In gathering and analyzing mobility data, traditional methods like traffic counters have always had certain limitations. Those gaps are widening as the pace of change quickens, and new modes arrive. In our first excerpt from our eBook, Big Data for Transportation, we explored the challenges facing today’s transportation experts. Here, we look at why traditional measurement methods can’t keep up with the pace of change.
Traffic Counter Sensors Are Incomplete
The traditional way to gather traffic counts is to send staff onto a handful of targeted roadways to either manually count vehicles, or install a temporary “tube” sensor across the roadway to capture counts for the vehicles that drive over it. Some areas install permanent traffic counters on priority roadways.
Unfortunately, transportation experts are well-acquainted with the limitations of this type of traffic counter data, which include:
- Lower-trafficked and rural roads are often overlooked, which can skew the data.
- Sending staff onto busy roadways is dangerous to workers and distracts drivers.
- Small sample sizes can skew modeled results.
- Temporary traffic counters can drive inaccurate results.
- Permanent traffic counters are expensive to install and maintain.
Traffic Surveys Under-Sample
Traffic count studies often include survey data, asking respondents questions about their travel routes and habits. But surveys increasingly fall short in gathering sufficient traffic counter data:
- Surveys can be expensive, costing hundreds of dollars per household.
- Results are based on small sample sizes (often around 1% or less) and small sample periods (usually 1-5 days).
- Participants are increasingly difficult to recruit due to increased privacy concerns, and fewer households using landline phones.
- Hard-to-reach populations are systematically under-sampled.
- Individuals/households tend to underreport travel, especially for short trips, active mode, and non-work purposes.
- Error can be introduced via the weighting and expansion process.
Overall, surveys are more powerful tools for gathering subjective rather than objective data.
Modeling Has Limits
Traffic count data obtained from sensors and surveys has long provided transportation professionals with the necessary inputs for data modeling. Modelers assist planners by developing quantitative analyses that can create short- and long-term travel demand forecasts.
Historically, data to develop and validate models has been limited by availability, frequency, or acquisition costs and time. Big Data offers a more up-to-date and easy-to-use source of travel and traffic counter data that can be used to improve, calibrate, and validate models.
For some types of analysis to support planning and policy, modeling can be replaced entirely with Big Data analytics either because models were not intended to be used for certain purposes or it is too much effort to develop or customize a model for such a purpose.
Some agencies do not have sufficient resources to develop models at scale, yet they do need simplified models for occasional projects. In such cases, an agency can use Big Data as building blocks to develop a simplified model to support planning and policy decision-making
Existing Metrics Aren’t Flexible
Even with the afore-mentioned tools like modeling, sensors, and surveys, and modeling, there remain some glaring gaps in data from traditional traffic counters. One example is before-and-after scenarios. For example, say that residents react strongly to restrictions put into place to combat cut-through traffic on a specific street. A transportation department looking to evaluate the success of those measures can’t compare “before and after” scenarios unless they planned ahead and captured traffic volume and route information before putting the restrictions into place.
Imagine also that planners want to prepare for a special event that will draw a large amount of visitors from outside the area. Traditional transportation counters can’t measure past events to determine traffic load and optimal re-routing.
We’ve accepted the limitations because, until now, these methods were the best we had. But Big Data is addressing these gaps, giving transportation professionals information that they didn’t even realize they could access. Download our eBook to learn more about how Big Data goes beyond sensors, surveys, and modeling to provide full metrics for today’s transportation challenges.