Urban network traffic congestion can be caused by disturbances, such as fluctuation and disequilibrium of traffic demand. This paper designs a distributed control method for preventing disturbancebased urban network traffic congestion by integrating Multi-Agent Reinforcement Learning (MARL) and regional Mixed Strategy Nash-Equilibrium (MSNE). To enhance the disturbance-rejection performance of Urban Network Traffic Control (UNTC), a regional MSNE concept is integrated, which models the competitive relationship between each agent and its neighboring agents in order to improve the decisionmaking process of MARL. The learning rate is enhanced with a self-adaptive ability to avoid a local optimal dilemma; Jensen-Shannon (JS) divergence is utilized to define the learning rate of the modified MARL. A two-way rectangular grid network with nine intersections is modeled via a Cell Transmission Model (CTM). A probability distribution mechanism, which can update the turn ratio of each approach dynamically and discretely, is established to represent the segmented route-decision process of the vehicles. The effectiveness of the proposed control method is evaluated through simulations in the grid network. The results show the influence of major disturbances, such as fluctuation of vehicle arrival rate, fluctuation of traffic demand (e.g. a rapidly rising flow and extreme changes in origin-destination distribution), and disequilibrium of traffic demand (e.g. different arrival flows at each boundary of the urban network), on the performance of the suggested control method. The results can be used to improve the state of the art in order to reduce urban network traffic congestion due to these disturbances. INDEX TERMS Urban network traffic control, distributed traffic signal control system, multi-agent reinforcement learning, mixed strategy Nash-equilibrium, numerical simulation.

A taxi stand can effectively regulate the behavior of taxi picking up passengers, reduce empty-run rate, and provide a convenient and orderly waiting environment for the public. However, the unreasonable setting of the existing taxi stands in most cities leads to an extremely low utilization rate and a waste of public space resources. This paper presents a novel three-stage strategy to address the taxi stands location problem (TSLP) incrementally. First, taxi demands hotspots are mined from a massive taxi Global Positioning System (GPS) data with GIS platform, and the optimal area for taxi stands siting in the following stages is determined. Then, the spatial interaction between taxi demands and taxi stands is explored to generate demand subsections and stand candidates along both the sides of the road. At last, a taxi stand location model (TSLM) is developed to minimize the total cost, which contains the access cost of passengers and the construction cost of taxi stands. The genetic algorithm-based procedure is adopted for TSLM optimization. A case study conducted in China verifies the effectiveness of the location strategy and investigate the impact of the maximum acceptable distance for passengers on TSLP. The experimental results describe the number and layout of taxi stand under a different demand coverage, which indicates that the proposed approach is beneficial to provide scientific reference for the municipal department in taxi stand site decisions and make a tradeoff between the interests of planners and users.INDEX TERMS Taxi stand, location strategy, spatial-temporal demand, GPS big data, genetic algorithm.

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The Dynamic Traffic Assignment (DTA) is one of the important measures to alleviate urban network traffic congestion. The congestions are usually caused by stochastic traffic demands, which are generally unassignable from time dimension in the real-world but are assumed to be assignable in existing DTA methods (i.e. real-time travel demands). In this paper, a distributed DTA method for preventing urban network traffic congestion caused by stochastic real-time travel demands by improving Multi-Agent Reinforcement Learning (MARL). A team structure, which consists of decision-makers and advisers, is designed to learn parallelly in realistic DTA tasks. To reduce the size of the solution space adaptively, the dynamic critical values advised by adviser agents are adopted as constraints for the strategy space of decision-makers (i.e. main agents). A collaborative heterogeneous-adviser mechanism is designed to avoid deviation of guidance. To enhance the adaptability of DTA to the changeable external environment, the mixed strategy concept is introduced to improve the decision-making process of main agents. The respective mapping mechanisms are designed to define adaptive learning rates to improve the sensitivity of MARL. The Sioux Falls (SF) network is established as a test platform via a Dynamic Network Loading (DNL). The effectiveness of the suggested DTA method is assessed through numerical simulations SF network. Under the influence of the scenario with stochastic real-time travel demands, the results show that the proposed method outperforms in terms of the throughput of the network and the individual average travel time among the overall network. Additionally, the ability of the proposed method in response to the external environment rapidly has also been demonstrated. Adopting the suggested method can improve the state of the art to assign stochastic real-time travel demands dynamically and to avoid potential traffic congestion fundamentally. INDEX TERMS dynamic traffic assignment, intelligent transportation system, multi-agent system, reinforcement learning, multi-agent reinforcement learning, numerical simulation.

The unreasonable layout of taxi stands (TS) in urban areas not only fails to provide bidirectional guidance for drivers and passengers but also wastes spatial resources and aggravates the surrounding traffic. This paper compares the performance of three classical location models in optimizing TS spatial layout, and develops an extended model integrating the p-median and distance factor to support TS site selection in urban planning from multiple perspectives. To this end, taxi demand with spatial–temporal dynamics is extracted from taxi global positioning system (GPS) data to uncover the restrictive distribution characteristics of the setting areas and specific locations of TS with GIS platform. Taxi demand is then subdivided, and potential service points are set up on the road network. With the constraints of the supply and demand environment, we design the TS location models (TSLM) based on the set covering problem (SCP), the maximal covering location problem (MCLP), and the p-median problem (PMP), respectively. Furthermore, the TSLM based on PMP is extended to consider the maximum acceptable distance for passengers. A genetic algorithm-based procedure is introduced for solving the extended TSLM. An experiment conducted in China compares the facility coverage capacity, taxi demand allocation, and passenger access willingness of the optimal layout schemes obtained from four TSLMs. The number of parking spaces at TS is also evaluated. The result demonstrates that extended TSLM outperforms the other three models in the validity of locating TS.