Headway Distance Research Articles

Effect of Distracting Factors on Driving Performance: A Review

The number of traffic accidents because of distracted driving is increasing rapidly worldwide. Hence, the main objective of the present study is to review the effects of different distracting factors on driving performance indicators. Distracting factors considered in this study are roadside advertisements (billboards), mobile use, in-built vehicle systems, and sleepiness; and driving performance indicators are lane deviation, reaction time, and speed variation. Studies from existing literature reveal that all the distracting factors distract drivers from forwarding roadways in many ways. The location and content displayed on the billboard and the use of mobile phones increase reaction time. However, the former decreases the driver’s ability to control the vehicle, and the latter increases the speed variation and reduces lane-keeping capacity. Lateral vehicle control and reaction time are compromised when drivers engage in searching for songs or videos on music players. When sleepiness occurs, drivers exhibit a higher standard deviation of speed and a decreased headway distance. Nevertheless, most of the studies in this area are carried out in developed countries like the USA and European countries. Therefore, a detailed study and further research in developing countries like India, where activities like installing billboards and mobile phone use are increasing day by day due to the rapid urbanization of major cities in the country, are quite essential. Doi: 10.28991/CEJ-2022-08-02-014 Full Text: PDF

Experimental analyses of step extent and contact buffer in pedestrian dynamics

This study aims to quantify and develop a deeper understanding of the parameters that underpin the development of a new, predictive, microscopic model of pedestrian movement with the potential to accurately reflect the complexity of flow dynamics now and into the future. It presents the results and analyses of two single file experiments designed to quantify the physical space taken up by the extent of a person’s stepping movement (maximum step extent) and the minimum distance between points of inter-person contact (contact buffer) across a range of walking speeds.The experiments successfully used high-resolution optical motion capture and enhanced video analysis to quantify the dynamic changes in gait and spatial parameters, which were manifested as overlapping steps, and changes to step extent, step length, step frequency, and contact distance. The sum of the step extent and contact buffer, at different speeds, was found to be within a few centimetres of the inter-person distance (headway), leading to the conclusion that these parameters are therefore key components for the derivation of inter-person spacing and, hence, overall crowd movement. The work informs the longer term aim of developing the mathematical model which has the potential to include pedestrian demographics, walking ability and cognitive capabilities.

Socially Compatible Control Design of Automated Vehicle in Mixed Traffic

In the car-following scenarios, automated vehicles (AVs) usually plan motions without considering the impacts of their actions on the following human drivers. This letter aims to leverage such impacts to plan more efficient and socially desirable AV behaviors in human-AV interactions. Specifically, we introduce a socially compatible control design for the AV that benefits mixed traffic in the car-following scenarios. The proposed design enables the altruistic AV in human-AV interaction by integrating the social value orientation from psychology into its decision-making process. The altruistic AV generates socially desirable behaviors by optimizing both its own reward and courtesy to the following human driver’s original plan in the longitudinal motion. The results show that as compared to the egoistic AV, the altruistic AV significantly avoids disrupting the following human driver’s initial plan and leads the following human driver to achieve considerably smaller car-following gap distance and time headway. Moreover, we investigated the impacts of the socially compatible control design with different altruism levels of the AV using statistical assessments. The results collectively demonstrate the significant improvement in traffic-level metrics as a result of the AV’s altruistic behaviors in human-AV interactions.

An improved automated braking system for rear-end collisions: A study based on a driving simulator experiment

Introduction: To assist drivers in avoiding rear-end collisions, many early warning systems have been developed up to date. Autonomous braking technology is also used as the last defense to ensure driver’s safety. Method: By taking the accuracy and timeliness of automatic system control into account, this paper proposes a rear-end Real-Time Autonomous Emergency Braking (RTAEB) system. The system inserts brake intervention based on drivers’ real-time conflict identification and collision avoidance performance. A driving simulator-based experiment under different traffic conditions and deceleration scenarios were conducted to test the different thresholds to trigger intervention and the intervention outcomes. The system effectiveness is verified by four evaluation indexes, including collision avoidance rate, accuracy rate, sensitivity rate, and precision rate. Results: The results showed that the system could help avoid all collision events successfully and enlarge the final headway distance, and a TTC threshold of 1.5 s and a maximum deceleration threshold of −7.5 m/s2 could achieve the best collision avoidance effect. The paper demonstrates the situations that are more inclined to trigger the RTAEB (i.e., a sudden brake of the leading vehicle and a small car-following distance). Moreover, the study shows that driver characteristics (i.e., gender and profession) have no significant association with system trigger. Practical Applications: The study suggests that development of collision avoidance systems design should pay attention to both the real-time traffic situation and drivers’ collision avoidance capability under the present situation.

Using naturalistic driving data to identify driving style based on longitudinal driving operation conditions

Purpose An individual’s driving style significantly affects overall traffic safety. However, driving style is difficult to identify due to temporal and spatial differences and scene heterogeneity of driving behavior data. As such, the study of real-time driving-style identification methods is of great significance for formulating personalized driving strategies, improving traffic safety and reducing fuel consumption. This study aims to establish a driving style recognition framework based on longitudinal driving operation conditions (DOCs) using a machine learning model and natural driving data collected by a vehicle equipped with an advanced driving assistance system (ADAS). Design/methodology/approach Specifically, a driving style recognition framework based on longitudinal DOCs was established. To train the model, a real-world driving experiment was conducted. First, the driving styles of 44 drivers were preliminarily identified through natural driving data and video data; drivers were categorized through a subjective evaluation as conservative, moderate or aggressive. Then, based on the ADAS driving data, a criterion for extracting longitudinal DOCs was developed. Third, taking the ADAS data from 47 Kms of the two test expressways as the research object, six DOCs were calibrated and the characteristic data sets of the different DOCs were extracted and constructed. Finally, four machine learning classification (MLC) models were used to classify and predict driving style based on the natural driving data. Findings The results showed that six longitudinal DOCs were calibrated according to the proposed calibration criterion. Cautious drivers undertook the largest proportion of the free cruise condition (FCC), while aggressive drivers primarily undertook the FCC, following steady condition and relative approximation condition. Compared with cautious and moderate drivers, aggressive drivers adopted a smaller time headway (THW) and distance headway (DHW). THW, time-to-collision (TTC) and DHW showed highly significant differences in driving style identification, while longitudinal acceleration (LA) showed no significant difference in driving style identification. Speed and TTC showed no significant difference between moderate and aggressive drivers. In consideration of the cross-validation results and model prediction results, the overall hierarchical prediction performance ranking of the four studied machine learning models under the current sample data set was extreme gradient boosting > multi-layer perceptron > logistic regression > support vector machine. Originality/value The contribution of this research is to propose a criterion and solution for using longitudinal driving behavior data to label longitudinal DOCs and rapidly identify driving styles based on those DOCs and MLC models. This study provides a reference for real-time online driving style identification in vehicles equipped with onboard data acquisition equipment, such as ADAS.

A Novel Macroscopic Traffic Model based on Distance Headway

A new model is proposed to characterize changes in traffic at transitions. These changes are affected by driver response. The distance headway between vehicles is considered as it affects driver behavior. Driver response is quick with a small distance headway and slow when the distance headway is large. The variations in traffic are greater with a slow driver while traffic is smooth with a quick driver. A model is developed which characterizes traffic based on driver response and distance headway. This model is compared with the well-known and widely employed Zhang and PW models. The Zhang model characterizes driver response at transitions using an equilibrium velocity distribution and ignores distance headway and driver response. Traffic flow in the PW model is characterized using only a velocity constant. Roe decomposition is employed to evaluate the Zhang, PW, and proposed models over a 270 m circular (ring) road. Results are presented which show that Zhang model provides unrealistic results. The corresponding behavior with the proposed model has large variations in flow with a slow driver but is smooth with a quick driver. The PW model provides smooth changes in flow according to the velocity constant, but the behavior is unrealistic because it is not based on traffic physics. Doi: 10.28991/CEJ-SP2021-07-03 Full Text: PDF

An empirical study on heterogeneous traffic car-following safety indicators considering vehicle types

Considering different car-following characteristics of trucks and cars, this study verifies the heterogeneity of steady car-following behavior of four leader-follower vehicle-type combinations based on the naturalistic driving dataset. The best-fit distribution model of surrogate safety indicators (i.e., distance headway (DHW), time headway (THW), time to collision (TTC) and safety margin (SM)) of four combinations are identified. Then, the performance of surrogate safety indicators in various speed ranges is compared to select a homeostatic risk perception indicator. After that, the longitudinal control model is calibrated for four combinations. Results show that the heterogeneity of different leader-follower vehicle-type combinations has been confirmed by surrogate safety indicators and calibrated parameters of the longitudinal control model. SM is suitable as the homeostatic risk perception indicator for four combinations when the speed is less than 90 km/h, and the advised safety warning SM value of is 0.9 or 0.95 for four combinations.

Effect of Apolipoprotein E4 on the Driving Behavior of Patients with Amnestic Mild Cognitive Impairment or Mild Alzheimer's Disease Dementia.

The driving behavior of patients with mild Alzheimer's disease dementia (ADD) and patients with mild cognitive impairment (MCI) is frequently characterized by errors. A genetic factor affecting cognition is apolipoprotein E4 (APOE4), with carriers of APOE4 showing greater episodic memory impairment than non-carriers. However, differences in the driving performance of the two groups have not been investigated. To compare driving performance in APOE4 carriers and matched non-carriers. Fourteen APOE4 carriers and 14 non-carriers with amnestic MCI or mild ADD underwent detailed medical and neuropsychological assessment and participated in a driving simulation experiment, involving driving in moderate and high traffic volume in a rural environment. Driving measures were speed, lateral position, headway distance and their SDs, and reaction time. APOE was genotyped through plasma samples. Mixed two-way ANOVAs examining traffic volume and APOE4 status showed a significant effect of traffic volume on all driving variables, but a significant effect of APOE4 on speed variability only. APOE4 carriers were less variable in their speed than non-carriers; this remained significant after a Bonferroni correction. To further examine variability in the driving performance, coefficients of variation (COV) were computed. Larger headway distance COV and smaller lateral position COV were observed in high compared to moderate traffic. APOE4 carriers had smaller speed COV compared to non-carriers. The lower speed variability of APOE4 carriers in the absence of neuropsychological test differences indicates reduced speed adaptations, possibly as a compensatory strategy. Simulated driving may be a sensitive method for detecting performance differences in the absence of cognitive differences.

Quantification of cut-in risk and analysis of its influencing factors: a study using random parameters ordered probit model

In the cut-in scenario, drivers are forced to experience a smaller headway distance, which may easily lead to rear-end crashes and reduced road traffic efficiency. Quantitatively evaluating cut-in risks and considering the heterogeneity of driving maneuvers to explore the influencing factors of cut-in risks using microscopic driving behavior data are still limited. In this study, a cut-in risk index (CIRI) was proposed to evaluate the cut-in risk based on fault tree analysis (FTA). To consider the heterogeneity of driving maneuvers, a random parameter ordered probit (RPOP) model was employed to recognize the key determinants of risky cut-in maneuvers. The results obtained in this study show that during the cut-in process, the cut-in vehicle has the highest crash risk with the preceding vehicle in the current lane compared to other surrounding vehicles. The proposed surrogate measure can objectively quantify cut-in risk. The present study suggests that the driver not only needs to pay attention to the following vehicle in the target lane, but also pay more attention to the preceding vehicle in the current lane during cut-in. Quantifying cut-in risks and exploring its influencing factors are essential for road traffic control, thereby improving driving safety and traffic efficiency.

A collision-free model on the interaction between pedestrians and cyclists on a shared road

In this study, we aim to propose a collision-free model on the interaction between pedestrians and cyclists on a shared road. First, we design an experiment to investigate the behaviors of pedestrians and cyclists in the interaction process. Three principles are presented for modeling based on the experimental observations, including the perception difference in the bidirectional and unidirectional scenarios, the difference in sensitivity on safety distance of pedestrians and cyclists, and the effect of distance and moving direction on the deceleration and steering behaviors. The model is proposed under the concept of subjective perception and behavioral decision-making. The heuristic of the path decision is that if a pedestrian (cyclist) decides to detour, he/she prefers the shortest route on the premise that a minimum safety distance is kept from the cyclist (pedestrian). The speed adjustment of both pedestrians and cyclists includes two parts: the expected speed determined by the available headway distance, and the deceleration coefficient determined by the distance between the cyclist and the pedestrian for the safety concern. The direction-related parameters are calibrated directly through the trajectories, while the speed-related parameters are calibrated with the cross-entropy method. The microscopic validation implies that the proposed collision-free model can potentially be used to simulate the microscopic interaction between pedestrians and the cyclist. It is also worth noting that the perception uncertainty of pedestrians could generate a big challenge to physical models on simulating the microscopic behaviors of pedestrians when the potential collision comes from the behind. The simulation of the mixed traffic reveals the difference in lane formation phenomena and the consequential conflict intensity between a cyclist and the crowd in bidirectional and unidirectional situations.

Analysis and Modeling of Pedestrian Flow in a Confined Corridor Focusing on the Headway Distance and Velocity of Pedestrians

Analysis and Modeling of Pedestrian Flow in a Confined Corridor Focusing on the Headway Distance and Velocity of Pedestrians

Traffic-Aware Lane Change Advance Warning System for Delay Reduction at Congested Freeway Diverge Areas

This paper presents an on-board advance warning system for vehicles based on a probabilistic prediction model that advises them on when to change lanes to reach a highway diverge on time. The system is based on a model that estimates the probability of reaching a goal state on the road using one or multiple lane changes. This estimate is based on several traffic-related parameters such as the distribution of inter-vehicle headway distances as well as driver-related parameters like lane change duration. For an upcoming diverge, the advance warning system uses the model to continuously calculate the probability of reaching it and advise the driver to change lanes when the probability dips below a certain threshold. To evaluate the performance of the proposed system in reducing traffic delay at highway diverges, it was used on a segment of a four-lane highway to advise vehicles taking an off-ramp on when to change lanes. Results show that using the proposed system reduces average delay up to 6% and maximum delay up to 16%, depending on traffic flow and the ratio of vehicles taking the off-ramp.

A new heterogeneous traffic flow model based on lateral distance headway

Based on a density gradient model proposed recently by Imran and Khan, a new heterogeneous traffic flow model considering time and lateral distance is proposed. The type and stability of the equilibrium solution of the model are discussed by using the differential equation theory, and the global distribution structure of the trajectory in the phase plane is analyzed. In addition, the density wave stability conditions and saddle-node bifurcation conditions of the model are studied, and the solitary wave solutions of the KdV equation in the metastable region are derived by using the reduced perturbation method. The numerical results show that the new model cannot only reproduce the spatiotemporal oscillation phenomenon when walking and stopping, but also describe the sudden change behavior of traffic near the critical point of saddle-node bifurcation. It is shown that the model can reproduce some complex traffic phenomena qualitatively.

Calibrating Microsimulation Parameters for Vehicular Travel Time

Travel time is one of the simplest yet the most important parameter for transportation facility users as well as transportation engineers. Travel time data is valuable for widerange of transportation analysis including congestion management, transportation planning and passenger’sdecision making.Traffic simulation models are now becoming necessary tools to understand the behavior of traffic and reduce vehicular travel times, but it is very important to calibrate these models first. Thisstudy attempts to determines the values of those parameters,using microsimulation,that significantly affect the travel time. These parameters arethenused for calibrating the traffic simulation model that results in realistic travel time.Study was conducted on an urban road andfield data was collected during weekdays for peak hours. The traffic network was modelled usingVISSIM®.The calibration parameters were desired speed distribution, number of lanes,average standstill distance and minimum headway. After calibrating the model, the travel times collected from field data and those by simulations for different modes of transportation were in close agreement.

An integrated methodology for real-time driving risk status prediction using naturalistic driving data

Real-time driving risk status prediction is critical for developing proactive traffic intervention strategies and enhance driving safety. However, the optimal observation time window length and prediction time window length, which should be the prerequisite for the timeliness and accuracy of real-time driving risk status prediction model, have been rarely explored in previous studies. In this study, a methodology which integrates driving risk status identification, rolling time window-based feature extraction, real-time driving risk status prediction and driving risk influencing factors analysis was proposed to accurately evaluate and predict real-time driving risk status. The methodology was tested based on 1,440 car-following events from Shanghai Naturalistic Driving Study. Results show that four driving risk statuses (safe, low-risk, median-risk and high-risk) are most appropriate to establish risk labelling criteria. In addition, results from driving risk status prediction show that when the observation time window length is 0.5 s, the accuracy rate of predicting medium-risk or high-risk status occurring in the next 0.7 s is higher than 85 % using multi-layer perceptron model. Meanwhile, the results from the analysis of influencing factors show that the input variables related to the risk status score higher in the ranking of feature importance. A part from that, speed difference, headway distance, speed and acceleration are still important in predicting driving risk status. The proposed methods in this paper can be applied in connected and autonomous vehicle (CAV) to reduce driver cognitive workload and hence improve driving safety fed with naturalistic driving data collected using in-vehicle systems.

Saturation Flow Rate of Urban At-Grade Signalized Intersection Under Different Climatic Conditions (Case Study: Sattari-Mokhberi Intersection)

This research aimed to compare the saturation flow rates of an urban at-grade signalized intersection under different climatic conditions in order to optimize the traffic light control system of this intersection. Data collection was performed with a video camera. The case studied was the intersection of Sattari-Mokhberi Intersection, in Tehran. The road surface conditions occurring in this intersection were divided into three classes: dry, wet, and snowy. Modeling and simulations were performed with the help of the software Vissim and Synchro. Model calibration was performed using the parameters “desired speed” and “acceleration/deceleration at zero speed”. Data analysis was performed using descriptive statistics and inferential statistics, including analysis of variance (ANOVA), Kolmogorov–Smirnov test, and Tukey and Scheffe tests. The saturation flow for dry, wet, and snowy road conditions was estimated to 1967, 1722, and 1607, respectively. Also, the relationship between saturation headway and sight distance in meters was modeled linearly and logarithmically. The analysis of videos showed that the mean desired speed in wet and snowy road conditions was, respectively, 23% and 36% lower than in normal conditions. The mean desired acceleration at zero speed in wet and snowy road conditions was, respectively, 24% and 30% lower than normal. The simulation results showed a 15% reduction in the total delay of the intersection after the implementation of weather-responsive schedules. The results of this research show that the benefits of adjusting the schedule of traffic lights based on weather are more pronounced in snowy conditions than in rainy conditions.

Adaptations in driver deceleration behaviour with automatic incident detection: A naturalistic driving study

Traffic congestion and crash rates can be reduced by introducing variable speed limits (VSLs) and automatic incident detection (AID) systems. Previous findings based on loop detector measurements have revealed that drivers reduce their speeds while approaching traffic congestion when the AID system is active. Notwithstanding these behavioural effects, most microscopic traffic flow models assessing the impact of VSLs do not describe driver response accurately.This study analyses the main factors that influence driver deceleration behaviour while approaching traffic congestion with and without VSLs. The Dutch VSL database was linked to the driver behaviour data collected in the UDRIVE naturalistic driving study. Driver engagement in secondary tasks and glance behaviour were extracted from the video data. Linear mixed-effects models predicting the characteristics of deceleration events were estimated.The results show that the maximum deceleration is high when approaching a slower leader, when driving at high speeds and short distance headways, and close to the beginning of traffic congestion. The minimum time headway is short when driving at high speeds and changing lanes. Certain drivers showed higher decelerations and shorter time headways than others. Controlled for these main factors, smaller maximum decelerations were found when the VSLs were present and visible, and when the gantries were within close proximity. These factors could be incorporated into microscopic traffic simulations to evaluate the impact of AID systems on traffic congestion more realistically. Further research is needed to clarify the link between engagement in secondary tasks, glance behaviour and deceleration behaviour.

Intraindividual variability in driving simulator parameters of healthy drivers of different ages

Intraindividual variability is a fundamental behavioural characteristic of aging but has been examined to a very limited extent in driving. This study investigated intraindividual variability in driving simulator measures in healthy drivers of different ages using the coefficient of variation (COV) as a variability measure. Participants were healthy volunteers who were regular drivers, who were divided into a “young” group, a “middle-aged” group, and an “old” group. They drove in two environments (rural, 72 drivers; urban, 60 drivers), under conditions of moderate and high traffic load, without and with distraction (conversation). Significant differences in COV were observed in the rural condition for headway distance and lateral position as a function of traffic load, with high traffic (without and with distraction) resulting in increased COV of headway and decreased COV of lateral position. Significant differences in COV were observed in the urban condition for headway distance only, with high traffic (without and with distraction) resulting in increased COV of headway. No age effects were found for any of the driving conditions. The results indicate that traffic load affected headway distance and lateral position in opposite directions in all three age groups: high traffic resulted in increased variability of headway in both rural and urban conditions but in decreased variability of lateral position in the rural conditions compared to moderate traffic irrespective of distraction. The study indicates that driving conditions affect the intraindividual variability of driving measures in selective ways, which may be linked to the extent of automatization of the driving variables and to adaptive changes to traffic condition challenges.

A Generalized Framework for Connectivity Analysis in Vehicle-to-Vehicle Communications

In vehicular networks, the instantaneous connectivity between any two consecutive vehicles is a function of both the headway distance between them and the communication channel characteristics. We derive a generalized analytic expression for vehicle-to-vehicle link connectivity probability in highway vehicular networks that considers the change in vehicle headway distribution under different traffic conditions. The introduced formulation is derived under the Nakagami-m small-scale fading model which captures a wide range of small-scale fading conditions in freeway vehicular networks and was shown to well fit the empirical data. This formulation is of interest in analyzing and simulating vehicular communication systems.

A systematic review of definitions of motor vehicle headways in driver behaviour and performance studies

Headway is a safety measure commonly used to investigate driving behaviour and driver performance. Its purpose is to reflect the following distance or time between a leading and following vehicle in traffic. It is therefore associated with drivers’ response time, such as in braking or swerving, during safety critical events. In the literature, distance and time headway are defined in different ways, despite standard definitions in the traffic engineering literature, which prompted this systematic review of headway definitions across a range of study designs, in order to recommend approaches to improve the accuracy and reproducibility of headway definitions used in road safety contexts. PRISMA guidelines were followed to search four databases (EMBASE, COMPENDEX, SCOPUS and MEDLINE) for studies that reported on headways or discussed methodological approaches. The search and filtering of abstracts identified 110 articles for a qualitative synthesis. Four broad approaches to measuring headways were detected: studies using simulation, roadside external features, on-road features, and on-vehicle features. Studies were coded as to whether they included written explanation, mathematical statements, or pictorial depictions of headway. Only 49.6% of studies contextualised headway sufficiently for reproducibility. Reproducibility is crucial for accurate interpretation of research findings and comparisons across studies. It is recommended that headway definitions should a) exclude vehicle or parts of vehicle lengths, b) include reference points (e.g., bumper/axle/rear), c) have a consistent terminology, and d) include the accuracy of headway measuring devices to report the precision of a study’s findings.