Inclusion of phone use while driving data in predicting distraction-affected crashes

Introduction: Given the tremendous number of lives lost or injured, distracted driving is an important safety area to study. With the widespread use of cellphones, phone use while driving has become the most common distracted driving behavior. Although researchers have developed safety performance functions (SPFs) for various crash types, SPFs for distraction-affected crashes are rarely studied in the literature. One possible reason is the lack of critical distracted behavior information in the commonly used safety data (i.e., roadway inventory, traffic, and crash counts). Recently, the frequency of phone use while driving (referred to as phone use data) is recorded by mobile application companies and has become available to safety researchers. The primary objective of this study is to examine if phone use data can potentially predict distracted-affected crashes. Method: The authors first integrated phone use data with roadway inventory, traffic, and crash data in Texas. Then, the Random Forest (RF) algorithm was applied to assess the significance of the feature - phone use while driving - for predicting the number of distraction-affected crashes on a road segment. Further, this study developed two SPFs for distraction-affected crashes with and without the phone use data, separately. Both SPFs were assessed in terms of model fitting and prediction performances. Results: RF results rank the frequency of phone use as an important factor contributing to the number of distraction-affected crashes. Performance evaluations indicated that the inclusion of phone use data in the SPFs consistently improved both fitting and prediction abilities to predict distracted-affected crashes. Practical Applications: The phone use data provide new insights into the safety analyses of distraction-affected crashes, which cannot be achieved by only using the conventional roadway inventory and crash data. Therefore, safety researchers and practitioners are encouraged to incorporate the emerging data sources in reducing distraction-affected crashes.     

Relationships of ozone formation sensitivity with precursors emissions, meteorology and land use types, in Guangdong-Hong Kong-Macao Greater Bay Area, China

Due to the influences of precursors emissions, meteorology, geography and other factors, ozone formation sensitivity (OFS) is generally spatially and temporally heterogeneous. This study characterized detailed spatial and temporal variations of OFS in Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 2012 to 2016 based on OMI satellite data, and analyzed the relationships of OFS with precursors emissions, meteorology and land use types (LUTs). From 2012 to 2016, the OFS tended to be NO_x-limited in GBA, with the value of FNR (HCHO/NO₂) increasing from 2.04 to 2.22. According to the total annual emission statistics of precursors, NO_x emissions decreased by 33.1% and VOCs emissions increased by 35.2% from 2012 to 2016, directly resulting in OFS tending to be NO_x-limited. The Grey Relation Analysis results show that total column water (TCW), surface net solar radiation (SSR), air temperature at 2 m (T2) and surface pressure (SP) are the top four meteorological factors with the greatest influences on OFS. There are significant positive correlations between FNR and T2, SSR, TCW, and significant negative correlations between FNR and SP. In GBA, the OFS tends to be NO_x-limited regime in wet season (higher T2, SSR, TCW and lower SP) and VOCs-limited regime in dry season (lower T2, SSR, TCW and higher SP). The FNR displays obvious gradient variations on different LUTs, with the highest in “Rural areas”, second in “Suburban areas” and lowest in “Urban areas”.     

手机对人体的电磁生态效应与对策

介绍移动通信用便携式收发信机对人体电磁生态效应的原理，研究现状，并就如何减少手机对人体的电磁影响提出几点看法和建议。