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This study aims to examine the differences in drivers' reaction time (RTs) while driving on horizontal curves and straight roadway segments, among different driver classes, and in different driving environments to better understand human driver behavior in typical car-following situations. Therefore, behavioral measures were extracted from naturalistic car-following trajectories to estimate the RT. The RT was estimated for two stimulus–response pairs, namely, the speed–gap and relative speed–acceleration pairs, by using the cross-classification method. The RT was estimated separately for each driver and aggregated based on location and based on driver class. The results reveal that drivers' RTs on curves are consistently higher than their RTs on straight segments, and this difference is statistically significant. The comparison between normal drivers and aggressive drivers indicates that regardless of the location, aggressive drivers have a significantly longer RT than normal drivers, as aggressive drivers can accept closer gaps and higher relative speed. Also, cautious drivers have a longer RT compared with normal drivers; however, the difference is not significant in most cases. Furthermore, cautious and normal drivers have longer RTs on curves compared with their RTs on straight segments. Additionally, the RT on rural horizontal curves is longer than the RT on urban curves, yet the differences are insignificant. [ABSTRACT FROM AUTHOR]

Tunnel–interchange connecting sections pose significant safety challenges on mountainous expressways due to their high incidence of accidents. Improving road safety necessitates a comprehensive understanding of driver behavior in such areas. This study explores the influences of road characteristics, signage information volume, and traffic conditions on drivers' car-following and lane-changing behavior in tunnel–interchange diverging areas. Utilizing driving data from 25 subjects of 72 simulated road models, driving performance is assessed using the Friedman rank test and multivariate variance analysis. The results highlight the significant influence of both connection distance and signage information load on driving behavior. In tunnel–interchange scenarios, the reduction in velocity increased by 62.61%, and speed variability surged by 61.11%, indicating potential adverse effects on driving stability due to the environmental transitions. Decreased connection distances are associated with reduced lane-changing durations, larger steering angles, and increased failure rates. Furthermore, every two units of increase in signage information leads to a 13.16% rise in maximum deceleration and a 5% increase in time headway. Notably, the signage information volume shows a significant interaction with connection distance (F > 1.60, p < 0.045) for most car-following indicators. Hence, the study recommends a maximum connection distance of 700 m and signage information not exceeding nine units for optimal safety and stability. [ABSTRACT FROM AUTHOR]

To systematically study the influence of a propensity for a particular driving style on car-following risk, a safety potential field risk-quantification method that considers driving style is proposed. First, we classify driving behaviors and construct a field-based safety potential car-following model via analogy with intermolecular interactions; second, we establish a risk-quantification model by considering driving style, risk exposure, and risk severity and classify the consequent risk into four levels, high risk, medium risk, low risk, and safe, using the fuzzy C-means algorithm. Finally, we predict the car-following risk using the LightGBM algorithm in real time. The experimental results show that the LightGBM algorithm can recognize up to 86% of medium–high risk levels compared to the Decision Tree and Random Forest Algorithms. It can achieve effective prediction of car-following risk, which provides sufficient warning information to drivers and helps improve the overall safety of vehicle operation. [ABSTRACT FROM AUTHOR]

This paper investigates the ability of autonomous driving systems to predict outcomes by considering human factors like gender, age, and driving experience, particularly in the context of safety-critical events. The primary objective is to equip autonomous vehicles with the capacity to make plausible deductions, handle conflicting data, and adjust their responses in real-time during safety-critical situations. A foundational dataset, which encompasses various driving scenarios such as lane changes, merging, and navigating complex intersections, is employed to enable vehicles to exhibit appropriate behavior and make sound decisions in critical safety events. The deep learning model incorporates personalized cognitive agents for each driver, considering their distinct preferences, characteristics, and requirements. This personalized approach aims to enhance the safety and efficiency of autonomous driving, contributing to the ongoing development of intelligent transportation systems. The efforts made contribute to advancements in safety, efficiency, and overall performance within autonomous driving systems. To describe the causal relationship between external factors like weather conditions and human factors, and safety-critical driver behaviors, various data mining techniques can be applied. One commonly used method is regression analysis. Additionally, correlation analysis is employed to reveal relationships between different factors, helping to identify the strength and direction of their impact on safety-critical driver behavior.

Traditional car-following models usually prioritize minimizing inter-vehicle distance error when tracking the preceding vehicle, often neglecting crucial factors like driving economy and passenger ride comfort. To address this limitation, this paper integrates the concept of eco-driving and formulates a multi-objective function that encompasses economy, comfort, and safety. A novel eco-driving car-following strategy based on the deep deterministic policy gradient (DDPG) is proposed, employing the vehicle's state, including data from the preceding vehicle and the ego vehicle, as the state space, and the desired time headway from the intelligent driver model (IDM) as the action space. The DDPG agent is trained to dynamically adjust the following vehicle's speed in real-time, striking a balance between driving economy, comfort, and safety. The results reveal that the proposed DDPG-based IDM model significantly enhances comfort, safety, and economy when compared to the fixed-time headway IDM model, achieving an economy improvement of 2.66% along with enhanced comfort. Moreover, the proposed approach maintains a relatively stable following distance under medium-speed conditions, ensuring driving safety. Additionally, the comprehensive performance of the proposed method is analyzed under three typical scenarios, confirming its generalization capability. The DDPG-enhanced IDM car-following model aligns with eco-driving principles, offering novel insights for advancing IDM-based car-following models. [ABSTRACT FROM AUTHOR]

Tunnel–interchange connecting sections pose significant safety challenges on mountainous expressways due to their high incidence of accidents. Improving road safety necessitates a comprehensive understanding of driver behavior in such areas. This study explores the influences of road characteristics, signage information volume, and traffic conditions on drivers’ car-following and lane-changing behavior in tunnel–interchange diverging areas. Utilizing driving data from 25 subjects of 72 simulated road models, driving performance is assessed using the Friedman rank test and multivariate variance analysis. The results highlight the significant influence of both connection distance and signage information load on driving behavior. In tunnel–interchange scenarios, the reduction in velocity increased by 62.61%, and speed variability surged by 61.11%, indicating potential adverse effects on driving stability due to the environmental transitions. Decreased connection distances are associated with reduced lane-changing durations, larger steering angles, and increased failure rates. Furthermore, every two units of increase in signage information leads to a 13.16% rise in maximum deceleration and a 5% increase in time headway. Notably, the signage information volume shows a significant interaction with connection distance (F > 1.60, p < 0.045) for most car-following indicators. Hence, the study recommends a maximum connection distance of 700 m and signage information not exceeding nine units for optimal safety and stability.

Despite the potential benefits of autonomous vehicles (AVs) of reducing human driver errors and enhancing traffic safety, a comprehensive evaluation of recent AV collision data reveals a concerning trend of rear-end collisions caused by following vehicles. This study aimed to address this issue by developing a methodology that identifies the relationship between driving patterns and the risk of collision between leading and following vehicles using spectral analysis. Specifically, we propose a process for computing three indices: reaction time, stimulus compliance index, and collision-risk aversion index. These indices consistently produced reliable results under various traffic conditions. Our findings align with existing research on the driving patterns of following vehicles. Given the consistency and robustness of these indices, they can be effectively utilized in advanced driver assistance systems or incorporated into AVs to assess the likelihood of collision risk posed by following vehicles and develop safer driving strategies accordingly. [ABSTRACT FROM AUTHOR]

In this study, a car-following model is put forth to explain the car-following behavior of HDVs (human-driven vehicles), AVs (autonomous vehicles) and CAVs (connected and automated vehicles) in mixed traffic. Based on the IDM (intelligent-driver model) and molecular dynamic theory, the model includes the velocity of surrounding vehicles along with the difference of velocity and the headway between each pair of vehicles. The drivers' sensitivity to the deceleration of the nearest front vehicle was considered in HDV car-following modeling and the influences of various kinds of nearest front and rear vehicles were distinguished in the CAV model. Based on the information obtained from the real road test mixed with HDVs, AVs and CAVs, the optimum value of the model parameters was obtained and the accuracy of model was verified with simulation. The results show that the simulation for HDVs', AVs' and CAVs' car-following behavior under the proposed model is more accurate than that for the IDM, ACC (adaptive cruise control) model and CACC (cooperative adaptive cruise control) model. The model can be applied to car-following simulations of HDVs, AVs and CAVs in mixed traffic, in the model the tools and concepts of nonlinear dynamics especially molecular dynamic theory is applied which can benefit road traffic vehicle interaction analysis. In addition, this study provides valuable suggestions and guidance for effectively guiding AVs and CAVs to follow vehicles and improving the stability of car-following behavior. [ABSTRACT FROM AUTHOR]

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Drivers know that smartphones distract. Trying to limit distraction, drivers can use handsfree devices, where they onlybriefly glance at the smartphone. However, the cognitive cost of switching tasks from driving to communicating back todriving adds an underappreciated, potentially long period to the total distraction time. This project measured the effectsof handsfree smartphones on driving behaviors by engaging ninety-seven 21- to 78-year-old individuals whoselfidentified as active drivers and smartphone users in a simulated driving scenario that included smartphonedistractions. Peripheral-cue and car-following tasks were used to assess driving behavior, along with synchronized eyetracking. This research found that simulated driving performance drops to dangerous levels after smartphone distractionfor all ages and for both voice and texting. The participants swerved for 15.1 seconds after a voice distraction and for alonger 20.6 seconds after a text distraction. Participants from the 71+ age group missed seeing about 50% of peripheralcues within 4 seconds of the distraction. Coherence with the lead car during following task dropped from 0.54 to 0.045during distraction, and seven participants rear-ended the lead car.

Drivers know that smartphones distract. Trying to limit distraction, drivers can use handsfree devices, where they onlybriefly glance at the smartphone. However, the cognitive cost of switching tasks from driving to communicating back todriving adds an underappreciated, potentially long period to the total distraction time. This project measured the effectsof handsfree smartphones on driving behaviors by engaging ninety-seven 21- to 78-year-old individuals whoselfidentified as active drivers and smartphone users in a simulated driving scenario that included smartphonedistractions. Peripheral-cue and car-following tasks were used to assess driving behavior, along with synchronized eyetracking. This research found that simulated driving performance drops to dangerous levels after smartphone distractionfor all ages and for both voice and texting. The participants swerved for 15.1 seconds after a voice distraction and for alonger 20.6 seconds after a text distraction. Participants from the 71+ age group missed seeing about 50% of peripheralcues within 4 seconds of the distraction. Coherence with the lead car during following task dropped from 0.54 to 0.045during distraction, and seven participants rear-ended the lead car.

This paper proposes a hierarchical framework integrating deep reinforcement learning (DRL) and rule-based methods for multi-lane autonomous driving. We define an instantaneous desired speed (IDS) to mimic the common motivation for higher speed in different traffic situations as an intermediate action. High-level DRL is utilized to generate IDS directly, while the low-level rule-based policies including car following (CF) models and three-stage lane changing (LC) models are governed by the common goal of IDS. Not only the coupling between CF and LC behaviors is captured by the hierarchy, but also utilizing the benefits from both DRL and rule-based methods like more interpretability and learning ability. Owing to the decomposition and combination with rule-based models, traffic flow operations can be taken into account for individually controlled automated vehicles (AVs) with an extension of traffic flow adaptive (TFA) strategy through exposed critical parameters. A comprehensive evaluation for the proposed framework is conducted from both the individual and system perspective, comparing with a pure DRL model and widely used rule-based model IDM with MOBIL. The simulation results prove the effectiveness of the proposed framework.

To systematically study the influence of a propensity for a particular driving style on car-following risk, a safety potential field risk-quantification method that considers driving style is proposed. First, we classify driving behaviors and construct a field-based safety potential car-following model via analogy with intermolecular interactions; second, we establish a risk-quantification model by considering driving style, risk exposure, and risk severity and classify the consequent risk into four levels, high risk, medium risk, low risk, and safe, using the fuzzy C-means algorithm. Finally, we predict the car-following risk using the LightGBM algorithm in real time. The experimental results show that the LightGBM algorithm can recognize up to 86% of medium–high risk levels compared to the Decision Tree and Random Forest Algorithms. It can achieve effective prediction of car-following risk, which provides sufficient warning information to drivers and helps improve the overall safety of vehicle operation.

Numerical simulations have shown that the network fundamental diagram (NFD) of a signalized network is significantly affected by the green ratio. An analytical approximation of the NFD has been derived from the link transmission model. However, the consistency between these approaches has not been established, and the impacts of other factors are still unrevealed. This research evalutes the impacts of start-up and clearance behaviors in a signalized network from a network fundamental diagram approach. Microscopic simulations based on Newell’s car-following model are used for testing the bounded acceleration (start-up) and aggressiveness (clearance) effects on the shape of the NFD in a signalized ring road. This new approach is shown to be consistent with theoretical results from the link transmission model, when the acceleration is unbounded and vehicles have the most aggressive clearance behaviors. This consistency validates both approaches; but the link transmission model cannot be easily extended to incorporate more realistic start-up or clearance behaviors. With the new approach, this project demonstrates that both bounded acceleration and different aggressiveness lead to distinct network capacities and fundamental diagrams. In particular, they lead to start-up and clearance lost times of several seconds; and these lost times are additive. Therefore, the important role that these behaviors play in the NFD shape is studied to reach a better understanding of how the NFD responds to changes. This will help with designing better start-up and clearance behaviors for connected and autonomous vehicles.

Compared with traditional rule-based algorithms, deep reinforcement learning methods in autonomous driving are able to reduce the response time of vehicles to the driving environment and fully exploit the advantages of autopilot. Nowadays, autonomous vehicles mainly drive on urban roads and are constrained by some map elements such as lane boundaries, lane driving rules, and lane center lines. In this paper, a deep reinforcement learning approach seriously considering map elements is proposed to deal with the autonomous driving issues of vehicles following and obstacle avoidance. When the deep reinforcement learning method is modeled, an obstacle representation method is proposed to represent the external obstacle information required by the ego vehicle input, aiming to address the problem that the number and state of external obstacles are not fixed. [ABSTRACT FROM AUTHOR]

A recent empirical study (Shi and Li, 2021) showed that commercial automated vehicles (AVs) became more unstable as the headway was set to a smaller value, implying possible intrinsic tradeoffs between safety, mobility, and stability aspects in AV following control design. This study aims to analytically explain the underlying vehicle control mechanism that dictates these tradeoffs. To this end, a robust optimization model is formulated based upon a parsimonious linear AV following model to capture the first-order tradeoffs between safety, mobility, and stability. The robust optimization model aims to maintain a sufficient safety buffer to avoid collisions against all possible realistic preceding vehicle trajectories. As opposed to a numerical solution, we managed to solve this model to an analytical solution that captures relationships between the key parameters determining safety, mobility, and stability. The analytical solution reveals that improving AV mobility (or reducing AV following headway) would require overcoming more safety challenges (e.g., enhancing vehicle control to maintain a short safety buffer) while causing more string-instability. The theoretical findings are consistent with the empirical observations in previous studies. Further, they provide a possible explanation for the observed string instability of commercial AV following control (e.g., adaptive cruise control) as a tradeoff for a smaller headway. Overall, this study lays a new methodology foundation for incorporating safety in traffic flow analysis that traditionally focused on only mobility and stability. Further, the findings yield a set of managerial insights into reasonable AV following design and its implications to emerging AV traffic management. • Build a robust optimization model relating safety, mobility, and stability. • Capture the tradeoffs between safety, mobility, and stability for commercial AVs. • Solve an analytical formulation for these tradeoffs. • Reveal that improving AV mobility would require overcoming more safety challenges while causing more string instability. • Show the consistency between theoretical findings and empirical observations. [ABSTRACT FROM AUTHOR]

In the near future, the road traffic flow will consist of both human-driven vehicles (HDVs) and connected autonomous vehicles (CAVs). Since HDVs cannot communicate with CAVs and road side units (RSU), they are unobservable to CAVs if outside the range of sensing. In such case, the advantages of CAVs will be compromised and various high-level tasks in mixed autonomous traffic flow cannot be achieved. This study proposes a model to infer HDVs information using sensing data of CAVs. The rationale is that CAVs react to HDVs based on the car-following (CF) logic. Inversely, real-time locations of HDVs can be reconstructed using the data from CAVs. The Bayesian network is used to reflect the CF logic and develop a real-time vehicle location inference method for both single and multiple CAVs scenarios. Last, the method is tested using real-world dataset. Both time consumption and near-field estimation precision are validated. [ABSTRACT FROM AUTHOR]

This study aims to model traffic flow under weak lane based heterogonous (mixed) traffic conditions. Unlike homogeneous traffic, when a follower (subject) vehicle in mixed traffic moves closer to its leader vehicle, it tends to adjust its longitudinal movement or change its lane and acts discretely. Due to this phenomenon, traffic flow modeling under such conditions is always challenging. A new driver behavioral logic is conceptualized for the vehicles' movement within a combination of surrounding vehicles. In which the following behavior was dissected with the lateral shift distance between vehicles. Two car-following models for homogeneous traffic conditions, the IDM and Gipps models were adapted with relevant lateral behavior parameters to different vehicle classes under mixed-traffic conditions. The new driving behavior logic was incorporated externally in place of default logic. The results showed that the performance of the adapted models was better accurate than the classical models. [ABSTRACT FROM AUTHOR]

By safety supervision on dangerous driving behaviors, emergent braking in autonomous vehicles can effectively prevent collisions when using the car following algorithm based on deep reinforcement learning. However, the significant deceleration associated with emergent braking often results in an uncomfortable driving experience and high energy consumption. To address this issue, a soft collision avoidance based car following algorithm is proposed. Different from emergent braking, our approach introduces a deceleration adjustment value to the current acceleration output. This adjustment value is calculated by considering safe distance with attenuation coefficient in terms of multi-step prediction, while the attenuation coefficient and the predicted time step are discussed in detail. Comparative analysis, including statistical results and representative cases, demonstrates that the proposed algorithm significantly enhances driving comfort (improve 37.341 %) and reduces energy consumption (improve 11.244 %) without increasing collision risks. [ABSTRACT FROM AUTHOR]

In the early mixed traffic environment, connected and automated vehicles (CAVs) tend to follow simple control rules for the sake of traffic safety and stability. Comprehensive analysis of overall mixed traffic performance and vehicle to vehicle interactions around highway on-ramp bottleneck is a basis for further traffic management and control. And yet CAVs in existing researches are mainly in the form of cooperative vehicular platoon, and mixed traffic scenarios within simulation software are not applicable for in-depth investigation. The purpose of this paper is to develop a microscopic simulation model to study in detail lane-based mixed traffic performance around the highway on-ramp. This model firstly considers vehicle characteristics of individual human vehicles (HVs) and CAVs by combining different microscopic traffic models. After the hypothetical on-ramp bottleneck structure is constructed, various simulation scenarios are developed under varying traffic volumes and changing market penetration rates (MPRs) of CAVs. Simulation results show that mixed traffic flow is improved in terms of congestion patterns and traffic capacity. For oscillating and nearly homogeneous congested traffic, congestion degrees are greatly reduced when CAV MPR above 30 % and 50 % respectively. Mixed traffic capacity is increased by 19.3 % when a smaller desired time gap set for CAVs, while it shows no significant change in a larger value. In the HV-HV interactions, traffic conflicts are slightly influenced by CAVs. Comparatively, conflict situations in HV-CAV interactions get worse, the proportion of time to collision (TTC) greater than 2 s monotonically declining with the rise in CAV MPR. It is concluded that traffic characteristics overall show a good momentum when MPR of CAV and the desired time gap of CAVs in mixed traffic are appropriate. [ABSTRACT FROM AUTHOR]

• Develop a hybrid car-following (CF) model to probabilistically integrate multiple CF models rather than relying on a single model. • Develop a stochastic learning framework to represent a range of CF behaviour. • The proposed ABC-hybrid framework is highly adaptable to various datasets and CF model pool. Car following (CF) models are fundamental to describing traffic dynamics. However, the CF behavior of human drivers is highly stochastic and nonlinear. As a result, identifying the "best" CF model has been challenging and controversial despite decades of research. Introduction of automated vehicles has further complicated this matter as their CF controllers remain proprietary, though their behavior appears different than human drivers. This paper develops a stochastic learning approach to integrate multiple CF models, rather than relying on a single model. The framework is based on approximate Bayesian computation that probabilistically concatenates a pool of CF models based on their relative likelihood of describing observed behavior. The approach, while data-driven, retains physical tractability and interpretability. Evaluation results using two datasets show that the proposed approach can better reproduce vehicle trajectories for both human-driven and automated vehicles than any single CF model considered. [ABSTRACT FROM AUTHOR]

Advanced driver-assistance systems (ADAS) have matured over the past few decades with the dedication to enhance user experience and gain a wider market penetration. However, personalization features, as an approach to make the current technologies more acceptable and trustworthy for users, have been gaining momentum only very recently. In this work, we aim to learn personalized longitudinal driving behaviors via a Gaussian Process (GP) model. The proposed method learns from individual driver’s naturalistic car-following behavior, and outputs a desired acceleration profile that suits the driver’s preference. The learned model, together with a predictive safety filter that prevents rear-end collision, is used as a personalized adaptive cruise control (PACC) system. Numerical experiments show that GP-based PACC (GP-PACC) can almost exactly reproduce the driving styles of an intelligent driver model. Additionally, GP-PACC is further validated by human-in-the-loop experiments on the Unity game engine-based driving simulator. Trips driven by GP-PACC and two other baseline ACC algorithms with driver override rates are recorded and compared. Results show that on average, GP-PACC reduces the human override duration by 60% and 85% as compared to two widely-used ACC models, respectively, which shows the great potential of GP-PACC in improving driving comfort and overall user experience. [ABSTRACT FROM AUTHOR]

This study investigates a driving behavior referred to as the cooperative safety concept that assumes for strings of conventional vehicles traveling in nighttime or conditions with reduced visibility during a stable flow condition, the leading vehicles would bear much of the navigational challenges and risks while the following vehicles enjoy reduced driving workload and improved navigation safety. The study includes a comprehensive investigation of the safety risk levels and driver behaviors at intersections and freeway ramp locations in an attempt to verify this phenomenon using data from the large-scale Second Strategic Highway Research Program (SHRP 2) naturalistic driving study database. Overall, the driver behavior analysis showed that drivers following other vehicles tended to travel at lower speeds but with more acceleration activities than other vehicles. In addition, lighting during nighttime appeared to help alleviate the behavioral differences between the two types of travelers and resulted in more dispersed merging, diverging, and lane-changing behaviors. The safety event data analysis showed that higher traffic levels tended to correlate with more safety events in general but significantly fewer single-vehicle events. In addition, higher traffic levels correlated with a significantly lower likelihood of crashes in general when a safety event occurred. In the SHRP 2 data, safety events included crashes, near crashes, and statistically selected baseline events recorded during the data collection. The findings of this study, including in particular the event analysis, indicated that vehicles following other vehicles in a free-flow condition tended to drive slower and have lower safety risks in terms of crashes in general and single-vehicle crashes in particular. This knowledge can have significant implications for applications such as advanced lighting systems, cooperative vehicle features, and smart traffic control strategies. [ABSTRACT FROM AUTHOR]

In this study, 20 drivers, who maintain a safe distance at all speeds, are monitored to help determine the principal reason for trip delay. The aim was to develop a driver-assisted technology device or devices to enhance driver comfort and safety. The use of vehicle-tovehicle (V2V), vehicle-to-everything (V2X), or a combination of devices that can quickly collect and analyze data is contemplated. Collecting real-time driver response data is a challenge and extremely expensive. For these reasons, a stochastic car-following model, called a carfollowing network model (CFNM), was developed. It features 20 simulated passenger-car drivers traveling around a test track with 2 cruising zones and a bottleneck. Each driver strives to minimize the individual trip time. The model accounts for (1) driver decision-making lapses made over time, (2) a driver's inability to precisely control speed, and (3) a driver's desire to comfortably accelerate and decelerate. Vehicle acceleration and deceleration rates are constrained for (4) driver comfort, and (5) driver health, i.e., vehicle top and lower speeds are adjusted to limit g-forces. A linear acceleration model and stochastic differential equation are introduced into the CFNM. The stochastic contribution associated with a driver's inability to precisely control speed is called traffic noise. A model calibration scheme was developed that ensures that the CFNM forecasts of time, speed, and location are reliable. The relationships between traffic noise and delay and road design and delay are discussed. Analyses show that road design and traffic platooning, which are closely associated with traffic noise and driver concern for safety, are critical factors. No single factor can explain delay. The forecast results suggest that three separate assisted-driver technologies will be needed to deal with start-up, cruise, and arrival delay. DOI: 10.1061/JTEPBS.0000700. © 2022 American Society of Civil Engineers. [ABSTRACT FROM AUTHOR]

Due to the large fluctuations and poor stability of the vehicle in the congested environment, it is difficult to track the vehicle in front of it in time. This paper developed a multi-objective optimal car following algorithm. The algorithm used a vehicle lateral and longitudinal dynamic model and deep reinforcement learning to establish a car-following model. Then, according to the vehicle distance difference, speed difference, lateral deviation, and relative yaw angle, the deep deterministic strategy gradient algorithm obtained the acceleration and steering angle of the following vehicle. Therefore, the algorithm can control the following car more smoothly and safely. After testing and verification with the NGSIM driving data set, the results show that this method can effectively improve the stability, comfort, and safety of car following. It can promote traffic safety and improve road capacity. [ABSTRACT FROM AUTHOR]

Car-following (CF) modeling, a fundamental component in microscopic traffic simulation, has attracted increasing research interest in recent decades. In this study, we propose an adaptable personalized car-following framework —– MetaFollower, by leveraging the power of meta-learning. Specifically, we first utilize Model-Agnostic Meta-Learning (MAML) to extract common driving knowledge from various CF events. Afterward, the pre-trained model can be fine-tuned on new drivers with only a few CF trajectories to achieve personalized CF adaptation. We additionally combine Long Short-Term Memory (LSTM) and Intelligent Driver Model (IDM) to reflect temporal heterogeneity with high interpretability. MetaFollower can accurately capture and simulate the intricate dynamics of car-following behavior while considering the unique driving styles of individual drivers. We demonstrate the versatility and adaptability of MetaFollower by showcasing its ability to adapt to new drivers with limited training data quickly. To evaluate the performance of MetaFollower, we conduct rigorous experiments comparing it with both data-driven and physics-based models. The results reveal that our proposed framework outperforms baseline models in predicting car-following behavior with higher accuracy and safety. To the best of our knowledge, this is the first car-following model aiming to achieve fast adaptation by considering both driver and temporal heterogeneity based on meta-learning. • Introduced a car-following model based on meta-learning. • Segmented the driving behavior of different drivers. • Validated the effectiveness of our approach using real-world driving data. [ABSTRACT FROM AUTHOR]

• A novel model for describing the CF behavior of HDVs was proposed by combining the 2D-IDM with the improved physics-informed GAN based on experiment data of mixed traffic flow. • Desired time gap was input as noises in the generator, and the Monte Carlo sampling was combined to improve the Disout algorithm, which described the internal and external stochasticity of HDVs, so that the model could also carry out interval prediction. The effectiveness was demonstrated through ablation experiments. • The effectiveness of the model was verified through short-term trajectory prediction and macroscopic traffic flow evolution. On this basis, further analysis of experimental data and numerical simulations were conducted to observe the impact of AVs on mixed traffic flow. To model the car following (CF) behavior of mixed traffic flow composed of autonomous vehicles (AVs) and human-driving vehicles (HDVs) in the future, this paper calibrated the lower control algorithm of AVs and proposed a model named theory-data dual driven stochastically generative adversarial networks (TDS-GAN) to describe the CF behavior of HDVs based on experimental data from mixed traffic flow. Firstly, the experimental scenario and collected data were introduced. Secondly, two transfer functions for the lower control algorithm of AVs were compared. Then, to account for the stochasticity of HDVs, the idea of physics-informed deep learning (PIDL) was used to improve generative adversarial networks (GAN) and integrate it with two-dimensional intelligent driver model (2D-IDM). Finally, the effectiveness of the model was verified from both a micro prediction and a macro simulation perspective. The influence of AVs on the stability of mixed traffic flow at different Market Penetration Rate (MPR) was also observed. The results show that TDS-GAN can effectively describe the following behavior and stochasticity of HDVs. When combined with CF model of AVs, it can depict the evolution of mixed traffic flow more accurately. Additionally, AVs can improve traffic flow stability under different penetration rates, and the effect is more significant with higher MPR. However, the introduction of AVs may not necessarily be positive in terms of road capacity. [ABSTRACT FROM AUTHOR]

Integrating Automated Vehicles (AVs) into existing traffic systems holds the promise of enhanced road safety, reduced congestion, and more sustainable travel. Effective integration of AVs requires understanding the interactions between AVs and Human-driving Vehicles (HVs), especially during the transition period in which AVs and HVs coexist in a mixed traffic environment. Numerous recent empirical studies find reduced headways of human drivers following an AV compared to following an HV, and attribute this reduction to behavioural changes of drivers when they follow AVs. However, more factors may be at play due to the inherent inconsistencies between the comparison conditions of HV-following-AV and HV-following-HV. This study scrutinises three alternative explanations for the observed reduction in headways: (1) systematic differences in car-following states during data collection, (2) systematic differences in driving variability between leading AVs and HVs, and (3) systematic differences in driving characteristics of leading AVs versus HVs. We use a large-scale dataset extracted from Lyft AV motion data and examine each of these explanations through data stratification and simulation. Our results show that all three mechanisms contribute to the observed reduction in headways of human drivers following AVs. In addition, our findings highlight the importance of driving homogeneity and stability in achieving reliably shorter headways. Thereby, this study offers a more comprehensive understanding on the difference between HV–AV and HV–HV interactions in mixed traffic, and is expected to promote more effective integration of AVs into human traffic. • Observation biases are inherent in data collected by automated vehicles (AVs). • 3 explanations for shorter headways of human drivers following AVs are validated. • Systematic differences in data collection and AV driving play remarkable roles. • Driving homogeneity and stability contribute to reliable reduction in time headway. • Behavioural insights from non-behavioural data require scrutiny. [ABSTRACT FROM AUTHOR]

The car following behaviour of a driver is the process of following the drivers’ creation of an adjustment in the leading vehicle behaviour. In a condition, where the traffic volume is in a free-flowing situation, the selection of vehicles speed is typically limited by some factors such as weather conditions, lighting, and road geometry features. This study aims to investigate the effects of climates on driver’s car following behaviour and speed flow relationships for highways in Kuwait. The case study was conducted at Road 40 in Kuwait using RTMS Sx-300 device, which is known as a radar device particularly used for monitoring traffic. The data was gathered in the period from 29th December 2018 to 5th January 2019 in winter. MATLAB code was written to analyse and classify the gathered data. Then, the models were built using R-software. The study depicts that nearly 24.87% of the vehicles move between 60 km/h and 69 km/hour. Additionally, the vehicles were segmented according to their types, i.e., Truck, Small-, Medium-, and Large-Sized Cars, in order to find the impact of following pattern on the vehicle average. It has been found that no significant association remains amidst the type of following pattern and the headway. Ultimately, a liner regression of data was developed to calculate a liner equation that shows the average headway as an element of speed for sixteen diverse following patterns. It has been recognized that an association could be supposed in mediumsized and small-sized vehicles. It has been observed that headway average could be placed in a linear equation for large, medium, and small as well as truck vehicles. It is worthy denoting that when data is bigger, the exactitude of a study is enhanced. Findings from each model of liner regression have more than 80% confidence level. The models of regression are deliberated as statistically significant, where the R (square) figures lie amidst 0.99 till 0.6. As per the findings, speed is the key influencing factor for headway value. The type of car does affect headway with drivers behind Heavy Good Vehicles and cars at the similar speed. According to the data, cars are identified to keep more headway when behind Heavy Good Vehicles in contrast with when behind other cars. These results will help the drivers understand their behaviour associated with car crashes, thus, increasing road safety awareness and reducing traffic congestion in Kuwait. [ABSTRACT FROM AUTHOR]

Abstract The prerequisite for the effective operation of vehicle collision warning system is that the necessary operation is not implemented. Therefore, the behavior prediction that the driver should perform when the preceding vehicle braking is the key to improve the effectiveness of the warning system. This study was conducted to acquire characteristics in the car-following behavior when confronted by the braking of the preceding vehicle, including the reaction time and operation behavior, and establish a behavior prediction model. A driving experiment on the expressway was conducted using devices, such as millimeter-wave radars and controller area network (CAN) bus data, to acquire 845 segments of car following when the brake lamps of the car ahead are on. Data analysis demonstrates that the mean of time distance of car following, mean of car-following distance, and time-to-collision (TTC) mean are closely related with whether or not the driver slowed the car down. The operation states of the driver were divided into keeping the unchanged state of the degree of accelerator pedal opening, loosening of accelerator pedal without braking, braking, and other special situations with the input variables of car-following distance, speed of driver’s car, relative speed, time distance, and TTC using the support vector machine (SVM) method to build a prediction model for the operation behavior of the driver. The verification result showed that the model predicts driving behavior with an accuracy rate of 80%. It reflects the actual decision-making process of the driver, especially the normal operation of the driver, to loosen the accelerator pedal without braking. This model can help to optimize the algorithm of the rear-end accident warning system and improve intelligent system acceptance.

This study developed a pair of lane changing (LC) model and car following (CF) model. They are used to investigate mutually adaptive maneuver of LC vehicle (LCV) and its immediate CF vehicle during the LCV's inserting process. The two models are developed based on a multianticipative CF model to simulate both of the LC and LC-adaptive CF maneuvers. A group of pairwise parameters are applied in the models to reflect the stimuli perceived by the two vehicle drivers simultaneously. Based on the united model framework, the features of the two vehicle maneuvers which are mutually and intensively influenced can be compared and analyzed. Vehicle trajectories collected on the urban arterial are applied to calibrating and validating the two models. Results show that the developed models can fit the trajectories in a higher accuracy than the previous models. The estimates of the model parameters revealed that dynamics of lateral moving vehicle influence the LC and CV maneuvers in different ways. It is found that the lateral influence has the heaviest influence on the vehicle maneuvers than other stimuli. The vehicles also adjust their maneuvers along with the change of traffic signal or LC target.

Compared with traditional rule-based algorithms, deep reinforcement learning methods in autonomous driving are able to reduce the response time of vehicles to the driving environment and fully exploit the advantages of autopilot. Nowadays, autonomous vehicles mainly drive on urban roads and are constrained by some map elements such as lane boundaries, lane driving rules, and lane center lines. In this paper, a deep reinforcement learning approach seriously considering map elements is proposed to deal with the autonomous driving issues of vehicles following and obstacle avoidance. When the deep reinforcement learning method is modeled, an obstacle representation method is proposed to represent the external obstacle information required by the ego vehicle input, aiming to address the problem that the number and state of external obstacles are not fixed.

In recent years, the mileage of the tunnels has substantially increased with the rapid highway construction that led to increasing highway tunnels. Most studies on tunnel accidents have mainly focused on the external environments, such as tunnel structure, traffic volume, and lighting. In addition, although many studies on mental load of drivers have been conducted for public roads, such studies for highway tunnels have been limited. In this study, three scenarios with different front vehicle speeds (60, 45, and 30 km/h) in a two-lane long tunnel (one lane in each travel direction) were evaluated using a driving simulator. The experiment involved 24 participants (14 men and 10 women) with an average age of 25.8 years and an average experience of 3.2 years. The electroencephalogram (EEG) technology was used to collect the leading EEG indicators during the driving simulation of the scenarios: α, β, and θ waves and the wave ratio, (α + θ)/β. According to the β-wave energy measurements, the alertness of drivers was the lowest at 45 km/h after adapting to the tunnel environment, indicating that the drivers were more comfortable at this speed. This preliminary finding should help in determining the speed limit in this type of tunnel. [ABSTRACT FROM AUTHOR]

• A field experiment using instrumented motorcycles was performed. • Factors affecting the riders' behaviour in car following condition were analysed. • Two surrogate safety indicators – Time Headway and Time To Collision –– were calculated. • Time Headway was modelled by using the hazard-based duration analysis. • The model identified the significant covariates that affected the riders' behavior. Car-following (CF) maneuver plays an important role in both traffic flow analysis and road safety assessment. However, many studies on this issue have focused on the drivers' behaviour, neglecting the different driving behaviour of the motorcycle riders and the vulnerability of this type of user. The aim of the present paper is to analyse the factors affecting the riders' behaviour in CF condition, by using two surrogate safety indicators, Time Headway (TH) and Time To Collision (TTC). A field experiment was conducted to collect data. Twenty young motorcyclists participated in the study driving their own motorcycles, which were instrumented with a camera and a global positioning system device (GPS). A digital image processing algorithm was used to obtain CF related indicators. The results of the study showed that Time Headway (TH) and Time To Collision (TTC) are independent of each other, extending the result obtained in the literature for the four wheeled vehicles to the motorcycles. TTC and TH were therefore analyzed separately. As for TTC, it was observed that only in few cases (0.8%) the riders were found in risky condition (TTC < 5 s), confirming the role of this parameter for the detection of an impending risk. On the other hand, TH gives information about a potentially risky situation, and 99% of the riders were found in such condition (TH < 2 s). Due to the few cases in TTC risky conditions, only TH was further modelled by using the hazard-based duration analysis. Accelerated Failure Time (AFT) model specification was used, and the Weibull distribution provided the best fit of the data. The model identified four covariates affecting TH and all of them had the effect of reducing TH, increasing the collision risk. The time spent on CF condition, the speed of the motorcycle, the willingness of overtaking and the engine size of the motorcycle were the factors that reduced TH. The findings of this study could be considered in the development of microsimulation models; the factors that were found affecting the motorcyclists' behavior in car-following condition could be considered for a more realistic simulation of the riders' choice of Time Headway. [ABSTRACT FROM AUTHOR]

In recent years, the mileage of the tunnels has substantially increased with the rapid highway construction that led to increasing highway tunnels. Most studies on tunnel accidents have mainly focused on the external environments, such as tunnel structure, traffic volume, and lighting. In addition, although many studies on mental load of drivers have been conducted for public roads, such studies for highway tunnels have been limited. In this study, three scenarios with different front vehicle speeds (60, 45, and 30 km/h) in a two-lane long tunnel (one lane in each travel direction) were evaluated using a driving simulator. The experiment involved 24 participants (14 men and 10 women) with an average age of 25.8 years and an average experience of 3.2 years. The electroencephalogram (EEG) technology was used to collect the leading EEG indicators during the driving simulation of the scenarios: α, β, and θ waves and the wave ratio, (α + θ)/β. According to the β-wave energy measurements, the alertness of drivers was the lowest at 45 km/h after adapting to the tunnel environment, indicating that the drivers were more comfortable at this speed. This preliminary finding should help in determining the speed limit in this type of tunnel.

Congested traffic flow exhibits rather complex properties of spatiotemporal evolution, e.g. stop-and-go waves, phantom jams, wide scattering of flow-density data, etc. To the best of our knowledge, previous car-following experiments on circular tracks only studied heavily congested flow. The experimental study concerning lightly congested flow, which exhibits different stability characteristic from heavily congested flow, is still lacking. To fill this gap, very recently we conducted a large-scale car-following experiment on an 812-meter circumference circuit with densities of 0.049, 0.043 and 0.037 m−1. The flow-density relation of the experiment is highly consistent with the empirical one, which indicates that the driving behavior in the experiment is similar to that in real traffic. By comparing the macroscopic spatiotemporal evolution diagrams in different runs under the same global density, we found that the traffic flow exhibits stochastic features with even the same initial conditions. We analyzed the car-following behavior and its relation to the macroscopic traffic flow evolution. It was found that the indifference region, the sensitivity of drivers, and the strength of stochasticity all contribute to the macroscopic traffic flow evolution pattern. Specifically, the traffic flow becomes more homogeneous in time-space with the expansion of indifference region, the overall decrease of sensitivity and the decrease of stochasticity strength. The roles of stochasticity and speed adaptation were further investigated. It was found that stronger traffic oscillations correspond to stronger stochasticity and speed adaptation. The analysis for competition between the two effects indicated that the ratio between speed adaptation effect and stochastic effect grows with the increase of the oscillation amplitude. The stochastic effect plays a dominant role in car following when the oscillation amplitude is small; its influence wanes and the speed adaptation effect grows to outweigh the influence of the stochastic effect as oscillation amplitude grows. These findings are expected to improve our understanding of the underlying mechanism that produces the spatiotemporal evolution patterns of lightly congested flow and the data collected in this study can be used to test traffic models and theories. • A large-scale car-following experiment on an 812-meter circumference circuit is performed • Traffic flow exhibits stochastic features with even the same initial conditions • The indifference region, the sensitivity of drivers, and the strength of stochasticity all contribute to the macroscopic traffic flow evolution pattern • Stronger traffic oscillations correspond to stronger stochasticity and speed adaptation [ABSTRACT FROM AUTHOR]

Autonomous vehicles have the potential to revolutionize intelligent transportation by improving traffic safety, increasing energy efficiency, and reducing congestion. In this study, a novel framework termed DeepAD was proposed and validated for decision making in intelligent autonomous driving via deep reinforcement learning. This framework incorporates multiple driving objectives such as efficiency, safety, and comfort to make informed decisions regarding autonomous vehicles (AVs). The decision-making process utilizes the origin–destination information for macrolevel routing and determines microlevel car-following and lane-changing behaviors. The lane-changing behavior is discretized and learned through a deep Q-network, and the continuous car-following behavior is learned through a deep deterministic policy gradient. Comprehensive simulation experiments on a real-world network demonstrated that DeepAD outperformed human driving while maintaining a desirable level of efficiency, safety, and comfort. In the real-world road networks experiment, multiple indexes of vehicles in the high AVs penetration rate group significantly outperformed that of the group with lower AVs penetration rate. Overall, the proposed framework provides insights into intelligent autonomous driving to improve urban mobility. [ABSTRACT FROM AUTHOR]

• An analytical framework for modeling both the damping behavior and the stability of AV longitudinal control has been provided. • Optimal safety-oriented ACC parameters for AV longitudinal control have been evaluated. • Guide the design of AV longitudinal control algorithms and choose the optimal car-following headway settings. Extensive research has examined the potential benefits of Automated Vehicles (AVs) for increasing traffic capacity and improving safety. However, previous studies on AV longitudinal control have focused primarily on control stability and instability or tradeoffs between safety and stability, neglecting the importance of vehicle damping characteristics. This study aims to demonstrate the significance of explicitly considering safety in addition to stability in AV longitudinal control through damping behavior analysis. Specifically, it proposes a safety-oriented AV longitudinal control and provides recommendations on the control parameters. For the proposed AV control, an Adaptive Cruise Control (ACC) model is integrated with damping behavior analysis to model AV safety under continuous traffic perturbations. Numerical simulations are conducted to quantify the relationship between mobility and safety for AVs considering both damping behavior and control stability. Different ACC control parameters are evaluated in terms of damping and stability properties, and their safety impacts are assessed based on various surrogate safety measures such as Deceleration Rate to Avoid Crash (DRAC), Crash Potential Index (CPI) and Time-Integrated Time-to-collision (TIT). The results indicate that an underdamped state (ACC damping ratio < 1) is less safe than the critically damped state (ACC damping ratio = 1) and the overdamped state (ACC damping ratio > 1). Furthermore, given the same AV car-following time lag, ACC with a damping ratio between 1 and 1.2 provides better safety performance. Increasing the AV car-following time lag can improve both safety and stability when the remaining ACC control parameters are kept the same. In this case, the optimal safety-oriented ACC regions also increase. The findings of this study provide important insights into designing safe and stable AV longitudinal control algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology to estimate uncertainties in automated vehicle (AV) dynamics in real time via Bayesian inference. Based on the estimated uncertainty, the method aims to track the car-following (CF) performance of the AV to support strategic actions to maintain desired performance. Our methodology consists of three sequential components: (i) the Stochastic Gradient Langevin Dynamics (SGLD) is adopted to estimate parameter uncertainty relative to vehicular dynamics in real time, (ii) dynamic monitoring of car-following stability (local and string-wise), and (iii) strategic actions for control adjustment if anomaly is detected. The proposed methodology provides means to gauge AV car-following performance in real time and preserve desired performance against real time uncertainty that are unaccounted for in the vehicle control algorithm. • Real-time car-following (CF) performance monitoring framework is developed. • Probabilistic approach is adopted for uncertainty estimation in lower-level controller. • Strategic adjustments are offered for performance loss in CF behavior. [ABSTRACT FROM AUTHOR]

Before a car-following model can be applied in practice, it must first be validated against real data in a process known as calibration. This paper discusses the formulation of calibration as an optimization problem and compares different algorithms for its solution. The optimization consists of an arbitrary car following model, posed as either an ordinary or delay differential equation, being calibrated to an arbitrary source of trajectory data that may include lane changes. Typically, the calibration problem is solved using gradient free optimization. In this work, the gradient of the optimization problem is derived analytically using the adjoint method. The computational cost of the adjoint method does not scale with the number of model parameters, which makes it more efficient than evaluating the gradient numerically using finite differences. Numerical results are presented that show that quasi-Newton algorithms using the adjoint method are significantly faster than a genetic algorithm and also achieve slightly better accuracy of the calibrated model. [ABSTRACT FROM AUTHOR]

• Studies of safe driver behaviour and performance use varied definitions of headway. • From 1980, less than half of 110 reviewed studies provided reproducible definitions. • Definitions often depended on the measuring method, contributing to reporting gaps. • Vehicle length should be excluded when measuring headway as a safety behaviour. • Measurement points for both vehicles and device accuracy should always be reported. 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. [ABSTRACT FROM AUTHOR]

Low illumination is a special driving condition that negatively affects drivers' vision, information acquisition (IA) ability, distance recognition and risk perception. This study evaluated drivers' car-following behaviours and characteristics using questionnaire-based research conducted among 214 drivers in Hefei. In this study, exploratory factor analysis (EFA) was used to determine the factor structure of the scale, and the internal consistency of all factors was good. The results show that low illumination strongly influences drivers' following behaviour and that they tend to choose safe and conservative ways to follow leading vehicles. Street lights are beneficial, aiding drivers' IA and their grasp of surrounding or distant environments. Myopic drivers performed worse in car following when driving in a low illumination environment, regardless of the presence of street lights. Drivers with astigmatism performed worse when street lights were present. Drivers who reported more aberrant behaviours were more aggressive when driving and tended to adopt shorter following distances at night. [ABSTRACT FROM AUTHOR]

• Car following behavioral stochasticity has been analyzed based on two sets of field experimental trajectory data. • Wave travel time does not follow a consistent pattern. • Changing rate of wave travel time follows a mean reversion process. • A new stochastic Newell type model is proposed. • Stochastic factors exhibit common feature from different perspectives. This paper analyzes the car following behavioral stochasticity based on two sets of field experimental trajectory data by measuring the wave travel time series τ ˜ n (t) of vehicle n. The analysis shows that (i) No matter the speed of leading vehicle oscillates significantly or slightly, τ ˜ n (t) might change significantly; (ii) A follower's τ ˜ n (t) can vary from run to run even if the leader travels at the same stable speed; (iii) Sometimes, even if the leader's speed fluctuates significantly, the follower can keep a nearly constant value of τ ˜ n (t). The Augmented Dickey-Fuller test indicates that the time series ξ n (t) = d τ ˜ n (t) / d t follows a mean reversion process, no matter the oscillations fully developed or not. Based on the finding, a simple stochastic Newell model is proposed. The concave growth pattern of traffic oscillations has been derived analytically. Furthermore, simulation results demonstrate that the new model well captures both macroscopic characteristic of traffic flow evolution and microscopic characteristic of car following. [ABSTRACT FROM AUTHOR]

Smart mobility and transportation, in general, are significant elements of smart cities, which account for more than 25% of the total energy consumption related to smart cities. Smart transportation has seven essential sections: leisure, private, public, business, freight, product distribution, and special transport. From the management point of view, transportation can be classified as passive or non-cooperating, semi-active or simple cooperating, active or cooperating, contract-based, and priority transportation. This approach can be applied to public transport and even to passengers of public transport. The transportation system can be widely observed, analyzed, and managed using an extensive distribution network of sensors and actuators integrated into an Internet of Things (IoT) system. The paper briefly discusses the benefits that the IoT can offer for smart city transportation management. It deals with the use of a hierarchical approach to total transportation management, namely, defines the concept, methodology, and required sub-model developments, which describes the total system optimization problems; gives the possible system and methodology of the total transportation management; and demonstrates the required sub-model developments by examples of car-following models, formation motion, obstacle avoidances, and the total management system implementation. It also introduces a preliminary evaluation of the proposed concept relative to the existing systems. [ABSTRACT FROM AUTHOR]

• This work shows that the fundamental relationship can rapidly change shape. • The rapid change is a systematic response to conditions in adjacent lanes. • Drivers adopt larger spacing when faced with high relative speed between lanes. • This adjacent lane dependency is absent from most micro and macro models. • The new found dynamics provide mechanisms to sustain synchronized flow. Much of traffic flow theory and highway design is based upon the fundamental relationship, FR, between speed, flow, and density. Traditionally, the FR is assumed to be a static curve over time and space. This paper presents new empirical findings that show the FR in one lane of a freeway can actually change shape in a rapid and systematic response to conditions in the adjacent lanes. This paper demonstrates the dependencies for a general purpose freeway lane that is operating in the congested regime. The drivers become more conservative in response to slower speeds in either adjacent lane, conversely, they become less conservative in response to higher speeds in either adjacent lane. It is also shown that as drivers become more conservative the signals and waves move upstream slower. So this inter-lane dependency has a direct impact on how fast signals propagate through the traffic stream. For the study site the empirical relationship between the wave speed and relative speed is close to linear. This interdependence between lanes is an important factor that it is absent from most traffic flow and car following models. Given these findings the discussion explores hypothetical examples showing how the inter-lane dependencies can give rise to mechanisms for waves in adjacent lanes to synchronize, as well as a mechanism for a queue spreading from one lane to another without any lane change maneuvers. Finally, this work closes with an exploration of the possible physiological mechanisms that might be occurring in the drivers. [ABSTRACT FROM AUTHOR]