A decision model for optimal lane reservation with task merging and residual capacity constraints

Abstract

This study addresses a new lane reservation problem in a transportation network with task merging and residual capacity constraints called the CLRP-TM. It aims to optimally convert some general-purpose lanes in the transportation network to be reserved and design reserved lane-based routes for some time-efficient transportation tasks emerging in large-scale special events. Once reserved, the lanes can only be used by the special tasks and their travel times can be reduced. However, the general-purpose vehicles on adjacent non-reserved lanes will burden travel delay due to the reduction of available lanes. To pre-merging some tasks that are geographically close can greatly reduce such negative impact because the required number of time-efficient transport routes is reduced. Besides, each road segment has a limited residual capacity for task vehicles if no lanes are reserved on it. The objective of the CLRP-TM is to minimize the total negative impact caused by all reserved lanes. For this problem, an integer linear programming model is developed and solved using existing commercial solver CPLEX. Computational results for a real-life example from the Guangzhou Asian Games in 2009 confirm the effectiveness and efficiency of the proposed approach.