Li-Sian Tey

Measuring driver responses at railway level crossings

Railway level crossings are amongst the most complex of road safety control systems, due to the conflicts between road vehicles and rail infrastructure, trains and train operations. Driver behaviour at railway crossings is the major collision factor. The main objective of the present paper was to evaluate the existing conventional warning devices in relation to driver behaviour. The common conventional warning devices in Australia are a stop sign (passive), flashing lights and a half boom-barrier with flashing lights (active). The data were collected using two approaches, namely: field video recordings at selected sites and a driving simulator in a laboratory. This paper describes and compares the driver response results from both the field survey and the driving simulator. The conclusion drawn is that different types of warning systems resulted in varying driver responses at crossings. The results showed that on average driver responses to passive crossings were poor when compared to active ones. The field results were consistent with the simulator results for the existing conventional warning devices and hence they may be used to calibrate the simulator for further evaluation of alternative warning systems.

Evaluating driver behavior toward innovative warning devices at railway level crossings using a driving simulator

Control at railway level crossings, using either road rumble strips or in-vehicle auditory warning devices, has the potential to improve safety. This article evaluates driver behavior of these two devices and two conventional crossing devices (flashing lights and a stop sign) using a driving simulator. Rumble strips have been seen to encourage drivers to reduce speed earlier on approach to a level crossing but did not affect stopping compliance. In-vehicle auditory warnings have produced high compliance and, overall, have produced behavior comparable to that seen for conventional flashing lights. However, the detailed design features of such devices require further development before more concrete conclusions can be drawn. Contributing factors of age, gender, speed, and types of warning devices were all found to significantly affect driver behavior at crossings. Overall, the results indicate that drivers behave differently and demonstrate higher signal compliance at actively protected crossings than at passively protected crossings for alternative and conventional warning devices.

Modelling driver behaviour towards innovative warning devices at railway level crossings

Improving safety at railway level crossings is costly and as funds are often limited, it is important to search for cost-effective, evidence-based solutions. The effect that the many existing alternative systems have on driver behaviour is not always known. This paper compares driver behaviour towards two novel warning devices (rumble strips and in-vehicle audio warning) at railway level crossings with two conventional warning devices (flashing light and stop sign). Regression models were developed to reflect drivers responses towards the four different types of devices based on data collected from a driving simulation experiment. The regression models include a binary choice model for predicting the probability of a driver stopping or driving through a railway crossing, as well as mixed regression models for predicting the moment at which a driver will produce specific behavioural responses before stopping at a crossing (e.g. initiation of accelerator release and application of foot-pedal brake). Violation results indicated the active systems produced much higher levels of driver compliance than passive devices. Contributing factors, such as age, gender, speed and types of warning devices were found significant at different approach stages to the level crossings. With the application of such behavioural models and traffic conflict techniques in microscopic simulation tools, traffic safety indicators, such as collision likelihood and time-to-collision can be estimated. From these, relative safety comparisons for the different traffic devices are derived.

Microsimulation modelling of driver behaviour towards alternative warning devices at railway level crossings

Level crossings are amongst the most complex of road safety issues, due to the addition of rail infrastructure, trains and train operations. The differences in the operational characteristics of different warning devices together with varying crossing, traffic or/and train characteristics, cause different driver behaviour at crossings. This paper compares driver behaviour towards two novel warning devices (rumble strips and in-vehicle audio warning) with two conventional warning devices (flashing light and stop sign) at railway level crossings using microsimulation modelling. Two safety performance indicators directly related to collision risks, violation and time-to-collision, were adopted. Results indicated the active systems were more effective at reducing likely collisions compared to passive devices. With the combined application of driving simulation and traffic microsimulation modelling, traffic safety performance indicators for a level crossing can be estimated. From these, relative safety comparisons for the different traffic devices are derived, or even for absolute safety evaluation with proper calibration from field investigations.

Integration of driving simulator and traffic simulation to analyse behaviour at railway crossings

The use of state-of-the-art technology to collect and analyse data has significantly improved the effectiveness of safety studies. Currently, despite the fact that there are many safety systems deployed at railway crossings, only limited research has been conducted to evaluate which of these systems is the most effective in terms of costs and safety. This paper demonstrates a way to evaluate safety at railway crossings using a twin-pronged approach: a driving simulator and traffic simulation software. A number of outputs have been observed from a driving simulator, such as driver compliance rate, vehicle speed profile, acceleration profile, initial braking position and final braking position. The compliance percentage at passive crossings (67 and 72% for a stop sign and rumble strips, respectively) has lower compliance rates compared with active crossings (97 and 93% for flashing red light and in-vehicle audible warning, respectively) at an 80 km/h approach speed. Using a statistical analysis it is shown that speed and acceleration profiles can be used to differentiate the effectiveness of active and passive crossings. These indicators are interpreted and used as input to a traffic simulation, which assists in determining which safety device is more efficient. By integrating driving simulator and traffic simulation models, this approach can be applied to evaluate and compare safety performance without the need to install costly test beds at real railway crossings.

Evaluating Safety at Railway Level Crossings with Microsimulation Modeling

Safety at railway level crossings (RLXs) is a worldwide issue that increasingly attracts the attention of relevant transport authorities, the rail industry, and the general public. The differences in the operation characteristics of varying types of warning devices, together with differences in crossing geometry, traffic, or train characteristics, leads to different driver behaviors at crossings. The aim of this study was to use traffic microsimulation modeling based on field video recording data to compare the safety performance of varying conventional RLX warning systems. The widely used microsimulation model VISSIM was modified to produce safety-related performance measures, namely, collision likelihood, delay, and queue length. The results showed that RLXs with an active warning system were safer than those with a passive sign by at least 17%. Integration of surrogate measures in conjunction with traffic simulation models determined which safety approach was more efficient for specified traffic and train volumes.

Genetic Algorithm Based Microscale Vehicle Emissions Modelling

There is a need to match emission estimations accuracy with the outputs of transport models. The overall error rate in long-term traffic forecasts resulting from strategic transport models is likely to be significant. Microsimulation models, whilst high-resolution in nature, may have similar measurement errors if they use the outputs of strategic models to obtain traffic demand predictions. At the microlevel, this paper discusses the limitations of existing emissions estimation approaches. Emission models for predicting emission pollutants other than CO2 are proposed. A genetic algorithm approach is adopted to select the predicting variables for the black box model. The approach is capable of solving combinatorial optimization problems. Overall, the emission prediction results reveal that the proposed new models outperform conventional equations in terms of accuracy and robustness.