| The increasingly frequent rainstorms caused by global climate change have resulted in significant economic losses and casualties in many cities of China in recent years.As an essential component of urban infrastructure,the urban road transportation system plays a crucial role in ensuring daily travel for residents and facilitating rapid recovery after disasters.Therefore,measuring and enhancing the capacity of the existing urban road transportation system to resist and recover from rainstorms is of great theoretical and practical significance in pursuing resilient urban systems.The emergence of intelligent transportation systems has hastened the development of new data collection technologies,which provides massive amounts of data to researchers and practitioners in transportation engineering and management for investigating resilient transportation systems against natural disasters.In light of this,this dissertation develops a data-driven resilience measurement and enhancement framework for urban road transportation systems under rainstorm conditions by applying time-series data models,complex network theory,and optimization theory with taxi GPS data.The main research content and conclusions are as follows:The performance fluctuation of the urban road transportation system was systematically analyzed based on taxi GPS data.The characteristics of commonly used taxi,bus,and shared bicycle GPS data in characterizing the operation of urban road transportation systems were compared and analyzed,clarifying that taxi GPS data can more accurately reflect the operating characteristics of urban road transportation systems.Through data cleaning,coordinate system transformation,extraction of travel trip information,and road network matching,the required taxi travel trajectory dataset for this study was constructed.The dynamic changes in taxi passenger flow in the road transportation system of Xi’an were analyzed from both temporal and spatial dimensions.It was found that Xi’an’s taxi passenger flow and average travel speed showed significant periodic variations over time,and the hotspot areas for taxi use were relatively stable in spatial distribution,confirming that large-scale taxi GPS data can effectively reflect the dynamic performance changes of urban road transportation systems.A data-driven resilience measurement framework for urban road transportation systems was constructed.Considering the dynamic changes in the performance of urban road transportation systems,a performance curve of urban road transportation systems under rainstorm conditions was proposed based on the resilience triangle theory.In combination with relevant theoretical methods for time-series anomaly data identification and prediction,a datadriven urban road transportation system resilience measurement framework was constructed.Finally,the trip resilience and mobility resilience of the transportation system under the ‘7-24rainstorm’ were measured and analyzed based on passenger flow and average trip speed.The results show that the mobility resilience of the road transportation system of Xi’an is stronger than its trip resilience,and the impact of the rainstorm on the taxi passenger flow in the urban road transportation system is greater than the impact on the average trip speed under the ‘7-24rainstorm’.Critical nodes with low resilience in urban road transportation systems under rainstorm conditions were identified based on a dual-layer dynamic network of taxi trips.Utilizing taxi GPS data to characterize the flow relationships between different areas within the city,traffic zones were used as network nodes,and the temporal variation characteristics of inter-regional traffic flows were used as network edge weights,constructing a taxi traffic flow-time dual-layer dynamic network.Next,considering the effects of rainstorms on both the node passenger flow intensity and transport efficiency dimensions,measurement indices for node trip resilience and mobility resilience were constructed.On this basis,a method for identifying critical nodes in urban road transportation systems based on node resilience was proposed.Finally,a case study of the ‘7-24 rainstorm’ event in Xi’an was conducted.The study found that the topological structure indicators of the taxi travel flow layer and travel time layer networks constructed based on taxi trajectories exhibited regular periodic variations under normal circumstances,indicating that the constructed taxi travel dual-layer dynamic network can effectively reflect the dynamic operating characteristics of urban road transportation system performance as it changes over time.At the same time,there were significant differences in resilience among different areas of road transportation system of Xi’an,with critical nodes with low resilience all located in the southern regions of Xi’an and exhibiting spatial clustering.Resilience enhancement research was conducted for urban road transportation systems from the perspective of optimizing pre-disaster emergency resource allocation.Considering the predictable characteristics of rainstorm,it was proposed to enhance the resilience of urban road transportation system by optimizing pre-disaster resource allocation and setting up emergency resource facilities.Next,considering the heterogeneity of node emergency resource demand and the impact of rainstorms on post-disaster travel times among nodes,a cooperative covering facility location model was proposed to maximize the coverage of emergency resource demand,minimize travel time between emergency resource facilities and demand points,and minimize construction costs of emergency resource facilities.An algorithm was designed to solve the model.Finally,a case study of the ‘7-24 rainstorm’ event in Xi’an was conducted.The results showed that the cooperative covering facility location model and the designed algorithm could provide effective emergency resource facility location solutions,achieve multiple coverages of critical nodes,reduce the impact of rainstorms on urban road transportation system performance,and improve the resilience of urban road transportation systems under rainstorm conditions.Furthermore,a data-driven urban road transportation system resilience enhancement strategy was proposed.Aiming to systematically investigate the resilience measurement and enhancement of urban road transportation systems under rainstorm conditions from a data-driven perspective based on taxi GPS data,this research enriches the existing theories and methods for urban road transportation system resilience measurement and enhancement and provides new perspectives and methodological references for studying urban transportation system resilience using other large-scale transportation data.In addition,this research also provides a theoretical basis and practical guidance towards resilient transportation systems for urban road transportation management practitioners. |
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