An Optimal Design Method for Bus Line Network Applicable to Unobvious Demand Changes
A bus line network and optimization design technology, applied in the field of traffic engineering, can solve the problems of manpower and time waste, and achieve the effects of reasonable resource allocation, strong operability and sustainability, good optimization quality and search efficiency
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Embodiment 1
[0039] An optimal design method for bus network suitable for inconspicuous demand changes, such as figure 1 shown, including the following steps:
[0040] Step 1: Establish the objective function and set constraints,
[0041] The objective function is shown in the following formula:
[0042]
[0043] The constraints are as follows:
[0044]
[0045]
[0046] In the formula: V is the set of all sites, i means site i, j means site j, d ij Indicates the travel demand from station i to station j, L max Indicates the maximum length of the bus line, L min Indicates the minimum length of the bus line, Q max Indicates the maximum capacity of each vehicle, T d Indicates the time cost of each unsatisfied public transport demand, n indicates the nth line of a plan, tr indicates the transfer route when using more than two routes, L n Indicates the total length of the line n, Indicates that the bus demand from station i to station j is satisfied on route n, Indicates tha...
Embodiment 2
[0084] The difference between this embodiment and Example 1 is that the traditional genetic algorithm (TGA) in the field of traffic engineering includes selection, crossover and mutation processes, while our improved genetic algorithm (IGA) deletes the crossover process and only includes the improved Selection and mutation process, in the comparison of Example 1, we applied the improved IGA and traditional TGA to our case respectively. Each method is tested 10 times, and the predetermined number of iterations is set to 1000. The comparison results are as image 3 shown.
[0085] image 3 It can be seen that the fitness value of IGA is better than that of TGA, which should be because we have improved the selection and mutation process. The calculation time of IGA is generally higher than that of TGA, which may be because we eliminate the crossover process. This comparison can prove that: compared with TGA, IGA application can improve the optimization quality and search effi...
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