Previous studies have mostly hypothesised or provided preliminary evidence that crash risk during mandatory lane-changing manoeuvres in a connected environment would be reduced. However, the unavailability of crash data for a connected environment makes it challenging to confirm such a hypothesis. To this end, this study has adopted an Extreme Value Theory approach to estimate and compare crash risk using traffic conflicts during mandatory lane-changing manoeuvres in the traditional and connected environments. Using the CARRS-Q advanced driving simulator, seventy-eight participants performed mandatory lane-changing manoeuvres in three randomised driving conditions: baseline (without driving aids), connected environment with perfect communication (PC), and connected environment with communication delay (CD). Driving-related factors obtained from the driving simulator data such as speeds, spacings, lag gaps, and remaining distances were used as input to a block maximum (or generalised extreme value, GEV) model. The characteristics of the estimated GEV distributions and relative crash risk were employed as an indicator of safety. Results reveal that in the connected environment driving conditions, the mandatory lane-changing crash risk is significantly reduced compared to the baseline, with the highest reduction observed in the PC condition. The crash risk is found to be higher in the CD condition compared with the PC condition. Findings of this study confirm (i) that the connected environment has the potential to reduce the crash risk substantially by assisting drivers during mandatory lane-changing manoeuvres, and (ii) the efficacy of GEV models to quantify crash risk by using conflict data when crash records are unavailable.

The gradual deployment of Connected and Automated Vehicles (CAV) in traffic will result in a transition period in which vehicles with various levels of automation and connectivity will have to co-exist with non-connected and non-automated road users for quite some time. Consequently, new types of interactions will emerge (and old types of interactions are likely to become more complicated) between vehicles at different levels of automation and other road users which could have significant implications on traffic safety and efficiency. Understanding the nature of these interactions, how humans might adapt their behavior, how connectivity can be utilized to proactively enhance drivers char63 driving performance, and how automated vehicles can be programmed to behave in different driving situations to guarantee safety and efficiency remain among the key knowledge gaps that require scientific research. This knowledge is crucial for the development of adequate integration policies of connected and automated vehicles in mixed traffic environment, for updating and improving automated vehicles char63 algorithms and software, for designing the physical and digital road infrastructure, and for operating and managing traffic on the road network.

Previous empirical behavior analysis on Autonomous Vehicles (AV) mainly focused on vehicles with Adaptive Cruise Control (ACC) system due to the lack of high-level AV dataset. Recently released SAE Level-4 AV datasets such as the Waymo Open Dataset provide great opportunities to evaluate their behavioral impact on traffic flow. In this study, we aim to characterize the empirical Car Following (CF) behaviors of the Waymo autonomous vehicle and compare its feature with human-driven Vehicles (HV), and capture such behavioral differences using the IDM CF model. Our main findings include: (a) AV is much safer than HV, based on our analysis using surrogate safety measures, as time headways and jam spacings of the AV are significantly larger than HV; (b) the response time of AV is also significantly larger than that of HV in response to various types of stimuli; (c) despite the short length of trajectories in the Waymo Open Dataset, we have confirmed that these trajectories are suitable for calibrating some of the IDM parameters; and the calibration results of IDM are consistent with our empirical analysis. Moreover, the modelling results, reveal that the proportion of string unstable behavior of AV is less than that of HV; and (d) for HV, there is generally no significant difference between following AV and following HV except a smaller jam spacing when following AV. Overall, we conclude that currently AV behaves in a conservative way to ensure its safety at the cost of traffic efficiency.

Discretionary lane change (DLC) is a basic but complex maneuver in driving, which aims at reaching a faster speed or better driving conditions, e.g., further line of sight or better ride quality. Although modeling DLC decision-making has been studied for years, the impact of human factors, which is crucial in accurately modelling human DLC decision-making strategies, is largely ignored in the existing literature. In this paper, we integrate the human factors that are represented by driving styles to design a new DLC decision-making model. Specifically, our proposed model takes not only the contextual traffic information but also the driving styles of surrounding vehicles into consideration and makes lane-change/keep decisions. Moreover, the model can imitate human drivers’ decision-making maneuvers by learning the driving style of the ego vehicle. Our evaluation results show that the proposed model captures the human decision-making strategies and imitates human drivers’ lane-change maneuvers, which can achieve 98.66% prediction accuracy. Moreover, we also analyze the lane-change impact of our model compared with human drivers in terms of improving the safety and speed of traffic.

As a common phenomenon, overtaking behaviour is frequently observed on pedestrian flow, which not only reshapes pedestrian flow but also generates adverse impacts on pedestrian safety to some extent. Prior research focused on unidirectional pedestrian modelling, especially with overtaking behaviour, is limited. Moreover, pedestrian behaviour in the context of COVID-19 is rarely investigated. Inspired by the social force model, this paper proposes a dynamic sensitivity model for unidirectional pedestrian flow, which is able to describe the overtaking behaviour and analyse the potential impact of COVID-19 on pedestrian behaviour. In the proposed model, dynamic sensitivity and attention field of pedestrians are introduced to embody the effects of individual characteristics and surrounding environments on pedestrian behaviours. To calibrate the model and evaluate the effects of COVID-19 pandemic on pedestrian dynamics, real-life data collected by video recordings in Nanjing, China is used in this study. The simulation results indicate that the dynamic sensitivity model is able to reflect the variance of the adaptive velocity and route choice of overtaking pedestrians on unidirectional pedestrian flow. Our research findings show that the social distance during COVID-19 is higher than the value under normal conditions, and the majority of pedestrians tend to follow the suggested social distancing rules during COVID-19. Moreover, the overtaking pedestrians violate the suggested social distancing rules more frequently than the rest pedestrians.

The predictive cruise control (PCC) is a promising method to optimize energy consumption of vehicles, especially the heavy-duty vehicles (HDV). Due to the limited sensing range and computational capabilities available on-board, the conventional PCC system can only obtain a sub-optimal speed trajectory based on a shorter prediction horizon. The recently emerging information and communication technologies such as vehicular communication, cloud computing, and Internet of Things provide huge potentials to improve the traditional PCC system. In this paper, we propose a general framework for the enhanced cloud-based PCC system which integrates a data-driven traffic predictive model and the instantaneous control algorithms. Specifically, we introduce a novel multi-view CNN deep learning algorithm to predict traffic situation based on the historical and real-time traffic data collected from fields, and the time-varying adaptive model predictive control (MPC) to calculate the instantaneous optimal speed profile with the aim of minimizing energy consumption. We verified our approach via simulations in which the impact of various traffic condition on the PCC-enabled HDV has been fully evaluated.

This paper proposes a new car-following (CF) model to capture the realistic behaviors of connected and automated vehicles (CAVs), whose communication topology (CT) among vehicles is characterized by graph theory in the V2V communication environment. By considering the heterogeneous time delays under the fixed and switching CTs, a generalized CF model is proposed. Based on the Lyapunov–Krasovskii method, a convergence analysis has been implemented for this new CF model with multiple time delays to obtain the convergence condition. Meanwhile, provides an estimate of the time delay bound. Finally, numerical experiments are performed under three typical fixed CTs (i.e., PF topology, BDLF topology, and TPLF topology) and the corresponding switching topology. Results support that the proposed CF model is capable of accurately reproducing the velocity, acceleration, position, and space headway profiles of CAVs traffic flow.

A gradient boosting procedure in combination with hierarchical reconciliation is proposed in this study for short-term forecasting of traffic flow. Particular attention is paid to three main characteristics of traffic flow: the temporal and spatial patterns, interactions between the temporal and spatial patterns, and the dynamics of traffic flow at different spatial aggregation levels. The performance of the proposed forecasting framework is examined by comparing it with three frequently used methods (i.e., SARIMA, Kalman filter model and random forest) in the literature, and using three distinctive datasets. Overall, the gradient boosting based approach offers a highly flexible and automated way to learn useful information in large datasets, which is particularly advantageous for forecasting traffic flow in a complex road network at longer forecasting horizons.

In this paper we develop and test a novel stochastic algorithm SNE (Shrinking Neighborhood Evolution) based on the issue of bound constrained optimization problem. Its heuristic strategy is simple and direct-related to the search region of the solving problem based on the concept of “k-box-neighborhood” -defined in this paper. Our numerical experiments show that the optimization capability of SNE is competing to other congeneric algorithms such as Particle Swarm Optimizer (PSO), Evolution Strategy with Covariance Matrix Adaptation (CMA-ES) and Differential Evolution (DE). The new method requires few control parameters, easy to use, and has promising potentials to parallel computation.