Bicycle and Pedestrian Forecasting Tools: State of the Practice
Source: Pedestrian and Bicycle Information Center (PBIC), Fehr & Peers
Transportation forecasting models predict levels of activity, and help inform decisions on issues such as future facility use and the prioritization of projects. Travel and demand forecasting methods have long been used to estimate the number of vehicles traveling on a specific street or network and to estimate ridership for mass transit. Many jurisdictions and metropolitan planning organizations use forecasting methods to determine the potential impact of new development, changes to roadway capacity, or projected ridership for new transit.
However, these methods have traditionally excluded pedestrian and bicycle activity. For communities seeking to support walking and bicycling activity, quantifying the use and potential demand of facilities that support active transportation is increasingly important. To meet this need, bicycle and pedestrian forecasting models are being developed and integrated into planning projects focusing on facilitating mobility, managing resources, and improving health and safety.
These emerging forecasting approaches vary widely in the amount of data and level of effort required. The type, specificity, and reliability of data also vary between different forecasting approaches. For example, data used in forecasting models can range from readily available U.S. Census data to large sets of cell phone data. Simple forecasting techniques can be useful for basic estimating exercises, while more labor intensive modeling tools can provide fine-grained analysis on future demand, network connectivity, collision rates and a number of other topics.
This paper summarizes the state of the practice of bicycle and pedestrian forecasting tools, and suggests potential next steps to improve them. The forecasting tools discussed in this paper differ in geographical application as well as the accessibility of the tools and data required to complete the analysis. The objectives of this paper are to evaluate the state of bicycle and pedestrian forecasting tools to better inform their use and to contribute to an ongoing conversation about opportunities for development of future forecasting methods. All tools were evaluated based on the resources necessary to complete analysis, as well as the practical application of the resulting information. The evaluation found that though many forecasting tools and required data are publicly available, the more sophisticated tools often require high levels of experience, extensive amounts of time and sometimes costly software. To better forecast bicycle and pedestrian activity in the future, accurate and regularly-collected public and private data and accessible analysis platforms are essential.
