By: Paul Ruggeri on April 20th, 2017

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Travel Demand Models and Big Data

Big Data  |  Transportation

Building accurate travel demand models requires a detailed understanding of the ways behavior changes during specific conditions. Route choice, for example, can vary dramatically depending on time of day and other factors that influence drivers.

Let’s say that Sarah needs to drive from her home in Pittsburgh’s east end to the downtown business district. Most of the time, she finds Penn Avenue to be the fastest and most enjoyable route. But she also knows it would be foolish to take that route during rush hour. Or during a sports game, major concert or visit from the President.

Accurate travel demand models start with quality input data. Outdated or incomplete data results in inaccurate models, and your predictions will fail to account for the decisions that drivers like Sarah make almost subconsciously. Learn how to improve the accuracy of your travel demand forecasts by plugging in analytics derived from Big Data.


The Challenge of Forecasting Travel Demand 

Modern travel demand forecasting is used by regional transportation planning organizations to ask critical “what if” questions about their proposed plans and policies. The results, according to a guide by the Virginia Department of Transportation (VDOT), inform a number of important transportation planning decisions.

Complex models are used to estimate travel behavior as well as travel demand for a specific future time frame. Traditionally, travel demand models use a four-step process to analyze regional transportation planning, according to VDOT:

  1. Trip generation (the number of trips to be made)
  2. Trip distribution (where those trips go)
  3. Mode choice (how the trips will be divided among the available modes of travel)
  4. Trip assignment (predicting the route trips will take)

The results obtained through this four-step process vary widely, since they depend on the quality of the ideas and data used, as well as the particular model’s sophistication. “Small models generally provide users with forecasted highway volumes for roadways with functional classes of minor arterial and above,” according to VDOT, while large model regions “generally provide users with everything included in small models and transit forecasts.”

Sophisticated models can incorporate and analyze more granular data, “such as information on truck forecasts, college/university travel, HOV travel, and the effects of toll strategies on travel behavior.”


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Modeling with Dynamic Traffic Assignment 

A great example of more sophisticated travel demand modeling is Dynamic Traffic Assignment (DTA). Transportation planners have found DTA useful in forecasting the impact of transportation projects or investments and land development projects.

Such forecasting “is even more focused than corridor planning and requires a corresponding sharper focus and disaggregation of inputs and sometimes outputs,” according to a report published by the National Cooperative Highway Research Program (NCHRP).

“In many project planning studies, it is now common for a refined and study area-focused travel demand forecasting model to be one step in a larger forecasting effort,” according to the report. This effort “may take the output model forecasts and subsequently use them as inputs to mesoscopic or microscopic dynamic traffic assignment (DTA) or microscopic travel simulation.”

This detailed modeling technique is particularly effective for modeling user response to issues such as peak spreading, freight analysis and congestion, in fine resolution. To be effective, however, DTA modeling requires detailed, rigorous data.


Traditional vs. Big Data Approaches to Modeling 

The traditional method of DTA modeling requires collection from at least six different data sources, and many of these sources are extremely cumbersome and expensive. This method also requires a huge effort to integrate, calibrate and check the data integration. As a result, DTA with traditional methods is clunky and messy, as well as costly and time consuming. What’s worse, the results are still based on lots of assumptions.

In contrast, Big Data allows you to attack DTA with a direct, data-driven approach. Using fine-tuned origin-destination studies based on Big Data, modelers can uncover precise analytics, such as how left-hand turns are affected by time of day and type of trip.

Let’s dig a little deeper into origin-destination (O-D) studies -- a fundamental building block of many transportation models, studies and projects.


Big Data for Origin-Destination Matrices

Transportation experts can now use Big Data to quickly create precise, accurate and comprehensive O-D matrices, using algorithmic techniques that analyze “pings” from millions of mobile devices and organize them by location and time stamp.

When transportation experts have analytics about trips from millions of devices, they can create traditional O-D matrices that represent a far greater percentage of the population and a longer timespan than could be captured through surveys or license plate studies.

In a nutshell, trips and series of activities are created by:

  • Identifying the pings that occur when mobile devices begin moving (the origins);
  • Following the series of pings that occur on the devices’ journeys (the route); and
  • Identifying the final pings when devices come to rest (the destinations).

Using mobile Big Data for these matrices helps deliver the fine resolution needed for accurate travel demand modeling. But that’s only the beginning. Trip data (the time stamps that identify devices’ home and work locations) can be combined with contextual data sets, such as parcel boundaries and demographic information. Now, your traditional O-D matrices can be analyzed in terms of trip purpose.

It’s important, however, to recognize that these data points are messy at the outset. No single person could manage trillions of data points using Microsoft Excel! That means sophisticated processing techniques are critical to making these data sets manageable and effective for planning transportation projects.


Beyond Travel Demand Modeling 

As we’ve discussed, Big Data is an essential tool for understanding the ways behavior changes during specific conditions, and building accurate travel demand models. But there are a number of other applications that are transforming transportation.

Here are seven other ways to harness the power of Big Data in your planning and projects:

  1. Transportation Demand Management
  2. Internal/External Studies
  3. Congestion Studies
  4. Project Performance Evaluations
  5. Performance Measures
  6. Detour Planning
  7. Public Transit Design and Expansion

What is your biggest challenge when it comes to travel demand modeling? What tips or advice would you offer to others? Please share your thoughts in the comments section below.

Download our free guide today and learn how to improve transportation planning with Big Data.

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