Road User Interactions: Patterns of Road Use and Perception of Driving Risk

The goal of the Road User Interactions research programme is a better understanding of the human factors of our road transport system: road user demographics, risk perceptions of road users, and the driving attitudes of various road user groups. Our analysis of the 1989 and 1999 New Zealand Household Travel Surveys identified several fundamental road user differences and consistent demographic trends over the past 10 years. The driver characteristics of gender, age, and area of residence (urban, secondary urban, and rural) are the demographic factors which most clearly differentiate New Zealand road user groups. Analysis of the patterns of road use suggests that, although these road user groups do drive at distinctly different times, there are periods of conflict which are also associated with the greatest crash risk for these drivers. Our analysis of a sample of road user groups in Hamilton, Auckland, Gisborne, New Plymouth, and Palmerston North found significant differences in their perceptions of risk and driving behaviours. Rural drivers and women drivers rated a range of driving situations as having greater risk than did the other road user groups, and they rated the high risk scenarios as being much riskier. Men indicated the greatest willingness to accept the risk in driving situations and rated their own driving skill as higher. Older drivers also rated driving situations as having higher risk, and young drivers generally rated low risk situations much lower than other drivers. In the survey of driving behaviour, young men in our sample reported very high levels of violations and aggressive violations. The male drivers’ rates of violations and aggressive violations were significantly higher than the women drivers’ and the number of both decreased significantly with age. Finally, inspection of crash data show that young drivers’ and older drivers’ crashes have some characteristics in common; both groups have a disproportionate number of crossing, turning, and manoeuvring crashes at intersections in the mid-afternoon

Taking the High Road to Canalside: How Community Activism Has Shaped Buffalo’s Waterfront

This policy brief was drafted by Michelle Zhao, the 2017 Cornell High Road Fellow at Partnership for the Public Good. It documents the efforts of local advocates to bring “High Road” economic development to Canalside, to advance community benefits over corporate control. After setting out the historical context of Canalside and the fight that won its preservation, the brief focuses on the period of 2004 to 2015. It details the proposal to bring a Bass Pro Shop to the Inner Harbor, the leadership and governance of the Erie Canal Harbor Development Corporation (ECHDC), and the campaign for a Community Benefits Agreement for the waterfront. This campaign led to three years of negotiations with the ECHDC, leading to the formal adoption of a consensus document, “A Public Statement of Principles for High Road Development of Buffalo’s Waterfront,” to guide development practices in the future. The community activists who took the High Road to Canalside succeeded in changing the way that economic development is understood and practiced in Buffalo. The author extends her thanks to the organizers, advocates, and officials who shared their stories and insights for this brief

Sustainability of energy management of transport sector in Hungary

The high ratio of road transportation in CO2 emission caused by humanity made the research of relation between the road transportation and climate change reasonable. There is a justifiable demand by the society to moderate the environmental impacts caused by road transportation. Our aim is in this article to monetarise the impact caused by the road transport sector in Hungary. First we analysed the fuel consumption in Hungary then tried to estimate the carbon dioxide emission from these time series. According to the recent finalised EC founded research projects we tried to monetarise the impact of the transport sector on the climate. This is the monetarised value which has been paid by the society and not by the sector

High-Resolution Road Vehicle Collision Prediction for the City of Montreal

Road accidents are an important issue of our modern societies, responsible for millions of deaths and injuries every year in the world. In Quebec only, in 2018, road accidents are responsible for 359 deaths and 33 thousands of injuries. In this paper, we show how one can leverage open datasets of a city like Montreal, Canada, to create high-resolution accident prediction models, using big data analytics. Compared to other studies in road accident prediction, we have a much higher prediction resolution, i.e., our models predict the occurrence of an accident within an hour, on road segments defined by intersections. Such models could be used in the context of road accident prevention, but also to identify key factors that can lead to a road accident, and consequently, help elaborate new policies. We tested various machine learning methods to deal with the severe class imbalance inherent to accident prediction problems. In particular, we implemented the Balanced Random Forest algorithm, a variant of the Random Forest machine learning algorithm in Apache Spark. Interestingly, we found that in our case, Balanced Random Forest does not perform significantly better than Random Forest. Experimental results show that 85% of road vehicle collisions are detected by our model with a false positive rate of 13%. The examples identified as positive are likely to correspond to high-risk situations. In addition, we identify the most important predictors of vehicle collisions for the area of Montreal: the count of accidents on the same road segment during previous years, the temperature, the day of the year, the hour and the visibility

Legal Requirements for Admission to Public Schools

Advanced driver assistance systems for heavy duty vehicles, such as lookahead cruise and gearshift controllers, rely on high quality map data. Current digital maps do not offer the required level of road grade information. This contribution presents an algorithm for on-board road grade estimation based on fusion of GPS and vehicle sensor data with measurements from previous runs over the same road segment. An incremental update scheme is utilized to ensure that data storage requirements are independent of the number of measurement runs. Results of the implemented system based on six traversals of a known road with three different vehicles are presented.QC 20120216</p