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As mobile device location data become increasingly available, new analyses are revealing the significant changes of mobility pattern when an unplanned event happened. With different control policies from local and state government, the COVID-19 outbreak has dramatically changed mobility behavior in affected cities. This study has been investigating the impact of COVID-19 on the number of people involved in crashes accounting for the intensity of different control measures using Negative Binomial (NB) method. Based on a comprehensive dataset of people involved in crashes aggregated in New York City during January 1, 2020 to May 24, 2020, people involved in crashes with respect to travel behavior, traffic characteristics and socio-demographic characteristics are found. The results show that the average person miles traveled on the main traffic mode per person per day, percentage of work trip have positive effect on person involved in crashes. On the contrary, unemployment rate and inflation rate have negative effects on person involved in crashes. Interestingly, different level of control policies during COVID-19 outbreak are closely associated with safety awareness, driving and travel behavior, and thus has an indirect influence on the frequency of crashes. Comparing to other three control policies including emergence declare, limits on mass gatherings, and ban on all nonessential gathering, the negative relationship between stay-at-home policy implemented in New York City from March 20, 2020 and the number of people involved crashes is found in our study., Competing Interests: The authors have declared that no competing interests exist.

In previous studies, the safety-in-numbers effect has been found, which is a phenomenon that when the number of pedestrians or cyclists increases, their crash rates decrease. The previous studies used data from highly populated areas. It is questionable that the safety-in-numbers effect is still observed in areas with a low population density and small number of pedestrians. Thus, this study aims at analyzing pedestrian crashes in a suburban area in the United States and exploring if the safety-in-numbers effect is also observed. We employ a Bayesian random-parameter Poisson-lognormal model to evaluate the safety-in-numbers effects of each intersection, which can account for the heterogeneity across the observations. The results show that the safety-in-numbers effect were found only at 32 intersections out of 219. The intersections with the safety-in-numbers effect have relatively larger pedestrian activities whereas those without the safety-in-numbers effect have extremely low pedestrian activities. It is concluded that just encouraging walking might result in serious pedestrian safety issues in a suburban area without sufficient pedestrian activities. Therefore, it is plausible to provide safe walking environment first with proven countermeasures and a people-oriented policy rather than motor-oriented. After safe walking environments are guaranteed and when people recognize that walking is safe, more people will consider walking for short-distance trips. Eventually, increased pedestrian activities will result in the safety-in-numbers effects and walking will be even further safer., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Pedestrian safety is increasingly recognized as a major public health concern. Extensive safety studies have been conducted to examine the influence of multiple variables on the occurrence of pedestrian-vehicle crashes. However, the explicit relationship between pedestrian safety and road network characteristics remains unknown. This study particularly focused on the role of different road network patterns on the occurrence of crashes involving pedestrians. A global integration index via space syntax was introduced to quantify the topological structures of road networks. The Bayesian Poisson-lognormal (PLN) models with conditional autoregressive (CAR) prior were then developed via three different proximity structures: contiguity, geometry-centroid distance, and road network connectivity. The models were also compared with the PLN counterpart without spatial correlation effects. The analysis was based on a comprehensive crash dataset from 131 selected traffic analysis zones in Hong Kong. The results indicated that higher global integration was associated with more pedestrian-vehicle crashes; the irregular pattern network was proved to be safest in terms of pedestrian crash occurrences, whereas the grid pattern was the least safe; the CAR model with a neighborhood structure based on road network connectivity was found to outperform in model goodness-of-fit, implying the importance of accurately accounting for spatial correlation when modeling spatially aggregated crash data., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

This study was performed to investigate the spatially varying relationships between crash frequency and related risk factors. A Bayesian spatially varying coefficients model was elaborately introduced as a methodological alternative to simultaneously account for the unstructured and spatially structured heterogeneity of the regression coefficients in predicting crash frequencies. The proposed method was appealing in that the parameters were modeled via a conditional autoregressive prior distribution, which involved a single set of random effects and a spatial correlation parameter with extreme values corresponding to pure unstructured or pure spatially correlated random effects. A case study using a three-year crash dataset from the Hillsborough County, Florida, was conducted to illustrate the proposed model. Empirical analysis confirmed the presence of both unstructured and spatially correlated variations in the effects of contributory factors on severe crash occurrences. The findings also suggested that ignoring spatially structured heterogeneity may result in biased parameter estimates and incorrect inferences, while assuming the regression coefficients to be spatially clustered only is probably subject to the issue of over-smoothness., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

The widely adopted techniques for regional crash modeling include the negative binomial model (NB) and Bayesian negative binomial model with conditional autoregressive prior (CAR). The outputs from both models consist of a set of fixed global parameter estimates. However, the impacts of predicting variables on crash counts might not be stationary over space. This study intended to quantitatively investigate this spatial heterogeneity in regional safety modeling using two advanced approaches, i.e., random parameter negative binomial model (RPNB) and semi-parametric geographically weighted Poisson regression model (S-GWPR). Based on a 3-year data set from the county of Hillsborough, Florida, results revealed that (1) both RPNB and S-GWPR successfully capture the spatially varying relationship, but the two methods yield notably different sets of results; (2) the S-GWPR performs best with the highest value of Rd(2) as well as the lowest mean absolute deviance and Akaike information criterion measures. Whereas the RPNB is comparable to the CAR, in some cases, it provides less accurate predictions; (3) a moderately significant spatial correlation is found in the residuals of RPNB and NB, implying the inadequacy in accounting for the spatial correlation existed across adjacent zones. As crash data are typically collected with reference to location dimension, it is desirable to firstly make use of the geographical component to explore explicitly spatial aspects of the crash data (i.e., the spatial heterogeneity, or the spatially structured varying relationships), then is the unobserved heterogeneity by non-spatial or fuzzy techniques. The S-GWPR is proven to be more appropriate for regional crash modeling as the method outperforms the global models in capturing the spatial heterogeneity occurring in the relationship that is model, and compared with the non-spatial model, it is capable of accounting for the spatial correlation in crash data., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

With the rapid development of portable and wearable electronics, safety concerns over flexible energy devices are inevitable. Therefore, it is important to develop energy supplies that are safe for use. In this work, a highly safe, durable, adaptable, and flexible air‐breathing direct methanol fuel cell (DMFC) is successfully prepared by synthesizing and applying a new composite material with agar gel and wood sponge, that is, a gel/sponge composite. The gel/sponge composite has a high absorption rate, high cyclic performance, high methanol absorption capacity, high energy content, and high flexibility. Moreover, the gel/sponge composite with 1.5% agar gel retains approximately 90% of the methanol solution at a pressure of 29.4 kPa, and the areal energy density of the proposed DMFC approaches 13.7 mWh cm−2. Both the single‐cell and stack of DMFC with the new composite material successfully survive a series of destructive tests, including needle penetration, cutting, and compression. Therefore, it is successfully demonstrated that absorbent materials can greatly boost the safety, adaptability, flexibility, and energy density of air‐breathing DMFCs. Furthermore, this concept shows promise in improving the safety of other fuel cells by using absorbent materials to solidify their gaseous or liquid fuels. [ABSTRACT FROM AUTHOR]

Background: Perceived as a minor transportation mode mainly for recreation, cycling and its related safety issues have not been treated as a citywide concern in Hong Kong and have thus received inadequate research efforts. Our study aimed to illuminate the safety challenges faced by cyclists in Hong Kong. Methods: We examined the police crash records from 1998 to 2017 and developed a Bayesian Poisson state space model to evaluate the longitudinal change in traffic injuries to cyclists. We then used quasi-induced exposure to measure the annual relative risk of crash involvement for cycling. Based on an officially published travel characteristics survey, we further measured the risk of injury for cycling per minutes cycled. Results: Between 1998 and 2017, Hong Kong witnessed a more than twofold increase in the number of cyclist injuries, with an average annual increase rate of 5.18% (95% CI: 0.53%–12.77%). By 2017, cyclists were 2.21 (1.82–2.69) times more likely to be involved in traffic crashes than in 1998. Per 10 million minutes, the injury rates for cycling were 28.64 (27.43–29.70) and 42.54 (41.07–44.02) on weekdays during 2001–2003 and 2010–2012. After adjusting for sex and age groups, cyclists were 1.95 (1.43–2.61) times more likely to be injured in 2010–2012 than in 2001–2003. Per minutes traveled, cyclists also sustained significantly higher risks of fatality and injury than pedestrians, private car drivers and passengers, taxi passengers, public bus passengers, and minibus passengers. A comparison of Hong Kong with other regions suggests that Hong Kong is among the most dangerous areas for cycling in terms of fatality rate per minutes cycled. Conclusions: Cyclist injuries have become a substantial public health burden in Hong Kong. A range of countermeasures with proven effectiveness should be promptly implemented to improve the safety of these vulnerable road users. [ABSTRACT FROM AUTHOR]