Transport Network Design Problem under Uncertainty: A Review and New Developments

Abstract

This paper aims to provide a state-of-the-art review of the transport network design problem (NDP) under uncertainty and to present some new developments on a biobjective reliable network design problem (BORNDP) model that explicitly optimizes the capacity reliability and travel time reliability under demand uncertainty. Both are useful performance measures that can describe the supply-side reliability and demand side reliability of a road network. A simulation-based multi-objective genetic algorithm (SMOGA) solution procedure, which consists of a traffic assignment algorithm, a genetic algorithm, a Pareto filter, and a Monte-Carlo simulation, is developed to solve the proposed BORNDP model. A numerical example based on the capacity enhancement problem is presented to demonstrate the tradeoff between capacity reliability and travel time reliability in the NDP.

Game theoretical transportation network design among multiple regions

“…There are a vast number of literatures of NDP focusing on advanced model formulations, effective solution algorithms and engineering applications. For a comprehensive review of literature on the NDP, the readers are referred to the following papers, Magnanti and Wong (1984), Yang and Bell (1998), Chen et al (2011) and Meng et al (2014) etc.…”

“…The inevitable and attractive challenges including stochastic parameters and inherent traffic dynamics revoke a new round popularity on the NDP studies. Herein, some representative works include stochastic NDPs taking account of impacts of network uncertainties (Chen and Yang 2004;Chen et al 2010Chen et al , 2011Lo and Tung 2003;Sharma et al 2009;Sumalee et al 2009;Yin et al 2009), time-dependent NDPs considering different time-dimension Szeto 2003, 2009;Szeto and Lo 2005;Ukkusuri and Patil 2009;Ukkusuri and Waller 2008) and some combined NDPs simultaneously considering uncertain parameters and time-dimension (Karoonsoontawong and Waller 2007;Waller and Ziliaskopoulos 2001). In addition, global optimum oriented solution algorithm and multiobjective NDP have also been addressed and analyzed in recent studies, for example, Wang and Lo (2010), Luathep et al (2011), Li et al (2012a and Wang et al (2013) for global solution algorithms, Chen and Yang (2004), Chen et al (2010), Meng and Yang (2002) and Sharma et al (2009) for multi-objective NDPs.…”

“…A number of notable developments have been made in theories, models, and solution algorithms (Magnanti and Wong 1984;Yang and Bell 1998;Chen et al 2011;Meng et al 2014). The NDP is typically characterized as a Stackelberg game or bi-level programming model.…”

Optimal Investment Timing and Size of a Logistics Park: A Real Options Perspective

“…The real option (RO) valuation approach considers the opportunity during an investment period that goes unrecognized in NPV analysis [24]. There is a compelling need to account for time-dependent uncertainty within network designs and project investment due to their irreversible characteristics [25,26]. Li et al [10] proposed a RO model to address investment and selection in transit technology given a volatile city population by considering the spatial use equilibrium of a city.…”

Population-driven Urban Road Evolution Dynamic Model

“…But where best to invest the infrastructure, known as the network design problem (NDP), has long been a great interest to decisionmakers, as well as been recognized to be one of the most difficult and challenging mathematical problem to solve. For the detailed reviews of NDP, the readers are referred to Boyce (1984), Magnanti and Wong (1984), Friesz (1985), Migdalas (1995), Yang and Bell (1998), Chen et al (2011) and Zanjirani et al (2013).…”