Rosa P: A digital library for transportation research

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SJSU SAN JOSE STATE UNIVERSITY 
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1MINETA TRANSPORTATION INSTITUTE 
Latent Active Transportation Methodology 
Maria Chierichetti, PhD Project 2309 
Fatemeh Davoudi Kakhki, PhD April 2025 
Introduction 
To create better sidewalks, bike lanes, and other 
infrastructure and to design better policies, it is 
important to understand how many people would 
walk or bike if safe and convenient options to do so 
were available to them—this is called “latent demand.” 
Unlike traditional demand models that focus on 
observed trips, latent demand estimation tries to 
measure the unrealized potential for active travel 
caused by barriers such as inadequate infrastructure, 
safety concerns, or lack of connectivity. Latent demand 
estimation for active transportation has gained 
signifcant attention in transportation research and 
planning because of its role in identifying unrealized 
travel potential. Traditional travel demand models 
have often underestimated walking and cycling trips 
due to data limitations and infrastructural constraints. 
Recent eforts have tried to refne methodologies for 
estimating latent demand using spatial modeling, 
behavioral analysis, and multimodal integration.Tis 
study develops a comprehensive latent demand model 
for California, integrating geospatial analysis and 
multimodal accessibility assessments. 
Study Methods 
Te Latent Demand Method quantifes the potential,
yet unrealized, demand for walking and cycling trips 
and assesses its impact on transportation systems.Tis 
approach estimates the number of trips that could 
occur within a given area if optimal pedestrian and 
cycling infrastructure were in place. Using a GIS-
based corridor analysis, the methodology analyzes 
how easy it is to reach places on foot or by bike,
estimating potential demand based on distance and 
accessibility. Te model assumes that the closer a road 
is to key destinations, the more people will want to 
walk or bike there, with demand decreasing as distance 
increases. Key factors incorporated into the analysis 
include employment density, population distribution,
school and university enrollments, and proximity to 
parks and trails, which help estimate the likelihood of 
walking and cycling trips.
To further refne the model, cyclists are classifed 
into four behavioral categories: strong and fearless,
enthused and confdent, interested but concerned,
and no way, no how.Tese classifcations help account 
for diferent levels of willingness to cycle based on 
M I  N E  T  A  T  R  A N S P O R  T  A  T  I O N  I  N S T  I  T  U  T  E  transweb.sjsu.edu 
Project 2309   April 2025 M I  N E  T  A  T  R  A N S P O R  T  A  T  I O N  I  N S T  I  T  U  T  E  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
      
 
  
 
 
 
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MINETA 
TRANSPORTATION 
INSTITUTE 
perceived safety and comfort. Te methodology has 
been tested in Douglas City, El Centro, and downtown 
San Jose, demonstrating its applicability across urban,
suburban, and rural environments, highlighting how 
diferent settings infuence latent demand for walking 
and biking. 
Findings 
Findings show that employment centers and 
commercial areas are major drivers of bicycle demand,
while schools and recreational spaces signifcantly 
infuence pedestrian activity.Te methodology assigns 
a Latent Demand Score to highway segments based 
on potential active transportation trafc, considering 
factors such as work commutes, shopping trips, school 
enrollments (including colleges and universities), and 
proximity to parks and trails. For each of the three 
case studies in California—San Jose (Downtown),
El Centro, and Douglas City—three highway 
segments were identifed where active transportation 
infrastructure would have the greatest impact on 
both pedestrian and cycling trafc. Tese locations 
represent priority areas for investment in walking and 
biking facilities, where improvements would likely 
result in the highest increase in active transportation 
trips.
Overall, the method serves as a valuable tool for 
estimating the efectiveness of new pedestrian and 
cycling infrastructure. However, urban planners 
are encouraged to consider existing and planned 
infrastructure before making new investments. By 
integrating these insights with broader transportation 
planning eforts, cities can maximize the benefts 
of active transportation projects, ensuring that new 
facilities are placed where they will have the greatest 
impact on mobility and accessibility. 
Policy Recommendations 
Te results emphasize the importance of strategically 
improving active mobility infrastructure to convert 
latent demand into actual walking and cycling trips.
Te fndings highlight specifc areas where people are 
likely to walk or cycle if safer, more convenient, and 
well-connected pathways were available.Tis supports 
the justifcation for investing in bike lanes,pedestrian-
friendly streets, and other active transportation facilities 
that enhance accessibility and safety. By identifying 
locations where new or improved infrastructure would 
have the most signifcant impact, the method helps 
state and local governments prioritize investments 
efectively. Instead of implementing projects without 
clear data on demand, planners can use these insights 
to ensure that limited transportation funds are 
allocated to areas where they will yield the highest 
return in terms of increased pedestrian and bicycle 
trafc. Additionally, integrating these fndings into 
broader transportation and land-use planning eforts 
can promote sustainable mobility, reduce congestion,
and encourage healthier, more active lifestyles. 
Overall, these results serve as a valuable tool for 
decision-makers, helping them design and implement 
infrastructure projects that maximize the benefts of 
active transportation, fostering more walkable and 
bike-friendly communities. 
About the Authors 
Dr. Maria Chierichetti is an Associate Professor in 
the Department of Aerospace Engineering at San 
José State University. Her current research interests 
focus on integrating machine learning to current 
analysis tools to improve the safety of transportation 
systems.
Dr. Fatemeh Davoudi is an Associate Professor in 
General Engineering and (by courtesy) in Electrical 
and Computer Engineering at Santa Clara University.
Dr. Davoudi is currently leading the Machine 
Learning & Safety Analytics Lab. 
To Learn More 
For more details about the study, download the full 
report at transweb.sjsu.edu/research/2309 
MTI is a University Transportation Center sponsored by the 
U.S. Department of Transportation’s Ofce of the Assistant 
Secretary for Research and Technology and by Caltrans. Te 
Institute is located within San José State University’s Lucas 
Graduate School of Business. 
transweb.sjsu.edu M I  N E  T  A  T  R  A N S P O R  T  A  T  I O N  I  N S T  I  T  U  T  E  


Latent Active Transportation Methodology [Brief]

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