Road Traffic Forecasting: Recent Advances and New Challenges

Laña, Ibai; Ser, Javier Del; Vélez, Manuel; Vlahogianni, Eleni I.

doi:10.1109/mits.2018.2806634

Cited by 283 publications

(161 citation statements)

References 104 publications

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“…Suhas et al [31] followed a systematic study to aggregate previous works on traffic prediction, highlight marked changes in trends, and provide research direction for future work. Lana et al [32] summarized the latest technical achievements in traffic prediction field, along with an insightful update of the main technical challenges that remain unsolved. The readers interested in details of models that applied in traffic prediction field could refer to review reference paper.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Traffic Flow Prediction with KNN and LSTM

Luo

Yang

et al. 2019

Journal of Advanced Transportation

205

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The traffic flow prediction is becoming increasingly crucial in Intelligent Transportation Systems. Accurate prediction result is the precondition of traffic guidance, management, and control. To improve the prediction accuracy, a spatiotemporal traffic flow prediction method is proposed combined with k-nearest neighbor (KNN) and long short-term memory network (LSTM), which is called KNN-LSTM model in this paper. KNN is used to select mostly related neighboring stations with the test station and capture spatial features of traffic flow. LSTM is utilized to mine temporal variability of traffic flow, and a two-layer LSTM network is applied to predict traffic flow respectively in selected stations. The final prediction results are obtained by result-level fusion with rank-exponent weighting method. The prediction performance is evaluated with real-time traffic flow data provided by the Transportation Research Data Lab (TDRL) at the University of Minnesota Duluth (UMD) Data Center. Experimental results indicate that the proposed model can achieve a better performance compared with well-known prediction models including autoregressive integrated moving average (ARIMA), support vector regression (SVR), wavelet neural network (WNN), deep belief networks combined with support vector regression (DBN-SVR), and LSTM models, and the proposed model can achieve on average 12.59% accuracy improvement.

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Section: Introductionmentioning

confidence: 99%

Spatiotemporal Traffic Flow Prediction with KNN and LSTM

Luo

Yang

et al. 2019

Journal of Advanced Transportation

205

View full text Add to dashboard Cite

show abstract

“…Thanks to the advancement of data collection techniques, an abundance of traffic data can be obtained including loop detector data, GPS data [3], automated fare collection (AFC) data [4], cellular signaling data [5] and automatic number plate recognition (ANPR) data [6]. High-quality traffic data can support a diversity of transportation applications like dynamic traffic forecasting [7], route planning [8] and accident warning [9].…”

Section: Introductionmentioning

confidence: 99%

Vehicle Identity Recovery for Automatic Number Plate Recognition Data via Heterogeneous Network Embedding

Chen

2020

Sustainability

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Automatic number plate recognition (ANPR) systems, which have been widely equipped in many cities, produce numerous travel data for intelligent and sustainable transportation. ANPR data operate at an individual level and carry the unique identities of vehicles, which can support personalized traffic planning. However, these systems also suffer from the common problem of missing data. Different from the traditional missing cases, we focus on the problem of the loss of vehicle identities in ANPR records due to recognition failure or other environmental factors. To address the issue, we propose a heterogeneous graph embedding framework that constructs a travel heterogeneous information network (THIN) and learns the embeddings of the entities to find the best matched vehicles for the unknown records. As a result, the recovery of vehicle identities is cast as an entity alignment task on a THIN. The proposed method integrates the vehicle group entities and context relations into the THIN for capturing the spatiotemporal relationships in vehicle travel and adopts a holographic embeddings model for better fitting the network structure. Empirically, we test it with a real ANPR dataset collected from Xuancheng, China, which has a densely-distributed camera network. The experiments demonstrate the effectiveness of the proposed graph structure under different missing rates. Further, we compare it with other embedding methods and the results support the superiority of holographic embeddings.

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“…Com o avanço da comunicação móvel, especialmente das tecnologias de comunicação sem fio e processamento embarcado nos veículos,é possível o desenvolvimento de um sistema de transporte inteligente (ITS) com o objetivo de utilizar a comunicações entre veículos para resolver diversos problemas nos transportes tradicionais dos centros urbanos como, por exemplo, reduzir engarrafamentos, diminuir o tempo de viagem dos veículos, diminuir acidentes, aumentar a capacidade de fluxo das vias etc [Lana et al 2018]. Para isso, as Redes Veiculares Ad-Hoc (VANETs -Vehicular Ad-Hoc NEtworks) compõem um conjunto de serviços de comunicação onde uma rede ad-hoc sem fioé criada entre veículos [Ahmed and Gharavi 2018, Wu et al 2015, Dietzel et al 2014.…”

Section: Introductionunclassified

SGTD: Sistema de Gerenciamento de Tráfego Distribuído para Redes Veiculares

Gomides

Lourenço

Souza

et al. 2019

Anais Do XXXVII Simpósio Brasileiro De Redes De Computadores E Sistemas Distribuídos (SBRC 2019)

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O gerenciamento da mobilidade é um importante e recorrente desafio dos centros urbanos, principalmente, devido à intensificação do agrupamento populacional nas grandes cidades. Nesse sentido, um dos reflexos desse impasse está no crescimento desproporcional do número de veículos em relação à infraestrutura urbana, responsável pelo surgimento ou pela intensificação dos congestionamentos. O objetivo deste trabalho é propor um algoritmo totalmente distribuído capaz de reduzir esses impactos a partir da reorganização do fluxo veicular. Dessa forma, os veículos, de maneira colaborativa, são responsáveis pela classificação e pelo compartilhamento de informações sobre os deslocamentos realizados para a tomada de decisão em um ambiente distribuído. Os resultados das simulações indicam que a solução apresentada consegue reduzir o tempo de viagem, o tempo em congestionamentos, além de aumentar a velocidade média alcançada com baixo impacto no número de mensagens transmitidas, de modo a possibilitar um bom desempenho do sistema proposto.

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Road Traffic Forecasting: Recent Advances and New Challenges

Cited by 283 publications

References 104 publications

Spatiotemporal Traffic Flow Prediction with KNN and LSTM

Spatiotemporal Traffic Flow Prediction with KNN and LSTM

Vehicle Identity Recovery for Automatic Number Plate Recognition Data via Heterogeneous Network Embedding

SGTD: Sistema de Gerenciamento de Tráfego Distribuído para Redes Veiculares

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