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:
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.
- Low processing costs, can be less expensive on smaller or less frequented roads
- Longer questionnaires with more in-depth questions are possible
- Can disrupt traffic
- Hard to accomplish on busy roads
- Low response rates
Roadside interviews involve stopping vehicles on the roadway to conduct short, in-person interviews where questions are asked directly to the motorist.
- High response rates
- Data is available sooner than other roadside methods
- 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.
- 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
- 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.
- 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
- 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.
- 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
- 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.
- No hardware or manual labour
- Highly accurate with no geographic limits
- No network reliance
- Most complete route information
- No disruption of traffic
- 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.
- No hardware or manual labour
- No disruption of traffic
- Higher road coverage than ALPR or connected car methods
- More complete trip lists than ALPR
- 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.