Introduction to Origin-Destination (OD) Data

Introduction to Origin-Destination (OD) Data

What is Origin-Destination (OD) Data?

Origin-Destination (OD) data tracks the movement of people or goods between specific locations (origins and destinations) over a defined time period. It provides valuable insights into travel patterns, including where trips begin, end, their purpose, and the modes of transportation used.

This data is critical for transportation planning, enabling policymakers to optimise infrastructure, systems, and services to meet current needs and anticipate future demands.

Why Collect OD Data?

OD data helps with:

• Understanding Travel Demand: Revealing travel patterns, including high-demand routes and travel times.
• Infrastructure Planning: Informing decisions about where to invest in roads, public transit, and pedestrian or cycling infrastructure.
• Analysing Trends: Monitoring how travel behaviors evolve over time.
• Reducing Congestion: Identifying bottlenecks and optimising traffic flow.

OD data is often collected for strategic projects, such as planning a Bus Rapid Transit (BRT) or Mass Rapid Transit (MRT) system. However, some cities collect this data periodically – not just for specific projects but to maintain up-to-date transport models. This proactive approach supports better long-term planning and helps cities adapt to emerging transportation needs.

How is OD Data Collected?

OD data can be collected through traditional methods, such as household and roadside surveys, or through innovative approaches that leverage modern technology. Examples for both methods are provided below highlighting their relative strengths and limitations.

Traditional Methods

1. Household Surveys

Household surveys involve surveyors visiting a representative sample of homes to collect trip data directly from residents. Participants manually complete questionnaires or participate in interviews to detail their travel activities over a specified period.

Resource Requirements:

• Trained surveyors
• Physical forms or digital tools
• Time and financial investment

Strengths & Limitations:

Strengths:

• Comprehensive insights into travel behavior across a wide range of trip types (including non-motorised modes).
• Allows for detailed demographic and socioeconomic data, which enhances data analysis.

Limitations:

• Resource and time-intensive and laborious, especially for large areas, requiring significant personnel and infrastructure.
• Risk of recall bias, leading to incomplete or inaccurate responses.
• Sampling typically involves only 1–2% of the population in large cities and 2–3% in smaller cities, potentially affecting data representativeness.

Sample Household OD Survey Template:

2. Roadside Surveys

Roadside surveys involve stopping vehicles at selected locations (e.g., intersections or toll booths) to gather trip information directly from drivers.

Resource Requirements:

• Trained teams stationed at multiple survey points.
• Traffic management to minimise disruptions.
• Tools for efficiently recording and digitising data.

Strengths & Limitations

Strengths:

• Accurate data for motorised trips
• Fast collection from many respondents

Limitations:

• Disrupts traffic
• Focused on motorised travel only

3. Travel Diaries

Participants record their trips in real-time over a set period, providing richer details on trip purposes, modes used, and other travel characteristics.

Strengths & Limitations

Strengths:

• Real-time, detailed trip data
• Captures missed info from other methods

Limitations:

• Depends on participant diligence
• Potential for missing/incomplete data

Travel Diary Template Example:

Personal Information:

• Name: ___
• Age: ___
• Gender: ___
• Household size: ___

Trip Details:

• Date: ___
• Time of Departure: ___
• Time of Arrival: ___
• Trip Purpose (tick one):
  • [ ] Work
  • [ ] School
  • [ ] Medical/Health
  • [ ] Leisure
  • [ ] Other: ___
• Mode of Transport (tick one):
  • [ ] Car
  • [ ] Bus
  • [ ] Train
  • [ ] Bicycle
  • [ ] Walking
  • [ ] Taxi/Ride-Sharing
  • [ ] Other: ___
• Distance Travelled: ___ km
• Start Location: ___
• End Location: ___
• Route Taken: ___

(Continue for each trip taken within the specified period.)

Innovative Methods

1. Mobile Phone Data

Mobile phone data, collected from cellular networks, tracks anonymized device movements to infer travel patterns.

Resource Requirements:

• Agreements with telecom providers for access to aggregated data.
• Skilled analysts and tools for processing large datasets.

Strengths:

• Broad coverage, capturing diverse population segments.
• Real-time data for mobility analysis.
• Cost-effective for large-scale studies.

Limitations:

• Lacks trip-specific details (e.g., purpose, transport mode).
• Raises privacy concerns, requiring robust data anonymisation.

2. GPS Data

GPS devices or mobile apps collect precise routes, speeds, and travel times.

Resource Requirements:

• GPS-enabled devices or app participation.
• Infrastructure to process large-scale route data.

Strengths:

• High accuracy for route and speed tracking.
• Ideal for real-time traffic analysis.

Limitations:

• Limited to users with GPS devices
• Requires incentives for voluntary participation.

3. Floating Car Data (FCD)

FCD is collected from fleet vehicles (e.g., taxis or ride-hailing cars) to provide real-time insights into road usage.

Resource Requirements:

• Access to fleet tracking systems or partnerships with fleet operators.

Strengths & Limitations

Strengths:

• Real-time data on road usage.
• Cost-effective in cities with active fleets.

Limitations: * Biased toward routes frequently used by fleets. * Limited representation of pedestrian or public transit trips.

4. Location-Based Survey Apps

Apps allow participants to log trips digitally, streamlining data collection and minimizing errors.

Requirements:

• App development and maintenance.
• Smartphone access and digital literacy among participants.

5. Purchasing Data from Brokers

Private companies aggregate and package data from sources like telecom operators, app usage, and GPS systems for purchase.

Strengths and limitations

Strengths:

• Saves time and effort in data collection.
• Pre-processed for use

Limitations:

• High costs for comprehensive datasets.
• Limited transparency about data collection methods

OD Data Processing: Matrices, Mapping, and Modelling

Collected OD data is processed into an OD matrix, a tabular representation of trips between origin and destination zones. Each row represents an origin, each column represents a destination, and the cell values indicate trip volumes.

Example OD Matrix:

This matrix can be imported into GIS maps and transport modelling tools to visualise the movement of people and goods across space — this can be for an entire country, city, or specific districts. By integrating OD data with a transport network model, cities can analyse congestion hotspots, underutilised routes, and peak travel times.

Conclusion

OD data is a cornerstone of transportation planning, enabling a deep understanding of mobility patterns. Traditional methods like household and roadside surveys provide detailed, ground-level data but require significant resources. Innovative approaches, including GPS, mobile phone data, and data purchased from brokers, offer scalable and efficient solutions.

By combining methods and leveraging GIS and modeling tools, cities can develop data-driven strategies for sustainable and effective transport systems. This unit primarily focuses on OD data for private transport planning. In the next unit, we cover OD data measurement for public transport planning with help from a subject matter expert based in Hong Kong.

Suggested readings

• Origin-Destination Traffic Survey—Case Study: Data Analyse for Bacau Municipality (2023)
• Understanding traditional origin-destination data: A survey (2017) US Department of Transportation, Federal Highway Administration