Intermodal Passenger Facility Planning and Decision-Making for Seamless Travel (2024)
Chapter: 6 Data and Information Needs
CHAPTER 6
Data and Information Needs
Introduction
Intermodal passenger facility owners and providers rely on data to understand daily operations and trends and flag issues requiring attention. The quality of available data and the use of data to make decisions varies, in part due to the lack of established practices around data management and limited understanding of how to leverage insights obtained from data. Data-driven decision-making can enable those managing and operating intermodal passenger facilities to better adapt to change. This chapter explains how to plan and manage data collection, including methods, approaches to data stewardship, and the use of data-sharing agreements. It describes new sources of data and discusses data used for different travel modes, and it provides examples of using technology and systems to measure facility performance.
Data Collection Planning and Management
Data collection and management plans typically have the following elements:
- Purpose.
- Methods.
- Data stewardship.
- Staff responsibilities.
Purpose
Intermodal passenger facilities may include multiple modes of transportation, each with their own set of unique data outputs. Before deciding what data to collect, owners and operators should identify what information is important to understand, what decisions need to be made with those data, and what analyses will be performed.
Methods
Data collection can occur in different ways. With new technology and mobility options available, the transportation industry is relying less on manual data collection and more on automatic and continuous data collection. When possible, data should be made available without restrictions in order to simplify collection and analysis. This facilitates connections to open-source (unrestricted) APIs that enable computer programs to communicate directly.
Integrating New Methods
Integrating new information with existing data sources and data management processes can be challenging. This integration may require updating prior methods of data collection while still maintaining key performance indicators (KPIs) already in use.
A comprehensive data integration plan typically includes timelines, data details, validation, processes, and needed changes to database management and architecture. Staff should understand how to use and interpret new data sources and understand data limitations. For example, if an intermodal passenger facility previously relied on a regional transit operator to send monthly summaries of daily boarding and alighting at each stop and route serving the intermodal passenger facility, related KPIs should be updated to connect to the transit API providing real-time General Transit Feed Specification (GTFS) data. Improved real-time data could help facility operators respond to variations in demand and better manage staffing. Any methods of manual data collection should also include plans for updates on a recurring basis.
Data Stewardship
Data stewardship is a set of policies and practices for ensuring that data are well managed. Data stewardship includes collecting, storing, sharing, archiving, and deleting data. It helps to support accuracy and trust by creating a structure for staff and other stakeholders to access data for planning, analysis, summary, and reporting purposes. In defining an approach to data stewardship, the following topics should be explored:
- Privacy and security of incoming data.
- Policies to secure private personal information.
- Capabilities of data storage.
- Procedure for scraping, validating, and cleaning data to convert to usable format.
As new data sources expand and become more available, data stewardship helps to ensure that data can be processed and that generated reports are accurate. Moreover, as the measurement of facility use increasingly relies on real-time data and access to provider APIs, effective data stewardship will become more critical for responding quickly and effectively to changes and incidents.
Data Management Principles Checklist
NCFRP Web Resource 49: Understanding and Using New Data Sources to Address Urban and Metropolitan Freight Challenges suggests the following checklist of data management principles, which are applicable to intermodal passenger facilities:
- Does your agency retain and store information that it believes will be valuable for research or decision-making in the future?
- Does your agency prescribe or otherwise require how geographic or location-related attributes are recorded and used?
- Is your agency’s data frequently refreshed, and will it be updated or cleaned in the future?
- Have the data themselves been explained or described logically so that the data are understood and used appropriately?
- Are the data easy to find by those who need to use them?
- Are the data openly available, and are any restrictions clearly stated and justified?
The web resource suggests that answering these questions can help determine if the principle of data management applies and suggests possible actions or tools to aid in implementation (CPCS and Ludlow 2019).
NCFRP Web Resource 49: Understanding and Using New Data Sources to Address Urban and Metropolitan Freight Challenges includes a discussion of data stewardship and principles, including transparency and openness, purpose specification, data minimization, retention and use limitation, data quality and accuracy, accountability, security, and data management (CPCS and Ludlow 2019).
Staff Responsibilities
An effective data collection and management plan should outline the staffing resources and capabilities to execute the plan. If the plan involves use of multiple open-source APIs for collecting real-time data, hiring staff trained to write code and maintain API connections is essential to ensure collected data remains usable. Business intelligence dashboards are real-time information management tools that provide a visual representation of real-time data in support of data-driven decision-making (Patel 2023). Examples include PowerBI and Tableau.
Data Sharing
Intermodal passenger facility owners are in a unique position to be both generators of sharable data and recipients of external data. Expanded access to data may help facility owners make less-siloed operational decisions with greater efficiency and visibility. Connectivity between modes can be optimized, supply and demand patterns can be better understood, and planning and operations can be undertaken more effectively. However, there are barriers to data sharing, such as privacy and security concerns, concerns within for-profit industries about competition, and cost considerations since cleaning, refining, and maintaining big data is expensive. Data-sharing agreements can help to alleviate some of these barriers.
Agreements to use or share data are formal contracts that clearly document the shared data and the parameters for data use. Agreements protect the agency or organization providing the data, the data subjects, and the entity receiving the information. These agreements confirm who is authorized to use the data, what the purpose of the use is, and that the data transfer is compliant with legal and ethical standards.
To access and harness big data sources for operations and service delivery analysis, facility owners may engage mobility providers, data gatherers and aggregators, and other agencies. Data-sharing agreements should be clear and offer a reasonable degree of specificity about the purpose of the collected or acquired data, should identify which data points are critical, and should specify the granularity needed to meet objectives.
Understanding the purpose of the compiled data is paramount. This helps to structure agreements that allow facility planners and operators to gather and store the most helpful data without paying to store large volumes of information that do not add value.
Elements of Data-Sharing Agreements
At a minimum, data-sharing agreements should include the following:
- A detailed project description.
- Terms of use, including details on use, re-use, disclosure, and any exclusions or limitations.
- Roles and responsibilities of each party regarding:
- Data ownership, collection, maintenance and updates, storage, archiving, and disposal.
- Method and frequency of data transmission.
- Type and quality of data to be shared.
- Security and safeguard measures to ensure privacy of data subjects, including specific requirements about omission of certain types of payment or personally identifiable information as may be required by government entities.
- Agreement duration and circumstances for termination.
- Financial cost of the data sharing.
- General legal provisions, such as dispute resolution, liability, and indemnification.
TCRP Research Report 213: Data Sharing Guidance for Public Transit Agencies—Now and in the Future (Weisbrod et al. 2020) offers quick study results and guidance on sharing agency data and data from others. It also offers suggestions on how to evaluate benefits, costs, and risks.
Data-sharing agreements can specify data-sharing formats. Adopting recognized standards will streamline data management and allow for comparison across facilities and jurisdictions. Developed standards improve access, compatibility, and interoperability. Standards also benefit from privacy and data security. In addition, agencies that have not done so should consider adopting digital policies, which can then be integrated into data-sharing agreements.
New Data Resources
Geolocation technology has made it easier to track people and vehicles. Broad use of geolocation-enabled smartphones has in turn created new industries in data aggregation and analytics, known as big data, and this landscape is rapidly evolving. Private companies and government agencies now employ data scientists to gain meaning from vast amounts of data. The use and potential of artificial intelligence is also rapidly evolving. Machine learning, a subfield of artificial intelligence, enables computers to learn from data without being programmed to do so. Machine learning can be particularly helpful when working with large or complex datasets that require sophisticated analysis to gain usable insights (FasterCapital, n.d.).
Open Data Standards
Open standards enable ongoing and consistent communication of information between devices. Whenever possible, data inputs should follow open standards to facilitate consistent communication of information between devices (Feigon and Murphy 2016) through the GTFS, Mobility Data Specification (MDS), and General Bikeshare Feed Specification (GBFS). Table 8 explains how these standards can be used at intermodal passenger facilities.
Big Data
Big data is a term used to refer to datasets that are too large or complex for traditional data-processing application software to adequately deal with (Franzwa, n.d.). Numerous companies
Table 8. Applicability of open standards to intermodal passenger facilities.
| Standard | Applicability to Intermodal Passenger Facilities |
|---|---|
| General Transit Feed Specification (GTFS) | Integration of schedules into trip planners and digital information systems, tracking of real-time arrivals for curb management, and response to service disruptions. |
| General Bikeshare Feed Specifications (GBFS) | Integration of bikeshare dock and bicycle locations and availability into trip planning applications and for monitoring. |
| Mobility Data Standard (MDS) | Tracking of private mobility provider data for owner oversight and monitoring. |
| Curb Data Specification (CDS) | At airports, the CDS can be used to actively monitor curb activities. At intermodal ground passenger facilities, municipalities that typically have jurisdiction of the curb can adjust pricing and target enforcement activities. |
compile, analyze, and sell access to big data, offering information that can support intermodal passenger facility planning. Onboard vehicle-location computers in cars, buses, and trucks, and smartphones provide much of the data. The International Transport Forum’s Use of Big Data in Transport Modelling highlights the potential of mobile data to enhance travel modeling and overall decision-making (Willumsen 2021). The paper emphasizes how big data can explain trends and improve travel demand forecasts, particularly for micromobility use (Willumsen 2021).
Appendix B contains information on private companies that compile, repackage, and sell big data for transportation analysis applicable to intermodal passenger facilities.
Mode-Level Data
Modal data and performance measurement are unique to each mode (Margiotta et al. 2017). Recognizing modal differences and working to integrate certain measures is important for better managing intermodal passenger facilities, particularly for analyzing the user experience.
The following discussion notes any available open data standards and offers guidance on how to collect and analyze data for facility planning. Topics discussed are:
- Public buses and shuttles,
- Ridehailing/TNCs,
- Privately owned vehicles,
- Micromobility (bicycles, scooters, bikesharing, and scooter sharing),
- Carsharing,
- Pedestrian data, and
- EV infrastructure.
The findings of NCHRP Project 08-36/Task 131, “Transportation Data Integration to Develop Planning Performance Measures,” were published in 2017 by AASHTO as a report and slide presentation. The study found that measures of mobility were shifting to consider how individuals experience the transportation system for complete trips, and that modal performance measures are unique to each mode. The study also noted the need for data on movements at the individual level to support true multimodal performance measurement (Margiotta et al. 2017).
Public Buses and Shuttles
GTFS feeds can be integrated with real-time information throughout an intermodal passenger facility. GTFS data can support the following facility performance measures:
- Number of unique transit routes serving the facility, including fixed-route and demand-response services.
- Fixed-route headways throughout each service day.
- Timetable connections with other scheduled mobility services.
- Actual location of buses and arrival times.
GTFS data can also be used for locating and sizing bus loading/unloading zones, managing bus ingress and egress, coordinating among providers, and understanding the overall operating envelope, including when to assign staff to manage times of high demand.
Many additional transit performance measures are available. [See TCQSM (Kittelson & Associates, Inc., et al. 2013).]
TCRP Research Report 235: Improving Access and Management of Public Transit ITS Data (EBP et al. 2022) proposes a structure for storing data from bus and rail intelligent transportation systems (ITSs). It also describes how that data structure can facilitate a process by which transit
agencies can receive ITS data from vendors, organize and validate the data, and use the data to calculate KPIs to improve transit system operations. To support that process, the report describes procedures that transit agencies, researchers, and consultants can use to develop tools to transform, validate, and analyze ITS data using the data structure.
Ridehailing/TNCs
There are no universal data specifications for ridehailing (services provided by taxis or TNCs). The amount of data shared with government agencies is a function of disparate individual licensing agreements. A universal data specification is needed, particularly given the challenges of managing multiple providers picking up passengers. In locations where such data are available or where companies agree to share information as a condition of access, the way data is collected and shared also differs. Data can be used for space allocation and daily operations, including for TNC and taxi loading zones, pre-loading zones (e.g., taxi stacks), and staging lots (Curtis et al. 2019).
Privately Owned Vehicles
Collecting traffic vehicle volume data is a mainstay of transportation planning. Advanced technologies that count and classify vehicles, such as traffic cameras and license plate readers (LPRs), are a source of reliable and continuous vehicle data. Traffic cameras placed at intersections can collect data and adjust signal timing in response to demand or incidents. In most instances, the technology is under the purview of a local, regional, or state transportation agency. While most intermodal passenger facility operators may not currently participate in traffic operations management, some participation may be beneficial. For example, sharing schedules for modes served, coordinating events, or participating in incident management planning can be helpful when surges in demand could require signal timing adjustments or deployment of personnel to alleviate congestion.
LPR Technology
LPRs are used for tolling, parking management, and curb management. LPR data can help to allocate parking spots by category, set time limits, and automate parking payments. LPRs can replace or enhance parking control systems and can facilitate cashless and barrier-less tolling for facility access by commercial vehicles. [See TCRP Research Report 235 (EBP et al. 2022).]
Measuring Landside Access at Airports
Reliable data on modes used to travel to or from airports are limited. ACRP Report 4: Ground Access to Major Airports by Public Transportation noted that fewer than 20 U.S. airport operators regularly conduct passenger surveys that compile this information and noted the significant effort and cost to plan and conduct surveys and analyze results (Coogan et al. 2008).
ACRP Report 266: Airport Roadway Analysis and Curbside Congestion Mitigation Strategies (InterVISTAS Consulting, Inc., forthcoming) noted that information about the number and distribution of arriving and departing passengers by airport, hour, day, or season is not readily available. It stressed the importance of collecting such data and conducting periodic surveys, particularly to monitor trends and for planning improvements (InterVISTAS Consulting, Inc., forthcoming). The same principles apply to other intermodal passenger facilities.
Real-Time Vehicle Tracking
Big data vendors offer services to help facility owners understand real-time vehicle movement and density. Available platforms can monitor speeds and travel times and other travel patterns.
Such data can support reallocation of space or suggest the need for different staffing deployments. For example, at LaGuardia Airport, operations control center staff use Waze and other tools to analyze travel times on pre-identified routes. The tools illustrate the severity of driver delays during peak periods compared to typical conditions (Sam Schwartz 2020).
Micromobility (Bicycles, Scooters, Bikesharing, and Scooter Sharing)
Quantifying micromobility demand and usage can offer intermodal passenger facility owners and others valuable insights on needed infrastructure requirements, curb management, and safety planning. The MDS is a possible resource to inform:
- Placement of bicycle/scooter lanes,
- Size and placement of zones for customers to park e-scooters (drop zones) and demarcation of restricted areas,
- Establishment of vehicle caps,
- Travel speed limits, and
- Distribution of devices (Open Mobility Foundation 2020).
Ongoing NCHRP Project 08-165, “Use of Active Transportation Data in Decision-Making,” will ultimately provide informed technical direction to transportation and other professionals on how to identify, access, collect, store, interpret, understand, and apply active transportation data in transportation planning, design, operations and maintenance, safety, performance management, funding, and other decision-making areas.
Important data metrics include temporal trip demand (i.e., peak hour/period trips), connectivity to nearby bicycle infrastructure such as bicycle lanes or bicycle paths, and how demand changes with other modal demand (such as surges that occur when intercity buses, trains, or ferries arrive), which can support analysis of mode of access/egress trends.
Using MDS Data in San Jose
The San Jose Department of Transportation uses the MDS to gather data under its shared micromobility permit regulations, which are specific to e-scooters. San Jose requires scooter-sharing operators within the City of San Jose to provide an automated mechanism to integrate their services with the MDS within 30 days of receiving a permit. The city requires the base data MDS requests as well as additional information such as a maintenance log and response time, including time of request and time of resolution. Vendors must make these data available to city-specified partners.
Compiling and Sharing Bikesharing Data in New York
The New York City Department of Transportation operates Citi Bike in partnership with Lyft. To facilitate access to data on ridership trends and station operations, the Citi Bike website reports historical trip data for the previous 10 years, real-time access to its GBFS feed, and monthly operating reports. The largest stations in the Citi Bike network are at Grand Central Terminal, Penn Station, and the Barclays Center.
Carsharing
For intermodal passenger facilities where carsharing is available on-site or nearby, supply and demand data can be beneficial for facility management. Some agencies have used both MDS and GBFS data specifications to collect and share carsharing data. For example, the city of Seattle tracks the number of participating individuals, size of fleet (gas-powered and electric), supply and location of on- and off-street parking spaces, and other data (Seattle Department of Transportation, n.d.).
Pedestrian Data
Unlike with other modes of transportation, pedestrians are not bound by fixed routes or schedules, making their patterns of movement and use of intermodal passenger facilities more fluid. An ongoing understanding of pedestrian patterns and volumes helps those who plan and operate facilities respond to the needs of passengers and other facility users.
Counting how many people walk and analyzing desire lines can support the management and modification of space allocated to walkways, crosswalks, hallways, waiting areas, gathering spaces, and other places where people walk. In addition, analysis of dwell times—time pedestrians spend waiting at crossings—can aid in reducing congestion and improve safety. Other pedestrian measures to consider are travel distances and time required to walk between connections when transferring since these affect the customer experience.
Pedestrian and Bicyclist Road Safety Audits
FHWA’s Pedestrian and Bicyclist Road Safety Audit (RSA) Guide and Prompt List provides guidance for auditing pedestrian and bicyclist safety and includes information on safety risks for both modes, the RSA process, necessary data, and the roles and responsibilities of the RSA team. Facility owners looking to examine internal or adjacent sidewalks and streets can use this tool to conduct audits (Goughnour et al. 2020).
EV Infrastructure
The ongoing shift toward EVs may require integration of charging infrastructure at intermodal passenger facilities. EV performance measures may encompass tracking the number of charging stations deployed and their utilization, the adoption rate of EVs, and energy consumption data. (For more information, see Appendix B.)
