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Road safety has become a worldwide public health concern. Although many factors contribute to collisions, pedestrian behaviors can strongly influence road safety outcomes. This paper presents results of a survey investigating the effects of age, gender, attitudes towards road safety, fatalistic beliefs and risk perceptions on self-reported pedestrian behaviors in a Chinese example. The study was carried out on 543 participants (229 men and 314 women) from 20 provinces across China. Pedestrian behaviors were assessed by four factors: errors, violations, aggressions, and lapses. Younger people reported performing riskier pedestrian behaviors compared to older people. Gender was not an influential factor. Of the factors explored, attitudes towards road safety explained the most amount of variance in self-reported behaviors. Significant additional variance in risky pedestrian behaviors was explained by the addition of fatalistic beliefs. The differences among the effects, and the implications for road safety intervention design, are discussed. In particular, traffic managers can provide road safety education and related training activities to influence pedestrian behaviors positively.

Currently, Storage-as-a-Service (StaaS) clouds offer multiple data storage and access pricing options which usually consist of hot and cold tiers. The cold tier storage option offers a lower storage price while the hot tier storage option offers a lower access price. Cloud users need to choose an optimal tier to store their data objects economically based on the frequency of accesses to their data objects. Besides, StaaS cloud users can transfer data objects between these two tiers to save cost according to the varying frequency of accesses to their data objects. Therefore, in order to make optimal transferring decisions, future access curves are needed to be predicted. However, for cloud users, it is difficult to precisely predict future access frequencies for their data objects. In this paper, we propose an online algorithm to guide StaaS cloud users in making decisions on whether and when to transfer their data objects between cold and hot tiers for achieving cost optimizations, while users do not need to have any prior knowledge of future access frequencies. We prove theoretically that the proposed online algorithm can achieve guaranteed competitive ratios for data objects stored in a two-tier StaaS cloud. Finally, through extensive experiments, we validate the effectiveness of our proposed online algorithm and show that it can save costs significantly compared with always keeping data objects in one tier or always transferring data objects from one tier to the other when their access frequencies begin to vary.INDEX TERMS Cost optimization, online algorithms, competitive analysis, StaaS cloud, tiered cloud storage.

Purpose The purpose of this paper is to investigate whether responsible purchasing (relational commitment and supplier evaluation) and responsible supply (supplier firm information sharing and supplier performance) affect the two factors of supply chain responsiveness including process efficiency and customer knowledge management capability, which, in turn, affect other three factors of supply chain responsiveness, such as dyadic quality performance, innovation capability and buyer‒supplier relationship improvement. Design/methodology/approach This study used questionnaire survey and statistical analytical methods. Employing path analysis, this study tested hypothesized relationships using data collected from manufacturers. Findings The findings of this study support the theorized links. Responsible purchasing and supply enhance supply chain responsiveness, which is reflected through process efficiency, customer knowledge management capability, dyadic quality performance, innovation capability and buyer‒supplier relationship improvement. Originality/value Grounded in the goal interdependence theory, this study investigates the effects of responsible purchasing and supply on supply chain responsiveness in the context of Chinese manufacturers. This study offers managerial implications and theoretical contribution.

PurposeThis study examines the operational and relational governances as antecedents of cooperation commitment in buyer–supplier exchanges. It also assesses the impact of cooperation commitment on operational performance.Design/methodology/approachPath analysis was performed on the data collected from manufacturers.FindingsThe results of this study show that both operational and relational governances exert impact on cooperation commitment, which, in turn, is associated with operational performance improvement.Originality/valueFirst, this is the first study employing the reciprocity theory to theorize the conceptual framework of the governance antecedents of cooperation commitment and operations excellence effect. Second, the study highlights how the research framework can enrich the reciprocity theory in exploring the mechanisms of the operational and relational governances of buyer–supplier exchanges and their impact on the commitment to the cooperation. Third, this study extends the reciprocity theory to examine in detail how cooperation commitment exerts impact on the operational performance.

Autonomous vehicles (AVs) have been reported to improve road safety, reduce traffic congestion, and increase urban mobility. However, the high price of AVs is currently a challenge for most consumers. Robo-taxi services, with ride-sharing services and AVs, are regarded as a good approach to solving this problem. As some companies have started testing Robo-taxis on the actual road, it has become important to investigate public adoption of Robo-taxi services before they are more widely introduced to the market. This study aims to explain and predict users’ acceptance of Robo-taxis by extending the Technology Acceptance Model by including the construct of social influence. Data were collected from an online survey in China and analyzed using linear regression models. The results indicate that perceived usefulness, perceived ease of use, and social influence have significant positive correlations with people’s behavior intentions to use Robo-taxis. Perceived ease of use further has an indirect effect on intention to use via perceived usefulness. The results of this study can serve as good references for policymakers, operators, and future transport researchers.

Exploring the impact of the urban built environment on online car-hailing, an increasingly popular mode of urban transportation, is crucial for developing corresponding transportation strategies and addressing sustainable urban planning. This paper captures that the form and orientation of the neighborhood network have a certain degree of influence on travel behavior. Therefore, the road network orientation entropy indicator is introduced based on the originally built environment elements, and the orientation-order (OO) indicator calculation model is constructed to normalize it. This study further tests and verifies its impact on the travel behavior of online car-hailing, thereby improving the factor characterization of neighborhood design latitude in the urban built environment. From the perspective of spatial correlation and spatial heterogeneity, the optimal models are selected by model comparative analysis, namely, the spatial Durbin model (SDM) and the mixed geographically weighted regression model (MGWR). Based on the optimal model estimation results, the influence mechanism of the urban built environment on the spatial-temporal distribution of online car-hailing travel in three cases (considering road network density (RD), considering RD and OO, considering OO) is compared and analyzed. The results show that the model fitting considering the OO is the best in the above three cases, and the OO has a significant impact on DiDi travel compared with the RD in this study, which verifies the rationality and necessity of selecting the OO in this study. In addition, catering service, corporate business, and OO have significant positive spillover effects, while the spillover effects of sports and leisure service and land-use mix are negative. The indicators of the bus station, residential district, catering service, shopping service, corporate business, land-use mix, life service, and OO have significant spatial heterogeneity.