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Rail transit is developing rapidly in major cities of China and has become a key component of urban transport. Nevertheless, the security and reliability in operation are significant issues that cannot be neglected. In this paper, the network and station vulnerabilities of the urban rail transit system were analyzed based on complex network and graph theories. A vulnerability evaluation model was proposed by accounting metro interchange and passenger flow and further validated by a case study of Shanghai Metro with full-scale network and real-world traffic data. It is identified that the urban rail transit network is rather robust to random attacks, but is vulnerable to the largest degree node-based attacks and the highest betweenness node-based attacks. Metro stations with a large node degree are more important in maintaining the network size, while stations with a high node betweenness are critical to network efficiency and origin-destination (OD) connectivity. The most crucial stations in maintaining network serviceability do not necessarily have the highest passenger throughput or the largest structural connectivity. A comprehensive evaluation model as proposed is therefore essential to assess station vulnerability, so that attention can be placed on appropriate nodes within the metro system. The findings of this research are of both theoretical and practical significance for urban rail transit network design and performance evaluation.

Abstract:With the development of in-vehicle data collection devices, GPS trajectory has become a priority source to identify traffic congestion and understand the operational states of road network in recent years. This study aims to investigate the relationship between traffic congestion and built environment, including traffic-related factors and land use. Fuzzy C-means clustering was used to conduct an exhaustive study on the 24-hour congestion pattern of road segments in an urban area, so that the spatial autoregressive moving average model (SARMA) could be introduced to analyze the output from the clustering analysis to establish the relationship between built environment and the 24-hour congestion pattern. The clustering result classified the road segments into four congestion levels, while the regression explained the impact of 12 traffic-related factors and land-use factors on the road congestion pattern. The continuous congestion was found to mainly occur in the city center, and the factors, such as road type, bus station in the vicinity, ramp nearby, commercial land use, and so on, had large impacts on congestion formation. The Fuzzy C-means clustering is proposed to be combined with quantitative spatial regression, and the overall evaluation process will assist to assess the spatial-temporal levels of service regarding traffic from the congestion perspective.Keywords: Congestion pattern, taxi GPS data, fuzzy C-means clustering, spatiotemporal regression, built environment factor IntroductionIn urban road network, the recurrent or current congestion of a certain road segment may largely impact the local network and reduce travel efficiency. Consequently, it is important to identify the Compared with mobile phone data, floating car data, cargo transport vehicle record and navigation system, taxi GPS trace data is one of the easiest available sources for accurate travel route and travel time records for a wider area with more road details. Data mining based on taxi trip can be traced back to the 1970s (Goddard, 1970), which has been applied to a wide range of studies, mainly including activity-based and infrastructure-based fields. The activity-based studies mostly focus on driver behavior, supply-demand pattern, and traffic state analysis, while the infrastructure-based studies mainly focused on lanes channelization (Tang, Yang, Kan, & Li, 2015) and signal-timing estimation (Yu & Lu, 2016).From driver behavior perspective, Zhang, Qiu, Duan, Du, and Lu (2015) proposed a space-time visualization method to demonstrate taxi daily trajectories by GIS-T to recognize working time, operating range, and residence location without time division. Qing, Parfenov, and Kim (2015) compared direct extracted datas like travel distance, speed, demand, and supply mismatch of taxi trip between fair weather and extreme storm using Manhattan GPS data, and discovered the reduction in trip distance and supply of drives during the extreme storm. Meanwhile, Hwang, Wu and Jian (2006) used structural equation modeling technique...

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The prevention and control of COVID-19 in megacities is under large pressure because of tens of millions and high-density populations. The majority of epidemic prevention and control policies implemented focused on travel restrictions, which severely affected urban mobility during the epidemic. Considering the impacts of epidemic and associated control policies, this study analyzes the relationship between COVID-19, travel of residents, Point of Interest (POI), and social activities from the perspective of taxi travel. First, changes in the characteristics of taxi trips at different periods were analyzed. Next, the relationship between POIs and taxi travels was established by the Geographic Information System (GIS) method, and the spatial lag model (SLM) was introduced to explore the changes in taxi travel driving force. Then, a social activities recovery level evaluation model was proposed based on the taxi travel datasets to evaluate the recovery of social activities. The results demonstrated that the number of taxi trips dropped sharply, and the travel speed, travel time, and spatial distribution of taxi trips had been significantly influenced during the epidemic period. The spatial correlation between taxi trips was gradually weakened after the outbreak of the epidemic, and the consumption travel demand of people significantly decreased while the travel demand for community life increased dramatically. The evaluation score of social activity is increased from 8.12 to 74.43 during the post-epidemic period, which may take 3–6 months to be fully recovered as a normal period. Results and models proposed in this study may provide references for the optimization of epidemic control policies and recovery of public transport in megacities during the post-epidemic period.

Lane‐changing behavior has received increasing attention during the recent years in traffic flow modeling. Researchers have developed various algorithms to model the maneuvers on both highways and urban streets. However, the majority of these models was derived and validated using data such as vehicle trajectories, without many considerations of driver characteristics. In this study, an instrumented vehicle‐based experiment was carefully designed to observe the drivers’ action under various urban lane‐changing scenarios. The personal background data, and “in‐vehicle” driver behavior and trajectory data were obtained from the experiment, and used to classify 40 drivers into four general groups according to the lane‐changing maneuvers performed in an urban street environment. Additional comparisons and analysis were conducted to confirm the categorization results. The article concludes by providing recommendations related to the implementation of study findings into micro‐simulators, such as using only four driver types in CORSIM instead of the existing 10 types, to better replicate driver behavior in urban street networks.

Although bus passenger demand prediction has attracted increased attention during recent years, limited research has been conducted in the context of short-term passenger demand forecasting. This paper proposes an interactive multiple model (IMM) filter algorithm-based model to predict short-term passenger demand. After aggregated in 15 min interval, passenger demand data collected from a busy bus route over four months were used to generate time series. Considering that passenger demand exhibits various characteristics in different time scales, three time series were developed, named weekly, daily, and 15 min time series. After the correlation, periodicity, and stationarity analyses, time series models were constructed. Particularly, the heteroscedasticity of time series was explored to achieve better prediction performance. Finally, IMM filter algorithm was applied to combine individual forecasting models with dynamically predicted passenger demand for next interval. Different error indices were adopted for the analyses of individual and hybrid models. The performance comparison indicates that hybrid model forecasts are superior to individual ones in accuracy. Findings of this study are of theoretical and practical significance in bus scheduling.