Transportation systems are the backbone of our modern economy. The ease of urban mobility is crucial for the success of cities. The automobile, which revolutionized urban mobility in the 20th century, is now struggling to keep up with demands for safety, efficiency, and equity. With more than 36,000 lives lost annually to traffic crashes in the US, and the average American spending 97 hours every year stuck in traffic, there is an imminent need for innovative solutions.

Two recent technologies show promise in delivering the next leap in urban mobility: (a) transportation network services, and (b) automated vehicles. Transportation network services (TNS) are businesses that connect people spread across the transportation network for efficient provision of goods and services. Examples include ride-hailing services such as Uber, and delivery services such as Instacart. Automated vehicles (AVs), popularly known as self-driving cars, obviate the need for a human to be in the driver’s seat. The combination of AVs and TNS is expected to significantly reduce traffic fatalities, alleviate congestion, and improve accessibility.

Despite their transformative potential, the path to a bright AV-TNS future of urban mobility seems unclear. Companies have realized after years of effort that designing safe AVs is much more difficult than originally anticipated. Recent studies have found that TNS companies have increased congestion in major US cities. Moreover, a significant proportion of TNS workers are unable to earn the prevailing minimum wage in their jurisdictions. This thesis is an attempt to understand why the promised AV-TNS future has remained elusive.

We explore two broad themes along this path: (a) economics of transportation network services, and (b) automated vehicle safety. Our work on these themes constitute the two halves of this thesis. In the first half, we introduce a unified framework to analyze the economics of transportation network services. Using this framework, we analyze the impact of recent labor regulations on the ride-hailing ecosystem and suggest alternatives that can achieve better outcomes. Adapting this framework to the case of delivery services, we demonstrate how the trade-off between delivery times and the cost of delivery, mediated by the extent of order pooling, dictates their economic viability. In the second half, we use a combination of theoretical models and empirical data to argue why connectivity is indispensable for safe AV deployment. Recognizing that some crashes are inevitable without connectivity, we propose a risk assessment framework that leverages human driving data along with on-road AV testing data to develop insights into their safety capabilities. We discuss several applications of this framework that inform safe AV deployment.




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