5 Origin-Destination Study Methods

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Origin-Destination (OD) studies are used to determine traffic travel patterns in an area of interest for a select time. These studies provide traffic planners and agencies with information on where drivers begin and end their trips. While older OD data collection methods are well-studied, they are limited in the scope and detail of data that can be collected. Newer methods provide richer, more expansive data that allow planners to get a clear picture of trip routes from start to finish. With each method varying in data collection processes, here are the pros and cons of 5 origin-destination study methods:

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5 Origin-Destination Study Methods

1. Roadside Surveys

Roadside surveys are the oldest OD study method, as they require little to no technology or installation. Depending on the size of the roadway, the volume of traffic, and project goals, there are multiple roadside survey techniques.

Here are 3 common roadside survey methods, each with its pros and cons:

Roadside Handout/Mail Back Surveys

Roadside handout/mail-back surveys involve manually handing out survey cards to drivers while on the roadway. The motorists may then fill out the card and mail it back free of charge, or follow the instructions to fill out the survey online. The cards may be handed out at intersections, ramps, toll booths, or other areas where cars are naturally stopped, or stopping traffic causes less disruption.

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Pros

Low processing costs, can be less expensive on smaller or less frequented roads
Longer questionnaires with more in-depth questions are possible

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Cons

Can disrupt traffic
Hard to accomplish on busy roads
Low response rates

Roadside Interviews

Roadside interviews involve stopping vehicles on the roadway to conduct short, in-person interviews where questions are asked directly to the motorist.

Pros

High response rates
Data is available sooner than other roadside methods

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Cons

Very disruptive to traffic
Can cause safety hazards
Requires high staff labour
Interviews must be very short

License Plate Mail-Out Surveys

License plate surveys are conducted by collecting license plate numbers from vehicles on the desired roadway, and then manually matching them to their registered address and mailing a survey.

Pros

The only roadside survey method that does not stop or disrupt traffic
Can ask more questions in the survey than for other roadside survey methods

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Cons

Can require extensive manual labour to match the license plates and addresses
Matching must be done quickly to avoid loss of driver recall
A low response rate is likely

2. License Plate Recognition

Automatic license plate recognition (ALPR) can be used for OD studies by matching license plates at designated entry and exit points. To use license plate recognition, roadways need to be equipped with a vehicle sensor, camera, and image processor. When a vehicle is detected, a photo is taken and processed. The agency's data analytics application then cross-references photos to determine which vehicles were present at both the entry and exit points.

Pros

Can have high detection rates depending on the quality of sensor and camera equipment
Provides travel time information
No manual labour or disruption to traffic
Can capture data on various days and times

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Cons

No information on multiple stops or alternate routes
More expensive, and the most difficult method to install, due to suitable infrastructure requirements to support the sensor and camera
Privacy concerns regarding enforcement and misidentification
Requires some inference for stops along the way
Need to purchase and install equipment for entry and exit points, as well as any waypoints

3. MAC Address Detection

MAC address detection uses Wi-Fi and Bluetooth sensors to track and match vehicles based on their unique MAC address, emitted from smart devices, to determine individual trip patterns. Which days and times are monitored can be pre-selected, and reports are separated by days and time slices, making data easy to analyze. This study method provides travel times, all trips between points of interest, average trip data, and an OD matrix report.

Pros

Easy to install and remove, more flexible location-wise than ALPR
More complete route information than previous study methods
Highly accurate vehicle matching, even on high-volume roadways
No manual labour
Real-time data with no disruption of traffic
30-40% sample size using SMATS

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Cons

Need to install sensors around points of interest
Lower sample size than ALPR
Can only collect data in cars where there are smart devices

4. Connected Car Data

Connected cars send information to third-party providers in real-time, straight from the vehicle, making them a faster, less expensive option than the previous methods. Time-stamped location and speed data is captured directly from moving vehicles, providing complete, detailed trip lists and overviews. Connected car data can access any road driven by a car, meaning that origin-destination studies can be completed between cities or within neighbourhoods.

Pros

No hardware or manual labour
Highly accurate with no geographic limits
No network reliance
Most complete route information
No disruption of traffic

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Cons

Can only collect data from connected cars
Bias toward the type of cars used
Lower sample size

5. Mobile Based Location Data

Mobile-based location data is captured when connected mobile devices transmit location data to third-party providers. Location and speed data is transmitted in real-time, providing accurate travel time information. Like connected car data, mobile-based location data can be collected on any road segment where there are mobile-connected devices.

Pros

No hardware or manual labour
No disruption of traffic
Higher road coverage than ALPR or connected car methods
More complete trip lists than ALPR

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Cons

Spotty networks can lead to incomplete data
Can only collect data from vehicles with a connected mobile device
Can capture duplicate data from the same vehicle if multiple devices are connected

Origin-destination studies provide valuable information for traffic planning and choosing the right study method is integral for getting the most out of your data. While older methods are familiar, they do not provide the amount and quality of data that newer methods offer, and are often more expensive and difficult to implement.

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Agencies can get precise, detailed data through research methods like connected car data, which delivers start-to-finish complete trip lists and speed data.

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