| Principal Investigator | Luca Quadrifoglio |
| Final Report (DOI) | Available Soon |
| TRID | Available Soon |
| Policy Brief | Available Soon |
| RIP | View RIP entry |
Large and growing urban areas are dealing with an escalation of traffic congestion and all its consequences in terms of overall delay experienced by travelers. Ridesharing is intuitively a practical solution to improve the congestion issues in our modern cities. The largely unused capacity of the private vehicles would be utilized in place of other vehicles to satisfy the mobility needs of individual passengers. This project analyzes and quantifies the effect on congestion reduction of a large-scale application of ridesharing mode to serve metropolitan transportation demand of taxi services, which have been shown to be often unproductive and wasting car seating capacity. Specifically, we are evaluating the macroscopic effect of converting a sizeable portion of the taxi rides from conventional direct point-to-point service to a ridesharing modality. This will incur in a potentially significant reduction of vehicles on the network and consequently ease out congestion at the expense of the service level experienced by willing ridesharers, which would need to be incentivized by fare reductions.
The project will make use of an available large set of taxi data from the City of Chicago to perform simulation analyses under a variety of scenarios. Different ridesharing modalities, scheduling algorithms and participation levels will be utilized and assumed to infer traffic network usages. The effect on congestion will be estimated with a before and after analysis evaluating an integrated index that considers total miles traveled, total travel time and Level of Service.
The project aims to provide tools and detailed information for planners and policy makers to intervene by incentivizing a conversion, even if partial, of the taxi services into ridesharing service, presenting an opportunity to improve the congestion conditions, along with other clear environmental advantages. Impact is expected to be significant with a potential nationwide deployment of these policies, driven by our largescale findings.
The project will make use of an available large set of taxi data from the City of Chicago to perform simulation analyses under a variety of scenarios. Different ridesharing modalities, scheduling algorithms and participation levels will be utilized and assumed to infer traffic network usages. The effect on congestion will be estimated with a before and after analysis evaluating an integrated index that considers total miles traveled, total travel time and Level of Service.
The project aims to provide tools and detailed information for planners and policy makers to intervene by incentivizing a conversion, even if partial, of the taxi services into ridesharing service, presenting an opportunity to improve the congestion conditions, along with other clear environmental advantages. Impact is expected to be significant with a potential nationwide deployment of these policies, driven by our largescale findings.
