Fundamentals of Transportation/Data

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There are a variety of types of transportation data used in analysis. Some are listed below:

  • Infrastructure Status
  • Traffic Counts
  • Travel Behavior Inventory
  • Land Use Inventory
  • Truck/Freight Demand
  • External/Internal Demand (by Vehicle Type)
  • Special Generators

Revealed Preference[edit]

Household travel surveys which ask people what they actually did are a type of Revealed Preference survey data that have been obtained by direct observation of the choice that individuals make with respect to travel behavior. Travel Cost Analysis uses the prices of market goods to evaluate the value of goods that are not traded in the market.

Hedonic Pricing uses the market price of a traded good and measures of its component attributes to calculate value. There are other methods to attain Revealed Preference information, but surveys are the most common in travel behavior.

Travel Behavior Inventory[edit]

While the Cleveland Regional Area Traffic Study in 1927 was the first metropolitan planning attempt sponsored by the US federal government, the lack of comprehensive survey methods and standards at that time precluded the systematic collection of information such as travel time, origin and destination, and traffic counts. The first US travel surveys appeared in urban areas after the Federal-Aid Highway Act of 1944 permitted the spending of federal funds on urban highways [1]. A new home- interview origin-destination survey method was developed in which households were asked about the number of trips, purpose, mode choice, origin and destination of the trips conducted on a daily basis. In 1944, the US Bureau of Public Roads printed the Manual of Procedures for Home Interview Traffic Studies. [2]. This new procedure was first implemented in several small to mid-size areas. Highway engineers and urban planners made use of the new data collected after the 1944 Highway Act extended federally sponsored planning to travel surveys as well as traffic counts, highway capacity studies, pavement condition studies and cost-benefit analysis.

Attributes of a household travel survey, or travel behavior inventory include:

  • Travel Diary of ~ 1% sample of population (all trips made on one day) every 10 years
  • Socioeconomic/demographic data of survey respondents
  • Collection methodology:
    • Phone,
    • Mail,
    • In-Person at Home,
    • In-Person at Work,
    • Roadside

Many such surveys are available online at: Metropolitan Travel Survey Archive


Thought Question[edit]

What are the advantages and disadvantages of Revealed Preference surveys?

Stated Preference[edit]

In contrast with revealed preference, Stated preference is a group of techniques used to calculate the utility functions of transport options based on the response of an individual decision-maker to certain given options. The options generally are based on descriptions of the transport scenario or are constructed by the researcher

  • Contingent valuation is based on the assumption that the best way to find out the value that an individual places on something is known by asking.
  • Compensating variation is the compensating payment that leaves an individual as well off as before the economic change.
  • Equivalent variation for a benefit is the minimum amount of money one would have to be compensated to leave the person as well as they would be after the change.
  • Conjoint analysis refers to the application of the design of experiments to obtain the preferences of the individual, breaking the task into a list of choices or ratings that enable us to compute the relative importance of each of the attributes studied

Thought Question[edit]

What are the advantages and disadvantages of Stated Preference surveys?



Pavement conditions[edit]

adapted from Xie, Feng and David Levinson (2008) The Use of Road Infrastructure Data for Urban Transportation Planning: Issues and Opportunities. Published in Infrastructure Reporting and Asset Management Edited by Adjo Amekudzi and Sue McNeil. pp- 93-98. Publisher: American Society of Civil Engineers, Reston, Virginia.[3]

Road infrastructure represents the supply side of an urban transportation system. Pavement condition is a critical indicator to the quality of road infrastructure in terms of providing a smooth and reliable driving environment on roads. A series of indices have been developed to evaluate the pavement conditions of road segments in their respective jurisdictions: Pavement Condition Index (PCI) is scored as a perfect roadway (100 points) minus point deductions for “distresses” that are observed; Present Serviceability Rating (PSR) is measured as vertical movement per unit horizontal movement (e.g. millimeters of vertical displacement per meter of horizontal displacement) as one drives along the road; (SR) Surface Rating is calculated by reviewing images of the roadway based on the frequency and severity of defects; Pavement Quality Index (PQI) is calculated using the PSR and SR to evaluate the general condition of the road. A high PQI (up to 4.5) means a road will most likely not need maintenance soon, whereas a low PQI means it can be selected for maintenance [4].

These indices of pavement quality are basic measures for road maintenance and preservation, for which each county develops its own performance standards to evaluate pavement conditions and make decisions on maintenance and preservation projects. Typically, pavement preservation projects are prioritized based on PCI of road segments: the lower the PCI, the higher the likelihood of selection. Taking Washington County, Minnesota as an example [5], the county has determined that a reasonable standard to maintain is an average PCI of 72. Thus any road segment with its PCI below 72 has a chance to be selected for preservation. Dakota County, Minnesota on the other hand, scores its preservation projects according to the measure of PQI: a road segment will be allocated 17 points (out of a possible 100) if its PQI falls lower than 3.1.

The pavement data structure is incompatible with the link-node structure of the planning road network used by the Metropolitan Council and other planning agencies. Typically, the measures of PCI, PSR, and PQI are stored in records indexed by “highway segment numbers” along each highway route. Highway sections with the same highway segment number are differentiated by their starting and ending stations. There is no exact match between highway segments in the actual road network and links in the planning network, as stationing is a position along the curved centerline of a highway while a planning network is a simplified structure consisting of only straight lines intersecting at points. Historic pavement data is generally unavailable in electronic format, although the information on pavement history such as pavement life and the duration since last repaving are important to estimate the cost of a preservation project, also affecting the decision whether a specific project will get selected and how much funding will be allocated.


Traffic flow[edit]

Traffic conditions reflect the travel demand loaded on a given road infrastructure. Traffic flows on roads, together with road capacity, can be used to calculate the volume/capacity (V/C) ratio, which is an approximate indicator for the level of service of road infrastructure, and is commonly adopted by the jurisdictions in their respective decision making processes. The traffic flows on the planning road network are predicted by the transportation planning model, but the results have to be calibrated with actual traffic data.

Loop detectors are the primary technology currently employed in many US metropolitan areas to collect actual traffic data. E.g. In the Twin Cities of Minneapolis-St. Paul, about one thousand detector stations have been buried on major highways, through which Mn/DOT’s Traffic Management Center collects, stores, and makes public traffic data every 30 seconds, including measured volume (flow) and occupancy, and calculated speed data for each detector station. Although the estimates of Annual Average Daily Traffic (AADT) for the planning road network are readily available, loop detectors provide more accurate measures of traffic volume, since they are collecting real-time data on a continuous basis. It also allows for calibrating models to hourly rather than daily conditions.

Due to the limited ability to convert raw data collected by loop detectors, however, most forecasting models rely on AADT data. The raw data are stored in a 30- second interval in binary codes. For planning uses they have to be converted and aggregated into desired measures, such as peak hour average, average for a particular month or a particular season, etc, in a systematic way.

Another issue in integrating loop detector data into a planning road network is to match the detector stations with the links in planning networks. Similar to the problem encountered in translating pavement data, detectors are located along major highways and mapped on the actual geometry of the network, while the planning road network is a simplified structure with only straight lines.



Sampling Issues (Statistics)[edit]

  • Sample Size,
  • Population of Interest
  • Sampling Method,
  • Error:
    • Measurement,
    • Sampling,
    • Computational,
    • Specification,
    • Transfer,
    • Aggregation
  • Bias,
  • Oversampling
  • Extent of Collection
    • Spatial
    • Temporal
  • Span of Data
    • Cross-section,
    • Time Series, and
    • Panel

Metadata[edit]

Adapted from Levinson, D. and Zofka, Ewa. (2006) “The Metropolitan Travel Survey Archive: A Case Study in Archiving” in Travel Survey Methods: Quality and Future Directions, Proceedings of 5th International Conference on Travel Survey Methods (Peter Stopher and Cheryl Stecher, editors)[6]

Metadata allows data to function together. Simply put, metadata is information about information – labeling, cataloging and descriptive information structured to permit data to be processed. Ryssevik and Musgrave (1999)[7] argue that high quality metadata standards are essential as metadata is the launch pad for any resource discovery, maps complex data, bridges the gap between data producers and consumers, and links data with its resultant reports and scientific studies produced about it. To meet the increasing needs for the proper data formats and encoding standards, the World Wide Web Consortium (W3C) has developed the generic Resource Description Framework (RDF) (W3C 2002). RDF treats metadata more generally, providing a standard way to use Extended Markup Language (XML) to “represent metadata in the form of statements about properties and relationships of items” (W3C 2002) [8]. Resources can be almost any type of file, including of course, travel surveys. RDF delivers detailed and unified data description vocabulary.

Applying these tools specifically to databases, the Data Documentation Initiative (DDI) for Document Type Definitions (DTD) applies metadata standards used for documenting datasets. DDI was first developed by European and North American data archives, libraries and official statistics agencies. “The Data Documentation Initiative (DDI) is an effort to establish an international XML-based standard for the content, presentation, transport, and preservation of documentation for datasets in the social and behavioral sciences” (Data Documentation Initiative 2004). As this international standardization effort gathers momentum it is expected more and more datasets to be documented using DDI as the primary metadata format. With DDI, searching data archives on the Internet no longer depends on an archivist's skill at capturing the information that is important to researchers. The standard of data description provides sufficient detail sorted in a user-friendly manner.

References[edit]

  1. Weiner, Edward, (1997) http://tmip.fhwa.dot.gov/resources/clearinghouse/158 Urban Transportation Planning in the United States: An Historical Overview]. U.S. Department of Transportation. Fifth Edition.
  2. US Department of Commerce (1944) Manual of Procedures for Home Interview Traffic Studies. US Bureau of Public Roads. Washington, DC
  3. Xie, Feng and David Levinson (2008) The Use of Road Infrastructure Data for Urban Transportation Planning: Issues and Opportunities. Published in Infrastructure Reporting and Asset Management Edited by Adjo Amekudzi and Sue McNeil. pp- 93-98. Publisher: American Society of Civil Engineers, Reston, Virginia
  4. Hammerand, J. (2006). “Rating Minnesota’s Roads.” The Minnesota Local Technical Assistance Program (LTAP): Technology Exchange, vol.24, No.1, 3
  5. Washington County Transportation Division (2004). “Washington County Annual Performance Report for 2004.” <http://www.co.washington.mn.us/client_files/documents/adm/PerfMeas-2004//ADM- PM-04-Transportation.pdf> (May 20, 2006)
  6. Levinson, D. and Zofka, Ewa. (2006) “The Metropolitan Travel Survey Archive: A Case Study in Archiving” in Travel Survey Methods: Quality and Future Directions, Proceedings of 5th International Conference on Travel Survey Methods (Peter Stopher and Cheryl Stecher, editors)
  7. Ryssevik J, Musgrave S. (1999) The Social Science Dream Machine: Resource discovery, analysis and delivery on the Web. Paper given at the IASSIST Conference. Toronto, May.
  8. World Wide Web Consortium (2002) Metadata Activity Statement

Appendix: Typical Household Survey Questions[edit]

(source: Denver Regional Council of Governments 2001
)

  1. What is/verify home address
  2. Assigned survey day
  3. Is your residence a single-family home, duplex/ townhouse, apartment/condominium, mobile home, or other?
  4. How many people live in this household?
  5. How many overnight visitors from outside of the region stayed with you on your survey day?
  6. How many motor vehicles are available to your household?
  7. In total, how many telephone lines come into your home?
  8. How many of the lines are used for voice communication?
  9. Has telephone service in your home been continuous for the past 12 months?
  10. What was the combined income from all sources for all members of your household for 1996?

Vehicle Questions[edit]

  1. Vehicle model year
  2. Vehicle make
  3. Vehicle model
  4. Body type
  5. Fuel type
  6. Who owns or leases this vehicle?
  7. Prior to the survey day, when was the last day it (the vehicle) was used?
  8. Odometer reading (mileage) at the start of the survey day
  9. Odometer reading (mileage) at the end of the survey day

Person Questions[edit]

  1. Person’s first name (used for identification purposes only during the survey; not saved on final data files)
  2. Relation to head of household
  3. Age
  4. Sex
  5. Licensed to drive?
  6. Student status (not a student, part time, full time)
  7. Grade level
  8. Employment status
  9. Primary job description (nurse, sales, teacher)
  10. Primary employer’s name
  11. Primary employer’s address
  12. Primary Employer’s business type (hospital, retail, etc.)
  13. Does your primary employer offer flextime?
  14. If flextime offered (primary employer), type of deviation allowed at start

of day

  1. If flextime offered (primary employer), type of deviation allowed at end of

day

  1. Number of other jobs or employers
  2. Do you have a transit pass?
  3. Monthly cost [of transit pass] to you
  4. Did you make trips on the survey day?
  5. If trips were made, did you use E-470 on the travel day?
  6. If trips were made, did you use the HOV lanes on the travel day?
  7. Did you work at your main job on the survey day?

Environment Questions[edit]

Using a 1 to 10 scale, with 10 the best, how would you describe the walking and bicycling environment around your:

  1. home
  2. work
  3. school
  4. Was the person interviewed by the surveyor?
  5. Based on responses and the survey, did the person appear to use the activity diary?

Travel Diary Questions[edit]

  1. This place is my home, my regular workplace, or another place
  2. What kind of place is this (bank, grocery, park etc.)?
  3. Place address
  4. At what time did you arrive at this place?
  5. What did you do at this place (main activity)?
  6. What else did you do at this place (up to eight secondary activities)?
  7. Was this your last place for the 24-hour day?
  8. At what time did you leave this place to go to the next place?


Travel Method[edit]

  1. Travel method used to make this trip and related travel details

Auto Trip Questions[edit]

  1. Which household vehicle was used (if a household vehicle

was used)?

  1. Total number of people in the vehicle
  2. Total number of household members in the vehicle
  3. If more than one person was in the vehicle, “is this a formal

carpool/vanpool”?

  1. Were HOV lanes used on this trip?
  2. Was E-470 used for this trip?
  3. What was the parking cost paid at the end of this trip?
  4. What period is covered by the parking cost paid?
  5. Was the parking cost fully or partly reimbursed?
  6. What was the parking location (cross streets, lot name, if applicable, and city)

Transit Trip Questions[edit]

The following four questions were asked if the travel method was transit

  1. What was the transit route number?
  2. What was your wait time for transit?
  3. What was the transit fare paid?
  4. How did you pay the transit fare?

The following four questions were asked if the travel method was walk or bicycle

  1. What was the bicycle or walk time?
  2. Was a bike path used?
  3. Where did you store this vehicle?
  4. Was a walk path used?

References[edit]

  1. Weiner, Edward, (1997) http://tmip.fhwa.dot.gov/resources/clearinghouse/158 Urban Transportation Planning in the United States: An Historical Overview]. U.S. Department of Transportation. Fifth Edition.
  2. US Department of Commerce (1944) Manual of Procedures for Home Interview Traffic Studies. US Bureau of Public Roads. Washington, DC
  3. Xie, Feng and David Levinson (2008) The Use of Road Infrastructure Data for Urban Transportation Planning: Issues and Opportunities. Published in Infrastructure Reporting and Asset Management Edited by Adjo Amekudzi and Sue McNeil. pp- 93-98. Publisher: American Society of Civil Engineers, Reston, Virginia
  4. Hammerand, J. (2006). “Rating Minnesota’s Roads.” The Minnesota Local Technical Assistance Program (LTAP): Technology Exchange, vol.24, No.1, 3
  5. Washington County Transportation Division (2004). “Washington County Annual Performance Report for 2004.” <http://www.co.washington.mn.us/client_files/documents/adm/PerfMeas-2004//ADM- PM-04-Transportation.pdf> (May 20, 2006)
  6. Levinson, D. and Zofka, Ewa. (2006) “The Metropolitan Travel Survey Archive: A Case Study in Archiving” in Travel Survey Methods: Quality and Future Directions, Proceedings of 5th International Conference on Travel Survey Methods (Peter Stopher and Cheryl Stecher, editors)
  7. Ryssevik J, Musgrave S. (1999) The Social Science Dream Machine: Resource discovery, analysis and delivery on the Web. Paper given at the IASSIST Conference. Toronto, May.
  8. World Wide Web Consortium (2002) Metadata Activity Statement
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