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Efficient traffic incident management is needed to alleviate the negative impact of traffic incidents. Accurate and reliable estimation of traffic incident duration is of great importance for traffic incident management. Previous studies have proposed models for traffic incident duration prediction; however, most of these studies focus on the total duration and could not update prediction results in real-time. From a traveler’s perspective, the relevant factor is the residual duration of the impact of the traffic incident. Besides, few (if any) studies have used dynamic traffic flow parameters in the prediction models. This paper aims to propose a framework to fill these gaps.

This paper proposes a framework based on the multi-layer perception (MLP) and long short-term memory (LSTM) model. The proposed methodology integrates traffic incident-related factors and real-time traffic flow parameters to predict the residual traffic incident duration. To validate the effectiveness of the framework, traffic incident data and traffic flow data from Shanghai Zhonghuan Expressway are used for modeling training and testing.

Results show that the model with 30-min time window and taking both traffic volume and speed as inputs performed best. The area under the curve values exceed 0.85 and the prediction accuracies exceed 0.75. These indicators demonstrated that the model is appropriate for this study context. The model provides new insights into traffic incident duration prediction.

The incident samples applied by this study might not be enough and the variables are not abundant. The number of injuries and casualties, more detailed description of the incident location and other variables are expected to be used to characterize the traffic incident comprehensively. The framework needs to be further validated through a sufficiently large number of variables and locations.

The framework can help reduce the impacts of incidents on the safety of efficiency of road traffic once implemented in intelligent transport system and traffic management systems in future practical applications.

This study uses two artificial neural network methods, MLP and LSTM, to establish a framework aiming at providing accurate and time-efficient information on traffic incident duration in the future for transportation operators and travelers. This study will contribute to the deployment of emergency management and urban traffic navigation planning.

This article will select 25 years of subdivided data to perform Kaplan–Meier survival analysis on the export trade relations of Chinese wooden flooring, use discrete-time cloglog models to analyze influencing factors, use logit and probit models to test the robustness, and try to systematically reveal the duration of China's wood flooring export trade and its influencing factors.

This study used Kaplan–Meier survival function estimation method. In the survival analysis, survival function and hazard rate function are often used to characterize the distribution of survival time.

The continuous average export time of China's wooden flooring is relatively long, about 14 years. China's wooden flooring has a negative time dependency. After the export trade exceeds the threshold value of 15 years, the failure rate of trade greatly decreases, which has a “threshold effect.” Gravity model variables have a significant impact on the duration of China's wooden floor export.

Studying the duration of forest products trade is of great significance for clearing deep-level trade relations and promoting sustainable development of forest products trade.

This paper examines the role of illiquidity and duration factor in understanding the momentum profit in the Korean stock market. We find that the foreigner/institutional illiquidity factor explains the momentum effect. In addition, this paper finds that duration factor defined as the difference in returns of short-duration and long-duration stocks captures well the momentum profits. That is, a two-factor model with the market and duration factor performs much better than competing asset pricing models in explaining the momentum effect. Finally, when controlling for the duration factor, the explanatory power of the foreign/institutional illiquidity factor on the momentum profits disappears. In sum, our empirical finding indicates that the duration factor is the most important ingredient in understanding the momentum effect in the Korean stock market.

The purpose of this study is to analyze the preferences of potential Japanese cruise ship tourists and identify the factors influencing their participation in cruise ship tourism. In the analysis, preference for cruise ports in East Asia is also examined.

The behavioral model of potential cruiser is developed through a mixed ordered logit approach. The data are collected by means of the stated preference method with the application of a Web-based questionnaire. Multiple answers are collected from each respondent. Hence, panel effects between answers are considered so as to obtain a robust model.

The results show that Nagasaki and Hong Kong ports are preferred, and other domestic ports, namely, Kobe, Kagoshima, and Naha are also relatively popular places to visit. However, potential Japanese cruisers are reluctant to visit two South Korean destinations which are frequently selected as cruise lines by avoiding Cabotage rule. Besides, shorter cruise duration and lower prices increase the possibility of participation in cruise tourism, particularly for working people. Retirees tend to have less interest in cruise tourism. However, Japanese-related services will increase retirees’ intentions to participate in cruise tourism.

This study attempts to analyze potential cruisers’ behavior toward cruise ship tourism in Japan and discusses how to increase the number of cruisers participating in cruise ship tourism. In this vein, repeat behavior should also be analyzed. Repeat behavior contributes to the maintenance and increase in cruisers in Japan.

There is no study on potential cruiser’s behavior analysis in Japan which is the emerging country as cruise market. Thus, the number of potential cruisers is expected to be high. This study reveals that potential cruisers’ preferences on cruise ship services (e.g. duration, price, on board services, etc.), which are separately analysed for working ages and retirees. Besides, preferences on port of calls in East Asian context are revealed. These results are useful for cruise ship industries, especially for cruise lines.

Over the past two decades, online booking has become a predominant distribution channel of tourism products. As online sales have become more important, understanding booking conversion behavior remains a critical topic in the tourism industry. The purpose of this study is to model airline search and booking activities of anonymous visitors.

This study proposes a stochastic approach to explicitly model dynamics of airline customers’ search, revisit and booking activities. A Markov chain model simultaneously captures transition probabilities and the timing of search, revisit and booking decisions. The suggested model is demonstrated on clickstream data from an airline booking website.

Empirical results show that low prices (captured as discount rates) lead to not only booking propensities but also overall stickiness to a website, increasing search and revisit probabilities. From the decision timing of search and revisit activities, the author observes customers’ learning effect on browsing time and heterogeneous intentions of website visits.

This study presents both theoretical and managerial implications of online search and booking behavior for airline and tourism marketing. The dynamic Markov chain model provides a systematic framework to predict online search, revisit and booking conversion and the time of the online activities.

This paper aims to examine the effect of both inflation rate and interest rate on stock prices using quarterly data on non-financial firms listed in DJIA30 and NASDAQ100 for the period 1999-2016. The stock duration model is used to measure the sensitivity in variations in inflation rates and interest rates on stock prices.

The authors use standard statistical tools that include Johansen cointegration test, linearity, normality tests, cointegration regression, Granger causality and vector error correction model.

The results of panel Johansen cointegration analysis show that cointegration exists between the stock prices, the changes in stock prices due to inflation rates and the changes in stock prices due to real interest rates. The results of cointegration regression show that inflation rates are negatively associated with stock prices, the real interest rates and stock prices are positively associated, changes in real interest rates and inflation rates Granger cause significant changes in stock prices, significant speed of adjustment to long run equilibrium between observed stock prices and real interest rates and significant speed of adjustment to long run equilibrium between changes in stock prices due to real interest rates and changes in inflation rates.

This paper contributes to the empirical literature in three ways. The paper examines the effects of inflation and interest rates on stock prices differently from other related studies by separating inflation from real interest rates. The paper examines the causality between stock prices, interest and inflation rates. This paper offers significant updated validity to extended literature that a negative association exists between stock prices and inflation rates. This validity can be considered as an existence a theory of stock prices, inflation rates and interest rates.

Dynamic advertising, including television and online video ads, demands new theory and tools developed to understand attention to moving stimuli. The purpose of this study is to empirically test the predictions of a new dynamic attention theory, Dynamic Human-Centred Communication Systems Theory, versus the predictions of salience theory.

An eye-tracking study used a sample of consumers to measure visual attention to potential areas of interest (AOIs) in a random selection of unfamiliar video ads. An eye-tracking software feature called intelligent bounding boxes (IBBs) was used to track attention to moving AOIs. AOIs were coded for the presence of static salience variables (size, brightness, colour and clutter) and dynamic attention theory dimensions (imminence, motivational relevance, task relevance and stability).

Static salience variables contributed 90% of explained variance in fixation and 57% in fixation duration. However, the data further supported the three-way interaction uniquely predicted by dynamic attention theory: between imminence (central vs peripheral), relevance (motivational or task relevant vs not) and stability (fleeting vs stable). The findings of this study indicate that viewers treat dynamic stimuli like real life, paying less attention to central, relevant and stable AOIs, which are available across time and space in the environment and so do not need to be memorised.

Despite the limitations of small samples of consumers and video ads, the results of this study demonstrate the potential of two relatively recent innovations, which have received limited emphasis in the marketing literature: dynamic attention theory and IBBs.

This study documents what does and does not attract attention to video advertising. What gets attention according to salience theory (e.g. central location) may not always get attention in dynamic advertising because of the effects of relevance and stability. To better understand how to execute video advertising to direct and retain attention to important AOIs, advertisers and advertising researchers are encouraged to use IBBs.

This study makes two original contributions: to marketing theory, by showing how dynamic attention theory can predict attention to video advertising better than salience theory, and to marketing research, showing the utility of tracking visual attention to moving objects in video advertising with IBBs, which appear underutilised in advertising research.

The role of list price is often discussed in a narrative describing sellers’ preferences or sellers’ price expectations. This paper aims to investigate a set of list price strategies that real estate brokers have available to influence the outcome of the sale, which may be many times self-serving.

By analyzing real estate brokers’ arguments on the choice of the list price level, a couple of hypotheses are formulated with regard to different expected outcomes that depend on the list price. This study empirically tests two hypotheses for the underlying incentives in the choice of list price from the real estate broker’s perspective: lower list price compared to market value leads to the higher sales price, lower list price compared to market value leads to a quicker sale. To investigate the two hypotheses, this paper adopts different methodological frameworks: H1 is tested by running a classical hedonic model, while H2 is tested through a duration model. This study further tests the hypotheses by splitting the full sample into two different price segments: above and below the median list price.

The results show that H1 is rejected for the full sample and for the two sub-samples. That is, contrary to the common narrative among brokers that underpricing leads to a higher sales price, underpricing lower sales price. H2, however, receives support for the full sample and for the two sub-samples. The latter result points to that brokers may be tempted to recommend a list price significantly below the expected selling price to minimize their effort while showing a high turnover of apartments.

Although there are a large number of previous studies analyzing list price strategies in the housing market, this paper is one of the few empirical studies that address the effect of list price choice level on auction outcomes of non-distressed housing sales.

Interest rate risk hedge strategies usually assume that term structure of interest rates in moving under a specific way. If a real term structure is moving differently from the assumed term structure movement, the interest rate risk hedge strategies assuming the special term structure movement may incur unexpected large loss for a bond portfolio manager. Hence an interest rate risk hedge strategy which could be effective under various types of term structure movements has been strongly needed by bond portfolio managers. Duration vector strategies have been developed to satisfy this practical need. To allow various types of term structure movements, duration vector strategies assume multi-factor models for the term structure movement. When a duration vector strategy is considered as a generalization of a duration strategy which is a single factor model for the term structure movement, there will be a generalized concept which measures convexity of a bond under the duration vector model. This study identifies the convexity property of an option embedded bond portfolio under ‘key rate duration model‘ which is a kind of duration vector model suggested by Ho (1992).