Public transport systems are expected to reduce pollution and contribute to
sustainable development. However, disruptions in public transport such as
delays may negatively affect mobility choices. To quantify delays, aggregated
data from vehicle locations systems are frequently used. However, delays
observed at individual stops are caused inter alia by fluctuations in running
times and propagation of delays occurring in other locations. Hence, in this
work, we propose both the method detecting significant delays and reference
architecture, relying on stream processing engines, in which the method is
implemented. The method can complement the calculation of delays defined as
deviation from schedules. This provides both online rather than batch
identification of significant and repetitive delays, and resilience to the
limited quality of location data. The method we propose can be used with
different change detectors, such as ADWIN, applied to location data stream
shuffled to individual edges of a transport graph. It can detect in an online
manner at which edges statistically significant delays are observed and at
which edges delays arise and are reduced. Detections can be used to model
mobility choices and quantify the impact of repetitive rather than random
disruptions on feasible trips with multimodal trip modelling engines. The
evaluation performed with the public transport data of over 2000 vehicles
confirms the merits of the method and reveals that a limited-size subgraph of a
transport system graph causes statistically significant delays

公共交通系统延迟问题的探测方法和流处理引擎的参考架构可以在线计算显著和重复延迟，以及对有限的位置数据质量具有弹性。

公共交通系统中重大延迟变化的流式检测

Streaming detection of significant delay changes in public transport  systems

Ongoing traffic changes, including those triggered by the COVID-19 pandemic,
reveal the necessity to adapt our public transport systems to the ever-changing
users' needs. This work shows that single and multi objective stances can be
synergistically combined to better answer the transit network design problem
(TNDP). Single objective formulations are dynamically inferred from the rating
of networks in the approximated (multi-objective) Pareto Front, where a
regression approach is used to infer the optimal weights of transfer needs,
times, distances, coverage, and costs. As a guiding case study, the solution is
applied to the multimodal public transport network in the city of Lisbon,
Portugal. The system takes individual trip data given by smartcard validations
at CARRIS buses and METRO subway stations and uses them to estimate the
origin-destination demand in the city. Then, Genetic Algorithms are used,
considering both single and multi objective approaches, to redesign the bus
network that better fits the observed traffic demand. The proposed TNDP
optimization proved to improve results, with reductions in objective functions
of up to 28.3%. The system managed to extensively reduce the number of routes,
and all passenger related objectives, including travel time and transfers per
trip, significantly improve. Grounded on automated fare collection data, the
system can incrementally redesign the bus network to dynamically handle ongoing
changes to the city traffic.

通过基于遗传算法的多目标和单目标方法，以及公共交通系统中自动收费数据为基础，设计一种能够动态适应城市交通变化、减少线路数量和改善乘客通勤体验的交通网络设计问题优化方案。