A bus network design procedure with elastic demand for large urban areas

Efficient transit network design and frequencies setting multi-objective optimization by alternating objective genetic algorithm

“…The results show that the design obtained by the proposed solution method is robust under demand uncertainty, and the design is better than both the current design and the design obtained by solving the route design problem and the frequency-setting problem sequentially. Cipriani et al (2012) described a procedure and its application to TNDFSP in the city of Rome, considering a complex road network, multimodal public transport systems and many-to-many transit demand. The procedure includes a route set generation algorithm and a parallel genetic algorithm for finding the sub-optimal set of routes with the associated frequencies, reporting an improvement over existing network.…”

Planning, operation, and control of bus transport systems: A literature review

“…Fan and Machemehl (2006a) develop a GA implementing the NAP as the fitness function while Fan and Machemehl (2008) design a local search algorithm consisting of (i) implementing an heuristic procedure to generate a feasible solution and (ii) implementing an iterative local search to generate the lines and their frequency as well as the NAP to re-assign the demand based on the current lines. Cipriani et al (2012) address the TND with elastic demand to define lines, frequencies, and vehicle sizes maximizing the total welfare. The authors propose a solution approach consisting of two stages: (i) implementing a heuristic algorithm to generate potential lines and their frequencies and (ii) a GA that recombines lines to generate new population individuals while the fitness function evaluates them using a probabilistic modal split model which determines the mode choice behavior of users and a hyper-path transit assignment model which determines the route choice behavior of users.…”

A review of technological improvements in bus rapid transit (BRT) and buses with high level of service (BHLS)

“…Table 5 presents a qualitative comparison of emission levels, based on a bus technology meta-analysis of internal combustion engines (Cooper et al 2012). No single technology outperforms the others across all pollutants; it is very important to check technologies according to various pollutants.…”

“…Itinerary (physical design) of transit routes is usually done without information technologies. There are, however, proposals to use heuristics to help optimize transit networks (see, for example, Cipriani et al 2009). Such a type of heuristic algorithm was used in the design process of the Transantiago bus network.…”