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Flexible polymer composites with high dielectric constants and low dielectric losses at high frequencies are highly desired in microwave and RF applications. However, a high dielectric constant is often obtained at the expense of flexibility because a high loading of filler is needed. In this work, we synthesize a core-shell structured 1D filler by coating high-dielectric-constant PbTiO 3 onto the surface of low-thermal-expansion-coefficient SiC nanofibers, which are then incorporated into the epoxy matrix together with BaTiO 3 nanoparticles to form the multi-phase BaTiO 3 /SiC@PbTiO 3 /epoxy composite film. A high dielectric constant (35 at 100 Hz and 20 at 5 GHz) and a low dielectric loss (0.023 at 100 Hz and 0.13 at 5 GHz) are achieved as the filling content of SiC@PbTiO 3 and BaTiO 3 is 5.24 wt% and 80 wt%, respectively. Prediction models of the effective dielectric constant of polymer-based composites reveal that a continuous polarization network is constructed in the composites owing to the physical contact between BaTiO 3 and PbTiO 3. The construction of the multi-phase filler provides a feasible way to effectively adjust and improve the dielectric properties of polymer-based composite films. [ABSTRACT FROM AUTHOR]

Electrification of end-use energy consuming sectors is becoming a widespread choice to combat climate change, yet the decarbonization of electricity determines the quality of carbon emissions reductions from electrification. This study evaluates the changes in carbon emissions from building operations due to electrification in China's 30 provinces over the period 2004–2020. Considering electricity decarbonization, electricity consumption, population and floor area, an assessment framework for carbon emissions reductions from building electrification has been built by Kaya-LMDI method. The results show that (1) The promotion of building electrification causes an increase of approximately 46.88 MtCO 2 over the period 2004–2020. The urban residential buildings contribute the largest share, about 54.78%; (2) Almost half of the provinces have failed to achieve carbon emissions reductions with promotion of building electrification; (3) The change in carbon emissions caused by the promotion of building electrification shows a clustering effect in space, with the northern region characterized by high-high clusters and the southern region by low-low clusters. In addition, this study reviews the strategy for power decarbonization and building electrification in China. The overall strategy for building electrification is to accelerate the proportion of electricity consumption in existing buildings and to promote the electrification of new public buildings across the board. Overall, this study provides a reference for other economies and end-use energy sectors to evaluate electrification for carbon emissions reductions, and provides insights and strategies for policy makers on the promotion of building electrification. [Display omitted] • The building electrification contributed 46.88 MtCO 2 of carbon emissions in 2004–2020. • Decarbonization of electricity can effectively reduce building operation carbon emissions. • The building electrification in half of provinces has failed to reduce carbon emissions. • Recommendations are given from cross-sectoral cooperation, energy efficiency and energy plus house. [ABSTRACT FROM AUTHOR]

Understanding the impact of contractors' cultural preferences on green construction performance (GCP) can contribute to sustainable development in the construction industry. However, existing research relies primarily on Hofstede's framework of national culture, which treats culture as a collection of independent dimensions, thereby ignoring the complex notion of cultural profiles, which refer to distinct patterns of interrelated dimensions that cannot be considered in isolation but only in combination. Thus, based on Hofstede's cultural theory, this study adopts a qualitative comparative analysis (QCA) approach to fill this gap by exploring how multiple dimensions of culture interact and combine to influence green construction performance. A relationship framework between contractors' cultural preferences and GCP could be developed based on the results. Findings go beyond the existing theoretical and empirical evidence on the relationship between unique cultural preferences and GCP, which enriches the theoretical system of sustainability research in the construction industry and assists contractors in changing their attitudes and behaviors to improve GCP. [Display omitted] • Different dimensions of contractors' cultural preferences have jointly affected GCP. • Cultural preferences and GCP are divided into four dimensions respectively. • QCA method helps obtain combinations of cultural dimensions that improve GCP. • Four propositions for improving the conformational effects of GCP are proposed. • A relationship framework between cultural preferences and GCP is proposed. [ABSTRACT FROM AUTHOR]

• Latent class cluster and random parameters model were integrated to explore factors influencing motorcyclists' injury levels. • Homogeneous clusters help to reveal new information such as important contributing factors hidden in the general model. • Although observations in each cluster are homogeneous in some aspects, heterogeneity was still found in each subgroup. • The proposed model is proved to have great potential for accounting for the source of heterogeneity. Due to the wide existence of heterogeneous nature in traffic safety data, traditional methods used to investigate motorcyclist rider injury severity always lead to masking of some underlying relationships which may be critical for the formulation of efficient safety countermeasures. Instead of applying one single model to the whole dataset or focusing on pre-defined crash types as done in previous studies, the present study proposes a two-step method integrating latent class cluster analysis and random parameters logit model to explore contributing factors influencing the injury levels of motorcyclists. A latent class cluster approach is first used to segment the motorcycle crashes into relatively homogeneous clusters. A mixed logit model is then elaborately developed for each cluster to identify its unique influential factors. The analysis was based on the police-reported crash dataset (2015–2017) of Hunan province, China. The goodness-of-fit indicators and the Receiver Operating Characteristic curves show that the proposed method is more accurate when modeling the riders' injury severities. The heterogeneity found in each homogeneous subgroup supports the application of the random parameters logit model in the study. More importantly, the results demonstrate that segmenting motorcycle crashes into relatively homogeneous clusters as a preliminary step helps to uncover some important influencing factors hidden in the whole-data model. The proposed method is proved to have great potential for accounting for the source of heterogeneity. The injury risk factors identified in specific cases provide more reliable information for traffic engineers and policymakers to improve motorcycle traffic safety. [ABSTRACT FROM AUTHOR]

• Assessment of the roles of human factors in motorcycle KSI crashes. • The findings help better understand how motorcycle KSI crashes occur. • The findings highlight the top priority of monitoring factors. • The finding provide a new direction for research about motorcyclists' behavior. Although the importance of human factors to crash occurrence has been demonstrated previously, the roles played by human factors in motorcycle killed and severely injured (KSI) crashes have remained unclear. One aim of our study is therefore to empirically determine the relative contribution of illegal behavior to motorcycle KSI crashes, conditional on real-world collisions between motorcycles and motor vehicles. Given that a crash is typically the synthetical result of human, vehicle, roadway, and environmental factors, another aim is to identify high-risk scenarios where inappropriate behavior is more likely to result in severe injuries for motorcyclists through interactions with other related factors. Based on a comprehensive dataset of 4587 police-reported crashes involving motorcycles during 2015–2017 in Hunan province, China, a data mining technique namely classification and regression tree was elaborately employed. Our results demonstrated the illegal behavior of the striking motor-vehicle drivers as one of the most dominant factors contributory to motorcycle KSI crashes, with a normalized importance value of 36.9%. We also confirmed collision object (i.e., collision with heavy or light vehicles) and helmet use of motorcyclists as determinants influencing motorcycle rider injury severities. Two types of extreme high-risk traffic scenarios were identified accordingly. A motorcycle rider was hit at weekends by a heavy motor-vehicle driver who was driving without license, driving a substantial vehicle, speeding, changing lanes illegally or driving in the wrong direction, and a motorcyclist was hit on weekdays by a heavy motor-vehicle driver aged 18–34 or 45–54, who was driving without license, driving a substantial vehicle, speeding, changing lanes illegally or driving in the wrong direction. Our findings are expected to shed more light on a deeper understanding of the illegal driving behavior as causation of motorcycle KSI crashes. [ABSTRACT FROM AUTHOR]

The Chinese government has deployed ambitious plans for its power system and renewable energy development to achieve carbon neutrality. To assess the decarbonization processes of regional power grids, this study develops an inventory of the power generation structure at the provincial level based on current official power planning, and simulates the power generated and carbon emissions intensity in China's regional power grid from 2020 to 2050 using a dynamic scenario. Results found the following: First, compared with 2020, carbon emissions from the power system in 2050 decreased by approximately 1.8268 BtCO 2 , and the North China regional power grid contributes the most to emission reduction. Second, except for Tibet power grid, the life-cycle carbon emissions coefficients in China's regional power grids maintained a downward trend, with carbon emission coefficients ranging from 0.1439 to 0.2385 kgCO 2 eq/kWh in 2050. Third, cross-regional power exchange can effectively reduce the carbon emission intensity of regional power grids dominated by installed fossil energy and Northwest regional power grid is the largest power output region, reaching 1077.78 TWh in 2050. Overall, this study assesses the prospective decarbonization of China's regional power grids. The findings contribute to the development of power decarbonization planning and provide data references for electrification of energy-consuming sectors. • The projected carbon emissions intensity of each regional power grid is simulated. • Our model considers cross-regional power exchange. • Dynamic scenarios are used to achieve uncertainty in power generation. • The power system is not decarbonized fast enough under current official power planning. • Recommendations are given from CCS technology and power transmission. [ABSTRACT FROM AUTHOR]

• Introduced a multi-level perspective into construction industry system studies • Identified driving factors for implementing low-carbon transitions in construction industry • Used an integrated ISM-MICMAC method to explore the interactions among factors • Formed a multi-stakeholder collaborative system to clarify the main responsibility Climate change caused by carbon emissions is a concern for many countries. As the world's largest carbon emitter, China's construction industry generates substantial carbon emissions. However, previous research has primarily focused on promoting low-carbon building products or calculating carbon emissions to propose low-carbon transition measures. There has been limited research on low-carbon transitions in the construction industry from an industry system perspective. This study aimed to identify the driving factors and their interrelationships for low-carbon transitions in the construction industry. A multi-level perspective (MLP) was presented as a heuristic structure to analyze driving factors. An integrated interpretative structural model (ISM) and cross-impact matrix multiplication applied to classification (MICMAC) technique was adopted to explore the interactions among factors. The results demonstrate that a six-level hierarchy of 22 drivers was constructed, and relationship degrees between the factors were discovered. The strongest drivers were low-carbon legal regulation, followed by industrial structure and organizational characteristics connected to industrial development. Improving these fundamental factors will increase the probability of successful transitions. The results also demonstrate that transitions are a collaborative process that involves multiple stakeholders. These findings can provide suggestions for low-carbon practices in the construction industry. [ABSTRACT FROM AUTHOR]

This study aimed to identify the factors affecting the crash-related severity level of injuries in taxis and quantify the associations between these factors and taxi occupant injury severity. Casualties resulting from taxi crashes from 2004 to 2013 in Hong Kong were divided into four categories: taxi drivers, taxi passengers, private car drivers and private car passengers. To avoid any biased interpretation caused by unobserved spatial and temporal effects, a Bayesian hierarchical logistic modeling approach with conditional autoregressive priors was applied, and four different model forms were tested. For taxi drivers and passengers, the model with space-time interaction was proven to most properly address the unobserved heterogeneity effects. The results indicated that time of week, number of vehicles involved, weather, point of impact and driver age were closely associated with taxi drivers’ injury severity level in a crash. For taxi passengers’ injury severity an additional factor, taxi service area, was influential. To investigate the differences between taxis and other traffic, similar models were established for private car drivers and passengers. The results revealed that although location in the network and driver gender significantly influenced private car drivers’ injury severity, they did not influence taxi drivers’ injury severity. Compared with taxi passengers, the injury severity of private car passengers was more sensitive to average speed and whether seat belts were worn. Older drivers, urban taxis and fatigued driving were identified as factors that increased taxi occupant injury severity in Hong Kong. [ABSTRACT FROM AUTHOR]

Nuclear import is a highly selective process that involves the specific recognition of appropriate import signals by suitable receptors. Many nuclear transport pathways are mediated by importin β superfamily members. Among them, IPO4 is a nuclear import receptor for many cargoes. However, its transcriptional regulation remains largely unknown. In the present study, we identified a core region encompassing nt − 118 to + 108 that is necessary for its promoter activity. Transcription factors binding to this region were screened, resulting in the identification of two members of the Ets family, Ets-like transcription factor-1 and GA binding protein, which repress or activate its promoter activity, respectively. Within this promoter region, two Ets binding sites were identified and shown to be required for promoter activity. Ets-like transcription factor-1 and GA binding protein compete with each other to regulate its promoter activity via its downstream Ets binding sites, as evidenced by EMSA and a luciferase reporter assay. Overexpression of Ets-like transcription factor-1 or GA binding protein results in its down-regulation or up-regulation in cells. Therefore, both Ets-like transcription factor-1 and GA binding protein regulate IPO4 transcription. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• We further explain the used data sets and the principle and function of T-SNE. • We further explain the method to solve the over fitting issue. • We retested the effectiveness of the model with a separate independent test set. • We calculate the time complexity of the model and examine the effectiveness of the proposed model. • We added a comparison analysis of the three models in the discussion section. The upper limb movement of stroke survivors has strong specificity and involuntary activation of muscles and other non-ideal factors. The prediction method suitable for healthy people often declines accuracy when applied to stroke survivors. The precise perception of the patient's motion intention is helpful for the patient to use the rehabilitation robot for rehabilitation training. Current research focuses on data acquisition, preprocessing, feature extraction, and classifier selection. Some researchers have proposed effective methods, but they have disadvantages such as complexity, high cost, and low generalization. In this paper, we proposed a new solution to the problem of significant interference of patients' sEMG data: (i) Embedding the attention mechanism into the deep residual network so that the attention module can entirely focus on the key features to improve the network's learning ability of features. (ii) The soft thresholding module is embedded into the deep residual network as a building unit, and the threshold is automatically set to eliminate the interfering noise. We designed an experiment to acquire sEMG signals from eight muscles of ten patients during six preset movements and adopted a 10-fold cross-validation method to verify the feasibility of the proposed method. The length of the data processing window, the prediction accuracy of different movements, and various models' classification effect are compared. The results show that compared with ResNet (average accuracy = 84.94 %) and CNN (average accuracy = 78.47 %), the proposed method has higher classification accuracy, with an average accuracy of 93.11 %, which proves the feasibility of the proposed method. This study can be applied to improve the efficiency of rehabilitation training for stroke survivors. [ABSTRACT FROM AUTHOR]

In order to reduce Construction & Demolition (C&D) waste and prolong the landfill life in Hong Kong, a Construction Waste Disposal Charging Scheme (CWDCS) has been introduced since December 2005 by the government. This paper aims to establish a framework for assessing the eco-efficiency of C&D waste management performance through eco-efficiency indicators, based on the particular practice of Hong Kong. A comparison of waste management performance before and after implementation of the CWDCS was conducted based on the eco-efficiency framework proposed. It is found that the eco-efficiency of C&D waste management in Hong Kong becomes better under implementation of the CWDCS. Suggestions were also presented to enhance the sustainability of C&D waste management in Hong Kong. [ABSTRACT FROM AUTHOR]

Building Energy Efficiency Retrofit (BEER) is considered as a valuable way to improve energy efficiency of high-energy-consumption buildings. Sustainable BEER helps integrate sustainable development strategy into existing buildings and retrofit projects. To ensure BEER projects that can fulfill the sustainable development strategy, Energy Performance Contracting (EPC) is one possible market mechanism to deliver energy efficiency projects. Sustainable BEER under the EPC mechanism is a comprehensive system which involves the various demands of sustainable dimensions, performance criteria and groups of project success factors, making multi-criteria decisions become a challenging problem for decision makers. This paper aims to examine the interrelationships of sustainable BEER by focusing on an existing hotel building. EPC mechanism is applied in the case study and an effective Analytic Network Process (ANP) approach is also employed in the research. The result indicates that sustainable BEER in hotel buildings under the EPC mechanism is mainly determined by project control mechanism, available technology, organizing capacity of the team leader, trust, accurate Measurement and Verification (M&V), and team workers' technical skills. Decision makers should dedicate more attention to these aspects. From the research findings, several policy implications are proposed in this paper. [ABSTRACT FROM AUTHOR]

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 R d 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. [ABSTRACT FROM AUTHOR]

Highlights: [•] Conducting a sensitivity analysis to quantitatively investigate the modifiable areal unit problem in the context of regional safety modeling. [•] The Bayesian spatial model outperforms the Bayesian Poisson-lognormal model in accurately accounting for spatial autocorrelation effects, unbiased parameter estimates, and model performance. [•] Zoning schemes with higher number of zones tend to have increasing number of significant variables, more stable coefficient estimation, smaller standard error, whereas worse model performance. [•] The variables of population density and median household income show consistently significant effects on crash risk and are robust to variation in data aggregation. [•] The MAUP effects may be significantly reduced if we just maintain at about 50% of the original number of zones. [ABSTRACT FROM AUTHOR]

The present research analyzes the nonlinear fluid–structure interaction (FSI) of free surface waves with large-scale polymer offshore floating photovoltaics (LPOFPV). The floating structure is modeled as a nonlinear Euler Bernoulli–von Kármán (EBVK) beam coupling with water beneath. The EBVK theory takes the in-plane force into consideration to account for the moderately large deflection slopes in LPOFPV. A multi-time-scale perturbation method leads to hierarchic partial differential equations by introducing the wave steepness squared as the perturbation. The analytical solution of the proposed nonlinear FSI model is obtained up to the second order. Pontoon structures and LPOFPV are studied and compared. The asymptotic solution provides the expressions of the propagating wave through the coupled system and its frequency–amplitude dispersion relation in a closed-form. A property of the solution is that the progressive plane wave through the coupled system remains linear for small dimensionless amplitudes, and features a second order correction for moderately large dimensionless amplitudes. Furthermore, it is also theoretically proven that no resonance occurs in the considered infinite problem. The proposed approach can be extended to the nonlinear coupling between a EBVK beam and Stokes waves. • Nonlinear model for strong hydro-elastics of very large floating structures. • Coupling between the nonlinear Euler Bernoulli–von Kármán beam and Airy waves. • Fully analytical solution based on the multi-time-scale perturbation method. • Comparison between polymer membrane structures and steel pontoon structures. [ABSTRACT FROM AUTHOR]

• One major challenge of bicycle safety is lack of complete exposure data. • A simultaneous-equation model was proposed to tackle incomplete exposure data. • Our model can reveal link of built environment, cycling levels, and bicycle crashes. • Our model is promising in imputation of missing exposure values. • Ignoring uncertainty in exposure results in biased inferences. One major challenge faced by neighborhood-level bicycle safety analysis is the lack of complete and reliable exposure data for the entire area under investigation. Although the conventional travel-diary surveys, together with the emerging smartphone fitness applications and bike-sharing systems, provide straightforward and valuable opportunities to estimate territory-wide bicycle activities, the obtained ridership suffers inherently from underreporting. We introduced the Bayesian simultaneous-equation model as a sound methodological alternative here to address the uncertainty arising from incomplete exposure data when modeling bicycle crashes. The proposed method was successfully fitted to a crowdsourced dataset of 792 bicycle–motor vehicle (BMV) crashes aggregated from 209 neighborhoods over a 3-year period in Hong Kong. Our analysis empirically demonstrated the bias due to omission of activity-based exposure measures or to the direct use of cycling distance extracted from the travel-diary survey without correcting for incompleteness. By modeling bicycle activities and the frequency of BMV crashes simultaneously, we also provided new evidence that an expansion of bicycle infrastructure was likely associated with a significant increase in cycling levels and a substantial reduction in the risk of BMV crashes, despite a slight increase in the absolute number of BMV crashes. Our approach is promising in adjusting for the uncertainty in raw exposure data, extrapolating the missing exposure values, and untangling the linkage among built environment, bicycle activities, and the frequency of BMV crashes within a unified framework. To promote safer cycling, designated facilities should be provided to consecutively separate cyclists from motor vehicles. [ABSTRACT FROM AUTHOR]

Abstract: Building Energy Efficiency Retrofit (BEER) is an attractive way toward low carbon buildings through improving energy efficiency of high-energy-consumption existing buildings. Sustainable development strategy has been reaching many spheres of human activities. Sustainable BEER aims to integrate the sustainable development concept into existing buildings and retrofit projects. To ensure BEER projects that can fulfill the sustainable development strategy, a project delivery model is one major consideration and Energy Performance Contracting (EPC) is one possible market mechanism to deliver energy efficiency projects. This paper aims to use Analytic Network Process (ANP) to develop a model for sustainable BEER under the EPC mechanism. Key performance indicators (KPIs) for sustainable BEER in hotel buildings and critical success factors (CSFs) for EPC have been identified based on a set of interview and questionnaire survey previously conducted by the authors. In this study, the relationships among sustainable dimensions, KPIs, and CSFs are identified through a focus group discussion. Finally, an ANP model is built based on the data collected in the group discussion using the SuperDecision software. [Copyright &y& Elsevier]

[Display omitted] Urbanization is a complex economic and social phenomenon. Recently, rapid urbanization has not only caused rapid economic development in China, it has also posed a major threat to the ecological environment. Therefore, this study aims to determine how urbanization and its internal structure affect ecological efficiency (eco-efficiency) using panel data from China's 30 provinces from 2008 to 2017. First, the eco-efficiencies of 30 provinces in China were calculated using the super slack-based measure model (super-SBM model). Then, the entropy method was used to measure the comprehensive level of urbanization from different dimensions. Finally, the Spatial Durbin model was used to analyze the impact mechanisms of urbanization dimensions and the internal structure effect of each dimension on eco-efficiency. The results show that, from 2008 to 2017, the comprehensive level of China's urbanization in each dimension showed an upward trend, while the average eco-efficiency declined slightly. Further, local eco-efficiency exhibited a negative spatial effect on the surrounding areas. In particular, the local social urbanization level inhibited the eco-efficiency of the surrounding areas, while the local population urbanization level promoted the eco-efficiency of the surrounding areas. Moreover, population and ecological urbanization had significant positive impacts on local eco-efficiency, while social and spatial urbanization had significant negative impacts. [ABSTRACT FROM AUTHOR]

• A note on correctly using RP models for out-of-sample crash prediction. • We proposed an analytic solution to include both the means and variances of RP. • Using only the means of RP for out-of-sample prediction results in a downward bias. One challenge faced by the random-parameter count models for crash prediction is the unavailability of unique coefficients for out-of-sample observations. The means of the random-parameter distributions are typically used without explicit consideration of the variances. In this study, by virtue of the Taylor series expansion, we proposed a straightforward yet analytic solution to include both the means and variances of random parameters for unbiased prediction. We then theoretically quantified the systematic bias arising from the omission of the variances of random parameters. Our numerical experiment further demonstrated that simply using the means of random parameters to predict the number of crashes for out-of-sample observations is fundamentally incorrect, which necessarily results in the underprediction of crash counts. Given the widespread use and ongoing prevalence of the random-parameter approach in crash analysis, special caution should be taken to avoid this silent pitfall when applying it for predictive purposes. [ABSTRACT FROM AUTHOR]

• A Bayesian multivariate spatio-temporal interaction model-based approach is proposed on roadway traffic safety. • The spatio-temporal correlations and interactions are incorporated into the crash frequency model. • The overall goodness-of-fit performance is improved. • Significant differences are found between the proposed approach and the naïve ranking approach. • A Bayesian approach based on the multivariate Poisson-lognormal model is provided. Ranking sites with promise is an essential step for cost-effective engineering improvement on roadway traffic safety. This study proposes a Bayesian multivariate spatio-temporal interaction model based approach for ranking sites. The severity-weighted crash frequency and crash rate are used as the decision parameters. The posterior expected rank and posterior mean of the decision parameters are adopted as the statistical criteria. The proposed approach is applied to rank road segments on Kaiyang Freeway in China, which is conducted via programming in the freeware WinBUGS. The results of Bayesian estimation and assessment indicate that incorporating spatio-temporal correlations and interactions into the crash frequency model significantly improves the overall goodness-of-fit performance and affects the identified crash-contributing factors and the estimated safety effects for each severity level. With respect to the ranking results, significant differences are found between those generated from the proposed approach and those generated from the naïve ranking approach and a Bayesian approach based on the multivariate Poisson-lognormal model. Besides, the ranks under the posterior mean criterion are found generally consistent with those under the posterior expected rank criterion. [ABSTRACT FROM AUTHOR]

• A novel stochastic link-based fundamental diagram model was considered for considering speed heterogeneity. • Random-parameter structures were applied to reveal the unobserved heterogeneity. • Two-stage Bayesian inference was proposed for model calibration. • Effects of rainfall intensity on mean speed and speed variance were quantified. This study aims to establish a stochastic link-based fundamental diagram (FD) with explicit consideration of two available sources of uncertainty: speed heterogeneity, indicated by the speed variance within an interval, and rainfall intensity. A stochastic structure was proposed to incorporate the speed heterogeneity into the traffic stream model, and the random-parameter structures were applied to reveal the unobserved heterogeneity in the mean speeds at an identical density. The proposed stochastic link-based FD was calibrated and validated using real-world traffic data obtained from two selected road segments in Hong Kong. Traffic data were obtained from the Hong Kong Journey Time Indication System operated by the Hong Kong Transport Department during January 1 to December 31, 2017. The data related to rainfall intensity were obtained from the Hong Kong Observatory. A two-stage calibration based on Bayesian inference was proposed for estimating the stochastic link-based FD parameters. The predictive performances of the proposed model and three other models were compared using K -fold cross-validation. The results suggest that the random-parameter model considering the speed heterogeneity effect performs better in terms of both goodness-of-fit and predictive accuracy. The effect of speed heterogeneity accounts for 18%–24% of the total heterogeneity effects on the variance of FD. In addition, there exists unobserved heterogeneity across the mean speeds at an identical density, and the rainfall intensity negatively affects the mean speed and its effect on the variance of FD differs at different densities. [ABSTRACT FROM AUTHOR]

Highlights • The safety-in-numbers effects were investigated in an area with low pedestrian activities. • The safety-in-numbers effects were found only at 32 intersections out of 219. • Intersections with the safety-in-numbers effects have relatively higher pedestrian activities. • Intersections without the safety-in-numbers effects have extremely low pedestrian activities. Abstract 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. [ABSTRACT FROM AUTHOR]

Abstract: The construction industry is characterized by high-energy consumption and intensive-carbon emissions. With the promotion of energy-saving technologies in buildings combined with concerns for global-warming, there has been a gradual shift in energy conservation requirements from the operation stage to the construction stage. This shift is primarily delivered through the use of improved materials and technologies implemented during the building construction stage. Building industrialization is an innovative construction approach that has experienced rapid up-take in China. The use of prefabricated components in building construction is now a key feature of the building industrialization process, and thus warrants greater attention in terms of its energy consumption and carbon emission. This paper utilizes a process-based method to assess carbon emissions during the prefabrication manufacturing process in offsite factories. A life cycle assessment is applied to a case study concerning a prefabricated concrete interior wall board. Carbon sources during the manufacturing process is identified and the carbon emissions are quantified using a factor method. The results show that carbon emissions from the interior prefabricated concrete wallboard of volume 0.609 m3 is 427 kg, with the vast majority of the emissions originating from building materials, at 96.2%. The carbon emissions from electricity consumption constitutes 3.65%, and that from workers make up only 0.16%. The findings of this study offer a carbon emission benchmark for building industrialization, which in turn serve as a solid data foundation for carbon assessment of prefabricated buildings in China. Highlights • Building industrialization is an innovative construction approach. • A process-based method is developed to assess carbon emissions of pre-components. • A life cycle assessment is applied to a case study. • Carbon sources during the manufacturing process is identified. [ABSTRACT FROM AUTHOR]

China consumes more energy and emits more carbon dioxide than any other country. The building sector accounts for nearly 1/3 of the total national energy consumption in China; thus, it is critical to find a solution for improving buildings’ energy efficiency. Under the market-oriented mechanism, constrained by the energy performance contracting (EPC) mode, energy service companies (ESCOs) play an important role in improving energy efficiency for high-energy-consumption industries, including the building sector. The aim of this study is to determine the strategy for boosting the building ESCO industry. By conducting a strength, weakness, opportunity, and threat (SWOT) analysis, this paper presented the status of the building ESCO industry in China and proposed alternative strategies. By utilizing a conventional multi-criteria decision-making method, namely, the Analytic Network Process (ANP), this study provides a quantitative basis to analytically determine the ranking of the factors in SWOT analysis and select the best strategy to promote the development of the building ESCO industry. The results showed that the ANP-SWOT is a viable and highly capable methodology that provides invaluable insights for strategic management decisions in the building ESCO industry, and a set of strategies for promoting ESCO development in the building sector were proposed based on this approach. [ABSTRACT FROM AUTHOR]

Zonal crash prediction has been one of the most prevalent topics in recent traffic safety research. Typically, zonal safety level is evaluated by relating aggregated crash statistics at a certain spatial scale to various macroscopic factors. Another potential solution is from the micro level perspective, in which zonal crash frequency is estimated by summing up the expected crashes of all the road entities located within the zones of interest. This study intended to compare these two types of zonal crash prediction models. The macro-level Bayesian spatial model with conditional autoregressive prior and the micro-level Bayesian spatial joint model were developed and empirically evaluated, respectively. An integrated hot zone identification approach was then proposed to exploit the merits of separate macro and micro screening results. The research was based on a three-year dataset of an urban road network in Hillsborough County, Florida, U.S. Results revealed that the micro-level model has better overall fit and predictive performance, provides better insights about the micro factors that closely contribute to crash occurrence, and leads to more direct countermeasures. Whereas the macro-level crash analysis has the advantage of requirement of less detailed data, providing additional instructions for non-traffic engineering issues, as well as serving as an indispensable tool in incorporating safety considerations into long term transportation planning. Based on the proposed integrated screening approach, specific treatment strategies could be proposed to different screening categories. The present study is expected to provide an explicit template towards the application of either technique appropriately. [ABSTRACT FROM AUTHOR]

• A power-to-X system based on CO 2 /H 2 O co-electrolysis and trigeneration is assessed. • An exergy-cost-carbon nexus approach based on energy level is constructed. • Cost and carbon intensities of products are obtained in different carbon mechanisms. • Impacts of solar radiation on exergy cost and carbon are studied. • The chain exergy efficiencies of the power-to-X system are 13.73% and 53.19%. The carbon dioxide/water co-electrolysis presents an exciting opportunity for utilizing carbon dioxide alongside renewable energy sources, and the stable hydrocarbon syngas can then be fed into downstream systems to produce power, cooling, and heat for end-users. However, the entire power-to-X system is a complex chain composed of various interconnected parts and segments. Its overall economic and environmental performances remain unclear. To address this gap, this study introduces a novel exergy-cost-carbon approach involving a solar-full-spectrum-driven power-to-X system that relies on the combination of co-electrolysis using solid oxide electrolysis cells and a fuel cell trigeneration system. An exergy-cost-carbon nexus method is developed to reveal their coupling relationships, in which energy levels of streams as allocation principles are employed to distinguish their differences. Using the proposed method, the cost and carbon intensities of syngas and energy products are determined, and these metrics are then compared across various carbon costing models. The validity of this approach is confirmed through a comparison with the conventional exergo-economic method. The co-electrolysis and trigeneration subsystems exhibit exergy efficiencies of 13.73 % and 53.19 %, respectively. The unit exergy costs of syngas and power in the power-to-X system are determined to be 0.261$/kWh and 0.550$/kWh, respectively, based on the given design parameters. When factoring in carbon markets and taxes, these costs decrease by 9.02 % and 6.83 %, respectively. The exergy carbon footprint associated with syngas production is notably low at only 0.070 kg CO 2 -eq/kWh. [ABSTRACT FROM AUTHOR]

With the increasing popularity of prefabricated construction around the world, precast plants face increasingly complex production scenarios. Because it is important for precast components to be delivered on time and assembled according to the planned schedule, it is a common phenomenon to increase production shifts and break the routine production schedule to meet time-pressured orders. But there is still a lack of research on flowshop multi-shift scheduling methods in precast production. To address this issue, a flowshop scheduling optimization model of multi-shift precast production with on-time delivery is established, which is the application in civil engineering. A solution framework and its corresponding optimization processes are designed to avoid a combinatorial explosion of solutions and save solution time, which is contribution in artificial intelligence. The validity of the model is proven using a practical case. The results show that, compared to the conventional scheduling method, the proposed method significantly reduces costs under on-time delivery and stably obtains optimized feasible scheduling schemes. [ABSTRACT FROM AUTHOR]

• Over 70% of injuries to bus passengers were resulted from non-collision incidents in Hong Kong. • Inconsistences were found between severity models of non-collision injuries and collision injuries. • Ignoring unobserved heterogeneity resulted in biased estimations. • A 3A strategy was proposed to mitigate non-collision injuries to bus passengers. Introduction : Although public buses have been demonstrated as a relatively safe mode of transport, the number of injuries to public bus passengers is far from negligible. Existing studies of public bus safety have focused primarily on injuries caused by collisions. Surprisingly, limited effort has been devoted to identifying factors that increase the severity of passenger injuries in non-collision incidents. Method : Our study therefore investigated the injury risk of public bus passengers involved in collision incidents and non-collision incidents comparatively, based on a police-reported dataset of 17,383 passengers injured on franchised public buses over a 10-year period in Hong Kong. A random parameters logistic model was established to estimate the likelihood of fatal and severe injuries to passengers as a function of various factors. Results : Our results indicated substantial inconsistences in the effects of risk factors between models of non-collision injuries and collision injuries. The severity of passenger injuries tended to increase significantly when non-collision incidents occurred due to excessive speed of bus drivers, on double-decker buses, in less urbanized areas, in winter, in heavy rains, during daytime, and at night without street lighting. Elderly female passengers were also found more likely to be fatally or severely injured in non-collision incidents if they lost their balance while boarding, alighting from, or standing on a bus. In comparison, the following factors were associated with a greater likelihood of fatal or severe injuries in collision incidents: elderly female passengers, standing passengers who lost balance, buses out of driver control, double-decker buses, collisions with vehicles or objects, and less urbanized areas. Practical Applications: Based on our comparative analysis, more targeted countermeasures, namely "4E" (engineering, enforcement, emergency, and education) and "3A" (awareness, appreciation, and assistance), were recommended to mitigate collision injuries and non-collision injuries to public bus passengers, respectively. [ABSTRACT FROM AUTHOR]

• Provided a new insight to model the safety state transition processes. • Compare the fatality risk state transition between different exposures. • Evidence supports trends towards a homogeneity of fatality risk states in a neighborhood. • The strength and duration of spatial effect on temporal dynamics of fatality risk states is investigated. The rate of road traffic fatalities has long served as a regular indicator to evaluate and compare road safety performance for different administrative divisions. This article introduces a novel method known as the Markov chain spatial model to incorporate the spatial effects into the temporal dynamic of the fatality rates. Compared to the traditional Markov chain model, the proposed spatial Markov chain model can quantify the influence of neighboring sites explicitly in the transition process. A case study using a long duration dataset, from 1975 to 2015 in the 48 lower states of the United Sates, was conducted to illustrate the proposed model. The fatality rates were measured as the number of traffic fatalities per 100 million vehicle miles or per 10,000 residents. The results show that the probability of transition for one state between different levels of traffic fatality risks depends largely on the context of its surrounding neighbors. Another important finding is that relative to the estimates of traditional Markov chain models, states surrounded by neighborhoods with relatively low fatality rates take a longer time to transform to a higher level of fatality risk in the spatial Markov chain model. On the other hand, those with high-risk neighborhoods takes less time to deteriorate. These findings confirm that it is imperative to incorporate spatial effects when modeling the temporal dynamic of safety indicators to assess and monitor the safety trends in the areas of interest. [ABSTRACT FROM AUTHOR]

• Spatiotemporal logistic models were proposed to analyze the severity of pedestrian injuries. • Adjusting spatiotemporal correlations achieves unbiased estimates and accurate predictions. • Modeling spatiotemporal correlations can reveal evolution pattern and identify hotspots. • 3A strategy was proposed to mitigate pedestrian injuries at urban intersections. This paper conducted a comprehensive study on the injury severity of motor vehicle–pedestrian crashes at 489 urban intersections across a dense road network based on high-resolution accident data recorded by the police from 2010 to 2019 in Hong Kong. Given that accounting for the spatial and temporal correlations simultaneously among crash data can contribute to unbiased parameter estimations for exogenous variables and improved model performance, we developed spatiotemporal logistic regression models with various spatial formulations and temporal configurations. The results indicated that the model with the Leroux conditional autoregressive prior and random walk structure outperformed other alternatives in terms of goodness-of-fit and classification accuracy. According to the parameter estimates, pedestrian age, head injury, pedestrian location, pedestrian actions, driver maneuvers, vehicle type, first point of collision, and traffic congestion status significantly affected the severity of pedestrian injuries. On the basis of our analysis, a range of targeted countermeasures integrating safety education, traffic enforcement, road design, and intelligent traffic technologies were proposed to improve the safe mobility of pedestrians at urban intersections. The present study provides a rich and sound toolkit for safety analysts to deal with spatiotemporal correlations when modeling crashes aggregated at contiguous spatial units within multiple years. [ABSTRACT FROM AUTHOR]

Highlights • A measurement error model was developed to analyze pedestrian crash counts. • The presence of uncertainties in pedestrian and vehicle volumes was confirmed. • The omission of pedestrian volume would lead to biased and incorrect inferences. • The presence of exclusive signals would reduce pedestrian crash risks by 45%. Abstract This study intended to identify the potential factors contributing to the occurrence of pedestrian crashes at signalized intersections in a densely populated city, based on a comprehensive dataset of 898 pedestrian crashes at 262 signalized intersections during 2010–2012 in Hong Kong. The detailed geometric design, traffic characteristics, signal control, built environment, along with the vehicle and pedestrian volumes were elaborately collected. A Bayesian measurement errors model was introduced as an alternative method to explicitly account for the uncertainties in volume data. To highlight the role played by exposure, models with and without pedestrian volume were estimated and compared. The results indicated that the omission of pedestrian volume in pedestrian crash frequency models would lead to reduced goodness-of-fit, biased parameter estimates, and incorrect inferences. Our empirical analysis demonstrated the existence of moderate uncertainties in pedestrian and vehicle volumes. Six variables were found to have a significant association with the number of pedestrian crashes at signalized intersections. The number of crossing pedestrians, the number of passing vehicles, the presence of curb parking, and the presence of ground-floor shops were positively related with pedestrian crash frequency, whereas the presence of playgrounds near intersections had a negative effect on pedestrian crash occurrences. Specifically, the presence of exclusive pedestrian signals for all crosswalks was found to significantly reduce the risk of pedestrian crashes by 43%. The present study is expected to shed more light on a deeper understanding of the environmental determinants of pedestrian crashes. [ABSTRACT FROM AUTHOR]

National building energy savings (NBES) plays an essential role in policymaking of China's building energy–efficiency (BEE) work. Numerous factors, such as technological progress and users' behavior, affect NBES while most of them are unquantifiable. One missing possibility along this direction is that there is currently no method to calculate China's NBES by summarizing all driving factors. To arrive at a solution, we proposed a concept of comparable building energy consumption per unit area and a method of China's NBES calculation based on an extended version of IPAT model (I = PAT, I = Human Impact, P = Population, A = Affluence, T = Technology) and LMDI decomposition (Logarithmic Mean Divisia Index, LMDI). Calculation revealed that China's NBES in “The 10th Five–Year Plan” period (2001–2005), “The 11th Five–Year Plan” period (2006–2010), and the first four years of “The 12th Five–Year Plan” period (2011–2014) was 165, 158, and 127 million tce, respectively. Based on these calculation results, we checked NBES data then successfully proved the validity of this calculation method. Furthermore, after comparing the calculated NBES with the official planned NBES, we found that China surpassed its BEE targets. In the third stage of China's BEE work (2006–2015), the implementation of BEE policies obtained good results. [ABSTRACT FROM AUTHOR]

There is a growing interest in a sustainable built environment and green technologies are gaining worldwide attention. However, implementing green technologies usually requires more investment than using traditional technologies and the payback period tends to be very long. In China, most developers sell their properties and subsequently transfer the ownership to customers. Hence, a crucial problem for promoting green construction is that developers cannot obtain economic profits (“gold”) from implementing “green” technologies. Meanwhile, other stakeholders, such as property management companies, are also hesitant to adopt green technologies because of the large investment involved and their lack of experience. To tackle this problem, an energy performance contracting (EPC) based framework is proposed, to turn “green” investment into “gold” benefit by careful sharing of the risks existed. The framework was developed by conducting a focus group meeting with three experienced representatives from developer, property management company and energy service company respectively. A real-life case study, which includes both energy efficient system retrofit and new construction, is used to estimate the costs and benefits involved to demonstrate and verify the conceptual framework. The results show that the proposed framework can support the application of green technologies in both types of projects. Economic benefits as well as environmental benefits can be gained for the project stakeholders and the whole society. [ABSTRACT FROM AUTHOR]

• The overall question is what effects the human behavior shift brings on the crash occurrence. • The increased percentage of people staying at home improves pedestrian and cyclist safety. • Human mobility factors were found not related to crash fatalities. • Control NPIs implemented increased the motor vehicle drivers' crash risk. The COVID-19 pandemic has reshaped our cities in many ways. The number of motor vehicles on the road has plummeted during lockdowns, and an increasing number of people are turning to walking and biking. From a road safety perspective, the overall question is what effects the human behavior shift brings on the crash occurrence and, more importantly, how to support decision-makers on safer mobility policies? Based on anonymous mobile phone location and crash report data in New York City, this study attempts to provide some new insights by using survival analysis (the hazard function approach) to explore the effects of human mobility changes due to the pandemic on crashes that involve injuries and fatalities (of pedestrian, cyclist or motorist). (1) the increased percentage of people staying at home improves pedestrian and cyclist safety, which adds evidence for making walking and cycling more appealing; (2) the increased percentage of people staying at home raises the likelihood of injuries for motor vehicle drivers, suggesting that it will be critical to monitor the driving behavior and establish new speed limits during the future pandemic waves and in the post-pandemic era as well; (3) non-work trips (e.g., shopping, recreation, personal business, etc.) are positively associated with crash injuries for motor vehicle drivers as well as pedestrian and cyclist; (4) human mobility factors were found not related to crash fatalities; (5) control NPIs implemented increased the motor vehicle drivers' crash risk. [ABSTRACT FROM AUTHOR]

• The cascading failure model is proposed based on disaster spreading theory. • Five kinds of influence factors are considered in the model. • Failures caused by attacks and by passenger flow congestion are considered respectively. • Eight simulation scenarios are established. • Five emergency resources allocation strategies are proposed. A new dynamic model is established to formulate the cascading failure in the urban rail transit network based on the disaster spreading theory. Firstly, the weighted urban rail transit network by considering the time cost of each effective path is established, the transfer station and turn back station on the topological network are handled specifically, the Dijkstra algorithm is designed to solve the shortest path of each Origin-Destination. Then, the cascading failure model based on disaster spreading theory is established. Five factors including the failure evolution process with time, self-recovery ability of the nodes, failures spreading mechanism, passenger volume changes and the internal random noises by other influence factors are fully considered in this model. Finally, a real-world case study is conducted by using Chengdu Metro Network as the background. Eight simulation scenarios are established, the output is statistical number of failed stations. The results show that, the failed stations number has the greatest scale when fixed transfer stations are attacked. There is no obvious functional relationship between the scale of failure stations and self-recovery factor, and there is a positive correlation between self-recovery factor and cascading failure scale. Based on the results, five emergency resources allocation strategies are proposed. [ABSTRACT FROM AUTHOR]

• Empirical evidence of pedestrian-looking habit and maladaptation was solicited. • Face-to-face survey of looking habit and maladaptation was conducted in Hong Kong. • Looking habit partially mediated the effect of hometown driving rule on maladaptation. • Longer length of stay enhanced looking habit and reduced maladaptation. Both left-driving (LD) and right-driving (RD) rules are used around the world. When traveling to places with different driving rules, pedestrians are likely to make mistakes. To investigate the frequency of such mistakes, a case study was conducted with pedestrians in Hong Kong, which follows LD rules, i.e., traffic drives on the left. The study aimed to probe the effects of hometown driving rules and length of stay on pedestrians' right-looking habit and maladaptation to the Hong Kong LD system and determine the mediating effect of the right-looking habit. A face-to-face survey was conducted with 581 respondents at seven locations in Hong Kong. A structural equation model was applied to determine the relationship among hometown driving rules, length of stay, right-looking habit, and maladaptation. The model exhibited good fitness ( χ 2 / d e g r e e s o f f r e e d o m = 2.154 ; c o m p a r a t i v e f i t i n d e x = 0.989 ; T u c k e r - L e w i s I n d e x = 0.980 ; and r o o t m e a n s q u a r e e r r o r o f a p p r o x i m a t i o n = 0.045). The results revealed that hometown driving rules and length of stay had positive effects on the right-looking habit, and hometown driving rules had a direct negative effect on maladaptation. The right-looking habit partially mediated the effect of hometown driving rules and fully mediated the effect of length of stay on maladaptation to the Hong Kong LD system. It was found that when foreign pedestrians were in areas with unfamiliar driving rules, they tended to practice their hometown looking habits, especially foreign pedestrians who had stayed only for a short time; this behavior differed significantly from that of local pedestrians, and it led to more severe maladaptation. The findings of this study provide empirical evidence of pedestrians' looking habits and maladaptation in areas with unfamiliar driving systems and have significant implications for improving the safety of foreign pedestrians. [ABSTRACT FROM AUTHOR]