Fax:

2 ABSTRACT For public transportation agencies, Big Data particularly floating traveller data (FTD) opens up a world of possibilities. FTD locates travellers using signals from mobile phones, GPS, and/or Bluetooth systems. Traveller location, speed, and direction of travel are aggregated, producing high-quality travel information in real-time. In recent years, sample sizes have increased, technologies have improved, and analytics have become more sophisticated, making FTD an affordable and reliable source of information. Using FTD, agencies can track vehicle travel patterns, volumes and speeds across the full network. Information on pedestrians and cyclists is not far behind. Agencies can use FTD data to improve the efficiency and effectiveness of service provision, and to develop new services that meet users needs. Applications include: analyzing system performance; planning transportation infrastructure; evaluating operational interventions; conducting active traffic management; forecasting travel conditions; and providing enhanced traveller information systems. At the same time, Big Data will bring new challenges for public transportation agencies, many of whom lack the analytical expertise, baseline investments in IT, and organizational culture required to capture Big Data s value. This paper discusses the evolution and future directions of travel data in transportation agencies. It illustrates the wide variety of FTD applications and describes how agencies are harnessing FTD to understand and improve transportation system performance. The paper identifies the investments and organizational changes that transportation agencies may need to make to harness the value of Big Data. Finally, it explores the big questions that accompany Big Data and provides recommendations for getting started with Big Data.

3 Buckley and Lightman INTRODUCTION Over the last few years, the volume of data has exploded. From 0 to 01, there was approximately a seven-fold increase in the volume of data generated, largely due to the proliferation of devices such as smartphones and tablet computers with embedded sensors and GPS systems (1). Technological advances have produced the infrastructure and analytics to integrate and make sense of all of this information. The era of Big Data has arrived. At the same time, transportation agencies are facing unprecedented pressures. Particularly in major cities, infrastructure is aging, budgets are being squeezed, and investments in transportation have not kept pace with population and employment growth. Citizens expect government to become more efficient, effective and responsive, and to balance the needs of all modes of travel. Big Data provides transportation agencies with significant opportunities to address these demands and improve the operation of urban transportation systems. In particular, floating traveller data enables agencies to track travel patterns, volumes and speeds in real-time, across the full transportation network. Transportation agencies can use this data to understand transportation systems better than ever before, to respond to changes in real-time, and to better meet the needs of all transportation system users. This paper discusses the evolution of travel data and identifies the possibilities that Big Data brings. It describes how transportation agencies are harnessing new types of travel data to understand and improve transportation system performance. The paper also presents some of the challenges that agencies will face as they seek to capture Big Data s value. Finally, it explores the big questions that accompany these uses of Big Data and provides recommendations for getting started with Big Data. THE EVOLUTION OF TRAVEL DATA IN THE BIG DATA ERA Travel data is integral to understanding and improving urban transportation systems, and Big Data is fundamentally changing our data collection methods. Historically, travel data has been collected using fixed point sensors on roadways (e.g. loop detectors, tube counters, etc.) and/or through manual counts of vehicles and pedestrians at intersections. Numerous estimation methods have been developed to estimate vehicle speeds and to fill in gaps between data points. This data and modelling process has underpinned most transportation planning and traffic management activities. However, these methods are very expensive and provide an incomplete picture of how our transportation systems operate (). With the dawn of Big Data, these conventional in-situ technologies are rapidly being replaced with floating traveller data (FTD). FTD locates travellers in real-time using signals from mobile phones, GPS, and/or Bluetooth systems (). Traveller location, speed, and direction of travel are sent anonymously to a central processing centre and aggregated, producing highquality traffic information in real-time. While there are limitations to FTD, the advantages of FTD over conventional fixed point sensors are very significant (Table 1).

4 TABLE 1 Comparison of Fixed-Point Data and Floating Traveller Data () Characteristic Fixed-Point Data Floating Traveller Data How is travel time measured? Inferred from a network of sensors Measured directly How is speed measured? Inferred from volume Inferred from travel time How is volume measured? Measured directly Inferred from sample size What determines data quality? Density of sensor network Sample size Data processing/analytics What are the main advantages? Direct information on traffic volumes Captures all vehicles, not a sample Covers entire network Large time period of data Detailed information on origin, destination and route choice Low cost, no physical infrastructure required What are the main disadvantages? Covers only select locations where sensors are installed High capital, installation and maintenance costs Limited information on origin, destination and route choice Volumes inferred from sample size The quality of FTD has increased dramatically since the early days of GPS technology. Sample sizes are growing as more and more people travel with mobile phones, and GPS systems themselves have become more accurate and more precise. At the same time, FTD analytics have become increasingly sophisticated, enabling traveller data to be used in new ways (Figure 1).

5 01? 0 s 000 s 10 s FIGURE 1 Evolution of Floating Traveller Data For the past two decades, transportation agencies have been using GPS data to track transit vehicles and fleet vehicles equipped with automatic vehicle location (AVL) systems. Roughly a decade ago, floating car data (FCD) from all types of vehicles started to be used to provide information on expressway travel conditions. This innovation proved invaluable for agencies such as the I- Corridor Coalition, described in the next section. However, as of 00, FCD was only considered reliable for expressway applications (). The FCD analytics were not able to account for traffic control, turning and parking movements, and non-vehicular data sources (e.g. pedestrians and cyclists). Now, the required algorithms have been developed and floating car data enjoys widespread use for analysis of traffic on signalized arterials. This means that FCD can be used by municipal governments to manage urban congestion and balance the needs of drivers, transit users, cyclists and pedestrians. As sample sizes continue to grow and algorithms continue to become more sophisticated, floating car data will be supplemented by floating bike and pedestrian data. Applications such as Strava and Cycletracks that require active user participation are already creating a foundation for cycling data collection. Within the next few years, it is expected that FTD algorithms will enable transportation agencies to track pedestrians and cyclists carrying mobile devices based on their travel speed and location within the right-of-way. Armed with the full array of FTD, transportation agencies will have an unprecedented understanding of how people travel. Finally, FTD is very affordable. Already, third party providers are offering highresolution, / data across million miles of road in over 0 countries at a fraction of the price of traditional traffic counts. According to North Carolina DOT where previous approaches to gathering traffic data had a life cycle cost of nearly $0,000 per mile, FCD has delivered more coverage at about percent of the per mile life cycle cost (). MAKING USE OF TRAVELLER DATA IN THE BIG DATA ERA Large samples of high-quality FTD are now available at reasonable prices. Furthermore, sophisticated analytics enable agencies to gauge traveller speeds, volumes and travel times and to

6 distinguish between different modes of travel. Transportation agencies can use this data in innumerable ways to better meet the needs of transportation system users (Table ). TABLE Uses of Floating Traveller Data in the Big Data Era Purpose Use of Floating Traveller Data Timing Network performance analysis Analyze congestion, delay and reliability o Duration o Intensity o Geographic extent Identify bottlenecks Historical Transportation infrastructure planning and demand management Intervention prioritization and evaluation Active traffic management Traveller information systems Traffic forecasting Develop origin-destination matrices Analyze route and mode choices Create robust network modelling tools Design transportation demand management initiatives Evaluate need for operational interventions Quantify impacts of interventions using before/after analysis Acquire automatic incident detection systems Implement active traffic management strategies o Dynamic signing and rerouting o Surface transit operations management o Junction control o Speed harmonization / variable speed limits o Managed lanes / temporary shoulder use o Variable pricing Post information on variable message signs and transit passenger information signs Share traffic data with media outlets Enable private sector application development by publishing Open Data Develop web-based/mobile traveller information tools Forecast traffic conditions and incidents based on date, time, weather, current conditions, local events, etc. Adjust system operations based on forecasts Historical Historical Real-time Real-time Predictive FTD provides transportation agencies with an opportunity to deliver existing services more efficiently and effectively. For example, agencies can plan new transit routes based on

7 1 accurate, detailed origin-destination and route choice information. Agencies can identify bottlenecks and determine where traffic signal timing needs to be adjusted. And agencies can identify and respond to incidents more quickly, using FTD-based automatic incident detection systems. FTD also provides transportation agencies with an opportunity to deliver new services. For example, agencies can provide the public with real-time information on travel times using different routes and/or different modes. Agencies can implement variable speed limits and managed lanes to improve the overall flow of traffic. And agencies can develop demand responsive transit services where routes are optimized based on passenger destinations and traffic conditions. Moving forward, transportation agency staff might make daily use of FTD-based tools and products such as the following: Map of real-time congestion, with alerts where congestion exceeds expected levels based on historical averages Map of cycling volumes on different routes, winter versus summer Map of pedestrian volumes relative to sidewalk width Map of real-time incidents and lane closures Forecast of daily travel times on key routes Before-after travel time analysis tool to evaluate the impacts of operational changes Real-time list of transit routes experiencing significant delays Application with real-time parking availability Enhanced origin-destination maps and models (discussed further below) Next-generation transportation demand management programs (discussed further below) Changing Tools: From Four-Step Modeling to Real-Time Network Visualization The four step model has underpinned most travel demand models since its creation in the 10s. These travel demand models have, in turn, underpinned regional transportation planning and network analysis. The four steps are as follows: 1. Trip generation estimating the number of trips generated by a traffic analysis zone based on socioeconomic data and the propensity to travel.. Trip distribution determining where trips will end based on trip attraction distributions and travel impedance, yielding trip tables of person-trip demands.. Mode choice distributing trips between different modes of travel, based on the relative proportions of trips by alternative modes.. Trip assignment assigning modal trip tables to particular routes/mode-specific networks. In the Big Data era, transportation agencies will not need to model the number of trips, their origins and destinations, and the modes used. Instead, agencies will have access to real data on the complete transportation network trips made at all times of day using all routes and all modes of travel, including walking and cycling. Agencies will be able to watch their transportation systems operate and change in real-time. While network models will still be used for forecasting purposes, these models will be based on very different methodologies and data.

8 Changing Tools: A New Generation of Transportation Demand Management Initiatives Transportation demand management (TDM) is the use of a wide range of policies, programs, services and products to influence how, why, when and where people, with the goal of making travel behaviours more sustainable. Information and promotion are paired with incentives and disincentives to drive changes in travel modes, trip frequency and trip timing. TDM initiatives have traditionally been developed based on crude data about origins, destinations and user preferences. Personalized individual travel planning programs have proven effective, but have been extremely resource-intensive to deliver. In the Big Data era, transportation agencies will be able to provide personalized, realtime travel information to help users plan their travel at low cost. Furthermore, transportation agencies will be able to adapt incentives and disincentives to travel (e.g. transit fares, parking costs and road tolls) based on real-time network conditions. Big Data combined with other technologies and tools therefore has the potential to dramatically improve agencies demand management capabilities. BIG DATA IN ACTION Transportation agencies around the world are already using Big Data to track changes in system performance, to identify bottlenecks, and to evaluate the impacts of interventions. This section describes the steps taken by Dublin, the I- Corridor Coalition, Singapore, San Francisco, Toronto and Oregon to capture value from FTD. While these examples are just a starting point, they are helpful in illustrating Big Data approaches and applications. Taking Action to Optimize Public Transit in Dubuque, Iowa In 00, the City of Dubuque and IBM Research announced a partnership to make Dubuque a living laboratory and smarter sustainable city. Since then, four Smarter Sustainable Dubuque pilot projects have been completed, including the 01 Smarter Travel Pilot Study. The Smarter Travel Pilot Study aimed to optimize public transit service based on detailed origin-destination and transit use data. The Pilot Study used a smartphone application developed by IBM Research and RFID technology to collect anonymous data on how, when and where volunteer participants travel within the community. Buses were outfitted with RFID and GPS-enabled systems; as volunteers with RFID tags entered and exited the bus, the tracking system created an anonymous record of their trip. Together, smartphone app data and vehicle data painted a dynamic, real-time picture of the state of transit activity across the city. Volunteers also received information on their travel patterns and recommendations to save money and conserve resources (). During the Smarter Travel Study, public transit ridership in Dubuque increased by.% (). The City is now working with state and federal partners to analyze the anonymized and aggregated data. This data will provide Dubuque s traffic planners with the tools to make informed tactical decisions such as how to reroute buses to avoid traffic, and how to optimize bus routes and schedules to better meet public transit demand. The long-term goal is an increase in public transit ridership and more efficient utilization of Dubuque s transit fleet (). Breaking New Ground in Coordination and Traveller Information along the I- The I- Corridor Coalition began in the 10's as an informal group of transportation professionals working across jurisdictional boundaries to enhance regional transportation

9 mobility, safety, and efficiency. The Coalition had an early focus on intelligent transportation systems, and their Vehicle Probe Project (VPP) is one of North America s best examples of technological innovation through interagency cooperation. Launched in July 00, the I- Corridor Coalition's VPP was a ground-breaking initiative to provide comprehensive and continuous real-time travel information (travel times and speeds) to Coalition member agencies. Originally covering 1,00 freeway miles and 1,000 arterial miles from New Jersey to California, the project was viewed by the Coalition and its member states as an experiment to determine if privately sourced traffic data was "ready for prime time." Since then, the VPP has facilitated inter-agency cooperation and has enabled member agencies to monitor the entire highway network at a fraction of the cost of traditional detection. The VPP has also underpinned a suite of traveller information tools, and has improved incident monitoring and emergency response. Now, the VPP covers over,000 miles, with traffic data updated every minute in 1 states from Maine to Florida. Using New Technologies to Manage Demand in Singapore Singapore s population has more than doubled since 10, but expanding the road network to address the growing transportation demands has not been seen as a sustainable option. Instead, Singapore has focused on maximizing the capacity of the existing road network through Intelligent Transportation Systems (ITS), public transit, and demand management including road pricing. Road pricing was first implemented in Singapore in 1 as a flat charge on all vehicles entering the Central Business District. In 1, this was replaced by the current Electronic Road Pricing (ERP) System that uses Radio Frequency Identification technology to automatically deduct a congestion charge on any vehicle passing under a road pricing gantry during operating hours. ERP rates are determined by a quarterly review of traffic speeds of priced roads, and are adjusted based on an optimal speed range of 0-0 km/h on arterial roads and - km/h on expressways (). Thanks in part to ITS, Singapore is one of the least congested major cities, with an average vehicle speed on main roads of km/h, compared to an average speed of 1 km/h in London and km/h in Tokyo (). However, the current ERP technology has its limitations; road users have pointed out that the implementation of an ERP gantry on a given road shifts some of the traffic elsewhere, causing traffic bottlenecks along unpriced roads. The next generation satellite-based ERP system (ERP II) will address this issue by permitting more targeted and effective placement of charging points now that there is no need for a physical charging gantry (C. Kian Keong, Singapore Land Transport Authority, unpublished data). The new system will also provide an option of charging users based on distance travelled on congested roads. Singapore s Land Transport Authority is expected to issue a tender for the installation of the next generation, real-time satellite-based system in 01. Using Real-Time Data to Manage Parking Supply and Demand in San Francisco In San Francisco s downtown core, parking management matters. Parking is often difficult to find, so many people double park or circle to find a space. As they circle, they waste time and fuel, and may drive distractedly, creating a traffic safety risk. San Francisco s public transit system is sometimes stuck in the middle, negotiating double parked cars or waiting for circling cars to turn. In response to these parking pains, the San Francisco Metropolitan Transportation Agency (SFMTA)'s designed the SFpark pilot.

10 SFpark used smart meters, parking sensors, and a sophisticated data management tool to create demand-responsive parking pricing. With SFpark, the SFMTA was able to gradually and periodically adjust rates at meters and in garages up or down in order to achieve a target level of occupancy. The goal was to increase the amount of time that there was parking available on every block and to improve the utilization of garages. Funded through the Department of Transportation s Urban Partnership Program, SFpark tested its new parking management system at,000 of San Francisco s metered spaces and 1,0 spaces in garages. The evaluation report revealed that the pilot project achieved its objectives. The amount of time that blocks were too full to find parking decreased by 1% in pilot areas while increasing by 1% in control areas. In addition, over the course of the pilot, the SFMTA lowered average hourly rates at meters from $. to $. and at garages from $. to $.0 (). Harnessing Insights from Historical Data in Toronto Toronto s population and employment are growing rapidly, while the capacity of the transportation system is not. In the face of increasing congestion, the City of Toronto s Transportation Services department is seeking new ways to reduce demand and to improve the efficiency of the transportation system. Alongside these efforts, the City is partnering with McMaster University to understand the state of the system and to track how it has changed in recent years. Using INRIX data, the City and McMaster are profiling vehicle speeds, travel times, delay, and reliability on expressways, arterial corridors, and in the downtown core. The project also includes identifying bottlenecks in the system, analyzing recurrent versus non-recurrent congestion, and conducting before-after analysis of the impacts of City interventions (e.g. parking regulations, traffic signal timing). This type of detailed, high-quality analysis will help build the business case for future interventions and will underpin the City of Toronto's congestion management efforts in the coming years. Using Private Sector Applications to Support Cycling in Oregon For years, the Oregon Department of Transportation (ODOT) has been struggling to develop new cycling facilities based on limited data. Although ODOT and a few local jurisdictions collect bike counts, counts only provide usage data of a one location for a short period of time (). Bike counts are generally time intensive, resource intensive and do not provide data about bicycle travel behavior i.e., where, when and how people ride. ODOT is looking to address this data deficiency in 01. ODOT is piloting a public/private partnership with Strava to understand how and where cyclists are riding in Oregon. Strava is a smartphone application that tracks users rides to help users analyze and quantify their cycling. Strava s 01 dataset includes over 00,000 bike trips made by,000 cyclists in Oregon, totalling over million bicycle miles travelled. ODOT is paying Strava $0,000 for a one-year license of this dataset and intends to use it in a variety of ways. The data will inform local and regional transportation planning, to identify appropriate locations for new cycling facilities. Strava data will also be used for project management and project delivery, to verify the use of new routes by cyclists and to analyze travel patterns. The data will assist with maintenance, by providing feedback on when cyclist ride in the context of sweeping and other maintenance schedules. Finally, the pilot will test public-private partnerships related to data and will inform the long-term use of Big Data by ODOT ().

11 MAKING THE SHIFT TO BIG DATA IN TRANSPORTATION AGENCIES Though the era of Big Data is just beginning, it is already clear that Big Data particularly FTD holds big promise for transportation agencies. However, transportation agencies will need to make significant investments in talent, business systems and organizational culture in order to exploit Big Data s potential. Finally, agencies will need to build the business case for investments in Big Data. These challenges are described below, along with areas for further research and recommendations for getting started with Big Data. Investing in Talent First, transportation agencies will need to step up their talent attraction, retention and development efforts. Converting data into understandable, useful and actionable information requires strong analytical skills, and the supply of people with these skills is finite. According to a recent McKinsey report, the demand for people with deep analytical skills in big data for example, machine learning, and advanced statistical analysis could outstrip current projections of supply by 0% to 0% in the United States (1). These skilled data analysts are needed to analyze large volumes of data and identify patterns and trends. Furthermore, McKinsey projects a need for 1. million additional managers and analysts in the United States who can ask the right questions and consume the results of data analysis effectively (1). This will likely be a challenge for the public sector. As the market for skilled data analysts and managers becomes increasingly competitive, transportation agencies will need to aggressively recruit employees with the right skills. Transportation agencies will also need to ensure that staff have opportunities to develop analytical and managerial skills and to upgrade their skills over the course of their careers. Big data tools, technologies and analytical techniques will undoubtedly evolve rapidly, and transportation agencies will need to keep pace with these developments. Investing in Business Systems Transportation agencies will not necessarily need to collect and process data in-house. As demonstrated by the examples in the previous section, many agencies have partnered with private companies, consulting firms, and academic institutions to gather and analyze big data. However, to harness the value of big data, transportation agencies will need to establish effective business processes and data management systems. Big data and the associated analytics will need to be procured in a timely manner, stored in appropriate places, shared with the right people, integrated with other data sources and used for decision-making. Many transportation agencies will undoubtedly struggle to establish the business systems for data management and use. Governments already collect huge amounts of data, which is good news for big data, but government departments often keep data in silos. Furthermore, public sector agencies frequently lack modern information technology (IT) assets and face challenges related to legacy systems and restrictive procurement processes (1). Some agencies will need to upgrade their IT systems, while others will need to focus on business processes to support IT. In particular, major investments will be required where real-time data is being used for transportation system management. Transportation agencies may also benefit from creative approaches to procurement, to expedite data and technology acquisition and to ensure that they obtain the best tools for the job at hand.

12 Investing in Organizational Culture Finally, transportation agencies will need to foster an organizational culture that provides a visions of how big data can be used, and provide support to enables them to derive real value from big data. This involves adopting a data-oriented mindset and practicing data-driven decision-making. This also involves nurturing innovation and taking thoughtful and calculated risk when appropriate and creating space for improvements to policies, technologies and practices linked to big data. Finally, creating dynamic, innovative, data-driven work environments will help agencies to attract and retain top talent, and to access the IT infrastructure that underpins big data. Making the Case for Big Data In the current climate of government austerity, transportation agencies will need to carefully build the business case for investments in Big Data whether for procuring the actual data or hiring staff to make use of data. In most cases, this will involve clearly articulating to decisionmakers including budget staff and political leadership how Big Data will actually improve decision-making and service provision. Agencies will need to tell stories in a new way, replacing technical jargon with clear value propositions. Big Questions about Big Data It is clear that Big Data will become an integral component of the work of all transportation agencies in the coming years. It is also clear transportation agencies will evolve in response to the opportunities and challenges that Big Data presents. However, it is not yet clear what the Big Data era will look like within transportation agencies. Questions remain regarding the role of the public versus private sector in the Big Data era. What types of partnerships with the private sector will be needed to generate and share the value of Big Data? What Big Data functions will be outsourced and what will be kept in-house? Are public sector purchasing procedures equipped to handle the new relationships between the private and public sector? There are also significant unknowns regarding transportation agency jobs, tools, and management techniques in the Big Data era. What jobs will become obsolete and what new jobs will be created? What changes to administrative structures, hierarchies and reporting relationships will be needed to function in the fast-paced world of Big Data? What tools and methodologies will become obsolete? As described below, travel demand models are among the tools that could change dramatically in the Big Data era. Finally, there is uncertainty regarding the industry standards that will be needed to enable flexibility and modernization. Historically, local governments have struggled to select and maintain new technology, given the challenges of procuring new hardware and software. Agencies have struggled to avoid proprietary systems that make them "captive" to any one provider and prevent future migrations. Agencies would therefore benefit greatly from industry standards that lead to open and flexible data platforms. It is not yet clear if and how these types of standards will be created.

13 Recommendations for Getting Started Given the near-infinite opportunities that Big Data creates, transportation agencies may struggle with where to start. The six strategies below may therefore help agencies prepare to capture the value of Big Data. 1. Start with a well-defined, high value project. This project should have a clear value proposition, address a demonstrated need, and be relatively easy to understand. Starting with the why means that the initial project is more likely to be supported by senior managers, politicians and the public, even if there is a cost.. Pick a project where you can "prove" success. Proving success has always been a challenge in urban transportation; systems are complex and it is very difficult to demonstrate causation. When selecting a project, it is therefore important to consider how success will be defined and measured, and to build evaluation into the project design.. Identify the data that will be required and evaluate the strategies available to collect and analyze this data. Once you understand what is needed, you can compare the costs associated with different data collection and analysis options. If possible, avoid locking into one particular format or platform, as technologies are still evolving rapidly.. Take advantage of expertise outside of government. Universities and private sector organizations have valuable Big Data skills and tools. They are often willing to make these assets available to the public sector at relatively low cost. Making datasets available to the public as Open Data can also generate impressive results, as demonstrated by applications such as RocketMan, Beat the Traffic, Parkmobile, and Spotcycle.. Allocate resources to the project. Even for projects of modest size, significant human and/or financial resources will be required to navigate the landscape, develop business processes and set up IT systems. This is particularly true when delving into the world of Big Data for the first time.. Build on successes strategically. Develop a longer-term plan for the use of Big Data, prioritizing initiatives based on their value and ease of implementation. Consider including other government groups in this planning; collaborating across government can increase the efficiency of initiatives and yield unexpected benefits. Revisit your Big Data plan regularly, as tools and techniques change. CONCLUSION Transportation agencies are harnessing Big Data to understand and improve how their transportation systems operate. They are using FTD to analyze system performance, to plan and evaluate interventions; to conduct active traffic management; and to provide enhanced traveller information systems. As data and analytics continue to improve, agencies will be able to use FTD in even more ways to support mobility for all modes of travel. At the same time, Big Data will bring new challenges for public transportation agencies, many of whom lack the analytical expertise, business systems, and organizational culture required to capture Big Data s value. Most transportation agencies will need to make significant investments in order to flourish in the Big Data era. Agencies will also benefit from building the business case for Big Data and taking advantage of the expertise of external organizations. Questions remain regarding the changes that will occur in transportation agencies and the roles

14 of the public versus private sector. However, it is clear that Big Data will soon become integral to the work of transportation agencies. It is also clear that transportation system users have much to gain from the exciting opportunities presented by the Big Data era. All sectors of society will benefit from public sector efforts to build capacity and bring Big Data to a city near you.

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