A Dynamic Traffic Assignment Method Based on Connected Transportation System

Zhang, Li; Liu, Jiaming; Yu, Bin; Chen, Gang

doi:10.1109/access.2019.2915993

Cited by 6 publications

(5 citation statements)

References 39 publications

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“…Moreover, no driver in the UE can reduce its travel time by unilaterally changing paths. Equation ( 8) is the classic variational inequality (VI) form of UE [56], which can also be used in DTA [57].…”

Section: Static Traffic Assignmentmentioning

confidence: 99%

“…In terms of the model type, DTA can be categorized into macroscopic, mesoscopic and microscopic models [58,59]. The microscopic level model mainly focuses on individual driver behaviour modelling [57,60,61]. Mesoscopic traffic flow describes vehicle behaviour in aggregate terms, such as inprobability distributions [62][63][64].…”

Section: Dynamic Traffic Assignmentmentioning

confidence: 99%

See 1 more Smart Citation

Spatial and temporal modelling of coupled power and transportation systems: A comprehensive review

Sun

Zhao

Wang

et al. 2021

Energy Conversion and Econom

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The large-scale application of electric vehicles can lead to new challenges in the operation of the two complex and coupled networks of electricity and transportation systems, thus creating a new concept-a power-traffic network. The purpose is to provide a comprehensive survey of this emerging field. First, both the transportation system and power grid models are briefly introduced in terms of time and space scales. Then, the power traffic network model and applications at long-term, mid-term, and short-term time scales are summarized, and provided a unified view of these achievements. Based on this analysis, recommendations for future research are presented.

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Section: Static Traffic Assignmentmentioning

confidence: 99%

Section: Dynamic Traffic Assignmentmentioning

confidence: 99%

Spatial and temporal modelling of coupled power and transportation systems: A comprehensive review

Sun

Zhao

Wang

et al. 2021

Energy Conversion and Econom

View full text Add to dashboard Cite

show abstract

“…Over the past decades, various dynamic traffic assignment (DTA) models have also been developed as part of intelligent transportation systems (ITSs), which can precisely describe the time-varying traffic flows of real road networks [24][25][26][27]. Compared with STA models, DTA models are better able to efficiently describe congestion effects and can therefore produce far more accurate traffic flows.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Quasi-Dynamic Traffic Assignment Model Integrating Multi-Source Traffic Sensor Data on the Expressway Network

Zeng

Guan

et al. 2021

IJGI

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Static traffic assignment (STA) models have been widely utilized in the field of strategic transport planning. However, STA models cannot fully represent the dynamic road conditions and suffer from inaccurate assignment during traffic congestion. At the same time, an increasing number of installed sensors have become an important means of detecting dynamic road conditions. To address the shortcomings of STA models, we integrate multi-source traffic sensor datasets and propose a novel data-driven quasi-dynamic traffic assignment model, named DQ-DTA. In this model, records of toll stations are used for time-varying travel demand estimation. GPS trajectory datasets of vehicles are further used to calculate the dynamic link costs of the road network, replacing the imprecise Bureau of Public Roads (BPR) function. Moreover, license plate recognition (LPR) data are used to design a statistical probability-based multipath assignment method to capture travelers’ route choices. The expressway network in the Hunan province is selected as the study area, and several classic STA models are also chosen for performance comparison. Experimental results demonstrate that the accuracy of the proposed DQ-DTA model is about 6% higher than that of the chosen STA models.

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“…Existing traffic assignment problems studied by researchers can be classified into two categories: (a) Static Traffic Assignment (STA) [3]; (b) Dynamic Traffic Assignment (DTA) [4].…”

Section: Introductionmentioning

confidence: 99%

A Distributed Assignment Method for Dynamic Traffic Assignment Using Heterogeneous-Adviser Based Multi-Agent Reinforcement Learning

Pan

Chen

et al. 2020

IEEE Access

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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.

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A Dynamic Traffic Assignment Method Based on Connected Transportation System

Cited by 6 publications

References 39 publications

Spatial and temporal modelling of coupled power and transportation systems: A comprehensive review

Spatial and temporal modelling of coupled power and transportation systems: A comprehensive review

A Data-Driven Quasi-Dynamic Traffic Assignment Model Integrating Multi-Source Traffic Sensor Data on the Expressway Network

A Distributed Assignment Method for Dynamic Traffic Assignment Using Heterogeneous-Adviser Based Multi-Agent Reinforcement Learning

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