Investigating the long- and short-term driving characteristics and incorporating them into car-following models
Introduction
The car-following (CF) modeling aims to describe the longitudinal interactions of vehicles on the road, and is the core component of microscopic traffic flow modeling and simulation. Over the past decades, numerous CF models have been developed. Most of these models attempted to understand the traffic characteristics by approximating vehicles’ physical dynamics in the traffic flow, and can be categorized as engineering CF models (Saifuzzaman and Zheng, 2014). In these models, all the movements are the continuous application of a single control law, whereas no emotional fluctuation or psychological changes of drivers are reflected. Just like a ‘driving machine’, drivers always drive according to the unified initial parameters.
The observable driving behavior parameters in the real world, such as speed, acceleration, trajectory, are the joint impact results of drivers’ physiological and psychological characteristics, vehicle dynamic characteristics and external traffic environment (van Lint and Calvert, 2018). However, as the primary decision maker and vehicle operator in the driving process, the human drivers were rarely studied in depth from the psychological perspective and incorporated in the CF models (Boer, 1999, Saifuzzaman and Zheng, 2014).
Recently, some studies highlighted the significance of considering human factors (HF) in CF modeling to characterize realistic CF behavior in complex driving situations. Saifuzzaman and Zheng (2014) reviewed recent developments and research needs of CF models from the perspectives of both engineering and HF. They pointed out that most studies attempt to incorporate these HF into CF models by calibrating one or two indirect parameters such as modified reaction time and estimation errors (Treiber et al., 2006a, Treiber et al., 2006b, Lindorfer et al., 2017), while other studies focus on taking into account individual phenomenon and driving errors, such as distraction (Bevrani et al., 2012, Przybyla et al., 2012, Saifuzzaman et al., 2015, van Lint and Calvert, 2018) and fatigue driving (Yang and Peng, 2010), and make efforts to enable CF models to describe these specific behaviors. There also exist some studies (Taylor et al., 2015, Kazemi and Abdollahzade, 2016, Zheng et al., 2019) investigated the driver heterogeneity, some studies (Treiber and Helbing, 2003, Treiber et al., 2006a, Treiber et al., 2006b) introduced dynamic parameters (e.g., desired speed, desired time headway, etc.) changes, and some others studies (Jiang et al., 2014, Huang et al., 2018a, Huang et al., 2018b) quantified that might change the desired time gap. However, the psychological characterization (such as short-term changes in driving behavior caused by psychological fluctuations) was largely neglected. The temporary changes, such as road-rage driving, aggressive driving, are considered as the intrinsic cause of many abnormal movements, traffic turbulences and even accidents (AAA, 2009, AAA, 2016). Some puzzling traffic flow phenomena, such as capacity drop, stop-and-go oscillations, and traffic hysteresis are also caused by fluctuating human driving behaviors (Saifuzzaman and Zheng, 2014, Sun et al., 2018, Sun et al., 2014a, Sun et al., 2014b, van Lint and Calvert, 2018). Therefore, it is essential to investigate short-term changes in driving behavior in CF modeling.
For a realistic and reliable CF model with HF, the observations/information about driver is the best research resources. Unfortunately, starting from the early days of traffic flow modelling, it has been a constant struggle for researchers to obtain such a complete set of data. In our understanding, this is also a main obstacle that prevents researchers to explicitly incorporate human factors into car following models. In the field of CF behavior, the most commonly used method is to investigate the driving style through driving behavior expressed by the vehicle. Generally, the driving styles can be categorized as timid, neutral and aggressive (Zheng et al., 2013, Bergasa et al., 2017, Martinez and Cao, 2018), and are regarded as long-term driving characteristics in this paper, as the 3 classes shown in Fig. 1(a). For each driver, the long-term driving characteristic is the intrinsic driving style under normal traffic conditions, and it is constant and unchanged. However, many studies (Ossen and Hoogendoorn, 2011, Taylor et al., 2015, Kazemi and Abdollahzade, 2016, Zheng et al., 2019) have indicated that the CF process has a time-varying nature. During the driving process, a driver could be influenced by many environmental factors, which are called stimuli, including traffic congestion, weather, other vehicles’ movement, etc. In this study, we named the time-varying nature as short-term driving characteristic, and it is a temporary change in driving behavior after external stimulus and psychological fluctuation. After the short-term changes, the driver will return to their long-term driving characteristics. The whole process is shown in Fig. 1(a). The “long-term” has fixed attribute while the “short-term” has temporary attribute, and the two jointly constitute a driver's driving attribute. But even in response to the same stimuli, different drivers can display various short-term characteristics, as shown in Fig. 1(b). Some drivers may temporarily change their driving styles, and then resume their initial and normal driving patterns (i.e. long-term driving characteristics) after different periods of short-term driving.
Given the significance of HF in CF modeling, in this paper, we firstly study drivers’ characteristics of intrinsic driving and temporarily changing. We then define the long- and short-term driving (LSTD) characteristics and incorporate them into CF models to accurately depict the human driving behavior and the subtle changes caused by external stimuli and psychological fluctuations. The main contributions are:
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Modeling concepts: through the analysis of driving behavior, we find that drivers have different LSTD characteristics which are classified into three long-term and two short-term, and totally six types of LSTD characteristics. From the field of psychology to human driving behavior, the LSTD model is proposed;
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Different findings: under the same stimulus, drivers with the same long-term characteristics also show differences in short-term changes, which is explicitly analyzed;
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Application performance improvement: the LSTD model is integrated with the two classical CF models (i.e. IDM, Gipps’ model), and demonstrates significant performance improvement as the errors decrease by 36.7% and 35.7%, respectively.
The remaining of this paper is structured as follows: Section 2 reviews the studies on the HF in driving behavior and CF models; Section 3 investigates the LSTD characteristics and proposes the LSTD model; Section 4 describes the data used in this paper; Section 5 integrates LSTD with IDM and Gipps’ model, respectively; Section 6 interprets and discusses the results; and Section 7 summarizes the main conclusions and provides suggestions for future research.
Section snippets
Literature review
Over the last decade, incorporating HF into mathematical CF models has been increasingly prevailing in the traffic flow research community (van Lint and Calvert, 2018). In this section, we summarize recent studies mainly from three perspectives: how to consider HF in driving behavior, how to model CF behavior with HF and the driver heterogeneity. In addition, this section also reviews the major findings of short-term changes in driving style from previous empirical studies.
Methodology
The objective of this study is to capture intrinsic LSTD characteristics, and incorporate these characteristics into the existing CF models. Three main challenges thus arise:
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How to describe the long-term driving characteristics and how to classify the long-term driving characteristics?
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How to interpret the dynamic short-term driving characteristics during the driving process?
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How to incorporate the long- and short-term driving characteristic into CF models?
To deal with these issues, two layers
Data preparation
The high-fidelity vehicle trajectory data selected in this study are extracted from video images of northbound traffic on I-80 in Emeryville, California, which are published in FHWA’s Next Generation Simulation (NGSIM) program (FHWA, 2008). The layout of study site is shown in Fig. 4(a). The HOV lane (lane 1) and acceleration lane (lane 6) on I-80 are excluded from the analysis because driving behaviors in these lanes are expected to be different from that in the other lanes. Given that the
Classification of LSTD characteristics
As the core component of microscopic traffic flow, the CF behavior is selected as the main driving scenario to investigate the LSTD characteristics in this study.
To investigate long-term CF behavior, the vehicles should maintain a steady CF state during the observation period without LC behavior. The stimulus we select for CF process is the LC behavior of another vehicle, more specifically, the cutting-in behavior, which is described as: when the ego vehicle is following a preceding vehicle,
Results analysis and discussions
In this section, we present the results of two LSTD models (LSTD-IDM and LSTD-Gipps) and their predecessors (IDM and Gipps’ model), and analyze the parameters for different LSTD characteristics. In addition, to validate the performance of LSTD model, we also compare the LSTD models with a recently-developed HF model, the task difficulty (TD) CF models (specifically, TDIDM and TDGipps), which were proposed by Saifuzzaman et al. (2015) using the Task-Capacity-Interface model (Fuller, 2011). To
Conclusion
Human factors (HF) are significant in driving behavior modeling, and incorporating HF into mathematical models to simulate complex driving situations has become an increasingly popular research direction. In this study, by considering the long-term personalities and short-term changes in driving behavior, we investigate and define the LSTD characteristics. The long-term driving characteristic is the intrinsic driving style under normal situations, which is divided into the aggressive, neutral
CRediT authorship contribution statement
Xiaoyun Chen: Methodology, Software, Data curation, Writing - original draft. Jian Sun: Conceptualization, Methodology, Writing - review & editing, Supervision, Project administration, Conceptualization, Methodology, Writing - review & editing, Supervision, Project administration. Zian Ma: Formal analysis, Writing - review & editing. Jie Sun: Writing - review & editing. Zuduo Zheng: Writing - review & editing.
Acknowledgement
This research was sponsored by the National Key Research and Development Program of China (2018YFB1600505), the National Natural Science Foundation of China (U1764261), and the “Shuguang Program” supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission (18SG21).
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