Abstract
Based on a state-of-the-art review of the Road Network Design Problem (RNDP), this paper proposes a bi-level programming model for the RNDP as well as algorithms for it. In the lower level of the proposed model, the elastic-demand Stochastic User Equilibrium (SUE) model is adopted to coincide well with characteristics of users behavior, and additionally, the parameter calibration method for the model is developed based on the Logit path choice model. In the upper level of the proposed model, the consumer surplus is maximized to improve the social benefit of a network in consideration of the travel demand, the construction cost, the off-gas emissions and the saturation level. The algorithm for the lower-level model is developed based on the descent iteration method, Dijkstra’s algorithm and linear search technology. A modified Genetic Algorithm (GA) is developed as the algorithm for the whole bi-level model, which takes designed elitist selection operator, adaptive cross operator, mutation operator and niche technology into consideration. The proposed model and algorithms are applied to a numerical example. The results demonstrate the validity and efficiency of the model and algorithms, which shows a bright prospect of the application in RNDP.
Highlights
Road Network Design Problem (RNDP) is regarded as one of the most complicated and challenging problems in transportation field
Uncertainty of travel demand indicates that travel demand is elastic which varies with network reformation, while uncertainty of path choice indicates that users are not always choosing the very path with minimum cost due to limitation of their knowledge level and information obtainment
To correspond with these two characteristics, model for Stochastic User Equilibrium (SUE) assignment with elastic demand is adopted in the lower-level subprogram in this research
Summary
Road Network Design Problem (RNDP) is regarded as one of the most complicated and challenging problems in transportation field. RNDP consists in optimizing a network to maximizing social benefit, by means of adding new links and improving existing links, with respect to a set of factors (traffic, cost, environment, etc.) (Cascetta 2001). X. Zhang et al Bi-level programming model and algorithms for stochastic network with elastic demand. Drezner, Salhi (2002) and Karoonsoontawong, Waller (2006) compared performance of these advanced heuristics in test problems, respectively Both studies demonstrate that GA is superior in finding optimal solutions of RNDP. Solution algorithms are developed for both elastic-demand SUE model and proposed bi-level programming model, based on some classic methods including descent iteration (Ortega, Rheinboldt 1970), Dijkstra’s algorithm (Kung et al 1984), linear search technology (Florian 1977), GA and so on. Some conclusions and future work are summarized in last section
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
