In This Section
Study Examines How Autonomous Vehicles May Change Morning Commutes
Findings on Traffic Congestion, Downtown Parking Can Inform Urban Planning Efforts
- Email ckiz@andrew.cmu.edu
- Phone 412-554-0074
Autonomous vehicles (AVs), which already operate on the roads of several major U.S. cities and in countries worldwide, are expected to play a large role in shaping the future of cities. In a new study, researchers investigated how AVs may change travel patterns during morning commutes and affect parking in business districts. By providing insights into the changes associated with parking and traffic congestion as the use of AVs rises, the study can inform urban planning efforts.
Conducted by researchers at 好色先生TV and the University of Texas (UT) at Dallas, "" is published in Management Science. 鈥淯rban planners have a rare window of opportunity to establish policies that pave the way for the inevitable mass arrival of AVs,鈥 suggests Soo-Haeng Cho, IBM Professor of Operations Management and Strategy at Carnegie Mellon鈥檚 Tepper School of Business, who coauthored the study.
To accommodate high demand for parking, cities surrender large spaces to build parking structures, but many morning commuters still struggle to find affordable, convenient places to leave their cars; traffic congestion further complicates many morning commutes. AVs have the potential to address these issues by dropping commuters at their workplaces in a central business district and parking in suburban areas at lower rates, all by themselves. In this way, commuters could avoid high parking fees, and cities could reduce the need to build or maintain large, costly, and largely underutilized parking structures in business areas.
In this study, researchers examined the effect of AVs on the morning commute to a central business district in general, using Pittsburgh, PA, as a case study. They developed a continuous-time game-theoretic traffic model that considered key economic deterrents to driving (e.g., parking fees, traffic congestion, curbside pickup and drop-off) and characterized commuters鈥 departure-time and parking-location (inside or outside the central business district parking area) patterns.
Based on the study鈥檚 model, commuters who all use AVs may choose to park outside the central business district, increasing both vehicle hours and vehicle miles traveled compared with human-driven vehicles, the study found. This change would increase total system cost and suggest potential changes in how land is used in business districts (e.g., repurposing parking spots for commercial and residential areas) after AVs are used more widely.
To reduce total system cost, urban planners may opt to regulate commuters鈥 decisions by adjusting parking fees or imposing congestion tolls as a short-term measure, or by adjusting infrastructure, for example, converting parking spaces to drop-off spots for AVs. In Pittsburgh, these measures are estimated to reduce total system cost by up to 28.5 percent.
鈥淚n our study, we sought not to propose city-specific solutions, but to highlight general tradeoffs and dynamics in human behavior that emerge when AVs, commuters, and infrastructure interact,鈥 explains Neda Mirzaeian, Assistant Professor of Operations Management at UT Dallas鈥檚 Jindal School of Management, who led the study. 鈥淥ur model can serve as a guide, or even an early warning system, to recognize how seemingly small shifts in technology, costs, or incentives can lead to large changes in commuter behavior and system-wide efficiency.鈥
Sean Qian, H. J. Heinz III Professor of Civil and Environmental Engineering at Carnegie Mellon鈥檚 College of Engineering and Heinz College, who coauthored the study adds, 鈥淚n providing guidance to urban planners鈥攊ncluding mobility and infrastructure departments of mayoralties, city councils, town councils, and town boards鈥攐ur results can identify when and where current policies need to adapt in light of the special needs and characteristics of AVs when AVs become widely deployed.鈥
###
Summarized from an article in Management Science, "" by Mirzaeian, N (University of Texas at Dallas), Cho, S-H (好色先生TV), and Qian, S (好色先生TV). Copyright 2026 INFORMS. All rights reserved.
听