Understanding the travel behaviors and activity patterns of the vulnerable population using smart card data: An activity space-based approach

Understanding the travel behaviors and activity patterns of vulnerable people is important for addressing social equity in urban and transportation planning. With the increasing availability of large-scale individual tracking data, new opportunities have emerged for studying people's travel behaviors and activity patterns. However, the data has not been fully exploited to examine the travel characteristics of vulnerable people and their implications for understanding transport-related disadvantage. This study proposes a methodological framework based on the concept of activity space that enables a comprehensive examination of vulnerable people's spatiotemporal travel characteristics and an investigation of the geographies of transport disadvantage. Using the proposed framework, a case study that investigates the bus activities of the vulnerable population using four-month smart card data is carried out in the city of Wuhu, China. The case study suggests that vulnerable people possess distinct travel behaviors that differ considerably from the mainstream population and that the implications of transport disadvantage, as revealed by the participation in bus activities, vary across different demographic groups and across different spatial contexts. Some of the empirical insights obtained from this study also differ from conclusions drawn from previous studies and will enrich our understandings of vulnerable people's activities. Overall, the paper makes two major contributions. Methodologically, the proposed framework can overcome some of the deficiencies of activity space-based approaches for understanding transport disadvantage and contribute broadly to the studies of travel behaviors and activities patterns using individual-level tracking data. Empirically, the study identifies varying spatial and temporal implications of transport disadvantage associated with different vulnerable groups, which could further shed light on public transit planning and service design.     

Improving forecasting performance using covariate-dependent copula models

Copulas provide an attractive approach to the construction of multivariate distributions with flexible marginal distributions and different forms of dependences. Of particular importance in many areas is the possibility of forecasting the tail-dependences explicitly. Most of the available approaches are only able to estimate tail-dependences and correlations via nuisance parameters, and cannot be used for either interpretation or forecasting. We propose a general Bayesian approach for modeling and forecasting tail-dependences and correlations as explicit functions of covariates, with the aim of improving the copula forecasting performance. The proposed covariate-dependent copula model also allows for Bayesian variable selection from among the covariates of the marginal models, as well as the copula density. The copulas that we study include the Joe-Clayton copula, the Clayton copula, the Gumbel copula and the Student’s $t$-copula. Posterior inference is carried out using an efficient MCMC simulation method. Our approach is applied to both simulated data and the S&P 100 and S&P 600 stock indices. The forecasting performance of the proposed approach is compared with those of other modeling strategies based on log predictive scores. A value-at-risk evaluation is also performed for the model comparisons.     

Non-linear associations between built environment and active travel for working and shopping: An extreme gradient boosting approach

Active travel has environmental, social, and public health-related benefits. Researchers from diverse domains have extensively studied built-environment associations with active travel. However, limited attention has been paid to distinguishing the associations between built environment characteristics at both the origins and destinations and active travel for working and shopping. Scholars have started to examine non-linear associations of built environment with travel behaviour, but active travel has seldom been a focus. Therefore, this study, selecting Xiamen, China, as the case, utilises a state-of-the-art machine learning method (i.e., extreme gradient boosting) to explore the non-linear associations between built environment and active travel for working and shopping. Our findings are as follows. (1) For both purposes, trip characteristics contribute the greatest, and the built environment is also quite important and has larger collective contributions for active travel than does socioeconomics. (2) The relative importance of built environment on active travel for shopping is evidently larger than that for working. (3) All built-environment variables have non-linear associations with active travel, and associations with active travel for working are generally in inverted U or V shapes, while those with shopping trips have much more complex patterns. (4) Differences in the threshold value and gradient exist between built-environment associations with active travel for working and shopping and between variables at origins and destinations. Decision makers are recommended to meticulously disentangle the complex influences of built environment on active travel and distinguish between diverse purposes to make informed and targeted interventions.     

Industry risk transmission channels and the spillover effects of specific determinants in China’s stock market: A spatial econometrics approach

This paper uses a spatial econometrics approach to study the industry risks in China’s stock market. We comprehensively consider the real linkage and information risk transmission channels and analyze the risk spillovers of specific determinants. Our empirical results show the following: 1) The real linkage channel and information channel are both effective transmission channels for driving spillover effects, and the information channel is of the utmost importance. 2) The spillover effects of specific determinants exist and are persistent. The superposition of spillover effects may lead to extreme risk. 3) The transmission channels and spillover effects are asymmetric in different regimes.     

Time-frequency co-movements between oil prices and interest rates: Evidence from a wavelet-based approach

This paper investigates time–frequency co-movements between crude oil prices and interest rates. To test this relationship, the study applied a continuous wavelet and cross wavelet approaches to data from West Texas Intermediate (WTI) crude oil prices and interest rates in the United States (U.S.). Results from the sample period revealed significant relationships, in the intermediate term, between WTI crude oil prices and U.S. interest rates. Moreover, co-movements between oil price and interest rate variables were especially sensitive during abnormal political events and periods of financial ‘meltdown’. We further use Partial Wavelet Coherence (PWC) and Multiple Wavelet Coherence (MWC) methods to investigate the impacts of five major control variables namely GDP growth, unemployment, three-month Treasury bill, CPI index and industrial production index. The results show a powerful impact of control variables on oil-interest rates co-movements under different frequencies. Finally, we show evidence of co-integrating long run relationship between oil markets and control variables. These results have important implications for energy investors and policy makers.     

A TVM-Copula-MIDAS-GARCH model with applications to VaR-based portfolio selection

This paper develops a novel time-varying multivariate Copula-MIDAS-GARCH (TVM-Copula-MIDAS-GARCH) model with exogenous explanatory variables to model the joint distribution of returns. The model accounts for mixed frequency factors that affect the time-varying dependence structure of financial assets. Furthermore, we examine the effectiveness of the proposed model in VaR-based portfolio selection. We conduct an empirical analysis on estimating the 90%, 95%, 99% VaRs of the portfolio constituted of the Shanghai Composite Index, Shanghai SE Fund Index, and Shanghai SE Treasury Bond Index. The empirical results show that the proposed TVM-Copula-MIDAS-GARCH model is effective to investigate the nonlinear time-varying dependence among those three indices and performs better in portfolio selection.     

Uncovering the global network of economic policy uncertainty

This study uncovers the static and dynamic network of economic policy uncertainty (EPU) across 17 developed and emerging economies. We build a centrality network using the minimal spanning tree (MST) as well as a dependency network using partial correlations. Results from the MST show that EPU exhibits some degree of geographical connections with EPUs in seven countries in the sample directly linked to the US EPU. Evidence from dynamic time-varying MST reveals that the nature and dominance of the EPU network have changed significantly over time. Further, the US and German EPUs dominate a close-knit global policy uncertainty network with the highest net (To and From) transmitter of information flow in the dependency network. Greece, Russia, and Brazil are the top three net receivers of information in the global network of EPU. The policy implication of these results relates to the renewed and ongoing international debate on policy coordination.     

Analysis of the impact of street-scale built environment design near metro stations on pedestrian and cyclist road segment choice: A stated choice experiment

The mismatch between the design of the micro-scale built environment around metro stations and pedestrian/cyclist preferences causes inconvenience and dissatisfaction. How to design streets near metro stations to provide a walking/biking friendly built environment is still a key question in promoting the use of metro systems. To identify which general attributes of the street-scale built environment are relevant for pedestrians/cyclists and increase walkability/cycle-ability, this paper reports the results of a stated choice experiment in which eight built environment attributes were systematically varied: street segment length, average number of building floors on both sides of the street, retail shops in frontage of streets, street crossing facilities for pedestrians/cyclists, width of sidewalks/bicycle paths, greenery, density of street lamps and crowdedness of pedestrian/cyclists to understand their influence on a road segment choice and preferences. A total of 803 respondents were recruited from Tianjin, China to complete the stated choice experiment through on-street face-to-face interviews. A multinomial logit model was estimated to unravel pedestrian/cyclist preferences using the stated choice data. The results indicate that pedestrians and cyclists have similar preferences for road segments with building lower than 6 floors, 50% retail shops in frontage, more greenery, lamps between 15 m and 30 m, more crossing facilities, wider sidewalk/bike lane and not crowded. These significant built environment attributes can be used in urban design projects with a walking/biking friendly built environment around a metro station.     

Literature review on urban transport equity in transitional China: From empirical studies to universal knowledge

Rapid urbanization and a drastic socioeconomic transition from the centrally planned system to a market system in China provide a rare opportunity of observing urban transport equity. Literature on urban transport equity in Chinese cities is increasing. However, our understanding of urban transport equity in China is dominated by individual empirical studies, and an overall picture remains absent. This paper contributes the first literature review on urban transport equity in transitional China. Two key questions are answered. They are how transport equity is understood and examined in China, and what progress can be made to obtain universal knowledge of urban transport equity. This paper addresses the two questions by engaging with recent theoretical dialogue between the political philosophy of justice and Western transport equity research. This theoretical dialogue reconceptualizes transport equity into the equitable accessibility distribution mediated by institutional architecture to achieve equality of social opportunity among people. Based on a critical review of equity-concerned China transportation research and its reconceptualization, we propose an agenda for furthering urban transport equity research in transitional China. This research agenda calls for a shifted research focus (a) on evaluating the accessibility distribution of specific transport projects and policy, (b) on unpacking the political economy of transitional urban transport governance that determines the triumph/failure of the pro-growth/pro-equity accessibility distribution in the real setting, (c) on tracking the impact of accessibility distribution on disadvantaged groups' socioeconomic status and social mobility and putting forward appropriate policy/institution to improve the accessibility of disadvantaged groups, and (d) on revealing how the emerging social trends (e.g., information technology revolution and the aging society) reshape the individual capability to move/accessibility distribution.     

Modelling dynamic dependence and risk spillover between all oil price shocks and stock market returns in the BRICS

This paper investigates the dynamic dependence and risk spillover between BRICS stock returns and different types of oil shocks, combining the Structural VAR model and time-varying copula-GARCH-based CoVaR approach. Our findings indicate that the dependence between BRICS stock returns and oil shocks is time-varying and exhibits different behaviours depending on the shock types in the oil market. In general, the shape of the CoVaRs in each country is comparatively different, depending on its special market situation and domestic policies. There is significant risk spillover from oil-specific demand shock to stock returns in all the BRICS countries. Finally, in Brazil, Russia and India, there is a significant asymmetric effect between upside and downside risk spillover based on oil aggregate demand shock and oil-specific demand shock.