![]() This study presents a distributed multi-agent-based traffic signal control for optimising green timing in an urban arterial road network to reduce the total travel time and delay experienced by vehicles. In this case, policies should be aimed at reducing the functional, psychological and cultural values of private cars, as well as increasing the performance of public transport and other (more) environmentally sound modes of transport on these aspects. In contrast, many efforts are needed to stimulate fervent car users to travel by public transport, because in their view, public transport cannot compete with their private car. Consequently, they may be open to use public transport more regularly. Infrequent car users judged less positively about the car and less negatively about public transport. So, for fervent car users, car use is connected with various important values in modern society. ![]() the car is a symbol of freedom and independence, a status symbol and driving is pleasurable. For them, the car outperformed public transport not only because of its instrumental function, but also because the car represents cultural and psychological values, e.g. Results revealed that especially fervent car users disliked public transport. A computerised questionnaire study was conducted among 1,803 Dutch respondents in May 2001. This paper describes who may be open to use public transport more often, and how people might be persuaded to use it. Public transport is often perceived to be a poor alternative for car use. We conduct several experiments with a transportation simulator the results of experiments show that the proposed framework reduces the average delay and travel time compared to the baseline methods. Second, for the discovered critical nodes, we introduce a novel traffic signal control approach based on deep reinforcement learning this approach can learn the optimal policy via constantly interacting with the road network in an iterative mode. This approach models the dynamic of road networks using a tripartite graph based on the vehicle trajectories and can accurately identify the city-wide critical nodes from a global perspective. Critical nodes are identified as nodes that would cause a dramatic reduction in the traffic efficiency of the road network if they were to fail. In this framework, we first use a data-driven approach to discover the critical nodes. To improve the traffic efficiency of city-wide road networks, we propose a traffic signal control framework that prioritizes the optimal control policies on critical nodes in road networks. It reduces the average travel delay and the time spent in the network compared to multi-agent-based adaptative signal control systems. Instantiation results and analysis denote that the designed system can significantly develop the efficiency at an individual intersection as well as in the multi-intersection network. An instance of the proposed framework was validated and designed in the ANYLOGIC simulator. It also uses fuzzy technology to handle the uncertainty of traffic conditions. Thus, the separate parts can be resolved rapidly by parallel tasking. The system profits from agent communication and collaboration as well as coordination features, along with decentralized organization, to decompose the traffic control optimization into subproblems and enable the distributed resolution. The optimizing of signal layouts is done in real time, and it is not only based on local stream factors but also on traffic stream conditions in surrounding intersections. The objective of this framework is to act on the phase layouts represented by its sequences and length to maximize throughput and fluidize traffic at an isolated intersection and for the whole multi-intersection network, through both inter-and intra-intersection collaboration and coordination. ![]() This paper presents a traffic simulation framework based on agent technology and fuzzy logic. Despite the fact that agent technologies have widely gained popularity in distributed systems, their potential for advanced management of vehicle traffic has not been sufficiently explored.
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