The future of road safety: A worldwide perspective

Estimates by the World Health Organization suggest that, on a yearly basis, road crashes kill 1.25 million people—nearly 3400 road fatalities per day—and injure up to 50 million. Traffic injuries are not equally spread over the world, however; some countries are hit harder than others, and the chance of being killed in a road crash depends on where one lives. Almost 90% of all traffic casualties occur in low- and middle-income countries (LMIC). Globally, the number of fatalities per 100,000 population (mortality rate) ranges from less than 3 to almost 40. The rate is less than 9 in high-income countries (HIC) but averages around 20 in LMIC, with the African region demonstrating the highest rate (26.6). While road safety trends have been positive in HIC over the last few decades, trends in LMIC are not telling a positive story: road fatalities are expected to increase to almost 2 million road fatalities per year by 2020.The United Nations has adopted several resolutions on road safety and proposes actions to tackle the global road safety crisis. Considering the current level of road safety to be unacceptable, the UN has taken several initiatives. One effort, the Decade of Action for Road Safety 2011–2020, has generated substantial activity around the world over the last couple of years. Furthermore, it is very encouraging that the UN included road safety in the Sustainable Development Goals that it laid out in September 2015. Road safety is part of the public health agenda and the urban development agenda. Measured in “real actions,” however, the responses so far from the overall global community and individual countries do not suggest that we are already on the right track to bringing down the death toll on roads.The future of road safety is uncertain and definitely not the same for all regions of the world. Countries with a mature road safety approach and an ambition to make further progress are expected to move in the direction of a pro-active approach: a Safe System approach. It is reported that many LMIC, meanwhile, are on the brink of designing road safety strategies and implementing action plans. The international community is willing to support LMIC, but LMIC cannot simply copy successful HIC strategies because local circumstances differ. The principles of successful HIC strategies are applicable, but the priorities and action plans should take root in and align with local conditions.     

A statistical analysis of consumer perceptions towards automated vehicles and their intended adoption

ABSTRACT

While automated vehicle (AV) development continues to progress rapidly, how the public will accept and adopt automated vehicles remains an open question. Using extensive survey data, we apply cluster analysis to better understand consumer perceptions toward potential benefits and concerns related to AVs with regard to factors influencing their AV adoption likelihood. Four market segments are identified – ‘benefits-dominated,’ ‘concerns-dominated,’ ‘uncertain,’ and ‘well-informed.’ A random parameters multinomial logit model is then estimated to identify factors influencing the probability of respondents belonging to one of these four market segments. Among other influences (such as socio-economic and current travel characteristics), it is found that ‘Millennials’ have a higher probability of belonging to the well-informed market segment, ‘Gen-Xers’ with a lower probability to the uncertain market segment, and ‘Baby Boomers’ with a higher probability to the concerns-dominated market (relative to the ‘Great Generation’). We also study the individuals’ expressed likelihood of AV adoption using separate random parameters ordered probit estimations for each of the four market segments. The substantial and statistically significant differences across each AV consumer market segment underscore the potentially large impact that different consumer demographics may have on AV adoption and the need for targeted marketing to achieve better market-penetration outcomes.     

Recent directions in automobile demand modeling

We identify seven areas in which recent research on auto demand has made significant progress. We discuss the importance of these areas in understanding and predicting auto demand and describe the methods that researchers have used to address them. Finally, we identify the issues that this research indicates require further investigation.     

Temporal stability of travelers' activity choice and home-stay duration: Some empirical evidence

This paper explores the temporal stability of activity type-choice models and models of travelers' home-stay duration. To empirically evaluate this stability, a nested logit model of activity-type choice and a proportional hazards model of home-stay duration are estimated using data from two-day travel diaries collected in the fall of 1989 and again, from the same individuals, in the fall of 1990. The results show that the models are not temporally stable over the one year time period separating the two travel-diary samples. A number of possible reasons for this instability are discussed.     

The relationship among highway geometrics,traffic-related elements and motor-vehicle accident frequencies

This research provides a demonstration of a statistical model of accident frequency that can eventually be used as part of a proactive program to allocate safety-related highway improvement funds. Negative binomial regressions of annual accident frequency on sections of principal arterials in Washington State were estimated using data from two years (1992 and 1993). In all, 31306 observations were used in model estimation (annual accident frequencies on specific sections of highway). Our estimation results isolated the effects of various highway geometric and traffic characteristics on annual accident frequency. Subsequent elasticity computations identified the relative importance of the variables included in our specifications. The findings show that the negative binomial regression used in this paper is a powerful predictive tool and one that should be increasingly applied in future accident frequency studies.     

Exploring the link between oil prices and tanker rates

Given the secular and sharp rise in oil prices over the past decade, this study analyses the impact that the spike in oil prices has on tanker rates. We investigate a dynamic model explaining spot tanker rates. The magnitude of the impact of oil prices on the shipping industry, in terms of the level and volatility of spot (voyage) under bull and bear market conditions. The West African–US Gulf Tanker Rates, West Texas Intermediate spot and 3-month futures contract, and US Weekly Petroleum Inventories are analysed using cointegration and Granger causality analysis, from 1997 through 2007, in order to examine the lead–lag relationship between oil prices and tanker freight rates. Our findings show a relationship between spot and future crude oil prices, crude oil inventories and tanker rates. The significant increase of freight rates, and the simultaneous increase in oil prices, during the recent years, provides an intriguing economic environment to identify relationships between shipping market rates and oil prices. These relationships have significant implications for the markets. At the practical level, the better understanding of the relationship between freight rates and crude oil prices can improve operational management and budget planning decisions.     

Discrete/continuous econometric models and their application to transport analysis

A wide range of transport‐related decisions involve the linking of discrete choices (e.g. of vehicle choice) and continuous choices (e.g. of vehicle use). In recent years econometricians have developed procedures for integrating such choices into a framework that is both economically and statistically sound. The literature is however somewhat technical. The objective of this paper is to provide a general overview of the basic elements of discrete/continuous econometric modelling with an emphasis on transport applications. It is hoped that such an introduction will demonstrate that the essence of the approach for the practitioner is quite straightforward and can be implemented with widely available computer software.     

Use of duration models for predicting vehicular delay at a US/Canadian border crossing

Delays caused by congestion at the US/Canadian border crossing between Washington state and British Columbia have underscored the need for some sort of intervention. One obvious congestion-mitigation measure would be to estimate delay times and then relay this information to motorists so that they could select among alternative border crossing sites, or delay their trips. This paper applies duration models to estimate vehicular delay and demonstrates the usefulness of such models as a basis for a fully automated motorist information system. The paper also explores the flexibility of duration models, in providing estimates of vehicle delay, by using alternate parametric forms and assessing prediction accuracy.     

Travelers' preferences for in-vehicle information systems: An exploratory analysis

A sample of travelers' preferences toward in-vehicle traffic information systems was undertaken and appropriate statistical models were estimated. Specifically, ordered logit and regression analyses were conducted to quantify travelers' ratings of the importance of in-vehicle system attributes, and the distance ahead that they prefer to be notified of various types of information provided by in-vehicle systems. Model estimation results show that travelers' socio-economics, habitual travel patterns, commute congestion levels and attitudes toward in-vehicle technologies are significant determinants of travelers' importance ratings and the preferred distance ahead of in-vehicle system information. These model results provide important information for both marketing and design of in-vehicle information systems.