Vulnerability assessment of urban rail transit in face of disruptions: A framework and some lessons from Hong Kong

Transport systems are confronting challenges under disruptive events. In transit-oriented cities, the heavy reliance on urban railways makes the transport system ever more vulnerable. Extant literature has paid much attention to urban rail transit vulnerability and resilience against natural disasters. Yet, less focus has been put on disturbances caused by people. In this study, by integrating the on- and off-road situations, we assess the urban rail transit vulnerability under the Anti-extradition Law Amendment Bill (Anti-ELAB) movement in Hong Kong in 2019, during which the Mass Transit Railway (MTR) services were severely disrupted. Our results show that very highly vulnerable stations are spatially concentrated in traditional urban core areas. It is also observed that roads around some new town stations became more congested during service disruption. When disruptive events occurred during non-peak hours, the level of congestion in more than half of stations’ ambient roads did not show significant changes. This study sets forth a rail transit vulnerability assessment matrix and suggests that future assessments of transport vulnerability should take a more holistic approach and consider both on- and off-road traffic situations. The findings also provide directions for building resilient cities in the future

Data-Driven Optimization of Piezoelectric Energy Harvesters via Pattern Search Algorithm

A data-driven optimization strategy based on a generalized pattern search (GPS) algorithm is proposed to automatically optimize piezoelectric energy harvesters (PEHs). As a direct search method, GPS can iteratively solve the derivative-free optimization problem. Taking the finite element method (FEM) as the solver and the GPS algorithm as the optimizer, the automatic interaction between the solver and optimizer ensures optimization with minimum human efforts, saving designers’ time and performing a more precise exploration in the parameter space to obtain better results. When employing it for the optimization of PEHs, the optimal length and thickness of PZT were 6.0 mm and 4.6 µm, respectively. Compared with reported high-output PEHs, this optimal structure showed an increase of 371% in output power, an improvement by 1000% in normalized power density, and a reduction of 254% in resonant frequency. Furthermore, Spearman’s rank correlation coefficient was calculated for evaluating the correlation among geometric parameters and output performance such as resonant frequency and output power, which provides a data-based perspective on the design and optimization of PEHs

Deep Encoder Cross Network for Estimated Time of Arrival

Estimated time of arrival (ETA) is essential to enable various intelligent transportation services and reduce passenger waiting time. Estimating the time of arrival of public transport in a highly dynamic and uncertain transportation system could be challenging. Many indirect factors beyond the remaining travel distance could dramatically deviate the time of arrival from the original schedule. Existing distance-based estimation methods disregarding those factors usually result in inaccurate estimations. In this paper, we propose a new deep learning model, called Deep Encoder Cross Network (DECN), to improve the ETA prediction based on multiple non-distance-based factors such as weather, road speed and congestion, and traffic composition. Unlike most regression tasks that output the target directly, we predict the ETA residual over the location-based ETA prediction. To effectively learn in the large and sparse input feature space, we use a new neural network structure consisting of three main components. First, a deep neural network is responsible for modeling explicit feature interactions. Second, an encoder network is constructed to reduce the input feature dimensionality. Third, a cross-network is introduced to learn from the implicit feature interactions. We conduct extensive experiments on a large real-world bus ETA dataset of Hong Kong, which contains about $2.95\times 10^{8}$ rows with 27 different features on an 84-dimensional space. The results show that the deep learning approach with the DECN model can improve the ETA error by 11% on average, and 49% for late arrival. The proposed approach can be further improved and extended to estimate other traffic information by incorporating non-distance-based related information

The path towards herd immunity: predicting COVID-19 vaccination uptake through results from a stated choice study across six continents

Despite unprecedented progress in developing COVID-19 vaccines, global vaccination levels needed to reach herd immunity remain a distant target, while new variants keep emerging. Obtaining near universal vaccine uptake relies on understanding and addressing vaccine resistance. Simple questions about vaccine acceptance however ignore that the vaccines being offered vary across countries and even population subgroups, and differ in terms of efficacy and side effects. By using advanced discrete choice models estimated on stated choice data collected in 18 countries/territories across six continents, we show a substantial influence of vaccine characteristics. Uptake increases if more efficacious vaccines (95% vs 60%) are offered (mean across study areas = 3.9%, range of 0.6%–8.1%) or if vaccines offer at least 12 months of protection (mean across study areas = 2.4%, range of 0.2%–5.8%), while an increase in severe side effects (from 0.001% to 0.01%) leads to reduced uptake (mean = −1.3%, range of −0.2% to −3.9%). Additionally, a large share of individuals (mean = 55.2%, range of 28%–75.8%) would delay vaccination by 3 months to obtain a more efficacious (95% vs 60%) vaccine, where this increases further if the low efficacy vaccine has a higher risk (0.01% instead of 0.001%) of severe side effects (mean = 65.9%, range of 41.4%–86.5%). Our work highlights that careful consideration of which vaccines to offer can be beneficial. In support of this, we provide an interactive tool to predict uptake in a country as a function of the vaccines being deployed, and also depending on the levels of infectiousness and severity of circulating variants of COVID-19