With the development of shared mobility (e.g., ride-sourcing systems such as Uber and Lyft), there has been a growing interest in pricing and empty vehicle relocation to maximize system performance (e.g., revenue, throughput, social welfare). Impatience of passengers during waiting is an...

This paper proposes a macroscopic fluid modeling framework to assist with strategic decision making of a platform for operating a large-scale on-demand app-based ride-hailing system. The framework captures the spatiotemporal characteristics of a ride-hailing system, and is flexible in...

A cooperative truck platoon is a set of virtually linked trucks driving with a small intra-vehicle gap enabled by connected and automated vehicle technologies. One of the primary benefits of truck platooning is energy savings due to the reduction of aerodynamic drag on the platooned vehicles....

This study considers dynamic parking management in the era of driving automation that users of privately-owned automated vehicles (AVs) come to a downtown area to conduct various activities and choose between three parking options: outskirt parking, on-street parking, and...

In ride-hailing markets, the spatial mismatch between supply and demand characterizes one of the most fundamental operational bottlenecks. The voluntary customer search of drivers is oftentimes inadequate for narrowing the demand-supply gaps and falls short in responding timely to the dynamic...

This paper investigates intersection control in a fully connected and automated vehicle (CAV) environment, which aims to coordinate in real-time conflicting traffic movements under safety and kinodynamic constraints to maximize intersection throughput or minimize traffic delay. Given the...

On-demand ride-hailing services are becoming a ubiquitous means of mobility in major cities. While convenient and accessible, they are costly for many, and thus ride-hailing providers are often offering pooling options that allow passengers to share a ride and split the fare. The downside of...

This paper investigates congestion pricing strategies in static networks with boundedly rational route choice behavior. Under such behavior, users do not necessarily choose a shortest or cheapest route when doing so does not reduce their travel times by a significant amount. A general path-based...

This paper is concerned with development of improvement schemes for road networks under future travel demand uncertainty. Three optimization models, sensitivity-based, scenario-based and min-max, are proposed for determining robust optimal improvement schemes that make system performance...