Public transit is highly promoted worldwide to reduce traffic congestion. An evidence-based planning of stop locations and routes with regard to residential and workplace locations can reduce walking distances to transit and the number of transfers, which can improve service quality of public transport and thus increase ridership. This paper proposes a novel method of identifying residential and workplace locations from smart card data. The proposed method identifies relevant stops first and then refines their catchments to narrow down residential and workplace locations in three steps: defining constraints from the design of the public transport network, movement logic, and land use. In 2017, we tested the method using Beijing smart card data. The results show close to 69% residential locations inference rates and more than 72% workplace locations inference rates. The mean value of inferred areas is approximately 20% of the areas derived by traditional methods. Available data on alighting stops verify the inferred results at least for flat fare systems.

Алгоритм прогнозирования моментов времени прибытия городских пассажирских автобусов на основе Марковских цепей

Many free-floating bicycle-sharing (FFBS) operators in cold region cities will put bicycles in warehouses and suspend services in winter due to factors such as safety and maintenance costs, resulting in the corresponding travel demand no longer be met. Considering the short-distance and green travel characteristics of FFBS, the flexible bus, as a sustainable demand-responsive transit service, is a suitable alternative transportation mode. To operate such a flexible bus system, service area and operation time planning is a key stage, however, the planning methods in relevant studies are not suitable for this research scenario. In view of the above, this paper proposed a data-driven method to determine the service area and operation time of flexible buses based on FFBS data. Firstly, an FFBS trip path reconstruction algorithm consists of fine-grained road network modelling and trajectory matching is proposed. Then, in the defined time slice, incorporating the idea of ride-sharing routes generation, according to the density-based clustering principle and considering topology between trajectories, a path clustering algorithm PATHSCAN is developed to generate the one-day path clusters. After that, a frequent pattern mining algorithm is applied to the multiday path clusters, and frequent pattern results with spatio-temporal correlation will be merged into the final service area. The generated planning results will cover ride-matching trips and high-frequency riding paths. Detailed application analysis and verification are carried out by using the real data from Tianjin, China. Through the evaluation and verification under the relatively limited experimental data set, the proposed data-driven method shows ideal planning results. Flexible bus service can supplement the green short-distance travel mode after the suspension of FFBS and can avoid FFBS travel demands switched to unsustainable transportation modes to a large extent. This study will contribute to urban sustainable transportation development and improving greenness.