Evaluation and Design of Transport Network Capacity under Demand Uncertainty

Abstract

This paper proposes a flexible transport network capacity evaluation and design problem (FNDP) under demand variability. The future stochastic demand is assumed to follow a normal distribution. Travellers' path choice behaviour is assumed to follow the Probit Stochastic User Equilibrium (SUE). The network reserve capacity is used to evaluate the performance of the network. Since the future demand is stochastic, the reserve capacity is measured by possible increases in both mean and standard deviation (SD) of the base demand distribution. The proposed model therefore represents the flexibility of the network in its robustness to OD demand variation (i.e. high SD). The proposed model can also determine an optimal network design to maximize the reserve capacity of the network in terms of both the mean and SD of the increased demand distribution. The paper applies the implicit programming approach to solve the FNDP. Sensitivity analysis is adopted to provide all necessary derivatives. The model and algorithm are tested with a hypothetical network to illustrate the merits of the proposed model.

Game theoretical transportation network design among multiple regions

“…Although the NDP is pertinent to many influential factors, including multi-period planning (e.g., Lo and Szeto 2003;Ukkusuri and Patil 2009), demand uncertainty (e.g., Sumalee et al 2009;Yin et al 2009;Chen et al 2011) and multi-modal choice (e.g., Li et al 2012a, b), the optimization objective is the most important factor for NDP modeling. A number of design objectives have been used in NDP, including minimization of total travel time (Leblanc 1975;Abdulaal and Leblanc 1979;Meng et al 2001;Meng and Yang 2002), maximization of reserve network capacity (Wong and Yang 1997;Yang et al 2000;Sumalee et al 2009;Zhang and van Wee 2012), and enhancing sustainability (Nagurney 2000;Meng and Yang 2002;Chen and Yang 2004).…”

“…A number of design objectives have been used in NDP, including minimization of total travel time (Leblanc 1975;Abdulaal and Leblanc 1979;Meng et al 2001;Meng and Yang 2002), maximization of reserve network capacity (Wong and Yang 1997;Yang et al 2000;Sumalee et al 2009;Zhang and van Wee 2012), and enhancing sustainability (Nagurney 2000;Meng and Yang 2002;Chen and Yang 2004). A single objective NDP model is typically formulated in early studies.…”

“…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.…”

Flexible Emergency Vehicle Network Design considering Stochastic Demands and Inverse-Direction Lanes

“…To properly characterize the effect of network stochasticity, simulation-based sampling (e.g., Monte-Carlo simulation) approximation technique is often used to estimate probabilistic statistics, including expectation and variance, of various system performance measures [8,[18][19][20][21][22][23][24]. Alternatively, probabilistic or reliability-based user equilibriums are proposed to incorporate the effect of network uncertainties into travelers' route choice behaviors in the lower-level traffic assignment problem and similar bilevel stochastic NDP models are then developed [25,26]. Some stochastic NDP models had also integrated other traffic factors and/or optimization targets (e.g., sustainability, traffic dynamics, and land use).…”

Large-scale road network vulnerability analysis: a sensitivity analysis based approach

“…SA has a long history in both non-linear programming and transport network analysis. Various applications of SA have been studied extensively for transport network design problems; e.g., trip matrix estimation (Yang et al 1992), capacity design problems (Sumalee et al 2009), and toll design problems (Yang 1997). Despite these applications, few studies of transport network vulnerability analysis have been conducted by using the SA technique.…”