Driver's Decision-making Process Research Articles

Injury severity analysis of rural vehicle crashes involving familiar and unfamiliar drivers

Familiar and unfamiliar drivers may exhibit different behaviours in response to the road environment. Overall familiarity with the road environment is a human factor believed to play a role in road crash injury severity due to its effect on a driver's decision-making process, reaction time, etc. Hence, there is a need to separately analyse familiar and unfamiliar drivers regarding the injury severity of crashes. Using a six-year database of 30,481 rural two-vehicle crashes in Guilan province, Iran, this research first defined four categories of crashes, reflecting various levels of the involved drivers’ familiarity with the environment (72% of drivers were from the same vs. 28% from a different province). Next, the injury severity of crashes in each familiarity crash category was analysed using both non-parametric (classification and regression trees) and parametric (logistic regression) methods. When both crash parties were unfamiliar, several results are different compared to when both parties were familiar or when ignoring driver familiarity. For instance, young at-fault drivers increased the injury severity of crashes if they were unfamiliar, while they decreased the crash severity if they were familiar. Also, crashes in winter tended to be more severe when one or especially both crash parties were unfamiliar, but winter crashes were less severe when both drivers were familiar or when driver familiarity was ignored. Overall, when both drivers were familiar, 63% of crashes were injury/fatal; however, when both drivers were unfamiliar, only 31% of crashes involved an injury or fatality.

The effect of traffic light spacing and signal congruency on drivers’ responses at urban intersections

Traffic lights are critical in regulating traffic flow and modulate the level of service and road user safety. As suggested by studies conducted with pedestrians, traffic light spacing and signal congruency could also impact a driver's decision-making process. However, requirements related to designing signalized intersections do not always consider the spacing between two consecutive traffic lights or the congruency of the information displayed. Here, using a classic traffic psychology paradigm, we developed a hybrid Go/No-go Flanker PC-based task to explore how traffic light spacing and signal congruency modulate drivers’ decisions in urban intersections. Real images of road intersections were edited to reproduce two specific conditions between traffic lights. Specifically, we manipulated both spacing (short vs. long) and congruency (congruent [e.g., red-red/green-green steady light] vs. incongruent [e.g., red-green/green-red steady light]). We found that incongruent information, displayed on short spacing traffic signals, delayed drivers’ responses without being detrimental to their decision-making processes. The results of this exploratory study could offer guidance to transportation engineers to simplify traffic light information readability and increase drivers’ awareness of traffic conditions and road safety.

How drivers respond to visual vs. auditory information in advisory traffic information systems

ABSTRACT To date, many efforts have been made to explore how to support driver's decision-making process with advisory information. Previous studies mainly focus on a single modality, e.g. the visual, auditory or haptic modality. In contrast, this study compares data from two simulator studies with 50 participants in total, where the visual vs. the auditory modality was used to present the same type of advisory traffic information under the same driving scenarios. Hereby we compare the effect of these two modalities on drivers' responses and driving performance. Our findings indicate that modality influences the drivers' behaviour patterns significantly. Visual information helps drivers to drive more accurately and efficiently, whereas auditory information supports quicker responses. This suggests that there are potential benefits in applying both modalities in tandem, as they complement each other. Correspondingly, we present several design recommendations on Advisory Traffic Information Systems.

Data-Driven Order Assignment for Last Mile Delivery

We study how delivery data can be applied to improve the on-time performance of last mile delivery services. Motivated by the delivery operations and data of a food delivery service provider, we discuss a framework that integrates the travel time predictors with the order assignment optimization. Such integration enables us to capture the driver's routing behavior in practice, where the driver's decision-making process is often unobservable or intricate to model. Focusing on the order assignment problem as an example, we discuss the classes of tractable predictors and prediction models that are highly compatible with the existing stochastic and robust optimization tools. We further provide reformulations of the integrated models, which can be efficiently solved with the proposed branch-and-price algorithm. Moreover, we propose two simple heuristics for the multiperiod order assignment problem, which are built upon the single-period solutions. Using the delivery data, our numerical experiments on a real-world application not only demonstrate the superior performance of our proposed order assignment models with travel time predictors, but also highlight the importance of learning the behavioral aspects from the operational data. We find that large sample size does not necessarily compensate for the misspecification of the driver's routing behavior

Driver's Behavior and Decision-Making Optimization Model in Mixed Traffic Environment

Driving process is an information treating procedure going on unceasingly. It is very important for the research of traffic flow theory, to study on drivers' information processing pattern in mixed traffic environment. In this paper, bicycle is regarded as a kind of information source to vehicle drivers; the “conflict point method” is brought forward to analyze the influence of bicycles on driving behavior. The “conflict” is studied to be translated into a special kind of car-following or lane-changing process. Furthermore, the computer clocked scan step length is dropped to 0.1 s, in order to scan and analyze the dynamic (static) information which influences driving behavior in a more exact way. The driver's decision-making process is described through information fusion based on duality contrast and fuzzy optimization theory. The model test and verification show that the simulation results with the “conflict point method” and the field data are consistent basically. It is feasible to imitate driving behavior and the driver information fusion process with the proposed methods. Decision-making optimized process can be described more accurately through computer precision clocked scan strategy. The study in this paper can provide the foundation for further research of multiresource information fusion process of driving behavior.

Logit-Based Merging Behavior Model for Uncontrolled Intersections in China

AbstractIt has been previously documented that under the condition of crossing an uncontrolled intersection, the decision-making process of drivers is rather complex and highly interactive: they need to decide about the timing, direction, and strategy to complete the required maneuver to avoid potential conflicts. Given the significant impact of this behavior on intersection safety and capacity, we model the merging behavior of a straight-moving vehicle facing a turning vehicle at an uncontrolled intersection in China. We expect that this model can predict the probability that a straight-moving driver has a preemptive status (i.e., arrive at the merging point before the turning vehicle). The factors which determine the drivers’ decision behavior are analyzed, and then we propose a logistic regression model using a dataset collected from an uncontrolled intersection in Kunming, China. Our model reveals that the speed difference, the distance between the two vehicles, and the distance of the turning vehicle...

Development of route choice behavior model using linkage of neural network and genetic algorithm with trip information

The decision-making behaviors of drivers have been clarified, as various researches on traffic behavior have been conducted until today. However, existing models set limits to examine non-linearity, ambiguity and the errors, which are occurred by variety of space-time contained in human conscious structure and process of decision-making. In particular, it is true that the route choice behavior of drivers is affected by external factors, such as past experience and change in diverse road environment information. However, taking into account the fact that various traffic problems are not being solved, it is proven that there are limitations in clearly describing traffic behavior. Accordingly, in order to construct a model that considers obscurity and non-linearity in the decision-making process of drivers, a model through neural network linkage and genetic algorithm was proposed in this study. In addition, this study was focused on proving the effectiveness of neural network theory and genetic algorithm, and studying the appropriateness of applying them into the field of transportation.

A Car-Following Model Based on a Self-Learning Neuro-Fuzzy Controller

This paper focuses on a car-following model that can be used in traffic simulation. A new approach is proposed for emulating the driver's decision-making process. The approach uses fuzzy reasoning for dealing with uncertainty and blending behaviors induced by simultaneous goals. An original algorithm is implemented in the system for imitating the driver's self-learning from actual driving experience and for mimicking the driver's braking and accelerating maneuvers.

Examination of the use of fuzzy sets to describe relative speed perception

In recent years a range of new methods have been proposed with which to describe and evaluate driver behaviour. One such method is that of fuzzy logic, where variables used in the driver decision-making process may be described linguistically, allowing a quantifiable degree of uncertainty to be introduced. This paper explores the use of such a formalism to describe the driver perception of ‘closing speed’ between two vehicles engaged in ‘car-following’ on a motorway, and by using data from an instrumented vehicle experiment, it tests a number of models using relative speed, visual angle and the time to collision. Several of these models fit the data quite well, and there is both a small positive perception bias present and a number of reversals in sign judgement. Additionally, a brief examination is made of potential variations on the methodology that may both make data collection easier and/or allow a ‘more fuzzy’ representation to be made.