Community Research and Development Information Service - CORDIS

Periodic Report Summary 1 - NG-DBM (Next Generation Driving Behaviour Models)

The goal of this project is to develop dynamic driving behaviour models that explicitly account for the effects of driver characteristics in his/her decisions alongside the effects of path-plan, network topography and traffic conditions. In a novel approach, the models will be calibrated by combining experimental data collected from the University of Leeds Driving Simulator (UoLDS) and actual traffic data collected using video recordings.
Driving behaviour models, which provide mathematical representations of how the drivers make decisions involving acceleration-deceleration, lane-changing, overtaking, etc., are increasingly being used for evaluation and prediction of road safety parameters and formulating remedial measures. Reliable driving behaviour models are also critical for accurate prediction of congestion levels in microscopic traffic simulation tools and analyses of emissions. While research in human factors and safety has confirmed that driving behaviour is significantly affected by drivers’ characteristics (such as age, gender, education level and experience); individual traits (such as aggressiveness, emotional instability) and their mental states/mood (such as anger, tension, depression, cognitive workload and distraction), the existing driving behaviour models being used in the leading microscopic simulators capture only the effects of the network and traffic conditions and tend to overlook the effect of these driver-specific factors on the decision framework. Ignoring the underlying heterogeneity in the decision making of different drivers as well as the same driver in different contexts can lead to significant noise because of the models’ structural inability to uncover underlying causal mechanisms. Implementation of models failing to capture the full diversity of driving behaviour in traffic micro-simulation tools can lead to unrealistic traffic flow characteristics and incorrect representation of congestion.
The proposed project, therefore, aims to address the current research gaps in driving behaviour modelling and develop next generation driving behaviour models which account for driver demographics, attitudes/traits and moods as well as effects of path-plan, network topography and traffic conditions in a single model framework.
Data and Methodology:
The project relies on data from two different settings:
(i) experimental data collected from the University of Leeds Driving Simulator (UoLDS) and
(ii) detailed trajectory data collected from the field.
UoLDS is one of the most advanced driving simulators in Europe. It has a Jaguar S-type vehicle cab with all driver controls fully operational. The vehicle’s internal Control Area Network (CAN) is used to transmit driver control information between the Jaguar and a network of nine high-performance computers that manages the complete simulation. Control feedback is generated so that the driver seated in the cab feels, sees and hears an appropriate simulation of the driving environment. As part of this project, 40 participant drivers are asked to drive a motorway and an urban scenario. In each case, the detailed driving decisions are recorded alongside the speed and positions of the ambient simulated traffic in a fully controlled setting. Given the aim to capture the effect of the mental state of the driver on their observed decisions, the driving scenarios are designed to have a variation in levels of workload and stress levels. The physiological indicators of the mental state of the driver (e.g. skin conductance, blood pressure, heart rate variability, eye blinking rate, eye gaze, etc.) are collected using non-intrusive sensors (e.g. wearable wristbands and dashboard cameras) alongside their socio-demographics, experience levels and risk-taking attitudes. The data collection is currently underway and expected to be completed by May 2017. The preliminary findings of the research have been accepted for presentation at the International Choice Modelling Conference (C1: Paschalidis et al. 2017).
While the simulator data offers the opportunity to fully control the driving scenarios and collect detailed data from the participant drivers, such data may lack behavioural realism. This prompted us to investigate the transferability of the car-following models between UoLDS and two comparable real-life traffic motorway scenarios, one from the UK (Motorway 1 near Leeds) and the other one from the US (Interstate 80 near California). In this regard, stimulus-response based car-following models have been estimated using the Maximum Likelihood Estimation technique and the transferability of the models are investigated using statistical tests of parameter equivalence and Transferability Test Statistics. Estimation results indicate transferability in the model level but not fully in the parameter level for both pairs of scenarios. The findings of this research have been presented at the World Conference of the Transport Research Society (WCTRS), Shanghai and the Annual Meeting of the Transportation Research Board. It has also been recently accepted for publication in the Transportation Research Record (Papadimitriou and Choudhury 2017).
Research is underway now to develop models estimated combining both data sources.
The Next Generation Driving Behaviour Models will lead to the improved representation of reality in microsimulation tools. Improving the driving behaviour algorithms will ultimately lead to more reliable and valid transportation decisions, which is critical in the current environment of both shrinking transportation budgets and growing demand for accountable and efficient transportation investments.
Journal paper:
1. Papadimitriou S. and Choudhury C. (2017) Transferability of Car-Following Models between Driving Simulator and Field Traffic, Transportation Research Record (forthcoming)
2. Choudhury CF; Islam MM (2016) Modelling acceleration decisions in traffic streams with weak lane discipline: A latent leader approach, Transportation Research Part C: Emerging Technologies, 67, pp.214-226.
Conference papers:
C1: Paschalidis E., Choudhury C. and Hess S. (2017) Investigating the effects of stress on choices: evidence from gap acceptance models, 5th International Choice Modelling Conference, Cape Town (3-5 April)
C2. Papadimitriou S. and Choudhury C. (2017) Transferability of car-following models between Driving Simulator and Field Traffic, Proceedings of the 96th Annual Meeting of the Transportation Research Board, Washington DC, USA.
C3: Paschalidis E., Choudhury C. and Hess S. (2016) Developing acceleration models combining driving simulator and real traffic data, 5th Symposium of the European Association for Research in Transportation (hEART), Delft, 13-16 September 2016.
C4. Papadimitriou S. and Choudhury C. (2017) Investigating the relative performances of driving behaviour models estimated from different data sources, 14th Annual meeting of the World Conference of the Transport Research Society (WCTRS), Shanghai, 7-11 July 2016.

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United Kingdom


Life Sciences
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