The Accessibility-Based Travel Model & West Station Area Pilot Study Project
The Accessibility-Based Travel Model & West Station Area Pilot Study project illustrates Renaissance’s evolving analysis frameworks that reflect shifting priorities within the planning profession. This evolution always reflects our best efforts to get to grips analytically with the trending issues that resonate with planners and the communities they serve. Our introduced innovations need not impose themselves as a new way of doing things, but they can reframe prevailing thinking on key topics to better address changing questions and concerns.
In 2019, the Metropolitan Area Planning Council (MAPC) of Massachusetts set out to develop a sketch travel planning model based on multimodal accessibility, an emerging analytical lens that combines land use and transportation factors to describe the ease with which travelers can reach destinations by different travel modes. Renaissance was retained to lead the study, and I took the reins as Project Manager. The goal was to offer planners an alternative to the regional travel demand model for local area analyses that would be sensitive to fine-grained demographic, land use, urban design, and transportation network and policy details.
It’s worth pausing to remind ourselves how difficult it is to understand how these aspects of place influence travel behaviors. Intuitively, we reckon they affect trip generation, mode choice, average trip length, etc. But tying them together in a comprehensive behavioral model? Let’s just acknowledge that no one on earth has perfected that yet.
Of course, perfection isn’t demanded. Sound, reasonable insight that can inform policy decisions is deeply valuable even when imperfect, especially in complex settings where numerous interests are represented. Our sketch travel model was to be pilot tested in the West Station planning area. Sticking strictly to the technical aspects of model development, Renaissance’s intention was to demonstrate sensitivity to alternative land use scenarios, walk and bike network configurations, transit service schedules, and pricing considerations in travel behavior forecasts. This would give planners critical insights to guide discussions with policymakers and stakeholders.
Our approach built on some of our prior experience using multimodal accessibility measurement and comparing the ease with which travelers could reach key destinations by walking, biking, driving, and transit. Accessibility offers several attractive features for this kind of sketch modeling application.
It’s pretty intuitive: the access offered by each mode relative to competing modes has a significant influence on whether travelers opt to use it.
It can incorporate a lot of other variables: access scores generally reflect proximity to destinations and mobility provided over travel networks. In this way, it combines land use and transportation factors in a coherent framework. It can be sensitive to travel time, parking charges, transit fares, tolls, vehicle operating costs, network connectivity, non-motorized facility characteristics, land use mix, and more.
We can flex geographies: when we want to understand how highly localized changes like walking directness or land use mix influence travel patterns, we need a granular analysis. Since it can be measured at various scales, accessibility offers the opportunity to achieve that detailed granularity for a local study area, all the while retaining an awareness of regional travel patterns.
The modeling approach and results are summarized here. We developed rich accessibility scores for each major mode of travel that accounted for its unique operating characteristics and costs, providing granular detail in the West Station area while maintaining a regional perspective. The access scores were used to develop behavioral models that also account for traveler socio-economic and demographic characteristics to forecast trip generation, mode choice, and distribution patterns. We also created a post-processing step to estimate where trips by transportation network companies (TNC’s) like Uber and Lyft would be made.
Finally, we constructed the model in a relatively lightweight package for sharing and reuse. We programmed the entire modeling framework using Python and R, well-known data analysis languages. We delivered the full toolkit and workflow for MAPC staff to use in developing and testing alternative scenarios for the West Station area and for use in similar future efforts.