Short-term traffic flow prediction using a self-adaptive two-dimensional forecasting method

Ma, Minghui; Liang, Shidong; Guo, Hui; Yang, Jufen

doi:10.1177/1687814017719002

Cited by 14 publications

(9 citation statements)

References 33 publications

(40 reference statements)

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“…Because studies that predict the related parameters in bus systems are relatively mature [38][39][40][41][42], the evolution of bus systems can be predicted with these methods in practice. In addition, the estimations of urban traffic parameters are also sufficiently mature, e.g., travel speed and queue length at signalized intersection [43,44]. erefore, the main object of this section is to propose an enhanced method that combines the bus holding control means and stop-skipping control means based on the self-adaptive equalizing headways control concept.…”

Section: Coordinated Control Strategymentioning

confidence: 99%

Sensitivity Analysis of Fleet Size for Dynamic Headway-Based Control Method Performance in terms of Passengers’ Experience

Liang

Zhang

et al. 2020

Journal of Advanced Transportation

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The headway-based control method is usually used to regulate the bus headways to improve the bus operation. However, these control methods only focus on equalizing bus headways, neglecting analysis for STD (standard deviation) of passengers on the buses. Therefore, in order to fix this gap, this paper analyzed performance of the control method in terms of regulating bus headways and number of passengers on the buses under variable number of buses on the route. First, as an enhanced version of headways-based control method, a coordinated control method based on the self-equalizing bus headways concept involving bus holding and stop-skipping is proposed. A discrete system is formulated to describe the bus operation, and the coordinated control method is imbedded into it. In order to reflect the performance in regulating bus headways and the number of passengers, two indexes are formulated, respectively. Finally, a set of numerical analysis was conducted. The test results showed that the regulated bus headways can make the number of passengers on buses equalized. As a by-product, the headway-based control method can make the bus capacity be well utilized by equalizing bus headways on the bus route.

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Section: Coordinated Control Strategymentioning

confidence: 99%

Sensitivity Analysis of Fleet Size for Dynamic Headway-Based Control Method Performance in terms of Passengers’ Experience

Liang

Zhang

et al. 2020

Journal of Advanced Transportation

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“…They found that their model outperformed the other advanced parametric models used in the study. Further, Ma et al [20] pointed out that accuracy is very important in short term traffic flow prediction. They proposed a 2-dimensional prediction method using Kalman filtering for historic traffic data.…”

Section: Literature Reviewmentioning

confidence: 99%

ANN based short-term traffic flow forecasting in undivided two lane highway

Sharma¹,

Kumar²,

Tiwari³

et al. 2018

J Big Data

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India is the second most dense and populated country in the world and one of the fastest growing economies. It is experiencing extreme congestion problems on road specifically on undivided two lane highways with mixed traffic. Facilitating infrastructure, imposing proper taxes to restrict personal vehicle growth and enhancing public transport facilities are long term solutions to this problem. These permanent solutions need government's involvement. The Indian government has spent a huge amount in the urban infrastructure sector. Many public transports like Bus Rapid Transit, Metro are being built in several new places to encourage the use of public transport. However, still there is a rapid growth of private vehicles [1]. The country's growing population is also one of the reasons for increased transportation needs. Meeting such needs with infrastructure growth is seemingly less viable because of space and cost constraints. In general, short term traffic forecasting deals with analyzing previous traffic data and predict the traffic flow for next 5-30 min. The duration of 5-30 min is very important because traffic can grow drastically sometimes in next 5 min and can be very congested

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“…The early methods for traffic flow forecast are model-driven, which work only if the data and model parameters satisfy specific assumptions. As a result, the model-driven methods cannot describe the complex nonlinearity of the traffic system, and have not been widely applied [5]. In this era of big data, many scholars have attempted to predict traffic flow based on the massive traffic data, eliminating the need for multiple assumptions, creating and implementing lots of data-driven forecast methods [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Traffic Flow Forecast Through Time Series Analysis Based on Deep Learning

Jiao

Huang

2020

IEEE Access

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Traffic congestion is a thorny issue to many large and medium-sized cities, posing a serious threat to sustainable urban development. Recently, intelligent traffic system (ITS) has emerged as an effective tool to mitigate urban congestion. The key to the ITS lies in the accurate forecast of traffic flow. However, the existing forecast methods of traffic flow cannot adapt to the stochasticity and sheer length of traffic flow time series. To solve the problem, this paper relies on deep learning (DL) to forecast traffic flow through time series analysis. The authors developed a traffic flow forecast model based on the long shortterm memory (LSTM) network. The proposed model was compared with two classic forecast models, namely, the autoregressive integrated moving average (ARIMA) model and the backpropagation neural network (BPNN) model, through long-term traffic flow forecast experiments, using an actual traffic flow time series from OpenITS. The experimental results show that the proposed LSTM network outperformed the classic models in prediction accuracy. Our research discloses the dynamic evolution law of traffic flow, and facilitates the decision-making of traffic management. INDEX TERMS Traffic flow forecast, time series analysis, deep learning (DL), long short-term memory (LSTM).

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Short-term traffic flow prediction using a self-adaptive two-dimensional forecasting method

Cited by 14 publications

References 33 publications

Sensitivity Analysis of Fleet Size for Dynamic Headway-Based Control Method Performance in terms of Passengers’ Experience

Sensitivity Analysis of Fleet Size for Dynamic Headway-Based Control Method Performance in terms of Passengers’ Experience

ANN based short-term traffic flow forecasting in undivided two lane highway

Traffic Flow Forecast Through Time Series Analysis Based on Deep Learning

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