The use of navigation systems in naturalistic driving.

OBJECTIVE: In this study, we assessed the use of portable navigation systems in everyday driving by applying in-vehicle naturalistic driving. METHOD: Experienced users of navigation systems, 7 females and 14 males, were provided with a specially equipped vehicle for approximately 1 month. Their trips were recorded using 4 cameras, Global Positioning System (GPS) data, and other sensor data. The drivers' navigation system use data were coded from the video recordings, which showed how often and for how long the system was activated and how often and for how long a driver operated the system. RESULTS: The system was activated for 23% of trips, predominantly on longer and unique trips. Analyses of the percentage of time for which the speed limit was exceeded showed no evidence of differences between trips for which the navigation system was used or not used. On trips for which the navigation system was activated, participants spent about 5% of trip time interacting with the device. About 40% of interacting behavior took place in the first 10% of the trip time, and about 35% took place while the car was standing still or moving at a very low speed; that is, 0-10 km/h. CONCLUSION: These results shed light on how and when drivers use navigation systems. They suggest that although drivers regulate their use of such systems to some extent, they often perform risky tasks while driving.

Speed choice and mental workload of elderly cyclists on e-bikes in simple and complex traffic situations: a field experiment.

To study the speed choice and mental workload of elderly cyclists on electrical assisted bicycles (e-bikes) in simple and complex traffic situations compared to these on conventional bicycles, a field experiment was conducted using two instrumented bicycles. These bicycles were identical except for the electric pedal support system. Two groups were compared: elderly cyclists (65 years of age and older) and a reference group of cyclists in middle adulthood (between 30 and 45 years of age). Participants rode a fixed route with a length of approximately 3.5 km on both bicycles in counterbalanced order. The route consisted of secluded bicycle paths and roads in a residential area where cyclist have to share the road with motorized traffic. The straight sections on secluded bicycle paths were classified as simple traffic situations and the intersections in the residential area where participants had to turn left, as complex traffic situations. Speed and mental workload were measured. For the assessment of mental workload the peripheral detection task (PDT) was applied. In simple traffic situations the elderly cyclists rode an average 3.6 km/h faster on the e-bike than on the conventional bicycle. However, in complex traffic situations they rode an average only 1.7 km/h faster on the e-bike than on the conventional bicycle. Except for the fact that the cyclists in middle adulthood rode an average approximately 2.6 km/h faster on both bicycle types and in both traffic conditions, their speed patterns were very similar. The speed of the elderly cyclists on an e-bike was approximately the speed of the cyclists in middle adulthood on a conventional bicycle. For the elderly cyclist and the cyclists in middle adulthood, mental workload did not differ between bicycle type. For both groups, the mental workload was higher in complex traffic situations than in simple traffic situations. Mental workload of the elderly cyclists was somewhat higher than the mental workload of the cyclists in middle adulthood. The relatively high speed of the elderly cyclists on e-bikes in complex traffic situations and their relatively high mental workload in these situations may increase the accident risk of elderly cyclist when they ride on an e-bike.

A road safety performance indicator for vehicle fleet compatibility.

This paper discusses the development and the application of a safety performance indicator which measures the intrinsic safety of a country's vehicle fleet related to fleet composition. The indicator takes into account both the 'relative severity' of individual collisions between different vehicle types, and the share of those vehicle types within a country's fleet. The relative severity is a measure for the personal damage that can be expected from a collision between two vehicles of any type, relative to that of a collision between passenger cars. It is shown how this number can be calculated using vehicle mass only. A sensitivity analysis is performed to study the dependence of the indicator on parameter values and basic assumptions made. The indicator is easy to apply and satisfies the requirements for appropriate safety performance indicators. It was developed in such a way that it specifically scores the intrinsic safety of a fleet due to its composition, without being influenced by other factors, like helmet wearing. For the sake of simplicity, and since the required data is available throughout Europe, the indicator was applied to the relative share of three of the main vehicle types: passenger cars, heavy goods vehicles and motorcycles. Using the vehicle fleet data from 13EU Member States and Norway, the indicator was used to rank the countries' safety performance. The UK was found to perform best in terms of its fleet composition (value is 1.07), while Greece has the worst performance with the highest indicator value (1.41).

The value of site-based observations complementary to naturalistic driving observations: a pilot study on the right turn manoeuvre.

Naturalistic driving studies are increasingly applied in different shapes and sizes. The European project PROLOGUE has investigated the value and feasibility of a large-scale naturalistic driving study in Europe. Within PROLOGUE several pilot studies have been conducted in different countries. The Dutch field trial investigated the value and feasibility of adding site-based observations to in-vehicle observations. In this trial, one intersection was equipped with cameras for site-based observation. Additionally eight cars were equipped of drivers crossing this intersection regularly. On this small scale, combining the two observation methods turned out to be technically feasible. It was possible to recognise the instrumented vehicles in the site-based video data, to match cases from the different observations and the speed measures from the separate studies appeared to be similar. The value of combining these two observation methods lies in the possibility to enrich the data from one study with complementary data from the other study. The study illustrated that each type of observation has its unique values. From in-vehicle data it is possible to look in detail at the driving behaviour of the participants over time and in different situations. The site-based study offers information about the position and speed of other road users surrounding the participant's vehicle, including vulnerable road users such as cyclists and pedestrians. Two values of adding site-based observations to in-vehicle observations were identified: to obtain more in depth understanding and to relate the behaviour of participants of the naturalistic driving study to behaviour of the full population of drivers (non-participants). For a future (large-scale) naturalistic driving study two research topics are identified that could benefit from these complementary observations: driving behaviour in relation to specific infrastructure and the interaction between drivers and vulnerable road users.