Examination of factors associated with the temporal stability assessment of crash severity by using generalised linear model-A case study.

Road crashes are a major public safety concern in Pakistan. Prior studies in Pakistan investigated the impact of different factors on road crashes but did not consider the temporal stability of crash data. This means that the recommendations based on these studies are not fully effective, as the impact of certain factors may change over time. To address this gap in the literature, this study aims to identify the factors contributing to crash severity in road crashes and examine how their impact varies over time. In this comprehensive study, we utilized Generalised Linear Model (GLM) on the crash data between the years 2013 to 2017, encompassing a total sample of 802 road crashes occurred on the N-5 road section in Pakistan, a 429-kilometer stretch connecting two big cities of Pakistan, i.e., Peshawar and Lahore. The purpose of the GLM was to quantify the temporal stability of the factors contributing crash severity in each year from 2013 to 2017. Within this dataset, 60% (n = 471) were fatal crashes, while the remaining 40% (n = 321) were non-fatal. The results revealed that the factors including the day of the week, the location of the crashes, weather conditions, causes of the crashes, and the types of vehicles involved, exhibited the temporal instability over time. In summary, our study provides in-depth insights aimed at reducing crash severity and potentially aiding in the development of effective crash mitigation policies in Pakistan and other nations having similar road safety problems. This research holds great promise in exploring the dynamic safety implications of emerging transportation technologies, particularly in the context of the widespread adoption of connected and autonomous vehicles.

Statistical estimation of fatal and serious injuries saved by iRAP protocols in 74 countries.

OBJECTIVE: Road traffic crashes cause 1.19 million deaths and millions more injuries annually. The persistently high burden has drawn attention from national and international stakeholders worldwide. Unsafe road infrastructure is one of the major risk factors for traffic safety, particularly in low- and middle-income countries. METHODS: Aiming to eliminate high-risk roads in all countries, the International Road Assessment Programme (iRAP) developed a robust and evidence-based approach to support country transportation agencies. RESULTS: Thus far, the iRAP protocols have been used to collect 1.8 million kilometers of Crash Risk Mapping and 1.5 million kilometers of Star Rating and FSI estimations in 128 countries. Deploying an observational before-and-after (or pre-post) study design, this report estimated the fatal and series injuries (FSI) saved through use of the iRAP protocols. The study is based on 441,753 kilometers of assessed roads from 1,039 projects in 74 countries. Our results show that the implementation of iRAP's proposed countermeasures saves about 159,936 FSI annually. Throughout the lifetime of the implemented countermeasures, a total of 3.2 million FSI could be saved. CONCLUSION: While quantifying the success of the iRAP protocols, our results suggest an opportunity to save many millions more lives on the roads through expanding iRAP implementation to more regions and countries.

Does the priority of ambulance guarantee no delay? a MIPSSTW model of emergency vehicle routing optimization considering complex traffic conditions for highway incidents.

Globally, traffic accidents on the highway network contribute significantly to a high fatality rate, drawing considerable attention from health institutions. The efficiency of transportation plays a vital role in mitigating the severe consequences of these incidents. This study delves into the issues of emergency vehicles experiencing delays despite having priority. Therefore, we construct mixed-integer linear programming with semi-soft time windows (MIPSSTW) model for optimizing emergency vehicle routing in highway incidents. We analyze the time-varying and complex traffic situations and respectively propose corresponding estimation approaches for the travel time of road segments, intersections on the urban road network, and ramp-weave sections on the highway network. Furthermore, we developed a modified cuckoo search(MCS) algorithm to solve this combinatorial problem. Optimization strategies of Lévy flight and dynamic inertial weight strategy are introduced to strengthen the exploration capability and the diversity of solution space of the CS algorithm. Computational experiments based on the Chinese emergency medical system data are designed to validate the efficacy and effectiveness of the MIPSSTW model and MCS algorithm. The results show that our works succeed in searching for high-quality solutions for emergency vehicle routing problems and enhance the efficacy of strategic decision-making processes in the realm of incident management and emergency response systems.

Using the dead to infer about the living: Amphibian roadkill spatiotemporal dynamics suggest local populations' reduction.

Roads represent one of the main sources of wildlife mortality, population decline, and isolation, especially for low-vagility animal groups. It is still not clearly understood how wildlife populations respond to these negative effects over space and time. Most studies on wildlife road mortality do not consider the spatial and temporal components simultaneously, or the imperfect roadkill detection, both of which could lead to inaccurate assumptions and unreliable mitigation actions. In this study, we applied a multi-season occupancy model to a 14-year amphibian mortality dataset collected along 120 km of roads, combined with freely available landscape and remote sensing metrics, to identify the spatiotemporal patterns of amphibian roadkill in a Mediterranean landscape in Southern Portugal. Our models showed an explicit general decrease in amphibian roadkill. The Iberian painted frog (Discoglossus galganoi) experienced roadkill declines over time of ∼70 %, while the spiny common toad (Bufo spinosus) and the fire salamander (Salamandra salamandra) had a loss of nearly 50 %, and the Southern marbled newt (Triturus pygmaeus) had 40 %. Despite the decreasing trend in roadkill, spatial patterns seem to be rather stable from year to year. Multi-season occupancy models, when combined with relevant landscape and remote sensing predictors, as well as long-term monitoring data, can describe dynamic changes in roadkill over space and time. These patterns are valuable tools for understanding roadkill patterns and drivers in Mediterranean landscapes, enabling the differentiation of road sections with varying roadkill over time. Ultimately, this information may contribute to the development of effective conservation measures.

Grain transportation and consumption reshapes the α-HCH exposure picture of China.

Socio-economic activities like food trade can increase the uncertainty of human risk of persistent organic pollutants (POPs). We compared the change in model predicted α-hexachlorocyclohexane (α-HCH) cancer risk (CR) with and without grain trade in mainland China. In scenario without grain logistics, α-HCH moved fast away from southern and southeastern China via northward atmospheric transport. However, the grain logistics from northeastern China delivers the α-HCH previously accumulated in northeastern sink back to densely populated areas in recent years, which enhance CR by >50 % in the southern seaboard of China. The northward movement of grain production center and recent grain deficiency in southern provinces induced by dietary pattern changes is identified as the major driving factors of the reversed transport of α-HCH. The finding highlights the potential of socio-economic activities that can otherwise offset the risk reduction effect of the geochemical cycle of POPs.

Implementation of a realistic artificial data generator for crash data generation.

In this paper, a framework is outlined to generate realistic artificial data (RAD) as a tool for comparing different models developed for safety analysis. The primary focus of transportation safety analysis is on identifying and quantifying the influence of factors contributing to traffic crash occurrence and its consequences. The current framework of comparing model structures using only observed data has limitations. With observed data, it is not possible to know how well the models mimic the true relationship between the dependent and independent variables. Further, real datasets do not allow researchers to evaluate the model performance for different levels of complexity of the dataset. RAD offers an innovative framework to address these limitations. Hence, we propose a RAD generation framework embedded with heterogeneous causal structures that generates crash data by considering crash occurrence as a trip level event impacted by trip level factors, demographics, roadway and vehicle attributes. Within our RAD generator we employ three specific modules: (a) disaggregate trip information generation, (b) crash data generation and (c) crash data aggregation. For disaggregate trip information generation, we employ a daily activity-travel realization for an urban region generated from an established activity-based model for the Chicago region. We use this data of more than 2 million daily trips to generate a subset of trips with crash data. For trips with crashes crash location, crash type, driver/vehicle characteristics, and crash severity. The daily RAD generation process is repeated for generating crash records at yearly or multi-year resolution. The crash databases generated can be employed to compare frequency models, severity models, crash type and various other dimensions by facility type - possibly establishing a universal benchmarking system for alternative model frameworks in safety literature.

Identification of critical links based on the optimal reliable path in stochastic traffic networks.

In urban stochastic transportation networks, there are specific links that hold great importance. Disruptions or failures in these critical links can lead to reduced connectivity within the road network. Under this circumstance, this manuscript proposed a novel identification of critical links mathematical optimization model based on the optimal reliable path with consideration of link correlations under demand uncertainty. The method presented in this paper offers a solution to bypass the necessity of conducting a full scan of the entire road network. Due to the non-additive and non-linear properties of the proposed model, a modified heuristic algorithm based on K-shortest algorithm and inequality technical is presented. The numerical experiments are conducted to show that improve a certain road link may not necessarily improve the overall traffic conditions. Moreover, the results indicate that if the travel time reliability is not considered, it will bring errors to the identification of key links.

Dynamic characteristics of scraper conveyor chain drive system under the impact condition of lump coal.

Scraper conveyor is the most important transportation equipment in the comprehensive mining equipment, and the chain drive system is its core subsystem, its dynamic characteristics will significantly affect the efficiency of coal transportation in the comprehensive mining face. In this paper, the dynamic characteristics of chain drive system when impacted by falling coal are investigated by means of test. The impact test bench of scraper conveyor was set up to analyze the effects of chain speed, impact height and impact load mass on the dynamic characteristics of the chain drive system of scraper conveyor under the working conditions of unloaded and loaded. The results show that the longitudinal vibration of the scraper conveyor is most obvious when it is impacted by the falling coal, and the chain speed, impact height and impact load mass of the scraper conveyor all play an excitation role on the vibration of the chain drive system, and the vibration of the chain ring is the most intense in the chain drive system, and the loaded coal pile conveyed on the scraper conveyor plays an inhibiting role on the vibration of the chain drive system. This study can help to identify the location where the scraper conveyor fails first in the impact condition, so as to provide a basis for its structural design and improvement, which is of great significance for the stable operation and structural optimization of the scraper conveyor.

FF-STGCN: A usage pattern similarity based dual-network for bike-sharing demand prediction.

Accurate bike-sharing demand prediction is crucial for bike allocation rebalancing and station planning. In bike-sharing systems, the bike borrowing and returning behavior exhibit strong spatio-temporal characteristics. Meanwhile, the bike-sharing demand is affected by the arbitrariness of user behavior, which makes the distribution of bikes unbalanced. These bring great challenges to bike-sharing demand prediction. In this study, a usage pattern similarity-based dual-network for bike-sharing demand prediction, called FF-STGCN, is proposed. Inter-station flow features and similar usage pattern features are fully considered. The model includes three modules: multi-scale spatio-temporal feature fusion module, bike usage pattern similarity learning module, and bike-sharing demand prediction module. In particular, we design a multi-scale spatio-temporal feature fusion module to address limitations in multi-scale spatio-temporal accuracy. Then, a bike usage pattern similarity learning module is constructed to capture the underlying correlated features among stations. Finally, we employ a dual network structure to integrate inter-station flow features and similar usage pattern features in the bike-sharing demand prediction module to realize the final prediction. Experiments on the Citi Bike dataset have demonstrated the effectiveness of our proposed model. The ablation experiments further confirm the indispensability of each module in the proposed model.

Spatio-temporal distribution and evolution of the Tujia traditional settlements in China.

The rapid modernization in China has aggravated the reduction of the traditional settlements and aroused concern about the protection and research. This study aims to examine the spatial-temporal variations Tujia traditional settlements in China and to delineate the driving mechanism of the settlement distribution. Previous studies have focused on the characteristics of settlements in provincial or smaller areas, providing lacked information regarding spatial distribution heterogeneity of Tujia traditional settlements in China. In this study, the spatial heterogeneity and influence factors of the distribution of traditional Tujia settlements were examined using the GIS platform and statistical methods. The results reveal that the spatial distribution of settlements exhibits clustering with the pattern of "scattered distribution in a large region, while concentrated in small areas". The settlements were generally built in low hills, gentle slopes, sunny slopes and low-relief terrain areas, with elevation, relief degree of land surface (RDLS), slope and aspect were the key factors affecting the distribution. In Song, Yuan, Ming and Qing dynasties, settlements showed significant clustering all through, though the location and number of clustering center kept changing. In this process, the history of the Tujia chieftain and the transportation and marketing lines of Sichuan salt had a profound influence on the historical evolution of the settlement.

Asymmetric modeling of regional tourism economic disparity in China.

China's tourism economy has experienced significant regional disparities. However, little attention has been paid to understanding the intricate mechanisms through which the interplay of influential factors gives rise to such disparities. Utilizing statistical data from the tourism economy of 31 provinces in mainland China, this study investigates regional tourism economic disparity through the asymmetric modeling of fuzzy set qualitative comparative analysis (fsQCA). The findings reveal that the causes of regional tourism economic disparity exhibit asymmetry; varying approaches contribute to the formation of high and low levels of tourism economy; the functioning of the most influential factors is impacted by other antecedent conditions, presenting an asymmetric non-linear effect; favorable transportation convenience is a necessary condition for a high level of tourism economy, while poor tourism attraction is a necessary condition for a low level of tourism economy. This research not only introduces a fresh perspective on regional tourism economic disparities, enabling an in-depth comprehension of the complex interactions and nonlinear functional mechanisms of influential factors, but also explores a region-based tourism development model, offering valuable practical guidance for policymakers in the tourism sector.

Scenario analysis of energy consumption and related emissions in the transportation industry-a case study of Shaanxi Province.

In the context of pursuing carbon neutrality and balancing the use of fossil fuels with renewable energy, the transportation industry faces the challenge of accurately predicting energy demand, related emissions, and assessing the effectiveness of energy technologies and policies. This is crucial for formulating energy management plans and reducing carbon dioxide (CO2) and atmospheric pollutant emissions. Currently, research on energy consumption and emission forecasting primarily relies on energy consumption quantities and emission factors, which lack precision. This study employs the low emissions analysis platform (LEAP) model, utilizing a "bottom-up" modeling approach combined with scenario analysis to predict and analyze the energy demand and related emissions in the transportation industry. Compared to previous studies, the methodological framework proposed in this research offers higher precision and can explore energy-saving and emission-reduction pathways for different modes of transport, providing a valuable energy forecasting tool for transport policy formulation in other regions. The forecast results indicate that under the business-as-usual (BAU) scenario, by 2049, the energy consumption and related emissions in Shaanxi Province's transportation industry are expected to increase by 1.15 to 1.85 times compared to the baseline year. In the comprehensive (CP) scenario, the industry is projected to reach a carbon peak around 2033. The study also finds that energy consumption and emissions predominantly originate from private passenger vehicles, highway freight, and civil aviation passenger, which have the greatest potential for emission reduction under the transport structure optimized (TSO) scenario. Therefore, policymakers should consider regional development characteristics, combine different transportation modes, and specifically analyze the emission reduction potential of the transportation industry in various regions, formulating corresponding reduction policies accordingly.

Heterogeneity in crash data: A cross comparison between ordered probability model and its variant using crashes at suburban type arterial.

Transportation researchers have long been using the statistical analysis of traffic crash data to create a proactive awareness of traffic safety, make important decisions about the design of vehicles and highways, and develop and implement safe preventive strategies to improve safety. Despite significant progress toward maintaining and analyzing traffic crash data, researchers still encounter several challenges and methodological barriers when conducting statistical analysis. One of these challenges is dealing with the issue of unobserved heterogeneity in crash data. This study uses state-of-the-art methodologies to model the injury severity of traffic crashes that occurred on a specific road segment, namely, a suburban-type road (STR), simultaneously addressing issues related to unobserved heterogeneity in data. Multiple heterogeneity ordered probit models are evaluated against Ohio crash data from the Highway Safety Information System (HSIS). The findings reveal the heterogeneous nature of some variables, such as the nighttime indicator, and demonstrate the distinctive feature of each model to capture the effect of unobserved heterogeneity in analyzing data with such variables. Furthermore, the result helps comprehend the contextual scenarios of crashes at STRs and formulate practical plans to lower the severity of such crashes.

Redefining Age-Friendly Neighbourhoods: Translating the Promises of Blue Zones for Contemporary Urban Environments.

The longevity and healthy ageing observed in Blue Zones offer valuable insights for contemporary urban neighbourhood planning and design. This paper reviews the age-friendly features of the built environment in Blue Zones, aiming to translate these insights into actionable strategies for urban neighbourhood development. Employing a systematic literature review and Convolutional Neural Networks (CNNs) analysis of Google Street View imagery, this study assesses the built environments in Blue Zones, including housing, public spaces, and transportation systems. Key findings highlight the presence of adaptable housing, building designs that foster a connection with nature, and semi-public spaces that encourage social interaction and physical activity. A notable emphasis on walkability and limited public transport access was observed. The CNN analysis corroborated these findings, providing a quantitative view of age-friendly features. This research contributes to the academic discourse in urban planning and design by providing practical insights for developing age-friendly neighbourhoods, inspired by Blue Zones. It concludes by offering policy advice and future research directions for creating sustainable and inclusive urban environments conducive to ageing populations.

How to improve the energy-saving performance of China's transport sector? An input-output perspective.

The transport sector proves a major energy consumer in China, but improving energy-saving performance in China's provincial transport sector from the lifecycle perspective remains unresolved. Thus, this study employs the environmentally extended multi-region input-output (MRIO) method, structural path analysis, and the newest MRIO table of China from 2017, to investigate how to improve the energy-saving performance from final demand structure, supply chain, and pathway perspectives. The relevant results are threefold. (1) Regarding the final demand structure level, the embodied energy consumption of China's transport sector is predominantly driven by investment from the production side, while that of the consumption side is primarily caused by exports. (2) At the supply chain level, production-side embodied energy consumption primarily occurs along a three-echelon supply chain, while that from the consumption side mostly occurs via a two-echelon supply chain. (3) At the pathway level, the production-side energy-saving performance of China's provincial transport sector is dominated by two pathways along the construction sector, including transport sector → construction sector → final demands, and transport sector → intermediate inputs → construction sector → final demands, while that of the consumption side is chiefly determined by three pathways along internal transportation chains.

Are electric vehicles really the optimal option for the transportation sector in China to approach pollution reduction and carbon neutrality goals?

Profound worldwide fleet electrification is thought to be the primary route for achieving the target of carbon neutrality. However, when and how electrification can help mitigate environmental impacts and carbon emissions in the transport sector remains unclear. Herein, the overall life-cycle environmental impacts and carbon saving range of two typical A-class vehicles in China, including electric vehicle (EV) and internal combustion engine vehicle (ICEV), were quantified by the life cycle assessment model for endpoint damage with localization parameters. The results showed that the EV outperformed the ICEV for the total environment impact after a travel distance of 39,153 km and for carbon emissions after 32,292 km. The ICEV was more carbon-friendly only when the driving distance was less than 3229 km/a. Considering a full lifespan travel distance of 150,000 km, the whole life-cycle average environmental impacts of EV and ICEV were calculated as 8.6 and 17.5 mPt/km, respectively, but the EV had 2.3 times higher impacts than the ICEV in the production phase. In addition, the EV unit carbon emission was 140 g/km, 46.8% lower than that of the ICEV. Finally, three potential reduction scenarios were considered: cleaner power mix, energy efficiency improvement and composite scenario. These scenarios contributed 19.1%, 13.0% and 32.1% reductions, respectively. However, achieving carbon peak and neutrality goals in China remains a great challenge unless fossil fuels are replaced by renewable energy. The research can provide scientific reference for the method and practice of emission reduction link identification, eco-driving choice and emission reduction path formulation.

Last-mile delivery increases vaccine uptake in Sierra Leone.

Less than 30% of people in Africa received a dose of the COVID-19 vaccine even 18 months after vaccine development1. Here, motivated by the observation that residents of remote, rural areas of Sierra Leone faced severe access difficulties2, we conducted an intervention with last-mile delivery of doses and health professionals to the most inaccessible areas, along with community mobilization. A cluster randomized controlled trial in 150 communities showed that this intervention with mobile vaccination teams increased the immunization rate by about 26 percentage points within 48-72 h. Moreover, auxiliary populations visited our community vaccination points, which more than doubled the number of inoculations administered. The additional people vaccinated per intervention site translated to an implementation cost of US $33 per person vaccinated. Transportation to reach remote villages accounted for a large share of total intervention costs. Therefore, bundling multiple maternal and child health interventions in the same visit would further reduce costs per person treated. Current research on vaccine delivery maintains a large focus on individual behavioural issues such as hesitancy. Our study demonstrates that prioritizing mobile services to overcome access difficulties faced by remote populations in developing countries can generate increased returns in terms of uptake of health services3.

Persona-PhysioSync AV: Personalized Interaction through Personality and Physiology Monitoring in Autonomous Vehicles.

The emergence of autonomous vehicles (AVs) marks a transformative leap in transportation technology. Central to the success of AVs is ensuring user safety, but this endeavor is accompanied by the challenge of establishing trust and acceptance of this novel technology. The traditional "one size fits all" approach to AVs may limit their broader societal, economic, and cultural impact. Here, we introduce the Persona-PhysioSync AV (PPS-AV). It adopts a comprehensive approach by combining personality traits with physiological and emotional indicators to personalize the AV experience to enhance trust and comfort. A significant aspect of the PPS-AV framework is its real-time monitoring of passenger engagement and comfort levels within AVs. It considers a passenger's personality traits and their interaction with physiological and emotional responses. The framework can alert passengers when their engagement drops to critical levels or when they exhibit low situational awareness, ensuring they regain attentiveness promptly, especially during Take-Over Request (TOR) events. This approach fosters a heightened sense of Human-Vehicle Interaction (HVI), thereby building trust in AV technology. While the PPS-AV framework currently provides a foundational level of state diagnosis, future developments are expected to include interaction protocols that utilize interfaces like haptic alerts, visual cues, and auditory signals. In summary, the PPS-AV framework is a pivotal tool for the future of autonomous transportation. By prioritizing safety, comfort, and trust, it aims to make AVs not just a mode of transport but a personalized and trusted experience for passengers, accelerating the adoption and societal integration of autonomous vehicles.

Before-school physical activity levels and sociodemographic correlates among Australian adolescents: A cross-sectional study.

Understanding adolescents' physical activity levels and underpinning contextual factors is crucial for health promotion. This cross-sectional study, using 24-hour time use diaries and sociodemographic variables from the Longitudinal Study of Australian Children, addressed gaps in understanding of physical activity in the before-school segment (the time between waking up and commencing classes). The study examined a) adolescents' time spent in before-school physical activity, focusing on location and shared presence, and b) sociodemographic correlates of before-school physical activity. Completed diaries by 12-13 year-olds (n = 3,201) revealed that adolescents reported an average of 10.8 minutes of daily before-school physical activity (average segment length: 114 min), mostly classified as active transport (5.7 min). Most before-school physical activity occurred in a location other than home or school (6.1 min) and with peers (6.1 min). Notably, 51% of boys and 60% of girls did not report any before-school physical activity. Through two-part regression, we found that boys, adolescents from single-parent households, and those with longer before-school segments are more likely to report before-school physical activity compared to their counterparts. Before-school initiatives should promote active transport and diverse opportunities in other settings. Research into barriers and facilitators may inform more inclusive and effective promotion strategies, including school-based initiatives.