Increasing traffic congestion and a shortage of funds available to build new roads are forcing the transportation infrastructure to function at its maximum capacity. The limited road space available on congested urban street networks in major cities in the United States as well as other parts of the world, notably in Eastern Asian countries, represents a challenge to transportation planners and traffic engineers. The available road space is typically partitioned according to a variety of modes: exclusive lanes for bicycles, buses, parking lanes, etc. The current road space allocation for most urban road networks has been modified throughout the years through a process of incremental changes, each tailored to meet a specific demand or to respond to a specific change at the time.

The questions in this research are: Is there a way to provide a solution to reduce congestion with minimum resources such as pavement markings and traffic signs? Should different modes of transportation be included in roadway lane designation? What are the best possible scenarios that would provide the best measures of effectiveness? And how can transportation professionals provide a comprehensive analysis to stakeholders to allow them to make an informed decision for lane-use allocation in urban transportation networks?

The approach in this study consists of investigating what relationships exist between the lane-use allocation on one hand and the traffic flow, traffic speed, environmental impact, safety impact, mobility, and accessibility on the other. Since not all of the objectives can be transformed into a single monetary dimension, a multi-objective decision-making framework is used to compare different road-allocation scenarios. This method is employed to incorporate multiple and conflicting objectives into a process where all of them are given credence regardless of how well they can be estimated in monetary terms. Further, the suggested decision-making method includes charts as visual tools to help decision-makers understand the results of each objective when corresponding to a specific scenario. The research provides a unique application for a multimodal analysis and a decision-making method not influenced by decision-makers' input, and contributes to the transportation community efforts to improve corridor and network efficiency.