A semi-decentralized control strategy for urban traffic

We present in this article a semi-decentralized approach for urban traffic control, based on the TUC (Traffic responsive Urban Control) strategy. We assume that the control is centralized as in the TUC strategy, but we introduce a contention time window inside the cycle time, where antagonistic stages alternate a priority rule. The priority rule is set by applying green colours for given stages and yellow colours for antagonistic ones, in such a way that the stages with green colour have priority over the ones with yellow colour. The idea of introducing this time window is to reduce the red time inside the cycle, and by that, increase the capacity of the network junctions. In practice, the priority rule could be applied using vehicle-to-vehicle (v2v) or vehicle-to-infrastructure (v2i) communications. The vehicles having the priority pass almost normally through the junction, while the others reduce their speed and yield the way. We propose a model for the dynamics and the control of such a system. The model is still formulated as a linear quadratic problem, for which the feedback control law is calculated off-line, and applied in real time. The model is implemented using the Simulation of Urban MObility (SUMO) tool in a small regular (American-like) network configuration. The results are presented and compared to the classical TUC strategy.Comment: 16 page

The State-of-the-art of Coordinated Ramp Control with Mixed Traffic Conditions

Ramp metering, a traditional traffic control strategy for conventional vehicles, has been widely deployed around the world since the 1960s. On the other hand, the last decade has witnessed significant advances in connected and automated vehicle (CAV) technology and its great potential for improving safety, mobility and environmental sustainability. Therefore, a large amount of research has been conducted on cooperative ramp merging for CAVs only. However, it is expected that the phase of mixed traffic, namely the coexistence of both human-driven vehicles and CAVs, would last for a long time. Since there is little research on the system-wide ramp control with mixed traffic conditions, the paper aims to close this gap by proposing an innovative system architecture and reviewing the state-of-the-art studies on the key components of the proposed system. These components include traffic state estimation, ramp metering, driving behavior modeling, and coordination of CAVs. All reviewed literature plot an extensive landscape for the proposed system-wide coordinated ramp control with mixed traffic conditions.Comment: 8 pages, 1 figure, IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE - ITSC 201

Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network

Accurate lane localization and lane change detection are crucial in advanced driver assistance systems and autonomous driving systems for safer and more efficient trajectory planning. Conventional localization devices such as Global Positioning System only provide road-level resolution for car navigation, which is incompetent to assist in lane-level decision making. The state of art technique for lane localization is to use Light Detection and Ranging sensors to correct the global localization error and achieve centimeter-level accuracy, but the real-time implementation and popularization for LiDAR is still limited by its computational burden and current cost. As a cost-effective alternative, vision-based lane change detection has been highly regarded for affordable autonomous vehicles to support lane-level localization. A deep learning-based computer vision system is developed to detect the lane change behavior using the images captured by a front-view camera mounted on the vehicle and data from the inertial measurement unit for highway driving. Testing results on real-world driving data have shown that the proposed method is robust with real-time working ability and could achieve around 87% lane change detection accuracy. Compared to the average human reaction to visual stimuli, the proposed computer vision system works 9 times faster, which makes it capable of helping make life-saving decisions in time

Exploiting Map Topology Knowledge for Context-predictive Multi-interface Car-to-cloud Communication

While the automotive industry is currently facing a contest among different communication technologies and paradigms about predominance in the connected vehicles sector, the diversity of the various application requirements makes it unlikely that a single technology will be able to fulfill all given demands. Instead, the joint usage of multiple communication technologies seems to be a promising candidate that allows benefiting from characteristical strengths (e.g., using low latency direct communication for safety-related messaging). Consequently, dynamic network interface selection has become a field of scientific interest. In this paper, we present a cross-layer approach for context-aware transmission of vehicular sensor data that exploits mobility control knowledge for scheduling the transmission time with respect to the anticipated channel conditions for the corresponding communication technology. The proposed multi-interface transmission scheme is evaluated in a comprehensive simulation study, where it is able to achieve significant improvements in data rate and reliability

Economics of Road Network Ownership

This paper seeks to understand the economic impact of centralized and decentralized ownership structures and their corresponding pricing and investment strategies on transportation network performance and social welfare for travelers. In a decentralized network economic system, roads are owned by many agencies or companies that are responsible for pricing and investment strategies. The motivation of this study is two-fold. First, the question of which ownership structure, or industrial organization, is optimal for transportation networks has yet to be resolved. Despite several books devoted to this research issue, quantitative methods that translate ownership-related policy variables into short- and long-run network performance are lacking. Second, the U.S. and many other countries have recently seen a slowly but steadily increasing popularity of road pricing as an alternative to traditional fuel taxes. Not only is the private sector encouraged to finance new roads, this transition in revenue mechanism also makes it possible for lower-level government agencies and smaller jurisdictions to participate in network pricing and investment practice. The issue of optimal ownership is no longer a purely theoretical debate, but bears practical importance. This research adopts an agent-based simulator of network dynamics to explore the implications of centralized and decentralized ownership on mobility and social welfare, as well as potential financial issues and regulatory needs. Components of the simulator: the travel demand model, cost functions, and key variables of pricing and investment strategies, are empirically estimated and validated. Results suggest that road network is a market with imperfect competition. While there is a significant performance lag between the optimal strategy and the current network financing practice in the U.S. (characterized by centralized control, fuel taxes, and budget-balancing investment), a completely decentralized network suffers from issues such as higher-than-optimal tolls and over-investment. For the decentralized ownership structure, appropriate regulation on pricing and investment practices is necessary. Further analysis based on simulation comparisons suggests that with appropriate price regulation, a decentralized road economy consisting of profit-seeking road owners could outperform the existing centralized control, achieve net social benefits close to the theoretical optimum, and distribute a high percentage of welfare gains to travelers. Decentralized control is especially valuable in rapidly changing environments because it promptly responds to travel demand. These results seem to favor the idea of privatizing or decentralizing road ownership on congested networks. Further tests on real-world transportation networks are necessary and should make an interesting future study.Network economics, Modeling network dynamics, Road pricing, Transportation financing, Privatization.

How blockchain impacts cloud-based system performance: a case study for a groupware communication application

This paper examines the performance trade-off when implementing a blockchain architecture for a cloud-based groupware communication application. We measure the additional cloud-based resources and performance costs of the overhead required to implement a groupware collaboration system over a blockchain architecture. To evaluate our groupware application, we develop measuring instruments for testing scalability and performance of computer systems deployed as cloud computing applications. While some details of our groupware collaboration application have been published in earlier work, in this paper we reflect on a generalized measuring method for blockchain-enabled applications which may in turn lead to a general methodology for testing cloud-based system performance and scalability using blockchain. Response time and transaction throughput metrics are collected for the blockchain implementation against the non-blockchain implementation and some conclusions are drawn about the additional resources that a blockchain architecture for a groupware collaboration application impose