14,539 research outputs found

    Decentralized Cooperative Planning for Automated Vehicles with Continuous Monte Carlo Tree Search

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    Urban traffic scenarios often require a high degree of cooperation between traffic participants to ensure safety and efficiency. Observing the behavior of others, humans infer whether or not others are cooperating. This work aims to extend the capabilities of automated vehicles, enabling them to cooperate implicitly in heterogeneous environments. Continuous actions allow for arbitrary trajectories and hence are applicable to a much wider class of problems than existing cooperative approaches with discrete action spaces. Based on cooperative modeling of other agents, Monte Carlo Tree Search (MCTS) in conjunction with Decoupled-UCT evaluates the action-values of each agent in a cooperative and decentralized way, respecting the interdependence of actions among traffic participants. The extension to continuous action spaces is addressed by incorporating novel MCTS-specific enhancements for efficient search space exploration. The proposed algorithm is evaluated under different scenarios, showing that the algorithm is able to achieve effective cooperative planning and generate solutions egocentric planning fails to identify

    Fully automated urban traffic system

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    The replacement of the driver with an automatic system which could perform the functions of guiding and routing a vehicle with a human's capability of responding to changing traffic demands was discussed. The problem was divided into four technological areas; guidance, routing, computing, and communications. It was determined that the latter three areas being developed independent of any need for fully automated urban traffic. A guidance system that would meet system requirements was not being developed but was technically feasible

    2nd Symposium on Management of Future motorway and urban Traffic Systems (MFTS 2018): Booklet of abstracts: Ispra, 11-12 June 2018

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    The Symposium focuses on future traffic management systems, covering the subjects of traffic control, estimation, and modelling of motorway and urban networks, with particular emphasis on the presence of advanced vehicle communication and automation technologies. As connectivity and automation are being progressively introduced in our transport and mobility systems, there is indeed a growing need to understand the implications and opportunities for an enhanced traffic management as well as to identify innovative ways and tools to optimise traffic efficiency. In particular the debate on centralised versus decentralised traffic management in the presence of connected and automated vehicles has started attracting the attention of the research community. In this context, the Symposium provides a remarkable opportunity to share novel ideas and discuss future research directions.JRC.C.4-Sustainable Transpor

    Modelling mixed traffic flow of autonomous vehicles and human-driven vehicles

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    Autonomous Vehicles (AVs) are bringing revolutionary opportunities and challenges to urban transport systems. They can reduce congestion, improve operational efficiency and liberate drivers from driving. Though AVs might bring attractive potential benefits, most benefits are evaluated at high AV penetration rates or an all-AV scenario. In practice, limited by price barriers, adoption rates and vehicle-renewal periods, AVs may not replace Human-Driven Vehicles (HDVs) to achieve a high penetration rate in a short time. It can be expected that the road network will operate with a mix of AVs and HDVs in the near to medium future. Therefore, there is a strong motivation to analyse the performance of road networks under mixed traffic conditions. The overall aim of this PhD research is to analyse mixed traffic flows of AVs and HDVs to help traffic managers and Local Authorities (LAs) improve the performance of urban traffic systems by right-of-way reallocation and dynamic traffic management. To achieve this aim, this PhD research is divided into four parts. Firstly, the impact of heterogeneity between AVs and HDVs on road capacity is investigated. A theoretical model is proposed to calculate the maximum capacity of heterogeneous traffic flow. Based on the theoretical model, it is shown that road capacity increases convexly with AV penetration rates. This finding provides a theoretical basis to support the hypothesis that right-of-way reallocation can increase road capacity under the mixed traffic flow. To cross-validate the above finding, different right-of-way reallocation strategies are evaluated on a two-lane road with SUMO simulation. Compared with a do-nothing scenario, the road capacity can be increased by approximately 11% with a proper RoW reallocation strategy at low or medium AV penetration rates. Secondly, whether CAVs can be used as mobile traffic controllers by adjusting their speed on a certain link is investigated. It is found that in some circumstances, system efficiency can be improved by CAVs adjusting their speed on a certain link to nudge the network towards the system optimum. According to a numerical analysis on the Braess network, total travel time can be reduced by 9.7% when CAVs actively slow down on a link. To take more realistic circumstances into account, a SUMO simulation case study is conducted, where HDVs only have partial knowledge about travel costs. The results of the simulation demonstrate that when CAVs are acting as mobile traffic controllers by actively reducing speed on a certain link, total travel time can be reduced by approximately 6.8% compared with the do-nothing scenario. Thirdly, whether travel efficiency can be improved with only a part of the vehicular flow cooperatively changing their routing under mixed conditions is investigated. It has been found that it is possible to use CAVs to influence HDVs’ day-to-day routing and push the network towards the system optimal distribution dynamically on a large network with multiple OD pairs. Taking non-linear cost-flow relationship and signal timing into account, an Optimal Routing and Signal Timing (ORST) control strategy is proposed for CAVs and tested in simulation. Compared with initial user equilibrium, total travel time can be reduced by approximately 7% when a portion of CAVs cooperatively charge their routing with the ORST control strategy at the 75% CAV penetration rate. This opens up possibilities, besides road pricing, to improve system efficiency by controlling routing and signal timing strategy for CAVs. Fourthly, whether additional travel efficiency can be achieved by jointly optimising routing and signal timing with information from CAVs is further investigated. Specifically, the impact of information levels on routing and signal timing efficiency has been investigated quantitatively. The results demonstrate that different levels of information will lead the road traffic system to reach different equilibrium points. Then the proposed ORST control strategy is compared with existing routing and signal timing strategies. The results present that ORST can reduce approximately 10% of the total travel time compared to user equilibrium. In addition, the proposed model has also been tested on a revised Nguyen-Dupuis network. At 25% CAV penetration rates, the proposed model can successfully reduce approximately 23% of total travel time. In summary, the mixed flow of AVs and HDVs is investigated in this PhD research. To increase the efficiency of urban traffic systems, novel strategies have been proposed and tested with numerical analysis and simulation, which provides inspirations and quantitative evidence for traffic managers and LAs to manage the mixed traffic flow efficiently.Open Acces

    Using Contraflow on a Road Segment to Improve Emergency Response Vehicle Speed in a Connected Vehicle Environment

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    Emergency response vehicles (ERVs) need to reach their destinations as fast as possible. Road congestion and unpredictable movement of non-emergency vehicles (non-ERVs) makes it challenging for the ERV to move quickly. By using the autonomous/connected vehicle environment, instructions can be disseminated to the non-ERVs in the vicinity of the ERV to facilitate its passage within a link. In this thesis, an extension to a previously developed mathematical program is proposed to enable the ERV to use a contraflow lane when considerable speed gains can be potentially achieved. An experimental analysis is conducted to evaluate the sensitivity of the model’s output to traffic congestion, downstream non-ERV positions, ERV starting position, road composition, road segment length, and the length of the feasible stopping range for every non-ERV. Results showed that usage of contraflow was provided the least travel times for the ERV when it started in the left-most lane of the normal direction. Also, when the normal direction of the road was heavily congested as compared to the contraflow segment, the usage of contraflow by the ERV provided it the least travel times. In addition, a comparative study is performed to compare the proposed formulation with previously developed non-contraflow strategies as well as the currently adopted strategy requiring vehicles to move to the nearest edge. Results showed that the use of contraflow by the ERV provides improved travel times and average ERV speeds in many situations when the contraflow segment volume was sparse whereas the normal direction was congested. However, the computation times for the newly developed contraflow strategy were greater than the previously developed non-contraflow strategies. So, a heuristic was developed to reduce computational effort by cutting off the solver at a specified point, which was decided by how far the current feasible solution found was from the possible optimal solution (optimality gap). This heuristic not only provided improved computation times, but also results which did not statistically differ from the optimal results. The paths provided by the heuristic were also similar with the only difference being the points at which the lane changes happened. Hence, the utilization of this approach can potentially save lives due to reduced emergency response times

    A Framework for Developing and Integrating Effective Routing Strategies Within the Emergency Management Decision-Support System, Research Report 11-12

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    This report describes the modeling, calibration, and validation of a VISSIM traffic-flow simulation of the San José, California, downtown network and examines various evacuation scenarios and first-responder routings to assess strategies that would be effective in the event of a no-notice disaster. The modeled network required a large amount of data on network geometry, signal timings, signal coordination schemes, and turning-movement volumes. Turning-movement counts at intersections were used to validate the network with the empirical formula-based measure known as the GEH statistic. Once the base network was tested and validated, various scenarios were modeled to estimate evacuation and emergency vehicle arrival times. Based on these scenarios, a variety of emergency plans for San José’s downtown traffic circulation were tested and validated. The model could be used to evaluate scenarios in other communities by entering their community-specific data

    Smart traffic control for the era of autonomous driving

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    This thesis aims to take on the challenges to address some of the key issues in traffic control and management, including intersection protocol design, congestion measurement, selfish routing and road infrastructure automation, under the assumption that all vehicles on the road are connected and self-driving. To design and test traffic control mechanisms for AVs, we introduced a formal model to represent road networks and traffic. Based on this model, we developed a simulation system on top of an existing open-source platform (AIM4) and used it to examine a number of traffic management protocols specifically designed for traffic with fully autonomous vehicles. Simulation outcomes show that traffic management protocols for AVs can be more subtle, sensitive and variable with traffic volumes/flow rate, vehicle safe distance and road configuration. In addition, by analyzing the real-world traffic data and simulation data, we found that measuring congestion with exponential functions has considerable advantages against the traditional BPR function in certain aspects. The deployment of autonomous vehicles provides traffic management with an opportunity of choosing either centralised control or decentralised control. The price of anarchy (PoA) of autonomous decision-making for routing gives an applicable quantitative criterion for selection between them. We extended the existing research on PoA with the ˙class of exponential functions as cost functions. We found an expression for the tight upper bound of the PoA for selfish routing games with exponential cost functions. Unlike existing studies, this upper bound depends on traffic demands, with which we can get a more accurate estimation of the PoA. Furthermore, by comparing the upper-bounds of PoA between the BPR function and the exponential function, we found that the exponential functions yield a smaller upper bound than the BPR functions in relatively low traffic flows. To specify traffic management systems with autonomous roadside facilities, we propose a hybrid model of traffic assignment. This model aims to describe traffic management systems in which both vehicles and roadside controllers make autonomous decisions, therefore, are autonomous agents. We formulated a non-linear optimization problem to optimize traffic control from a macroscopic view of the road network. To avoid the complex calculations required for non-linear optimization, we proposed an approximation algorithm to calculate equilibrium routing and traffic control strategies. The simulation results show that this algorithm eventually converges to a steady state. The traffic control scheme in this steady state is an approximately optimal solution
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