Modeling and Prediction of the Volatility of the Freight Rate in the Roadway Freight Market of China

The trucking sector is an essential part of the logistic system in China, carrying more than 80% of its goods. The complexity of the trucking market leads to tremendous uncertainty in the market volatility. Hence, in this highly competitive and vital market, trend forecasting is extremely difficult owing to the volatility of the freight rate. Consequently, there is interest in accurately forecasting the freight volatility for truck transportation. In this study, to represent the degree of variation of a freight rate series in the trucking sector over time, we first introduce truck rate volatility (TRV). This investigation utilizes the generalized autoregressive conditional heteroskedasticity (GARCH) family of methods to estimate the dynamic time-varying TRV using the real trucking industry transaction data obtained from an online freight exchange (OFEX) platform. It explores the ability of forecasting with and without reestimation at each step of the conventional GARCH models, a neural network exponential GARCH (NN-EGARCH) model, and a traditional forecasting technique, the autoregressive integrated moving average (ARIMA) approach. The empirical results from the southwest China trucking data indicate that the asymmetric GARCH-type models capture the characteristics of the TRV better than those with Gaussian distributions and that the leverage effects are observed in the TRV. Furthermore, the NN-EGARCH performs better in in-sample forecasting than other methods, whereas ARIMA performs similarly in out-of-sample TRV forecasting with reestimation. However, the Diebold–Mariano test indicates the better forecasting ability of ARIMA than the NN-EGARCH in the out-of-sample periods. The findings of this study can benefit truckers and shippers to capture the tendency change of the market to conduct their business plan, increase their look-to-buy rate, and avoid market risk.

Kamikaze Truckers in Postwar Japan

Many Japanese workers in lower-paying positions were drawn to the growing trucking sector in the 1950s and 1960s, characterized by contingency and the thrill of risk and reward, in contrast to the stasis of lifetime employment guarantees emerging in other sectors of the economy. The gamified reward structure in trucking, however, led to a spike in traffic accidents and a backlash against “kamikaze trucks.” Only after regulations and enforcement limited the most dangerous kinds of incentives did meaningful forms of play emerge at work from the bottom up, rather than the stultified forms imposed by businesses from the top down.

Driver turnover research: exploring the missing angle with a global perspective

Purpose – The purpose of this paper is to investigate and contribute to the understanding of the critical issue of “driver turnover” in the USA long-haul trucking environment which is becoming a malaise in the transportation sector not only in the USA but also worldwide. Most importantly, it accomplishes this through an exploration of the perceptions of the “drivers” themselves regarding the external customer-base in the trucking industry. This, to the best knowledge of the authors, has not yet been done and so becomes the missing angle in focus on driver turnover research. Design/methodology/approach – This exploratory study employs a qualitative research methodology via in-depth interviews of a select sample of drivers in a field setting followed by content analysis of the responses. This methodology, by and large, is the most suitable for this type of exploration. Findings – A content analysis of the perceptions of drivers reveals several important reasons (or causes) for driver turnover. This paper elaborates on those and offers customer-centric solutions to alleviate the plight of the driver and improve overall performance in the trucking sector. Research limitations/implications – Needless to say, this exploratory research should be replicated in several other locations within the USA, other settings (e.g. trucking in extreme conditions), and other countries in order to enhance the external validity of the findings and recommendations. Practical implications – All implications of this research are practical as they have direct managerial significance. Originality/value – The value of this research lies in the fact that, to the best knowledge of the authors, this is the first study that explores the perceptions of long-haul drivers regarding the external customer-base of the trucking industry. The findings have direct implications for management in the trucking sector.

Measuring Spatial and Temporal Market Structure in a Transportation Sector: For-hire Grain Trucking on the Alberta-Saskatchewan Border in Canada

While the trucking industry across North America is now fully de-regulated, truck markets and movements are diverse enough that the level of competition in truck transportation almost certainly varies across space, commodities, and even time. Most studies of market power in transportation do not measure or account for spatial or temporal variation in levels of competition, and in addition, it is not clear to what degree such variation affects shippers. For example, there is anecdotal evidence that trucking of certain commodities in Western Canada is characterized by considerable market power that only manifests at certain times of the year. In this light, we examine both spatial aspects as well as the dynamics of rates in the medium-to-long-haul grain trucking sector in West-Central Alberta and East-Central Saskatchewan on the Canadian prairies. We attempt to characterize market power over both space and time within this regional trucking sector. This is done using a unique and detailed data set of trucking rates charged to shippers (farmers) for grain transportation to a common destination (Lloydminster, AB) from the numerous dispersed farms in the region. To frame the unique spatial aspects of this issue, we begin by using geographic information systems (GIS) software to build freight rate contours for this trucking market through space. A set of suppositions regarding the possible shape of these contours as they relate to transportation market structure is also developed. Subsequently, a subset of the data is used to conduct an econometric estimation of short-run freight rate dynamics. These latter estimates reveal evidence of less than competitive transportation markets through time and space. Ultimately, we find that market power in trucking is not persistent within this market, but we do observe uncompetitive pricing behavior at certain times of the year. Given that trucking is deregulated, the latter finding is somewhat unexpected. We suspect that this set of conditions very likely affects trucking rates across more markets and regions than the one examined here.