The procedure further includes the identification of potential terminal nodes, an aspect vital for route design in an urban context. The down-scaling of the street network is achieved by devising simple and robust rules applicable to all urban layouts. For our modest desktop set up, Footnote 1 we determined 500 nodes, which would result in a runtime of about 600 h, to be the upper-most limit for practical work. Scaling down an urban street network is desirable principally to restrict the computation times needed for the passenger objective, which is usually the main bottleneck and leads to an increase of the run time with \(f(N^3)\), N being the number of nodes in the network. We therefore propose a network design procedure which scales down the network to a size manageable for meta-heuristic-based optimisations, while at the same time preserving the characteristics of the urban street network. The methods from Bagloee and Ceder ( 2011) and John ( 2016) are applicable to most urban areas, but both share the same disadvantage: as the locations of the selected stops within the street network are not fully taken into account, the chance that the resulting network does not reflect the real spatial layout of the city is high (John 2016), especially if the number of selected stops is only a fraction of the total number available.Īs the layout of the street network is essential for the design of bus routes, it is important that an instance network sufficiently reflects the characteristics of the street network. Here a fixed number of stops was selected randomly while a minimal distance between selected stops was ensured. A similar method was also used by John ( 2016) to generate networks for the UK cities of Nottingham and Edinburgh. They select every stop point as a node provided it is further than 300 m from another with a higher expected travel demand. Another node selection algorithm was proposed by Bagloee and Ceder ( 2011) to generate instances for Winnipeg, Canada, and Chicago, USA. This method is only suited to cities built in a strict grid pattern. The nodes were placed on street junctions close to the centres of housing blocks. One exception is the work by Mauttone and Urquhart ( 2009) who generated a network with 84 nodes to represent the city of Rivera, Uruguay. 2017).įew publications describe the rules of instance generation in detail. (Concepcíon, Chile, 2017) (Gutierrez-Jarpa et al. (Taoyuan-County, Taiwan, 2011) (Feng et al. (Groningen, Netherlands, 1988), Pattnaik et al. In addition to these studies, other researchers built their own test instances based on data from urban areas around the world. These have been used by several studies since (e.g. As these two instances are rather small (15 and 24 nodes), Mumford published four larger instances ranging from 30 to 127 nodes, based on the Chinese city Yubei and the two UK cities of Brighton and Cardiff (Mumford 2013). Another often used test instance, a 24-node network published by Leblanc ( 1975), is based on the city of Sioux Falls, USA. A prominent example is the instance published by Mandl ( 1979). Many researchers have tested their algorithms on the few fully published instances. These combined datasets are usually referred to as instances. Research for public transport route optimisation requires information on the available transport network and the travel demand. This approach will also be used in this paper. One common simplification is to focus on the route design phase under the assumption of a fixed transfer time between different routes. However, due to the high complexity of the task, most researchers focus their efforts on simplified versions of the problem. Solving all five phases simultaneously would be optimal. The task to generate efficient public transport networks can be treated as five interconnected phases (Ceder and Wilson 1986): (1) route design, (2) vehicle frequency setting, (3) timetabling, (4) vehicle scheduling and (5) crew scheduling. Multiple reports have pointed out the insufficiencies of this process and the need for a systematic computer-based approach (see e.g. Most often these networks have evolved over time rather than being designed as a whole (Mumford 2013). In the majority of cities around the world, public transport networks have been developed using a combination of local knowledge, planning experience and published guidelines.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |