Drivers’ speed behaviour in real and simulated urban roads – A validation study

https://doi.org/10.1016/j.trf.2017.06.001Get rights and content

Highlights

  • A behavioural validation study of a driving simulator is presented.

  • Speed values collected in field and during simulation experiments were compared.

  • The driving simulator induced similar behaviours in similar driving environments.

  • The regression technique has revealed to be efficient for the simulator validation.

  • The driving experience influences the speed recorded in the virtual environment.

Abstract

Traffic accidents and injuries constitute a growing problem for Europe’s urban transportation system. Driving simulators can be active support tools in the design process of urban driving environments, provided that they are appropriately validated. The aim of this study was to establish the behavioural validity, in relative and absolute terms, of the motion-base driving simulator of the Road Safety and Accident Reconstruction Laboratory (LaSIS) of the University of Florence in order to use it to evaluate the effectiveness of the urban road safety treatment design process.

The research was conducted by comparing the driving speed collected in field with those recorded in the 3D virtual reality experiments through conventional and integrative statistical methods. Speeds were recorded at twenty-one measurement sites located in homogeneous road sections characterized by a less or more demanding driving environment; thirty-four participants drove the virtual scenario which reproduced the real situation.

The results of the comparative and conventional statistical analysis established the relative validity and also revealed that absolute validity was obtained in the measurement sites in which physical constraints are imposed by engineering treatments. The integrative regression analysis confirms these outcomes and showed that the simulation system is a reliable predictor of the real speed data also in absolute terms. It was also found that the driving experience levels significantly affect the speed adopted by the motorists in the virtual environment.

These findings support the use of the driving simulator as a powerful approach to predict of the safety effectiveness of design solutions in urban areas, thus allowing important cost savings.

Introduction

Urban areas are very complex driving environments in which different types of road users compete continually. The constant interaction between motorized traffic and vulnerable road users creates serious implication for traffic safety.

In the last few years the problem of road crashes in urban areas has become increasingly relevant. In 2013, 26,090 people were killed in road accident throughout the European Union (EU),2 9923 of whom were killed in accidents on urban roads in the EU. This corresponds to 38% of the road fatalities in 2013. Although the total number of fatalities within urban areas decreased since 2005, the proportion has slightly increased (from 35% to 38%) (Traffic Safety Basic Facts, 2016). In Italy the situation is even more serious. According to Italian data published by ACI-ISTAT in 2014 (ACI-ISTAT, 2015), the majority of fatal and injury accidents occurred on urban roads: 133,598 accidents, corresponding 75.5% of the total accidents with injured and/or fatalities occurred in the whole Italian road network. These accidents caused the death of 1505 people, corresponding 44.5% of the total number of victims of road traffic accidents on the Italian road network (ACI-ISTAT, 2015).

Speeding has been identified as a key risk factor in traffic accidents on urban roads, influencing both the frequency and the severity of the injuries resulting from crashes.

Many safety measures have been proposed to tackle this problem whose effectiveness depends on the characteristics of each specific urban environment and their interaction with the driver’s capabilities and expectations.

The use of an interdisciplinary approach based on driving simulations is a promising method to study these interactions, to check the safety impact of the proposed interventions and to identify the most promising design alternatives.

The use of driving simulator provides important advantages in terms of experimental control, capabilities effectiveness, cost, ease of data collection and safety of the study implementation. However, it presents also limitations mainly related to the reliability of the data acquired. For this reason, any driving simulator study should be preceded by questioning whether the simulator is sufficiently valid for the task or ability to be investigated (Kaptein, Theeuwes, & Van Der Horst, 1996).

A wide range of driving parameters, environments and behaviours have been examined in driving simulator’s validation studies and generally a good correspondence between performance in the real world and during driving simulator experiments was obtained. Speed is the most commonly examined parameter in safety studies concerning road tunnels (Cao et al., 2015, Törnros, 1998), rural roads (Bella, 2008, Bittner et al., 2002, Godley et al., 2002, Santos et al., 2005), highway work zones (Bella, 2005, Bham et al., 2014, McAvoy et al., 2007), signalized intersections (Yan, Abdel-Aty, Radwan, Wang, & Chilakapati, 2008) and speed management measures (Godley et al., 2002, Riemersma et al., 1990). Few studies considered other indicators such as vehicle’s lateral position (Blana and Golias, 2002, Wade and Hammond, 1998), driver’s reaction time (Brown et al., 2007, Engen, 2008, Hoffman et al., 2002), time gap (Abe and Richardson, 2006, Boer et al., 2005) and driving errors (Meuleners & Fraser, 2015). The validity of the simulation tools for assessing driving behaviour of specific driver groups, such as young drivers (Brown et al., 2007, De Winter et al., 2009, Hoffman et al., 2002, Mayhew et al., 2011, Shechtman et al., 2009), older drivers (Hekamies-Blomqvist et al., 2001, Lee, Cameron, Lee, 2003, Lee, Lee, Cameron, 2003) and subjects with temporary or permanent disabilities (Lee et al., 2007, Lew et al., 2005) has been investigated also.

Few studies investigated the possible responsibility the drivers’ characteristics in differentiating the results of the real world and the driving simulator tests. The driving experience was found to be the most crucial driver characteristic (Gemou & Bekiaris, 2014) and validation study evaluation of driving errors showed that experienced drivers had the fewest errors compared to the beginner drivers (Mayhew et al., 2011).

Gender and age also affect the behavioural validity of the driving simulator. Reed and Green (1999) found that the performance difference in terms of speed variability between young and old participants was significantly larger in simulation experiments than in the real world. Moreover, they found that the behavioural validity of lane keeping measures depends on both age and gender. In fixed-base simulators, participants older than 60 years produced a significantly larger standard deviation of the lateral position, compared to the 20–30 year-old participants, whereas in the real study no gender or age effects were observed. Additionally, they found a significant interaction between age and gender in the simulator, older females performing significantly poorer than other groups.

The validation study carried out by Yan et al. (2008) revealed that driver age and gender significantly influence the operating speed in simulator experiments. They found that the mean speed registered with male participants was slightly higher than that with females and that, after the 20–24 age group, a decreasing trend in speed with increasing age was observed. Klüver, Herrigel, Heinrich, Schöner, and Hecht (2016) found no or only marginal age and gender differences between the real study and moving-base simulators. However, they found a significant interaction between age and gender in fixed-base simulators. Specifically, older participants showed a considerably higher headway and lane keeping variability, whereas the performance of younger participants was roughly the same as in the real world.

Section snippets

Framework for driving simulator validation

Defining the validity of a driving simulator is a complicated and multidisciplinary task. The issue of transferability of results acquired on driving simulator experiments has been a concern for at least 35 years. The quality of a driving simulator is normally defined in terms of physical and behavioural validity (Blaauw, 1982). The first, often referred to as simulator fidelity, measures the degree to which the simulator reproduces the sensory stimuli present in a real driving situation, and

Objective and methodology

The paper is referred to a preliminary activity of the SCUP (Speed Control in Urban Projects) research project conducted at the Department of Civil and Environmental Engineering (DICeA) of the University of Florence, aiming at assessing the safety impact of road design solutions on urban roads by means of driving simulator experiments.

In urban driving environments, speed reduction measures are often the most promising solution, especially on the roads encompassing both the mobility and the

Urban road characteristics

The speed monitoring activities were performed along Via Pistoiese, near Florence (Italy), which is two-lanes urban penetration collector linking the suburban areas located in the west boundaries of the Firenze Municipality to the city centre. Along both sides of Via Pistoiese two crowded residential and commercial districts are present, burdening the road with many intersections, pedestrian crossings and driveways. 3 intersections are signalized and n. 7 intersections are un-signalized. A

LaSIS driving simulator

The LaSIS Driving Simulator is a full scale, dynamic simulator, with a complete Lancia Ypsilon cabin installed on a 6 axes Stewart Stewart’s platform, capable to reproduce all the sensorial stimuli typical of driving. It is the model AS 1200, supplied by AutoSim (Norway), and it is running on the software SimWorld version 2.8.2. The vehicle interior is identical to the commercial version and includes all commands normally available in such kind of cars, with steering wheel with force feed-back.

Results and discussion

The speed values measured in field and those registered during the simulation experiments have been compared and statistically analyzed to ascertain the behavioural validity of the driving simulation results:

  • in relative terms (comparative analysis);

  • in absolute terms (statistical analysis).

The impact of the drivers’ characteristics on the behavioural validity of the simulation results was also analyzed.

Conclusion

A validation study has been performed to assess the behavioural validity of the LaSIS Driving Simulator when used to evaluate the speed related safety measures in urban roads. The relative and the absolute behavioural fidelity of the driving simulator were considered in the study.

The comparison of speed data measured in field with those registered at the simulator in the same sites allowed to find out a very good correspondence between the performance in the real world and in the virtual

References (43)

  • X. Yan et al.

    Validating a driving simulator using surrogate safety measures

    Accident Analysis and Prevention

    (2008)
  • ACI-ISTAT (2015). Incidenti stradali in Italia anno...
  • Bella, F. (2005). Validation of a driving simulator for work zone design. Transportation Research Record: Journal of...
  • G.H. Bham et al.

    Driving simulator validation of driver behavior with limited safe vantage points for data collection in work zones

    Journal of Safety Research

    (2014)
  • Bittner Jr., A. C., Simsek, O., Levison, W. H., & Campbell, J. L. (2002). On-road versus simulator data in driver model...
  • G. Blaauw

    Driving experience and task demands in simulator and instrumented car: A validation study

    Human Factors: The Journal of the Human Factors and Ergonomics Society

    (1982)
  • E. Blana et al.

    Difference between vehicle lateral displacement on the road and in a fixed-simulator

    Human Factors

    (2002)
  • Boer, E. R., Girshik, A. R., Yamamura, T., Kuge, N. (2000). Experiencing the same road twice: A driver comparison...
  • Boer, E. R., Ward, N. J., Manser, M. P., Yamamura, T., & Kuge, N. (2005). Driver performance assessment with a car...
  • Brown, T., Dow, B., Marshall, D., & Allen, S. (2007). Validation of stopping and turning behavior for novice drivers in...
  • Cao, C., Wang, J., & Luo, Y. (2015). Driving simulator validation for research on driving behavior at entrance of urban...
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