关键词: 城市交通, 公交线路规划, 轨迹数据驱动, 灵活式公交, 基准线路规划

Abstract: As a type of Flexible Transit (FT), Deviated Fixed Route (DFR) bus services allow vehicles to moderately deviate from a predetermined baseline route to pick up and drop off passengers. The current researches in this field primarily focus on the vehicle deviation rules, service constraints, and scheduling strategies based on the baseline route. Typically, the baseline route is either assumed to be an existing bus route or constructed virtually within a simulated network structure. It lacks data-driven planning methods that reflect the actual characteristics of passenger demand. Therefore, this paper proposes a planning method for baseline route driven by taxi trajectory data. The method comprises three stages: bus-stop generation, route generation, and route evaluation &selection. During the bus-stop generation, a multi-period origin-destination (OD) matrix is first constructed with taxi trajectory data to identify the high-frequency OD pairs. Candidate stops are then generated through grid clustering and merge-and-split strategies. During the route generation, a weighted point graph is constructed by integrating the candidate bus stops with the rail transit stations. Initial candidate routes are generated with the demand-intensity-guided probabilistic path search. These routes are then filtered through operational constraints. A Pareto analysis is subsequently applied to derive a feasible baseline route set under dual objectives. Subsequently, a multidimensional evaluation system is employed to quantitatively assess the candidate routes and select the optimal one as the benchmark route plan. The Shenzhen case study validated the feasibility of this method. The following results are demonstrated: the benchmark routes distributed along primary travel corridors and effectively connected subway stations, with over 14% of their stops located at the subway stations and over 89% non-overlapping with the existing bus routes; the benchmark routes for opposing directions during the same time period exhibit the differences in metrics such as stop locations, route alignment, passenger flow intensity, average travel time, and metro connection ratios. Such variations also occur among the benchmark routes generated for the same origin-destination pair but different time periods, demonstrating that this method can generate benchmark routes matching with spatio-temporal heterogeneity of passenger flow characteristics.

Key words: urban transportation, route planning of bus, driven by trajectory data, flexible transit, baseline route plannin