Estimation of Link Travel Time Distribution With Limited Traffic Detectors

Duan, Peibo; Mao, Guoqiang; Kang, Jun; Huang, Baoqi

doi:10.1109/tits.2019.2932053

Cited by 16 publications

(4 citation statements)

References 36 publications

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“…To simplify the problem, we consider a scenario where a taxi aims to travel along the route with the smallest distance. This assumption is also commonly adopted in numerous research studies, such as [35][36][37]. Hereafter, given x nm = 1, that is, the nth taxi is recommended to pick up the mth potential passenger from zone z i , we then have a group of possible cruising routes for the nth taxi, denoted by C z i nm , which has a size of |z i |.…”

Section: Estimation Of R C Nm and R O Nmmentioning

confidence: 99%

A Distributed Scheme for the Taxi Cruising Route Recommendation Problem Using a Graph Neural Network

Li,

Huang,

Liu

et al. 2024

Electronics

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Despite considerable research efforts being devoted to the taxi cruising route recommendation (TCRR) problem, existing studies still have some shortcomings. To begin with, the competition and collaboration between taxis are not sufficiently taken into account. Furthermore, the TCRR is heavily reliant on potential taxi demand, which is time-variant and difficult to accurately predict due to the underlying spatiotemporal correlation and dynamic traffic patterns. Moreover, the consideration of competition and cooperation among taxis increases the complexity of the TCRR problem, making conventional centralized algorithms computationally expensive. In this paper, we first formulate TCRR as a biobjective optimization problem to balance the collaboration and competition between taxis. Subsequently, we forecast short-term taxi demand using the proposed long-short-term-memory-based graph convolutional network (LSTM-GCN), which considers diverse factors such as road topology, points of interest (POIs), and multiple time-scale features. Lastly, we propose a distributed algorithm based on a Lagrange dual decomposition. The experimental and simulation results demonstrate that our TCRR scheme performs better than any other counterpart, (i) resulting in a 3% reduction in idle taxis per hour, (ii) performing four times faster than the centralized algorithms to obtain the optimal solution, and (iii) resulting in a 7% increase in average profit.

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Section: Estimation Of R C Nm and R O Nmmentioning

confidence: 99%

A Distributed Scheme for the Taxi Cruising Route Recommendation Problem Using a Graph Neural Network

Li,

Huang,

Liu

et al. 2024

Electronics

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“…The presence of a large number of neurons and weight parameters in the classical bilinear recurrent neural network makes the network structure complex and the convergence speed of the algorithm slow, and the convergence speed of the algorithm is related to the dynamic performance of the prediction model [20][21][22][23][24]. Therefore, in order to improve the convergence speed of the BRNN model and enhance the reliability of traffic flow prediction results, the particle swarm algorithm is integrated with the bilinear recurrent neural network algorithm, so as to prune the redundant neurons and weights in the classical BRNN model to reduce the complexity of the network, prevent it from falling into local optimum and improve the computational speed [25][26][27][28][29][30][31].…”

Section: Brnn Optimization Modelmentioning

confidence: 99%

Traffic flow prediction method based on bidirectional linear recurrent neural network

Xu¹,

Shen²

2021

Preprint

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In order to improve the rationality and effectiveness of intelligent traffic control and management on urban roads, a bidirectional linear recurrent neural network-based traffic flow prediction method is proposed from the perspective of spatial and temporal characteristics of traffic flow. The method effectively combines the characteristics of fast and accurate bilinear polynomial solution and dynamic calibration of recurrent neural network, and adopts particle swarm algorithm to realize the dynamic pruning process of redundant neurons and weights, which improves the convergence speed and prediction accuracy of the algorithm. The algorithm is trained and experimented with video data, and a comparative analysis is conducted. The results show that the method can achieve accurate prediction of road traffic flow, the traffic flow prediction accuracy reaches more than 90%, meeting the data accuracy requirements of actual traffic management and control, and the convergence speed of the algorithm has also been significantly improved.

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“…A maior parte dos modelos de previsão de tráfego veicular se aplica a dados de rodovias (VLAHOGIANNI; KARLAFTIS; GOLIAS, 2014), considerando que o tráfego urbano apresenta maior nível de complexidade. Para contornar essa di culdade, observou-se nos últimos anos o surgimento de métodos capazes de extrair as dependências temporais e espaciais entre arestas da malha viária para melhor compreender a dinâmica de tráfego veicular em contexto urbano (ERMAGUN; LEVINSON, 2018;RYU et al, 2018).…”

Section: Previsão Do Fluxo De Tráfegounclassified

“…Uma forma direta de fornecer intervalos de credibilidade e, portanto, a incerteza de previsões, é por meio de distribuições de probabilidade. Entretanto, distribuições de uxos de tráfego são pouco comuns na literatura, sendo mais frequentes a utilização de distribuições de headways (LI; CHEN, 2017) e tempos de viagem (GUESSOUS et al, 2014;DUAN et al, 2019;GHADER;DARZI;ZHANG, 2019). A primeira, e até então única aplicação de distribuições de uxos de tráfego na pesquisa em transportes foi proposta por Djenouri, Zimek e Chiarandini (2018) para detecção de outliers em séries temporais.…”

Section: Modelos Baseados Em Dadosunclassified

Método de previsão de fluxo de tráfego em rodovias urbanas a partir de dados de velocidade de fontes online

Stefani¹

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Estimation of Link Travel Time Distribution With Limited Traffic Detectors

Cited by 16 publications

References 36 publications

A Distributed Scheme for the Taxi Cruising Route Recommendation Problem Using a Graph Neural Network

A Distributed Scheme for the Taxi Cruising Route Recommendation Problem Using a Graph Neural Network

Traffic flow prediction method based on bidirectional linear recurrent neural network

Método de previsão de fluxo de tráfego em rodovias urbanas a partir de dados de velocidade de fontes online

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