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In the absence of accurate transit demand information, a demand responsive transit(DRT) route planning method based on taxi trajectory data was proposed to predict the “potential demand” of demand responsive transit and provide a feasible plan for route planning before transit operation. Firstly, taxi trajectory data in the study area was obtained through data mining, representing the “potential demand” for passenger travel in the area, and candidate station were determined using the K-means clustering algorithm. Secondly, a benchmark station network was established using these candidate station, with edge benchmark stations designated as the starting and ending points of routes. Utilizing the K-shortest pathes(KSP) algorithm constrained by route length, benchmark chains were generated. Finally, after determining the sub-chain set of the benchmark chains, demand response stations within each sub-chain were searched based on circumferential critical value constraints. Using this algorithm, alternative routes were generated repeatedly within specific time periods, and an initial optimal route was selected based on comprehensive evaluation indices for each alternative route.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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