Smart card automated fare collection systems have been effective for the collection of data about the travel behavior of users on public transit networks. Because some systems record only the boarding (origin) locations, a method is needed for estimating the alighting (destination) locations. Existing algorithms can estimate the destination for most trips. However, unlinked trips, which are not part of a trip chain during the day, are more difficult to analyze. The proposed improvement to the existing model for destination estimation, especially for unlinked trips, is based on kernel density estimation of the spatial and temporal probabilities of each destination. The Société de Transport de l'Outaouais, a medium-sized bus service near Ottawa, Ontario, Canada, provided data for a 1-month period in 2009 (908,303 total transactions). Existing algorithms can handle only 80.64% of the trips; the proposed method handles an additional 10.9%. These results are analyzed, and future research directions are discussed.
The characteristics of multi-standard rail transit systems coexistence of regional rail transit increase the risk probability of rail transit network. At the same time, the interaction between multi-standard systems and the integrity of the rail transit network increase the risk impact area and the risk consequences. Therefore, the risk bottleneck identification for regional rail transit is of great significance to realize targeted protection and optimization of key risk points and effectively reduce the global risk of the transportation system. In view of the fact that the existing risk assessment methods of rail transit network mostly use index fusion, which cannot reflect the global structural risk and cannot effectively evaluate the global risk bottlenecks of the rail transit network, this paper proposes a method for identifying global structural risk bottlenecks of regional rail transit based on sensitivity analysis. First, an assessment index for the global structural risk of the rail transit network is established, and then sensitivity analysis is used to assess the impact of the failure and optimization of station or section on the global structural risk of the rail transit network, so as to provide decision support for the protection and optimization of the rail transit network risk bottlenecks. Finally, the regional rail transit in Chengdu-Chongqing area is taken as an example to verify the effectiveness of the method.
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