A Multiclass Simulation-Based Dynamic Traffic Assignment Model for Mixed Traffic Flow of Connected and Autonomous Vehicles and Human-Driven Vehicles

One of the potential capabilities of Connected and Autonomous Vehicles (CAVs) is that they can have different route choice behavior and driving behavior compared to human Driven Vehicles (HDVs). This will lead to mixed traffic flow with multiple classes of route choice behavior. Therefore, it is crucial to solve the multiclass Traffic Assignment Problem (TAP) in mixed traffic of CAVs and HDVs. Few studies have tried to solve this problem; however, most used analytical solutions, which are challenging to implement in real and large networks (especially in dynamic cases). Also, studies in implementing simulation-based methods have not considered all of CAVs' potential capabilities. On the other hand, several different (conflicting) assumptions are made about the CAV's route choice behavior in these studies. So, providing a tool that can solve the multiclass TAP of mixed traffic under different assumptions can help researchers to understand the impacts of CAVs better. To fill these gaps, this study provides an open-source solution framework of the multiclass simulation-based traffic assignment problem for mixed traffic of CAVs and HDVs. This model assumes that CAVs follow system optimal principles with rerouting capability, while HDVs follow user equilibrium principles. Moreover, this model can capture the impacts of CAVs on road capacity by considering distinct driving behavioral models in both micro and meso scales traffic simulation. This proposed model is tested in two case studies which shows that as the penetration rate of CAVs increases, the total travel time of all vehicles decreases

Short-term traffic predictions on large urban traffic networks: applications of network-based machine learning models and dynamic traffic assignment models

The paper discusses the issues to face in applications of short-term traffic predictions on urban road networks and the opportunities provided by explicit and implicit models. Different specifications of Bayesian Networks and Artificial Neural Networks are applied for prediction of road link speed and are tested on a large floating car data set. Moreover, two traffic assignment models of different complexity are applied on a sub-area of the road network of Rome and validated on the same floating car data set

Dynamic Reroute Modeling for Emergency Evacuation: Case Study of the Brunswick City

The human behaviors during evacuations are quite complex. One of the critical behaviors which affect the efficiency (performance) of evacuation is route choice. Therefore, the respective simulation modeling work needs to function properly. In this paper, SUMO's current dynamic route modeling during evacuation, i.e. the rerouting functions, is examined with a real case study. Four influence factors (1) time to get information, (2) probability to cancel a trip, (3) probability to use navigation equipment and (4) rerouting and information updating period are considered to analyze possible traffic impacts during the evacuation and to examine the rerouting functions in SUMO. Furthermore, some behavioral characters of the case study are analyzed with use of the corresponding detector data and applied in the simulation. The experiment results show that the dynamic route modeling in SUMO can deal with the proposed scenarios properly. Some issues and function needs related to route choice are discussed and further improvements are suggested

Synergy between public space politics and mobility strategies

¿Hasta qué punto y en qué circunstancias movilidad, como aspecto funcional e inevitable del entorno humano, se puede convertir en un elemento afirmativo de espacio público dándole un nuevo significado y un valor añadido? Diálogo entre movilidad y espacio público se puede explicar mediante la comprensión de las estrategias de movilidad como partidario de la integración de diferentes lógicas urbanas, observando infraestructura como un elemento de configuración de espacio público y al cuestionar transporte como pivote del carácter e identidad de espacio público. El objetivo principal de esta discusión es la integración urbana y contextual de los sistemas de transporte vistos como confluencias de lógica urbana y lógica de transporte desarrolladas como una sola expresión. Armonizando esta paradoja es posible crear sinergias entre espacio público y transporte que ganan nuevas dimensiones.Up to which point and under which circumstances mobility, as a functional and an inevitable aspect of the human environment, can become an affirmative element of public space giving it a new significance and an additional value? Dialog between mobility and public space can be explained by understanding mobility strategies as a supporter of integration of different urban logics, by observing infrastructure as an element of public space configuration and by questioning transport as a pivot of public space character and identity. The main focus of this discussion is on mobility lines, specifically urban and contextual integration of transport systems seen as a crossroads between urban and transport logic, developed as a single expression. Harmonizing this paradox it is possible to create synergies between public space and mobility which gain new dimensions

Weak nodes detection in urban transport systems: Planning for resilience in Singapore

The availability of massive data-sets describing human mobility offers the possibility to design simulation tools to monitor and improve the resilience of transport systems in response to traumatic events such as natural and man-made disasters (e.g. floods terroristic attacks, etc...). In this perspective, we propose ACHILLES, an application to model people's movements in a given transport system mode through a multiplex network representation based on mobility data. ACHILLES is a web-based application which provides an easy-to-use interface to explore the mobility fluxes and the connectivity of every urban zone in a city, as well as to visualize changes in the transport system resulting from the addition or removal of transport modes, urban zones, and single stops. Notably, our application allows the user to assess the overall resilience of the transport network by identifying its weakest node, i.e. Urban Achilles Heel, with reference to the ancient Greek mythology. To demonstrate the impact of ACHILLES for humanitarian aid we consider its application to a real-world scenario by exploring human mobility in Singapore in response to flood prevention.Comment: 9 pages, 6 figures, IEEE Data Science and Advanced Analytic

WSN Location Privacy Scheme Enhancement through Epidemical Information Dissemination

Wireless Sensor Networks (WSNs) are commonly used for animal tracking. Over the years, a significant number of studies have been presented for monitoring moving targets through WSN. At the same time, the location / position information of each target should be available only to authorized entities, e.g., Animal Protection Centers, thus, the position should be kept private. The iHIDE is a location privacy mechanism that uses a non-geographical based routing scheme for packet delivery over WSN. In this paper, we elaborate on that scheme by introducing a routing plan construction algorithm. Furthermore, we enhance iHIDE by adopting the use of epidemical data dissemination as an enforcing privacy technique. Finally, we evaluate through simulations the scheme against other commonly used location privacy overlays in terms of network overhead and safety period

Identification of Air Traffic Flow Segments via Incremental Deterministic Annealing Clustering

Many of the traffic management decisions and initiatives in air traffic are based on "flows" of traffic in the National Airspace System (NAS), but the actual identification of the location and time of the flow segments are often left to interpretation based on observations of traffic data points over time. Having an automated method of identifying major flow segments can help to target traffic management initiatives, evaluate design of airspace, and enable actions to be taken on the collection of flights in a flow segment rather than on the flights individually. A novel approach is developed to identify the major flow segments of air traffic in the NAS that consists of a robust method for partitioning 4-dimensional traffic trajectories into a series of great circle segments, and clustering the segments using an Agglomerate Deterministic Annealing clustering algorithm. In addition, a very efficient algorithm to incrementally cluster the segments is developed that takes into account the spatial and temporal properties of the segments, and makes the method very suitable for real-time applications. Further, an enhancement to the algorithm is provided that requires only a small subset of the segments to be clustered, drastically reducing the run time. Results of the clustering technique are shown, highlighting various major traffic flow patterns in the NAS. In addition, organizing the traffic into the flow segments identified using the Incremental Clustering method is shown to have a potential reduction in the number of conflict points. An application of the flow information is presented in the form of a Decision Support Tool (DST) that aids traffic managers in establishing and managing Airspace Flow Programs. In addition, the flow segment information is applied to a low-level form of aggregated traffic management, showing that aggregating flights into the flow segments and rerouting the whole flow segment can be efficiently performed as compared to rerouting individual aircraft separately, and can reduce the number of conflict points. Considerations for implementing these techniques in real-time systems are also discussed