In urban centers, cycling is increasingly popular as an eco-friendly transportation mode and a short-distance transport option, driving higher demand for accurate bicycle travel time estimation. Policymakers need to understand bicycle traffic for urban traffic management and sustainable transport promotion, while cyclists benefit from better route planning and improved network efficiency. However, urban bicycle travel time estimation has not received as much attention as car traffic estimation and presents several challenges: 1) Limited availability of structural cycling data, which can be inaccessible due to privacy concerns and/or severely biased by user demographics. 2) The diverse and complex behaviors of cyclists. 3) The lack of strict road constraints for cyclists and frequent rule violations, complicating the model definition of a comprehensive cycling infrastructure network. This paper presents the first study on urban bicycle travel time estimation using GPS tracking data. Leveraging graph-based deep learning’s ability to learn from topological network information, we introduce the Dual Graph-based approach for bicycles (DG4b), which employs two parallel encode-process-decode pipelines: one for a shared undirected road network graph to capture intrinsic road characteristics, and another for a directed trip-specific graph reflecting unique trip features. The outputs are combined to estimate road segment speeds and overall trip travel time. When applied to a real-world dataset from Berlin, our method shows superior accuracy and reliability compared to baseline models, while maintaining low complexity. Our approach provides a novel perspective on integrating bicycling-specific characteristics and aims to inspire more future research in bicycle-related traffic estimation.

Bicycle transportation, a low-carbon option, is essential for promoting sustainable urban mobility. However, predicting bicycle traffic is challenging due to limited investments in data collection, especially in smaller cities. This paper proposes a multi-source transfer learning spatial-temporal graph neural network (Multi-TLSTGCN) for accurate bicycle traffic prediction in target cities with limited available data. This study first examines how to transfer knowledge from single source domain to the target domain while mitigating the risk of negative transfer. Following this, a multi-source adaptive transfer learning approach is developed to optimize traffic prediction in the target domain by adaptively integrating knowledge from multiple sources. Finally, the performance of the Multi-TLSTGCN model is evaluated under various levels of target data scarcity and compared with models that do not incorporate source domain knowledge. The experimental results demonstrate several key insights: 1) Models fine-tuned with a single-cluster pre-trained source model where the clusters are formed based on similar traffic patterns are more effective at minimizing negative knowledge transfer than those fine-tuned with single-city pre-trained source models. 2) The proposed Multi-TLSTGCN outperforms baseline models in bicycle traffic prediction, showing promise for accurate predictions in data-scarce environments; and 3) The Multi-TLSTGCN model remains robust across varying levels of data scarcity, exhibiting only a slight decrease in accuracy as the availability of target data decreases, in contrast to models relying solely on target domain data. These findings highlight the Multi-TLSTGCN model as an effective and promising solution for bicycle traffic prediction with limited data availability.

Future traffic will include automated vehicles (AVs) that will interact with other road users, including cyclists. These interactions need to be safe for AVs to be accepted by society. To accomplish this, the interaction process needs to be studied from both the AV’s point of view (AV’s passenger) and cyclists’ point of view. Insights from current interactions between drivers of conventional vehicles (CVs) and cyclists, and the factors contributing to safe interactions, can inform industry of the design of AVs to interact safely and in socially acceptable ways with cyclists. This paper provides a synthesis of the current literature on the interactions between AVs/CVs and cyclists, from four different points of view: 1) from CV drivers’ point of view when interacting with cyclists; 2) from cyclists’ point of view when interacting with CVs; 3) from AVs driver-seat passengers’ point of view when interacting with cyclists; and 4) from cyclists’ point view when interacting with AVs. The literature review included publications between the years 2015-2025 and resulted in 89 relevant scientific papers. Fifty-one papers focused CVs and cyclists interactions, at intersections, and in overtaking maneuvers, while thirty-eight papers focused on cyclists and AVs interactions. Key factors that influence AV-cyclist interactions were identified, including infrastructure, environment, factors influencing vehicle and cyclist behaviors, and rules and regulations. These elements and the factors influencing them were summarized in a conceptual framework. Future research directions are proposed based on the literature review and knowledge gaps identified and were structured following the proposed conceptual framework.

Sparse decision tree learning provides accurate and interpretable predictive models that are ideal for high-stakes applications by finding the single most accurate tree within a (soft) size limit. Rather than relying on a single “best” tree, Rashomon sets—trees with similar performance but varying structures—can be used to enhance variable importance analysis, enrich explanations, and enable users to choose simpler trees or those that satisfy stakeholder preferences (e.g., fairness) without hard-coding such criteria into the objective function. However, because finding the optimal tree is NP-hard, enumerating the Rashomon set is inherently challenging. Therefore, we introduce SORTD, a novel framework that improves scalability and enumerates trees in the Rashomon set in order of the objective value, thus offering anytime behavior. Our experiments show that SORTD reduces runtime by up to two orders of magnitude compared with the state of the art. Moreover, SORTD can compute Rashomon sets for any separable and totally ordered objective and supports post-evaluating the set using other separable (and partially ordered) objectives. Together, these advances make exploring Rashomon sets more practical in real-world applications.

Crowd management plays a vital role in urban planning and emergency response. Accurate crowd prediction is important for venue operators to respond effectively to adverse crowd dynamics during large gatherings. Although many studies have tried to predict crowd densities or movement dynamics with data-driven predictive models, their validation is often limited to data within the same scenario. As a result, the predictability of the data-driven model in unseen scenarios, such as evacuation scenarios, remains unknown due to the challenges of collecting out-of-distribution data regarding emergency conditions. To address this problem, we present an evaluation pipeline to evaluate different kinds of data-driven models. A method is proposed to generate realistic scenarios by simulation and collect synthetic data from these scenarios to acquire a comprehensive dataset. With these synthetic data, we evaluated different predictive models, from traditional machine learning methods to deep learning time-series prediction models, to explore their generalizability. Furthermore, we propose a weighted average metric, which is better suited to determine the performance of forecasting algorithms under adverse conditions. Through extensive experimentation, we showcase the heterogeneity and diversity of the simulation dataset. The evaluation results also revealed that all the data-driven models performed poorly in unseen scenarios, highlighting the urgent need to develop a robust and generalizable model for predicting crowd flow in indoor spaces.

Accurate short-term predictions of active mode traffic are crucial for effective urban traffic control and management, helping to reduce delays, stops, and improve travel time reliability, and optimize travel route choice. While most methods focus on motorized traffic, active modes like walking and cycling have been overlooked due to their complex dynamics and sensitivity to external factors like weather and individual choices, making them inherently less predictable. To address this, we propose a Dynamic Attention-based Spatial-Temporal Graph Convolutional Network (DyASTGCN) model that incorporates the impact of weather on graph spatial correlations within the active mode traffic network. Additionally, we introduce a fusion approach to integrate various heterogeneous spatial correlations, aiming to represent the optimal spatial correlations within the active mode network. Experimental results demonstrate that weather changes have a lagging effect on traffic network spatial correlations. Specifically, active mode traffic demonstrates significant sensitivity to precipitation, with notable changes in spatial correlations occurring within 5 minutes. Conversely, it takes approximately 20 minutes for spatial correlations to respond to wind speed influences. By incorporating both precipitation and wind speed with a 20-minute lag, our model outperforms those using only one feature, achieving the best traffic prediction performance. Given the uncertain traffic state and highly sparse nature of active mode data, our fusion approach adeptly captures the essential spatial correlations required for accurate traffic flow prediction. This allows our model to better understand complex graph correlations and traffic patterns, improving prediction accuracy and offering valuable insights into active mode network dynamics.

Identifying driving heterogeneity plays an important role in improving traffic safety and efficiency. This paper proposes a novel framework to identify driving heterogeneity from the underlying characteristics of driving behaviour. The framework includes three processes: Action phase extraction, Action pattern calibration, and Action pattern classification. The concepts of Action phase and Action patterns are proposed to decipher and interpret driving behaviours. Action phases are extracted by rule-based segmentation methods and Action patterns are calibrated based on an unsupervised learning approach. The extraction and calibration processes provide a rigorous labelling approach for the attention-based LSTM Action pattern classification process. Evaluation of the framework on a large-scale naturalistic driving dataset reveals six distinct Action patterns. The implementation of the attention mechanism to LSTM models significantly enhanced both the accuracy and time efficiency of Action pattern identification. The proposed framework offers benefits in detecting and reducing variability in driving behaviour through ITS applications such as user-based traffic management, personalised Advanced Driver Assistance Systems (ADAS), and advanced autonomous vehicles (AV) design, thereby enhancing road safety and traffic efficiency.

Presents corrections to the paper, (Corrections to “A Novel Framework for Identifying Driving Heterogeneity Through Action Patterns”).

Drivers initiate a discretionary lane change when they perceive an anticipated improvement in their own driving condition from moving to another lane. However, such a lane change can slow down other vehicles on the target lane, and even worse initiate a disturbance. In this work, we argue that the blocking effect triggered by individual lane changes results from the heterogeneity in the desired speeds of vehicles, and thus using desired speed information of vehicles when regulating lane-changing decisions can improve traffic efficiency. In doing so, our work also exemplifies the usefulness of incorporating user preferences into control decisions. The proposed lane guidance system uses an optimization-based approach to update the target range of desired speeds on each lane in real time, and accordingly recommends individual lane changes. The control system coordinates the lane-changing decisions at the link level, for which the road stretch is subdivided into multiple sections that are controlled independently. We evaluate the performance of the lane guidance system in micro-simulation, for different network demands and desired speed distributions. The results highlight that the proposed approach utilizing the desired speed preferences of drivers results in positive efficiency gains for most traffic compositions in free flow. Moreover, the highest gains are expected in medium to high demand, and when the traffic composition includes a higher proportion of vehicles desiring higher speeds. The gains also increase when the desired speeds of vehicles that want to drive fast and those that want to drive slower are more separated.

The increase in perception capabilities of connected mobile sensor platforms (e.g., self-driving vehicles, drones, and robots) leads to an extensive surge of sensed features at various temporal and spatial scales. Beyond their traditional use for safe operation, available observations could enable to see how and where people move on sidewalks and cycle paths, to eventually obtain a complete microscopic and macroscopic picture of the traffic flows in a larger area. This paper proposes a new method for advanced traffic applications, tracking an unknown and varying number of moving targets (e.g., pedestrians or cyclists) constrained by a road network, using mobile (e.g., vehicles) spatially distributed sensor platforms. The key contribution in this paper is to introduce the concept of network bound targets into the multi-target tracking problem, and hence to derive a network-constrained multi-hypotheses tracker (NC-MHT) to fully utilize the available road information. This is done by introducing a target representation, comprising a traditional target tracking representation and a discrete component placing the target on a given segment in the network. A simulation study shows that the method performs well in comparison to the standard MHT filter in free space. Results particularly highlight network-constraint effects for more efficient target predictions over extended periods of time, and in the simplification of the measurement association process, as compared to not utilizing a network structure. This theoretical work also directs attention to latent privacy concerns for potential applications.

Virtual Reality (VR) provides the possibility to study pedestrian wayfinding behaviour in multi-level buildings. Although VR has been applied increasingly to study pedestrian behaviour, it has remained unclear how different VR technology would affect behavioural outcomes in a multi-level building. The study compares the adoption of different VR technologies for pedestrian wayfinding studies, via investigating the difference in pedestrian wayfinding behaviour and user experience. Wayfinding experiments with two groups of participants were conducted using either HMD VR or Desktop VR. Pedestrian movement trajectory data were collected via the VR system and user experience data were recorded via a questionnaire. These data allow for direct comparison and detailed analysis of pedestrian behaviour and user experience between the adoption of two VR technologies. The results showed that technological differences have a significant impact on wayfinding task performance and head rotation change. However, the route choice, exit choice and user experience were overall similar between the two groups. These results provide empirical evidence supporting researchers to choose between immersive and non-immersive VR when study pedestrian wayfinding behaviour.

The literature on green mobility and eco-driving in urban areas has burgeoned in recent years, with special attention to using infrastructure to vehicle (I2V) communications to obtain optimal speed trajectory which minimize the economic and environmental costs. This article shares the concept with these studies but turns the spotlight on cyclists. It examines the problem of finding optimal speed trajectory for a cyclist in signalised urban areas. Unlike the available studies on motorised vehicles which predominantly designed for pre-defined, fixed traffic lights timing, this article uses an algorithm based on stochastic dynamic programming to explicitly address uncertainty in traffic light timing. Moreover, through a comprehensive set of simulation experiments, the article examines the impact of the speed advice’s starting point as well as the cyclist’s willingness for changing his/her speed on enhancing the performance. The proposed approach targets various performance metrics such as minimising the total travel time, energy consumption, or the probability of stopping at a red light. Hence, the resulting speed advice can be tailored according to the personal preferences of each cyclist. In a simulation case study, the results of the proposed approach is also compared with an existing approach in the literature.

In this paper a novel computationally efficient model predictive control (MPC) method for optimizing flows at urban intersections is proposed. Several linear and quadratic MPC approaches have been developed in the literature to reduce the computational complexity of the problem, but without considering the back-propagating waves associated with spillback. As the principal contribution of this work, a linear optimization problem for an MPC approach is formulated, which considers downstream propagating waves linked to free-flow traffic, queuing dynamics, and upstream propagating waves related to spillback (i.e. forward and backward moving waves, respectively). The linear optimization problem is obtained by describing link dynamics using the link transmission model, and aggregating the traffic dynamics to (several) tens of seconds. The performance of the proposed controller is compared with two other existing strategies; a store-and-forward model-based, and a cell transmission model-based approach. The total time spent (TTS) by all the vehicles in the network and the computation time is applied as performance indexes for the evaluation of the control strategies. Simulation results show that including upstream propagating waves results in better controller performance, due to the explicit modeling of the impact of link outflow on the maximum link inflow.

This paper extends an existing linear quadratic model predictive control (LQMPC) approach to multi-destination traffic networks, where the correct origin-destination (OD) relations are preserved. In the literature, the LQMPC approach has been presented for efficient routing and intersection signal control. The optimization problem in the LQMPC has a linear quadratic formulation that can be solved quickly, which is beneficial for a real-time application. However, the existing LQMPC approach does not preserve OD relations and thus may send traffic to wrong destinations. This problem is tackled by a heuristic method presented is this paper. We present two macroscopic models: 1) a non-linear route-specific model which keeps track of traffic dynamics for each OD pair and 2) a linear model that aggregates all route traffic states, which can be embedded into the LQMPC framework. The route-specific model predicts traffic dynamics and provides information to the LQMPC before the optimization and evaluates the optimal solutions after the optimization. The information obtained from the route-specific model is formulated as constraints in the LQMPC to narrow the solution space and exclude unrealistic solutions that would lead to flows that are inconsistent with the OD relations. The extended LQMPC approach is tested in a synthetic network with multiple bottlenecks. The simulation of the LQMPC approach achieves a total time spent close to the system optimum, and the computation time remains tractable.

Freeway congestion can reduce the freeway throughput due to the capacity drop or due to blocking caused by spillback to upstream ramps. Research has shown that congestion can be reduced by the application of ramp metering and variable speed limits. Model predictive control is a promising strategy for the optimization of the ramp metering rates and variable speed limits to improve the freeway throughput. However, several challenges have to be addressed before it can be applied for the control of freeway traffic. This paper focuses on the challenge of reducing the computation time of MPC strategies for the integration of variable speed limits and ramp metering. This is realized via a parameterized control strategy that optimizes the upstream and downstream boundaries of a speed-limited area and the parameters of the ALINEA ramp metering strategy. Due to the parameterization, the solution space reduces substantially, leading to an improved computation time. More specifically, the number of optimization variables for the variable speed limit strategy becomes independent of the number of variable message signs, and the number of optimization variables for the ramp metering strategy becomes independent of the prediction horizon. The control strategy is evaluated with a macroscopic model of a two-lane freeway with two ON-ramps and OFF-ramps. It is shown that parameterization realizes improved throughput when compared with a non-parameterized strategy when using the same amount of computation time.

In this paper, we develop a hierarchical approach to optimize the signal timings in an urban traffic network taking into account the different dynamics in all traffic regimes. The proposed hierarchical control framework consists of two layers. The first layer-the network coordination layer-uses a model predictive control strategy based on a simplified traffic flow model to provide reference outflow trajectories. These reference outflow trajectories represent average desired link outflows over time. These are then mapped to green-red switching signals which can be applied to traffic lights. To this end, the second layer-the individual intersection control layer-then selects at every intersection the signal timing stage that realizes an outflow which has the smallest error with respect to the reference outflow trajectory. The proposed framework is tested using both macroscopic and microscopic simulations. It is shown that the control framework can outperform a greedy control policy that maximizes the individual intersection outflows, and the control framework can distribute the queues over the network in a way that the network outflow is improved. Simulations using a macroscopic model allow the direct application of the reference outflows computed by the network coordination layer, and the results indicate that the mapping of the reference outflows to the detailed signal timings by the individual intersection control layer only introduces a small performance loss.

We design controllers and derive implementable algorithms for autonomous and cooperative car-following control (CFC) systems under a receding horizon control framework. An autonomous CFC system controls vehicle acceleration to optimize its own situation, whereas a cooperative CFC (C-CFC) system coordinates accelerations of cooperative vehicles to optimize the joint situation. To realize simultaneous control of many vehicles in a traffic system, decentralized and distributed algorithms are implemented in a microscopic traffic simulator for CFC and C-CFC controllers, respectively. The impacts of the proposed controllers on dynamic traffic flow features, particularly on formation and propagation of moving jams, are investigated through a simulation on a two-lane freeway with CFC/C-CFC vehicles randomly distributed. The simulation shows that the proposed decentralized CFC and distributed C-CFC algorithms are implementable in microscopic simulations, and the assessment reveals that CFC and C-CFC systems change moving jam characteristics substantially.

Route guidance instructions are crucial in the implementation of an evacuation plan. Considering travelers' compliance with these instructions is controllable by adopting traffic management at intersections, a simulation-based framework for optimizing traffic management is presented with the objective function of maximizing evacuation efficiency with uncertain budget constraint. A comprehensive case study illustrates the sensitivity of traffic simulation model with traffic demand, duration of hazard, and traffic management. The specific analyses on network performances provide some practical insights. In reality, mandatory traffic management is unnecessary as the optimal instructions are unavailable. Well-staged departure and appropriate enforcement of traffic management at intersections are recommended, which contribute to extensive distribution of traffic flow and then high-efficiency evacuation.

Uphill sections have often been identified as capacity bottlenecks in freeway networks. One of the main reasons seems to be that drivers reduce speed when they reach the beginning of an uphill section. With high traffic demand, the deceleration of the first vehicle of a platoon can generate a flow disturbance that amplifies as it propagates upstream, triggering the formation of a traffic jam. This paper presents a proof of concept by exploring whether equipping the leader of a platoon with an in-vehicle Gradient Compensation System (GCS) can improve traffic flow efficiency on uphill sections. The GCS assists the driver in performing the longitudinal driving task at uphill sections. We present the results of a series of traffic simulation experiments in which a platoon of vehicles drive on a single-lane freeway stretch containing an uphill section. The phenomenon of speed reduction is modeled by means of a sub-microscopic traffic simulation program. The results show that if the platoon leader is not equipped with the GCS, its speed drop at the beginning of the uphill section can cause a traffic breakdown, as observed in reality. However, if the platoon leader is equipped with the GCS, the magnitude of the speed drop is reduced, preventing congestion formation.

Freeway traffic state estimation and prediction are central components in real-time traffic management and information applications. Model-based traffic state estimators consist of a dynamic model for the state variables (e.g., a first- or second-order macroscopic traffic flow model), a set of observation equations relating sensor observations to the system state (e.g., the fundamental diagrams), and a data-assimilation technique to combine the model predictions with the sensor observations [e.g., the extended Kalman filter (EKF)]. Commonly, both process and observation models are formulated in Eulerian (space-time) coordinates. Recent studies have shown that this model can be formulated and solved more efficiently and accurately in Lagrangian (vehicle number-time) coordinates. In this paper, we propose a new model-based state estimator based on the EKF technique, in which the discretized Lagrangian Lighthill-Whitham and Richards (LWR) model is used as the process equation, and in which observation models for both Eulerian and Lagrangian sensor data (from loop detectors and vehicle trajectories, respectively) are incorporated. This Lagrangian state estimator is validated and compared with a Eulerian state estimator based on the same LWR model using an empirical microscopic traffic data set from the U.K. The results indicate that the Lagrangian estimator is significantly more accurate and offers computational and theoretical benefits over the Eulerian approach.

Probe vehicles with Global Positioning Systems (GPS) can provide accurate positions that enable spatial-average speed estimation. However, some probe vehicles cannot provide accurate positions but can provide location-specific information on when and where they are located at the segment or cell level. These topological position (TP) data with segment- or cell-level accuracy cannot provide the distance component that is necessary for traffic speed estimation. However, considering the wide availability of TP data in the existing telecommunications network, there is still hope and benefits to make use of the data for traffic state estimation. In this paper, an algorithm is proposed using low-resolution positioning data. The proposed method is capable of fusing low-resolution positioning data with other data sources, leading to more accurate and reliable speed estimation of relatively low bias. In addition, this method shows strong robustness and error tolerance and can reveal the magnitude of the estimation error, which is helpful for travel time prediction and traffic control.

This paper presents a continuous traffic-flow model for the explorative analysis of advanced driver-assistance systems (ADASs). Such systems use technology (sensors and intervehicle communication) to support the task of the driver, who retains full control over the vehicle. Based on a review of different traffic-flow modeling approaches and their suitability for exploring traffic-flow patterns in the presence of ADASs, kinetic traffic-flow models are selected because of their good representation on both the aggregate level (congestion dynamics) and the level of the individual vehicle (vehicular interactions either directly or through intervehicle communication). The human-kinetic modeling approach is presented. It is a multiclass variant of kinetic traffic-flow models that is strongly based on individual driver behavior, i.e., on fully continuous acceleration/deceleration behavior and explicit modeling of the activation level of the driver. The strength of this modeling approach is illustrated by application to a driver-assistance system that uses intervehicle communication. It warns drivers when approaching sharp decelerations in a queue tail. The explorative analysis shows that the system results in safer and smoother transition from free-flowing to congested traffic. It also avoids compression of the queue tail, thus preventing the emergence of stop-and-go congestion patterns.