Autonomous driving often suffer from a decoupled feedback loop between prediction and planning. While prediction losses focus on the accuracy of surrounding agents, planning losses typically imitate recorded Autonomous Vehicles’ (AVs) trajectories that may contain suboptimal or aggressive behaviors, leading to unstable interactions in mixed traffic. This paper presents HybridLoss, an adaptive planning-oriented objective that unifies prediction and planning through planner-in-the-loop supervision and interaction-aware consistency. HybridLoss integrates an adaptive motion-planning module which replaces ground-truth targets with optimized reference trajectories, and a multi-term loss combining prediction, adaptive planning, safety potential, and social force objectives. Evaluations on the INTERACTION dataset indicate that HybridLoss significantly outperforms strong baselines. Beyond standard metric improvements—reducing ADE/FDE from 1.36/1.64 m to 1.11/1.36 m and collision rates from 0.19% to 0.11%—extensive stress-testing reveals superior system maturity. First, HybridLoss exhibits the highest robustness under input perturbations, maintaining the lowest planning deviation and endpoint standard deviation. Second, it demonstrates strong generalization, maintaining stable success rates (87.7%) in unseen scenarios with high computational efficiency (64.3 Hz). Third, multi-objective analysis confirms that HybridLoss achieves the optimal Pareto trade-off between efficiency, safety, and comfort, avoiding the speed-safety collapse seen in baseline methods. Finally, social force evaluations highlight that HybridLoss fosters implicit cooperation, achieving higher yield rates and reduced conflict indices while maintaining safe interaction buffers. These results validate HybridLoss as a robust, socially compliant, and adaptive solution for end-to-end driving.

The preponderance of traffic accidents takes place within urban intersection areas. It is crucial to extrapolate accident risk maps specifically for these locations to proactively mitigate and prevent future occurrences of traffic accidents. Nevertheless, inferring fine-grained intersection risk remains a challenging task, primarily due to the intricate structure of the road network, variability in scene information, and the stringent requirements for high-quality data. In this work, we propose an end-to-end Adaptive Risk-Feature Aware Fusion Network (ARFAF-Net) based on multimodal data to achieve fine-grained inference of traffic risk maps in intersection areas. Specifically, we introduce a Heterogenous Feature Adaptive Fusion Module to extract complementary features of risk from satellite imagery and streetscape data. The Dynamic Correlation Analysis Module is used to capture large-scale changes in risk in the region to improve multi-scale information perception. In addition, the Macro-Aware Guided Fusion Module is used to introduce macro-satellite image data features to enhance the accuracy of perceiving risks at the periphery of intersections. Ultimately, pixel-level extrapolation maps of intersection crash risk are generated, thereby offering more cost-effective and rational guidance for crash prevention. Both quantitative evaluation and qualitative analysis on real-world datasets demonstrate that the proposed ARFAF-Net achieves superior performance. Finally, the codes and models used in this study for intersection crash risk map inference are available athttps://github.com/gwt-ZJU/ARFAF-Net.

Discretionary lane changing is one of the most important behaviors in vehicle dynamics. The analysis of discretionary lane changing can provide support for human-like autonomous driving system and microscopic traffic simulation. The rule based discretionary lane change decision-making model has the advantages of good interpretability and being in line with human intuitiveness, but the performance of the rule based models are relatively poor. The learning-based discretionary lane changing decision-making models perform better in lane changing analysis than that of the rule based models, while they require a large amount of training data and have poor interpretability. This study proposes a framework for discretionary lane changing decision-making based on cumulative prospect theory and decision tree model, and uses genetic algorithm to calibrate the parameters of the framework. The framework proposed in this study has good interpretability for discretionary lane changing behavior similar to rule-based models, and it can achieve good lane changing prediction performance. The Next Generation Simulation (NGSIM) dataset is used to validate the framework proposed in this study. The results show that the framework proposed in this study can achieve better performance of discretionary lane change prediction than the analysis model based on decision tree and the analysis model based on cumulative prospect theory. When predicting discretionary lane changing behavior 2–0 seconds in advance, the accuracy of the framework in the test set can reach 85.6%.

This paper employs lateral and longitudinal trajectory planning to generate candidate trajectories and discards those that do not satisfy the constraints imposed by single-vehicle conditions. Next, a collaborative trajectory combination set for multiple vehicles is derived from the candidate trajectories, with multi-vehicle constraints applied to eliminate combinations that fail to meet the required conditions. The objective function for each candidate trajectory set is first calculated using a single-vehicle objective function, after which a multi-vehicle objective function based on the Kaldor-Hicks improvement principle is constructed. Finally, the paper introduces an improved particle swarm optimization method for multi-vehicle collaborative trajectory planning. The results demonstrate that the dynamic spatiotemporal occupancy growth rate, under varying planning times and frequencies, is at least 19%. Furthermore, the proposed algorithm ensures efficient allocation of travel resources, preventing competition among vehicles that could compromise system feasibility. When verified with HighD trajectory data, the algorithm not only delivers superior optimization results but also exhibits lower standard deviations in dynamic spatiotemporal occupancy and speed compared to real-world data. Finally, the algorithm’s superiority in real-time decision-making and stability is confirmed. Note to Practitioners—In the context of mixed traffic comprising both autonomous and human-driven vehicles, this paper tackles the challenge of coordinating autonomous vehicles to improve traffic efficiency and safety in real-time environments. It presents a promising approach to enhancing cooperative behavior in complex scenarios by integrating real-time data streams to optimize adaptability and ensure equitable driving efficiency across different vehicle types.

Urban public transportation is a complex and open system integral to urban mobility. Its operation is often disrupted by various random factors, necessitating robust scheduling solutions. This study develops a bus robust scheduling model based on mixed-integer linear programming to enhance system resilience. First, an arterial signal coordination model is proposed for mixed traffic environments, enabling autonomous public transport vehicles to traverse intersections without stopping. Second, a demand-deterministic bus scheduling model is constructed, integrating timetables, trajectories, and origin-destination transfer schemes to balance passenger waiting time fairness and efficiency. Third, to address stochastic passenger demand during actual operations, a robust bus scheduling model is developed by incorporating robust constraints. Numerical experiments demonstrate that the demand-deterministic model generates optimal scheduling schemes when passenger demand remains within bus capacity. However, when passenger demand exceeds capacity, the demand-deterministic model becomes infeasible. In such scenarios, the robust scheduling model produces feasible schemes, albeit with reduced optimization, and its robustness can be tuned by adjusting model parameters. Additionally, practical management insights are provided for real-world applications.

Mobile Phone Signaling (MPS) data record the daily traces of urban residents, offering a cost-effective means to obtain travel information for urban traffic management and planning at low cost. However, despite the vast amount of data available, there remains a lag in the development of techniques for identifying fine-grained information such as travel modes. On the one hand, the high positioning error and irregular collection frequency make the identification of fine-grained modes challenging. On the other hand, the difficulties in collecting real labeled data limit the training and evaluation of advanced models. In this paper, we present an advanced Travel Mode Identification (TMI) framework and collect real labeled MPS data for evaluation. Specifically, a fast smoothing method is proposed to enhance noise reduction in large-scale trajectories while effectively mitigating positioning errors. We propose novel point-level bus route alignment features for advanced deep-learning models to improve differentiation between motorized modes. Furthermore, a deep learning model with ensembled feature encoding modules is designed to overcome training instability due to limited data amount. Our proposed framework achieves an accuracy of 83.06% in identifying fine-grained travel modes, including walking, riding, bus, car, and metro, with recall rates exceeding 78% for all modes except walking. We analyze the relationship between accuracy and the spatiotemporal characteristics of trajectories, revealing a significant impact from the collection frequency, while showing insensitivity to distance gaps and positioning errors. This study demonstrates the potential of MPS for TMI and promotes its application in intelligent transportation systems.

Vehicle trajectory prediction is crucial for ensuring the safety and reliability of autonomous driving systems. Due to the highly stochastic nature of road participants’ behaviors, it is vital that prediction models accommodate a wide range of possible scenarios to mitigate safety risks. To address this challenge, we propose a novel trajectory prediction model called DiffusionTrajPred, an innovative trajectory prediction model based on the diffusion model. This model uniquely combines forward and reverse processes, manipulating noise levels in trajectory data to forecast future paths. Through the application of a mask-based reverse process, the model can make full use of historical trajectory information and predict trajectories that combine accuracy and multiple possibilities. The model utilizes a Transformer architecture for learning the noise, which enables the model to extract richer temporal information from trajectory data, resulting in improved semantic comprehension. Furthermore, we have effectively encoded high-definition (HD) semantic map information and vehicle interaction dynamics as crucial input features, improving the model ’s predictive power. Extensive experiments on the widely recognized open-source dataset ’Argoverse’ reveal that our method outperformed the most existing state-of-the-art methods in terms of accuracy and multimodality, demonstrating the diffusion model’s unique advantage in addressing the stochastic nature of road scenarios in autonomous driving.

Analyzing discretionary lane-changing (DLC) behavior can provide support for intelligent connected vehicle driving behavior modeling and microscopic traffic simulation. The discrete choice model is the basic model in DLC modeling. However, the discrete choice model cannot fully consider risk preferences during the DLC decision making process. This study harnesses cumulative prospect theory to consider risk preference, constructing a feature vector that includes lane-changing benefits, lane-changing risks, and driving style of drivers. Based on this, a DLC decision making framework based on the discrete choice model is proposed. The framework considers risk preference in the DLC decision making process. It not only retains the interpretability of the discrete choice model but also enhances the predictive performance of DLC. The DLC decision-making framework proposed in this study is validated through the Next Generation Simulation (NGSIM) dataset. The results show that the DLC decision making framework proposed in this study can achieve better performance than the discrete choice model, with an accuracy reaching 77.25% in the test set.

The prevailing reinforcement-learning-based traffic signal control methods are typically staging-optimizable or duration-optimizable, depending on the action spaces. In this paper, we use hybrid proximal policy optimization to synchronously optimize the stage specification and green interval duration. Under reformulated traffic demands, the intrinsic imperfections of (implementing optimization in) discrete or continuous action spaces are revealed. By comparison, hybrid action space offers a unified search space, in which our proposed method is able to better balance the trade-off between frequent switching and unsaturated release. Experiments in both single-agent and multi-agent scenarios are given to demonstrate that the proposed method reduces queue length and delay by an average of 12.72% and 11.89%, compared to the state-of-the-art RL methods. Furthermore, by calculating the Gini coefficients of right-of-way, we reveal that the proposed method does not harm fairness while improving efficiency.

In this study, we introduce a novel hybrid trajectory planning algorithm for autonomous driving, specifically designed to mitigate the risks posed by spoofing attacks on Connected and Autonomous Vehicles (CAVs). The research begins by assessing the safety implications of attacks and developing an adaptive safety model that is grounded in the fundamental assessment of state and decision data. This model incorporates the establishment of a posterior probability distribution for decision data, rooted in the pre-existing prior distribution but adjusted to account for the influence of spoofing attacks. The adjustment is achieved through Bayesian maximum posterior estimation, thereby refining the model to better adapt to potential threats. The adaptive safety model is then optimized dynamically, taking into consideration a set of indices—safety, comfort, and efficiency—that are critical to trajectory planning. In the subsequent phase, we introduce a composite trajectory planning algorithm that integrates a lateral trajectory selection sampling method with a longitudinal trajectory optimization approach. The adaptive safety model is seamlessly integrated into the trajectory planning process, influencing target position selection, the setting of constraints, and the formulation of the optimization objective function. The results demonstrate that the algorithm effectively limits the average standard deviation of lateral displacement to 0.5851 and achieves a significant increase in longitudinal speed growth rate, by up to 7.30%, surpassing the performance of benchmark algorithms. The proposed solution consistently delivers optimal safety and efficiency across various scenarios and under different parameter conditions.

Deep Reinforcement Learning has shown some promise in dynamic traffic signal control by adapting to real-time traffic conditions. However, multi-intersection control presents challenges, primarily due to the need for efficient information exchange across increasing intersections, and the importance of spatiotemporal dynamics in traffic flows. Traditional methods often focus solely on spatial or temporal aspects, leading to suboptimal control strategies. This paper introduces a novel Multi-Agent Incentive Communication Deep Reinforcement Learning (MICDRL) method, designed for collaborative control across multiple intersections. MICDRL features an incentive communication mechanism, allowing agents to generate customized messages that influence other agents’ policies, thereby enhancing coordination and achieving globally optimal decisions. A key feature of MICDRL is its reliance on local information for message generation, effectively reducing communication overhead while ensuring collaboration. Additionally, MICDRL integrates a teammate module that leverages temporal data for predicting other agents’ actions, crucial for understanding collective dynamics and spatial environment characteristics. Empirical results show that MICDRL outperforms several state-of-the-art methods in metrics like queue length and throughput. Furthermore, we introduce a tailored three-layer Internet-of-Things architecture to enhance data collection and transmission.

Customized bus (CB) is a kind of demand-responsive and one-stop transit service for commuters with similar travel demands. In actual applications, the bus companies identify commuter demands including origin-destination (OD) flow and departure time using online surveys, and manually plan the CB routes, which is inefficient and costly. This study aims to identify spatial patterns of travel demands using a clustering algorithm based on vehicle trajectory data, and automatically extract the potential CB route from each cluster. The clustering algorithms are based on similarity measurements. However, the existing measurements cannot be used to synchronously identify the multiple characteristics of vehicle trajectories, such as OD location, direction, and multiple sub-sequences. To address this issue, we propose a comprehensive similarity (CS) to simultaneously evaluate all the characteristics. Subsequently, we utilize the density-based spatial clustering of applications with noise (DBSCAN) to divide the trajectories into several groups, and determine the trade-off parameters in the DBSCAN using vlseKriterijumska optimizacija i kompromisno resenje (VIKOR), which means a multi-criteria optimization and compromise solution. Then, a case study is conducted based on one-week taxi trajectory data collected in Suzhou, China. The extracted results are compared to the existing routes designed by bus companies, providing a coverage rate of 66.7%. Finally, the advantages of the proposed method were discussed from the two perspectives of clustering algorithm and trajectory similarity measurement. The proposed method can help traffic management authorities and bus companies to plan and design new CB routes.

With the development of intelligent transportation technology, more and more traffic information can be obtained to enhance arterial coordination efficiency. A time-varying arterial coordination control program is proposed in this paper. By extracting Origin-Destination (OD) information from Automatic number plate recognition (ANPR) data, an OD-based Multipath-Band method (MP-BAND) is developed to achieve the function of the program. The proposed method breaks vehicle routing at intersections, and treats one link as an analysis unit. The method can automatically select link paths for coordination, and multiple paths can be synchronized simultaneously according to the actual traffic condition. The overlap bandwidth is introduced to capture the connectivity between paths of adjacent intersections. The PM-BAND is formulated as a mixed-integer linear program, which can be solved by the standard branch-and-bound technique. Numerical tests are conducted to evaluate the performance of MP-BAND under different traffic conditions. The results have demonstrated that MP-BAND outperformed MULTIBAND and AM-BAND in various aspects. MP-BAND can improve network performance significantly almost in all scenarios. Moreover, the intersection efficiency was improved significantly, especially the efficiency of left-turning vehicles. Also, MP-BAND can well recognize major routes and improve traffic efficiency. Compared with Synchro, MP-BAND also had superiority when flow rate is not too high. Thus, MP-BAND has a much wider applicability, and can be treated as an optimization core to achieve time-varying arterial coordination.

Traffic signal control under oversaturated conditions presents a major challenge in metropolitan transportation networks. Previous works have demonstrated the ability of back-pressure methods to maximize network throughput and guarantee network stability. However, most of these methods are implemented adaptively. At present, fixed phase sequences are still widely used in traffic signal control systems. Herein, we propose a new back-pressure-based signal optimization method that combines fixed phase sequences with spatial model predictive control. First, a spatial prediction model for traffic flow was constructed to analyze the movement of vehicles between a central intersection and four peripheral intersections. Then, a multi-objective optimization model of traffic signal timing was developed with the purpose to reduce the risk of spillover and to balance the distribution of vehicles across the whole network. Next, a method based on the Multi-Objective Particle Swarm Optimization algorithm and Technique for Order Preference by Similarity to an Ideal Solution principle was used to achieve the Pareto frontier and the optimal solution. Finally, traffic simulations were performed in Paramics to assess the performance of the proposed method. The results of the simulations suggest the performance of the proposed method surpasses fixed-time control and cycle-based back-pressure schemes under oversaturated conditions.

This paper makes a basic assumption that energy conservation exists, between psychological potential and a vehicle's kinetic energy, in the driver's psychological field based on the driver's mental activities. A virtual spring is used to describe the storage and release of psychological potential energy. Under the aforementioned conditions, we established a macroscopic traffic flow model with conservation law. Each parameter in the new model is physically meaningful and explicit. Additionally, the model can fit field data consistently well, both in free-flow and congested situations. The results of this paper prove the rationality of the energy conservation concept in traffic flow, which improves the understanding of traffic flow and provides a new theoretical foundation.

Transport researchers and practitioners have long been seeking capable solutions to deal with the traffic oscillations caused by freeway merging. Although existing approaches based on ramp metering have improved the overall efficiency of on-ramps, their performance is still far below the theoretical capacity. The recently proposed detecting technology of autonomous vehicles (AVs) provides an alternative for maximizing the merging efficiency by developing and using appropriate controllers for AVs. In this paper, we develop a cooperative intelligent driver model in order to examine the system performance under different proportions of AVs. The results show that, with a proper vehicle-to-vehicle controlling mechanism, an increasing percentage of AVs will reduce the total travel time and smooth traffic oscillations.