Vehicular cyber-physical systems are implemented to share taxi resource eciently using intensive algorithms running on telematics devices. However, due to the lack of social interactions, conventional systems are hard to improve user experience without considering passengers inner connections. In this paper, we propose an optimization scheme for these vehicular cyber-physical systems which integrate social interaction with real time street data to improve the sharing eciency and user experience. To answer the sharing requirement from potential passengers, our system allocates the taxi resource under the trade-o between cost and social interactions. We state and solve the sharing arrangement problem by computing a heuristic algorithm called SONETS to satisfy overwhelming requests from streets with limited taxi resource in peak time. e simulation results show that our algorithm can increase the integrated benet than other solutions.
Real-time traffic modeling at national scale is essential to many applications, but its calibration is extremely challenging due to its large spatial and fine temporal coverage. The existing work mostly is focused on urban-scale calibration with complete field data from single data sources (e.g., loop sensors or taxis), which cannot be generalized to national scale, because complete single-source field data at national scale are almost impossible to obtain. To address this challenge, in this paper, we design MultiCalib, a model calibration framework to optimize traffic models based on multiple incomplete data sources at national scale in real time. Instead of naively combining multi-source data, we theoretically formulate a multi-source model calibration problem based on real-world contexts and multi-view learning. In particular, we design (i) convex multi-view learning to integrate multi-source data by quantifying biases of data sources, and (ii) context-aware tensor decomposition to infer incomplete multi-source data by extracting real-world contexts. More importantly, we implement and evaluate MultiCalib with two heterogeneous nationwide vehicle networks with 340,000 vehicles to infer traffic conditions on 36 expressways and 119 highways, along with 4 cities across China. The results show that MultiCalib outperforms state-of-the-art calibration by 25% on average with same input data. Based on the proposed national scale traffic model calibration, we design a novel application where we guide a fleet among national-scale highways with a routing strategy to reduce general traveling time. The results show that a routing strategy based on MultiCalib outperforms a routing strategy based on a state-of-the-art traffic model by 39% on average.
Travel time in urban centers is a significant contributor to the quality of living of its citizens. Mobility on Demand (MoD) services such as Uber and Lyft have revolutionized the transportation infrastructure, enabling new solutions for passengers. Shared MoD services have shown that a continuum of solutions can be provided between the traditional private transport for an individual and the public mass transit based transport, by making use of the underlying cyber-physical substrate that provides advanced, distributed, and networked computational and communicational support. In this paper, we propose a novel shared mobility service using a dynamic framework. This framework generates a dynamic route for multi-passenger transport, optimized to reduce time costs for both the shuttle and the passengers and is designed using a new concept of a space window. This concept introduces a degree of freedom that helps reduce the cost of the system involved in designing the optimal route. A specific algorithm based on the Alternating Minimization approach is proposed. Its analytical properties are characterized. Detailed computational experiments are carried out to demonstrate the advantages of the proposed approach and are shown to result in an order of magnitude improvement in the computational efficiency with minimal optimality gap when compared to a standard Mixed Integer Quadratically Constrained Programming based algorithm.
This article describes a system to facilitate dynamic en route formation of truck platoons with the goal of reducing fuel consumption. Safe truck platooning is a maturing technology which leverages modern sensor, control, and communication technology to automatically regulate the inter-vehicle distances. Truck platooning has been shown to reduce fuel consumption through slipstreaming by up to ten percent under realistic highway conditions. In order to further benefit from this technology, a platoon coordinator is proposed, which interfaces with fleet management systems and suggests how platoons can be formed in a fuel-efficient manner over a large region. The coordinator frequently updates the plans to react to newly available information. This way, it requires a minimum of information about the logistic operations. We discuss the system architecture in detail and introduce important underlying methodological foundations. Plans are derived in computationally tractable stages optimizing fuel savings from platooning. The effectiveness of this approach is verified in a simulation study. It shows that the coordinated platooning system can improve over spontaneously occurring platooning even under the presence of disturbances. A real demonstrator has also been developed. We present data from an experiment in which three vehicles were coordinated to form a platoon on public highways under normal traffic conditions. It demonstrates the feasibility of coordinated en route platoon formation with current communication and on-board technology. Simulations and experiments support that the proposed system is technically feasible and a potential solution to the problem of using truck platooning in an operational context.
Smart cities can be viewed as large-scale Cyber-Physical Systems (CPS) that different sensors and devices record the cyber and physical indicators of the urban environment. Those records are being used for improving urban life by offering improved efficiencies with accurate electric load forecasting, efficient traffic management, etc. Accurate forecasting is mostly dependent on the sufficient and reliable data. Traditional data collection methods are necessary but not sufficient due to their limited coverage and expensive cost of implementation and maintenance. For example, continuous traffic data collection is mostly limited to major highways only in many cities whereas secondary and local roadways are usually covered once or twice a year. The advances in sensor networks and recent technological developments such as methods based on vehicle locations and in-vehicle devices through mobile phones or GPS-based systems in transportation networks provide such an opportunity. Although these technologies also have the potential to connect the physical components and processes with the cyber world that leading to a Cyber-Physical Systems (CPS), they also have significant drawbacks. Specifically, they usually suffer from limited resolution due to limitations on time frame, cost, accuracy, and reliability. One way for improving the limited resolution is data fusion. Furthermore, a city should be considered as a collection of the layers of tangled city infrastructure networks which connects people, places, and resources. Therefore, the study of traffic or electricity consumption forecasting should go beyond the transportation and electricity networks, and merge with each other and even with other city networks such as environmental networks. As such, this paper proposes a traffic and electric load forecasting methodology which benefits from the data fusion techniques in order to fill the lack of sufficient information in any of these aforementioned networks. For this purpose, a Bayesian spatiotemporal Gaussian Process model is proposed which employs the most informative spatiotemporal interdependency among its own network, and covariates from other city networks. The proposed load forecasting fusion method is compared with other state-of-the-art methods including Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX), Multivariate Linear Regression, Support Vector Regression and Neural Networks Regression using real-life data obtained from the City of Tallahassee in Florida. Results show that multi-network data fusion framework improves the accuracy of load forecasting, and the proposed Bayesian spatiotemporal Gaussian Process model outperforms all the above-mentioned methods.
Model-based development is an important paradigm for developing cyber-physical systems (CPS). Early verification and validation of embedded software speeds up the development process and saves costs. This is especially challenging, since CPSs interact with complex environments through sensors and actuators requiring models of the relevant CPS and its context. Therefore, the strong underlying assumption is that models are adequate for the verification task. Conformance testing addresses this problem by checking that two models of the same CPS are conformant, i. e., produce equivalent behavior w. r. t. the verification task. Although conformance is in general undecidable, for the relevant models of CPSs in practice, non-formal conformance checking procedures typically succeed in verifying conformance. In this work, we survey conformance checking for CPS we do not only perform a comparison of approaches for the evaluation of conformance, but also survey the required input generation.
The rapid development of vehicular network and autonomous driving technologies provides opportunities to significantly improve transportation safety and efficiency. One promising application is centralized intelligent intersection management, where an intersection manager accepts requests from approaching vehicles (via vehicle-to-infrastructure communication messages) and schedules the order for those vehicles to safely crossing the intersection. However, communication delays and packet losses may occur due to the unreliable nature of wireless communication or malicious security attacks (such as jamming and flooding), and could cause deadlocks and unsafe situations. In our previous work, we considered these issues and proposed a delay-tolerant intersection management protocol for intersections with a single lane in each direction. In this work, we address key challenges in efficiency and deadlock when there are multiple lanes from each direction, and propose a delay-tolerant protocol for general multi-lane intersection management. We prove that this protocol is deadlock-free, safe and satisfying the liveness property. Furthermore, we extend the traffic simulation suite SUMO with communication modules, implement our protocol in the extended simulator, and quantitatively analyze its performance with the consideration of communication delays. Finally, we also model systems using smart traffic lights with back-pressure scheduling in SUMO, and compare our delay-tolerant intelligent intersection protocol with smart traffic lights in cases of a single intersection and a network of interconnected intersections. Simulation results demonstrate the effectiveness of our approach.
Embedded computing devices play an integral role in the mechanical operations of modern-day vehicles. These devices exchange information that contains critical vehicle parameters that reflect the current of state of operations. Such information can be captured for various purposes like diagnostics, fleet management, and even independent research. Although monitoring individual parameters can be useful for some applications, monitoring distinct combinations of parameters can reveal more complex and higher level states that may be worth observing. Existing monitoring systems either lack user configurability and control or present simple user interfaces that make it difficult to monitor and collate different parameters in order to observe high-level vehicle states. In this work, we present TruckSTM, a novel application that realizes user-defined states from messages seen in the embedded networks of medium and heavy duty vehicles and displays state transitions on an interactive user-interface. We begin by symbolically formulating some of the in-vehicle networking concepts and formally defining the concept of operational states and state transitions. We then elaborate on the operations performed by TruckSTM in mapping network obtained vehicle parameters to states that can be defined in standard JSON format. Finally, we evaluate TruckSTM's asymptotic performance and present the results for the worst-case scenario.
Many energy optimizations require fine-grained, load-level energy data collected in real-time, most typically by a plug-level energy meter. Online load tracking is the problem of monitoring an individual electrical load's energy usage in software by analyzing the building's aggregate smart meter data. Load tracking differs from from the well-studied problem of load disaggregation in that it emphasizes per-load accuracy and efficient, online operation rather than accurate disaggregation of every building load via offline analysis. In essence, tracking a particular load creates a virtual power meter for it, which mimics having a networked-connected power meter attached to the load, but notably does not require tracking every other load as well. We propose PowerPlay, a model-driven system for performing accurate, high-performance online load tracking. Our results from applying the system to real-world energy data demonstrate that PowerPlay i) enables efficient online tracking on low-power embedded platforms, ii) scales to thousands of loads (across many buildings) on server platforms, and iii) improves per-load accuracy by more than a factor of two compared to a state-of-the-art load disaggregation algorithm. Our results point to the potential of replacing physical energy meters by 'virtual' power meters using a system like PowerPlay.
Modern trains rely on balises (communication beacons) located on the track to provide location information as they traverse a rail network. Balises, such as those conforming to the Eurobalise standard, were not designed with security in mind and are thus vulnerable to cyber attacks targeting data availability, integrity, or authenticity. In this work, we discuss data integrity threats to balise transmission modules and use high-fidelity simulation to study the risks posed by data integrity attacks. To mitigate such risk, we propose a practical two-layer solution: at the device level, we design a lightweight and low-cost cryptographic solution to protect the integrity of the location information; at the system layer, we devise a secure hybrid train speed controller to mitigate the impact under various attacks. Our simulation results demonstrate the effectiveness of our proposed solutions.
The trend of connected / autonomous features adds significant complexity to the traditional automotive systems to improve driving safety and comfort. Engineers are facing significant challenges in designing test environments that are more complex than ever. We propose a test framework that allows one to automatically generate various virtual road environments from the path specification and the behavior specification. The path specification intends to characterize geometric paths that an environmental object (e.g., roadways or pedestrians) needs to be visualized or move over. We characterize this aspect in the form of linear or nonlinear constraints of 3-Dimensional coordinates. Then, we introduce a test coverage, called an area coverage, to quantify the quality of generated paths in terms of how wide area the generated paths can cover. We propose an algorithm that automatically generate such paths using a SMT (Satisfiability Modulo Theories) solver. On the other hand, the behavioral specification intends to characterize how an environmental object changes its mode changes over time by interacting with other objects (e.g., a pedestrian waits for a signal or start crossing). We characterize this aspect in the form of timed automata. Then, we introduce a test coverage, called an edge/location coverage, to quantify the quality of the generated mode changes in terms of how many modes or transitions are visited. We propose a method that automatically generates many different mode changes using a model-checking method. To demonstrate the test framework, we developed the right turn pedestrian warning system in intersection scenarios and generated many different types of pedestrian paths and behaviors to analyze the effectiveness of the system.