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This paper aims to evaluate the sensitivity of the proposed cooperative dynamic bus lane system with microscopic traffic simulation models. The system creates a flexible bus priority lane that is only activated on demand at an appropriate time with advanced information and communication technologies, which can maximize the use of road space. A decentralized multi-lane cooperative algorithm is developed and implemented in a microscopic simulation environment to coordinate lane changing, gap acceptance, and car-following driving behavior for the connected vehicles (CVs) on the bus lane and the adjacent lanes. The key parameters for the sensitivity study include the penetration rate and communication range of CVs, considering the transition period and gradual uptake of CVs. Multiple scenarios are developed and compared to analyze the impact of key parameters on the system’s performance, such as total saved travel time of all passengers and travel time variation among buses and private vehicles. The microscopic simulation models showed that the cooperative dynamic bus lane system is significantly sensitive to the variations of the penetration rate and the communication range in a congested traffic state. With a CV system and a communication range of 150 m, buses obtain maximum benefits with minimal impacts on private vehicles in the study simulation. The safety concerns induced by cooperative driving behavior are also discussed in this paper. National Research Foundation (NRF) This work was financially supported by the Singapore National Research Foundation under its Campus for Research Excellence and Technological Enterprise (CREATE) program.

The well-known Lighthill–Whitham–Richards (LWR) theory is the fundamental pillar for most macroscopic traffic models. In the past, many methods were developed to numerically derive solutions for LWR problems. Examples for such numerical solution schemes are the cell transmission model, the link transmission model, and the variational theory (VT) of traffic flow. So far, the eulerian formulation of VT found applications in the fields of traffic modelling, macroscopic fundamental diagram estimation, multi-modal traffic analyses, and data fusion. However, these studies apply VT only at the link or corridor level. To the best of our knowledge, there is no methodology yet to apply VT at the network level. We address this gap by developing a VT-based framework applicable to networks. Our model allows us to account for source terms (e.g. inflows and outflows at intersections) and the propagation of spillbacks between adjacent corridors consistent with kinematic wave theory (KWT). We show that the trajectories extracted from a microscopic simulation fit the predicted traffic states from our model for a simple intersection with both source terms and spillbacks. We also use this simple example to illustrate the accuracy of the proposed model, and the ability to model complex bottlenecks. Additionally, we apply our model to the Sioux Falls network and again compare the results to those from a microscopic KWT simulation. Our results indicate a close fit of traffic states, but with substantially lower computational cost. The developed methodology is useful for extending existing VT applications to the network level, for network-wide traffic state estimations in real-time, or other applications within a model-based optimization framework.

Explicit and implicit communication cues are part of the communication strategies often employed by bicyclists in traffic to reveal their intentions to other road users. For human drivers, such types of information may be understandable in a specific social and cultural context. Present automated driving functions mostly rely on vulnerable road user (VRU) dynamic and operational features to perform short-term predictions of nearby VRU behaviour. The understanding of bicyclist implicit and explicit communication behavior can provide an extra layer of information in predicting their movement in traffic, confine the solution space to specific maneuver archetypes and expand the short-term time window for the bicyclist trajectory prediction. This paper focuses on the problem of predicting the bicyclist maneuver at an intersection approach using exclusively bicyclist explicit and implicit communication cues. Data is first evaluated using random forests for feature selection to determine the most important features for the prediction task. The next step involves training and testing different classifiers such as k-nearest neighbors, extra trees, decision trees, random forests, logistic regression, linear support vector machines, and the gaussian naive-bayes classifier to define the most efficient methodology to address the maneuver classification problem. Results suggest that the gaussian naive-bayes classifier provides the best performance on the test data with a 69.4% accuracy score during training and 67.0% accuracy score during testing. The proposed method has the potential to support and cooperate with existing solutions for bicyclist trajectory prediction as part of automated driving functions, effectively increasing traffic safety.

Current Urban Traffic Control (UTC) systems rely heavily on inductive loop detectors. The emergence of Connected Vehicles (CV) opens new possibilities for improving signal control, while reducing the need for loop detectors. However, for low penetration rates, the CV measurements are sporadic and thus difficult to exploit by existing UTC systems. In this paper, a methodology that enables cycle-to-cycle traffic state estimation and prediction based on limited CV measurements is presented. Furthermore, the proposed formulation enables fusion of CV with other data sources and their integration in any UTC system. The developed Extended Observer (EO) is a discrete-time, variable-dimension implementation of the Extended Kalman Filter. It does not require loop detectors and is independent of the type of signal control. The evaluation focuses on the queue length estimation. Especially in oversaturation, the EO outperforms the CV measurements for all examined penetration rates. Moreover, the highest benefit is observed for the lowest penetration rates. The results show that for oversaturation and low penetration rates the EO improves the CV measurements 13-31%. In addition, the EO is tested with an adaptive UTC system by feeding the fused estimation from CV and camera measurements. The error in queue length estimation from the EO is significantly lower than the error based on stochastic arrivals. Additionally, the results show a reduction in delays at the examined signal and the complete intersection. Overall, this paper sheds light on the potential benefits from enhancing limited CV measurements in order to contribute immediately to current UTC systems.

Detailed specifications of urban traffic from different perspectives and scales are crucial for understanding and predicting traffic situations from the view of an autonomous vehicle (AV). We suggest a data-driven specification scheme for maneuvers at different design elements of the built infrastructure and focus on urban roundabouts in Germany. Based on real observations, we define classes of maneuvers, interactions and driving strategies for cyclists, pedestrians and motorized vehicles and define a matrix for merging different maneuvers, resulting in more complex interactions. The sequences of these interactions, which partially consist of explicit communications, are extracted from real observations and adapted into microscopic traffic flow simulations. The simulated maneuver sequences are then visualized in 3D environments and experienced by bicycle simulator test subjects. Using trajectory segments (in fictional space) from two conducted simulator studies, we relate the recorded movement patterns of test subjects with observed cyclists in reality.

The ongoing increase of bicycle traffic in urban areas forces transport authorities to reconsider the space allocation for different transport modes. Transport policies favor the introduction of high-quality bicycle infrastructure along urban corridors to improve the traffic quality and safety for bicyclists but more importantly to increase the attractiveness of bicycling and over vehicular modes. Especially in urban areas with an already established high and steadily increasing share of bicyclists, the introduction of bicycle highways is considered to further alleviate saturated interurban public transport and motor vehicle connections and increase the average traveled distance by non-motorized modes. Due to the expensive implementation costs and the space restrictions in already built-up urban environments, there should be an extensive planning phase for defining the expected changes in traffic efficiency and safety. However, the effects of urban bicycle highways on traffic performance metrics of bicyclists as well as other road users are not thoroughly studied. This paper aims to quantify and assess the potential effects of urban bicycle highway on road users. The study considers a possible inner-city pilot route in the city of Munich, where the present bicycle infrastructure is planned to be upgraded to a bicycle highway. A simulation model is designed using traffic data from field observations and future estimates for the traffic composition. Through microscopic traffic simulation, the potential effects of the introduced infrastructure on road users are determined for different study scenarios. Results show that traffic quality thresholds for bicycle highways, as defined in official guidelines, can only be fulfilled through the implementation of special bicycle traffic control measures such as bicycle coordination or bicycle passage time extension. Finally, unidirectional bicycle highways together with bicycle passage time extension provided the best overall traffic performance for bicycle traffic and motor vehicle traffic.

In mixed traffic, the popularity of public transport (PT) is still affected by relatively low operating speeds compared to private vehicles. To overcome this, PT priority measures have been proposed and adopted extensively. However, existing solutions such as exclusive bus lanes or traffic signal priorities are often limited in terms of available road space or large-scale feasibility. In this paper, we propose a Vehicle-to-Vehicle/Infrastructure (V2X)-based dynamic PT priority concept in mixed traffic called Virtual Right of Way (VROW). Private vehicles in front of a PT vehicle make spaces through collaborative lane changes within a dynamic clearing distance computed based on the current traffic situation. This allows a more efficient allocation of road space while still maintaining a high level of PT priority. In this paper, we evaluate the potential traffic impacts of VROW on both PT and private vehicles by conducting microscopic traffic simulations within a small urban network and a highway scenario. Comparisons with mixed traffic and other existing bus lane priority strategies, in terms of operation and safety concerns, are analyzed and highlighted. Simulation results show that VROW improves the PT operational performance with only a marginal influence on private vehicles measured by their average travel time and the number of lane changes.

In the present research, a bicycle simulator is used to study the behavior of bicyclists and to collect data that can be used for developing a Bidirectional Long Short-Term Network (B-LSTM) model that predicts a bicyclist’s intended maneuver type class (left turn, right turn, straight) at an intersection approach. First the bicyclists’ dynamic behavior and the explicit and implicit communication behavior is recorded. Features describing the bicyclists’ behavior are extracted and associated with the respective maneuver type. The B-LSTM model is then trained on the bicycle simulator dataset. Five different model cases are investigated in order to identify the optimal input feature set and evaluate the added value from the inclusion of the bicyclists’ explicit and implicit communication behavioral data in the classification task. A maximum f1-score value of 83.8% (prediction accuracy per maneuver type: 85.3% left-turn, 84% right turn and 82.5% straight) is achieved using classes of implicit and explicit communication behavior together with the dynamic behavior data for the maneuver type prediction. Possible application areas of the proposed model may include, but are not limited to, the expansion and support of existing models and functions in the field of automated driving and the improvement of traffic efficiency and safety through real-time monitoring and forecasting of bicyclist behavior at intersections.

The degradation of road network performance due to incidents is a major concern to traffic operators. The development of urban traffic incident management systems requires a comprehensive understanding of traffic dynamics during incidents. Recently, the concept of the macroscopic fundamental diagram (MFD) contributed to such an understanding and has been used in a wide range of applications. However, the MFD is merely reproducible under recurring traffic patterns. Motivated by a few studies which argue the existence of the MFD with a clockwise hysteresis loop during incidents, we tackle this limitation of the MFD and propose a framework to study the characteristics of the MFD under non-recurring congestion. More specifically, we introduce a criticality score (CS) which represents network redundancy and postulate that links with a higher level of CS impose a larger hysteresis loop on the MFD. We design an experiment in a microscopic traffic simulation to study the relation of closed links and the resulting MFDs. The results confirm our postulation and we observe that links with similar CS have a comparable impact on the shape of the MFD. The main contribution of this paper is the possibility to develop a framework for incident detection in urban networks under limited sensor coverage. However, the findings of the study may strongly rely on the assumptions, for instance, the network structure, the OD pairs, and drivers route choice during incidents. Thus, future studies are required to study other network topologies as well as more realistic driver route choice during incidents.

The well-known Lighthill-Whitham-Richards (LWR) theory is the fundamental pillar for most macroscopic traffic models. In the past, many methods were developed to numerically derive solutions for LWR problems. Examples for such numerical solution schemes are the cell transmission model, the link transmission model, and the variational theory (VT) of traffic flow. So far, the latter framework found applications in the fields of traffic modelling, macroscopic fundamental diagram estimation, multi-modal traffic analyses, and data fusion. However, these studies apply VT only at the link or corridor level. To the best of our knowledge, there is no methodology yet to apply VT at the network level. We address this gap by developing a VT-based framework applicable to networks. Our model allows us to account for source terms (e.g. inflows and outflows at intersections) and the propagation of spillbacks between adjacent corridors consistent with kinematic wave theory. We show that the trajectories extracted from a microscopic simulation fit the predicted traffic states from our model for a simple intersection with both source terms and spillbacks. We also use this simple example to illustrate the accuracy of the proposed model. Additionally, we apply our model to the Sioux Falls network and again compare the results to those from a microscopic simulation. Our results indicate a close fit of traffic states, but with substantially lower computational cost. The developed methodology is useful for network-wide traffic state estimations in real-time, or other applications within a model-based optimization framework.

Bicyclists are extremely flexible road users who employ various tactical behaviours to optimise comfort, directness and time efficiency while crossing a signalised intersection. Tactical choices faced by bicyclists at signalised intersections include whether to use the bicycle lane, roadway or sidewalk, to stop at or violate a red traffic signal, to ride with or against the mandatory direction of travel and the method of executing a left turn. The outcome of these choices has a direct impact on traffic safety and efficiency at intersections. In this paper, revealed choice data from 4710 bicyclists at four intersections in Munich, Germany are used to estimate binomial and multinomial logistic regression models to predict tactical choice outcomes. Optimal predictor sets are selected from the main and two-way interaction effects of 43 independent variables describing the situation, strategic behaviour and prior tactical choices of bicyclists using recursive feature elimination. A simplified model is estimated using the statistically significant variables of the optimal predictor set. The prediction power of the resulting regression model is assessed using k-fold cross validation. The models to predict response to a red signal and the type of left-hand turn exhibit high predictive power while the prediction of infrastructure selection and the direction of travel proves to be difficult.

This paper introduces two Kalman filter (KF) implementations for the estimation of turning rate at signalized intersections, assuming the availability of Floating Car Data (FCD). The developed filters are tested in microscopic simulation under various scenarios. The first filter is a typical (linear) KF that utilizes only real-time FCD. The second is an Extended (non-linear) Kalman filter that utilizes both real-time and offline (historical profiles) FCD. The aim of this paper is to demonstrate how Kalman filter can be used to estimate in real-time the total turning rate at an intersection, based on limited (and error-prone) measurements coming from FCD. Moreover, it is shown how to tune the filters to account for different errors in real-time measurements and in historical profiles, in order to use it in real-world applications.

This study proposes an integrated simulation approach, which consists of a microscopic traffic simulation model, a vehicle dynamics model, and an emission estimation model, in order to estimate emissions based on more reliable vehicle performance measures. The vehicle performance measures such as engine power and engine speed significantly relate to the amount of emissions, and road curvatures and inclinations are the core inputs affecting these vehicle performance measures. Therefore, providing reliable vehicle performance measures reflecting the road geometric attributes is critical for a reliable emission estimation. This study proposes to use the microscopic traffic simulation model for generating vehicle trajectories, which is advantageous in modeling various traffic situations, and the vehicle dynamics model for producing the vehicle performance measures based on the vehicle trajectories. Finally, the outputs from the vehicle dynamics model are fed into the emission estimation model to compu...

Zur Unterstutzung der digitalen Transformation im Bereich Smart Mobility und Smart City wurde das TUM Living Lab Connected Mobility (TUM LLCM) initiiert. Das Forschungsprojekt bundelt die einschlagigen Forschungs‑, Entwicklungs‑, und Innovations‐Kompetenzen der TU Munchen in der Informatik und in der Verkehrsforschung. Im Projekt wird die Konzeption und prototypische Implementierung offener und somit anbieterubergreifend nutzbarer digitaler Mobilitatsplattformen vorangetrieben. Die eigentliche Implementierung dieser Plattformen erfolgt durch kommerzielle Anbieter unter Berucksichtigung der Anforderungen des sich im Umbruch befindlichen kundenorientierten Mobilitatsmarkts. Dieser Artikel zeigt am Beispiel des TUM LLCM Projekts, wie die Fakultat fur Informatik der TU Munchen die Kompetenzen ihrer Forschungseinheiten in innovativen Anwendungsszenarien bundelt, um zeitnah gesellschaftlich relevante Ergebnisse im engen Austausch mit den betroffenen Akteuren auserhalb der Forschung zu erzielen.

The Macroscopic Fundamental Diagram (MFD) describes the relation of average network flow, density and speed in urban networks. It can be estimated based on empirical or simulation data, or approximated analytically. Two main analytical approximation methods to derive the MFD for arterial roads and urban networks exist at the moment. These are the method of cuts (MoC) and related approaches, as well as the stochastic approximation (SA). This paper systematically evaluates these methods including their most recent advancements for the case of an urban arterial MFD. Both approaches are evaluated based on a traffic data set for a segment of an arterial in the city of Munich, Germany. This data set includes loop detector and signal data for a typical working day. It is found that the deterministic MoC finds a more accurate upper bound for the MFD for the studied case. The estimation error of the stochastic method is about three times higher than the one of the deterministic method. However, the SA outperforms the MoC in approximating the free-flow branch of the MFD. The analysis of the discrepancies between the empirical and the analytical MFDs includes an investigation of the measurement bias and an in-depth sensitivity study of signal control and public transport operation related input parameters. This study is conducted as a Monte-Carlo-Simulation based on a Latin Hypercube sampling. Interestingly, it is found that applying the MoC for a high number of feasible green-to-cycle ratios predicts the empirical MFD well. Overall, it is concluded that the availability of signal data can improve the analytical approximation of the MFD even for a highly inhomogeneous arterial.

A bicycle dynamics model is developed that introduces characteristics of the bicycle dynamics in microscopic traffic simulation. The model alters the lateral position of simulated bicycles in space due to the stabilization efforts of the simulated bicyclist. The development of the proposed model is based on conclusions and simplifications derived from the study of single-track vehicle dynamic models. The proposed model is subsequently integrated with the microscopic traffic simulation software SUMO [1]. Results indicate that the operational width of a bicyclist is a decreasing function of the operational speed as a result of the bicyclist stabilization process. The proposed model can be used to improve the accuracy of simulation studies of the overtaking behavior of bicyclists and while studies focusing on the behavior of bicycles in platoons are expected to produce more accurate results.

Operational behavior models are used in traffic simulations to represent the subconscious, short-termdecisions made by road users to respond to other road users, the infrastructure and traffic control measures. Calibration and validation of these models can be achieved using observed trajectory data from real road users. For lane bound traffic, it is assumed that road users intend to follow a given lane with a certain desired speed across the intersection. Any deviation from this planned path is in response to other road users or the environment. It is difficult, however, to identify and separate the desired movement of more flexible road users that do not follow lane disciple, such as bicyclists, from movements made as a reaction to other road users or obstacles. This can lead to poor calibration of operational behavior models and unrealistic behavior in the simulation. Tactical behavior models recreate the conscious decisions made on a time horizon of seconds to minutes to cope with the immediate traffic situation. As such, tactical behavior models are responsible for selecting the planned path across an intersection.Here, SUMO is coupled with the simulation software DYNA4 to create a simulated road environment for a bicycle simulator. Trajectories observed in reality are displayed as potential prescribed pathways across the simulated intersection. Participants in the simulator study are instructed to select and follow one of the prescribed pathways as closely as possible while responding naturally to other road users and obstacles in the environment. The resulting trajectory data is used to calibrate existing operation al and tactical path finding behavior models for bicyclists at signalized intersection.