Dynamic Routing for Ridesharing

The transportation sector represents a major part in the current and future US economy, with more than 10% of the United States’ GDP directly related to transportation activity. The significant congestion and projected demand increases with limited infrastructure investment make necessary the development of significant improvement on transportation systems. Transportation planners must therefore find ways to improve transportation conditions in a cost-efficient manner. Significant advances have been made in the procurement and provision of real-time information that would be required for the effective control of a transportation system. Yet, this information is mostly used in a centralized transit system design and operation. These efforts have had limited success to date addressing congestion in most American cities, which have a dispersed demand due to a lack of single high-density business and residential centers. Congestion in the US continues to rise, stressing vital infrastructure, causing delayed shipments, late employees, and countless other problems. Although these new mobility options such as ride-sharing are not the complete answer to congestion nationwide, their ability to augment existing public infrastructure, such as mass transit, could help to solve many congestion related problems in urban areas like Los Angeles. This research will develop new dynamic routing models and algorithms to support the special features of ridesharing such as High Occupancy Vehicles (HOVs) lanes and based on the developed models and algorithms, we will study the impact of these lanes on facilitating ridesharing.

Traffic congestion is a significant social concern that is credited with considerable economic costs, wasted time, and associated public health risks. Efficient ridesharing solutions could help mitigate congestion. However, given a fleet of vehicles and ridesharing passenger requests, finding efficient ridesharing routes should take into account existing policies of discounted toll rates on High Occupancy Toll (HOT) lanes and the availability of HOV lanes, which could provide cost reductions and time savings under congestion. While there is a rich history of using optimization models to determine vehicles routes, the special features that could facilitate ridesharing such as the use of special links in the roadway, namely, the increasing use of High Occupancy Vehicle (HOV) lanes and reduced toll rates for high occupancy vehicles on many roads and bridges makes the nature of the routing solution different from other routing problems. In most routing problems the addition of an extra pickup request will usually increase the objective function (e.g., travel time) but in the ridesharing context the objective function could be reduced with the extra pickup due to being able to qualify for a HOV lane. Thus, the objective of this research is to develop dynamic routing algorithms to support ridesharing.


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