Abstract
Rapid urbanization and increasing demand for transportation burdens urban road infrastructures. The interplay of number of vehicles and available road capacity on their routes determines the level of congestion. Although approaches to modify demand and capacity exist, the possible limits of congestion alleviation by only modifying route choices have not been systematically studied. Here we couple the road networks of five diverse cities with the travel demand profiles in the morning peak hour obtained from billions of mobile phone traces to comprehensively analyse urban traffic. We present that a dimensionless ratio of the road supply to the travel demand explains the percentage of time lost in congestion. Finally, we examine congestion relief under a centralized routing scheme with varying levels of awareness of social good and quantify the benefits to show that moderate levels are enough to achieve significant collective travel time savings.
Highlights
Rapid urbanization and increasing demand for transportation burdens urban road infrastructures
Despite the abundance of research on optimal flow configurations and their implications in the transportation, urban planning and economics literature, there is a shortage of works that use big data sources to understand the role of travel demand and actual travel times in metropolitan regions when comparing cities
This highlights a need to build a framework that can be replicated to systematically generate meaningful travel times to understand cities better and test solutions to urban problems such as congestion or pollution. We address this issue by coupling travel demand profiles and travel time estimates to analyse how efficiently people move across cities
Summary
Rapid urbanization and increasing demand for transportation burdens urban road infrastructures. Despite the abundance of research on optimal flow configurations and their implications in the transportation, urban planning and economics literature, there is a shortage of works that use big data sources to understand the role of travel demand and actual travel times in metropolitan regions when comparing cities. This highlights a need to build a framework that can be replicated to systematically generate meaningful travel times to understand cities better and test solutions to urban problems such as congestion or pollution. We present our findings for ;five major cities around the world: Boston and San Francisco Bay Area in the United States, Rio de Janeiro in Brazil, and Lisbon and Porto in Portugal
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