Using an ARIMA-GARCH Modeling Approach to Improve Subway Short-Term Ridership Forecasting Accounting for Dynamic Volatility

Ding, Chuan; Duan, Jinxiao; Zhang, Yanru; Wu, Xinkai; Yu, Guizhen

doi:10.1109/tits.2017.2711046

Cited by 109 publications

(61 citation statements)

References 38 publications

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“…Clustering along with Exponential Weighted Moving Average models were used to predict passenger demand hot spots with a 79.6% hit ratio in [5]. The subway ridership demand was analysed in [25] using a combined ARIMA-GARCH model with a maximum error of 7%. This promising performance of regression and smoothing based models in the context of passenger demand modeling, along with high computational speeds, make them ideal choices for our comparison study.…”

Section: A Related Workmentioning

confidence: 99%

Taxi Demand Forecasting: A HEDGE-Based Tessellation Strategy for Improved Accuracy

Davis

Raina

Jagannathan

2018

IEEE Trans. Intell. Transport. Syst.

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A key problem in location-based modeling and forecasting lies in identifying suitable spatial and temporal resolutions. In particular, judicious spatial partitioning can play a significant role in enhancing the performance of location-based forecasting models. In this work, we investigate two widely used tessellation strategies for partitioning city space, in the context of real-time taxi demand forecasting. Our study compares (i) Geohash tessellation, and (ii) Voronoi tessellation, using two distinct taxi demand datasets, over multiple time scales. For the purpose of comparison, we employ classical time-series tools to model the spatio-temporal demand. Our study finds that the performance of each tessellation strategy is highly dependent on the city geography, spatial distribution of the data, and the time of the day, and that neither strategy is found to perform optimally across the forecast horizon. We propose a hybrid tessellation algorithm that picks the best tessellation strategy at each instant, based on their performance in the recent past. Our hybrid algorithm is a non-stationary variant of the wellknown HEDGE algorithm for choosing the best advice from multiple experts. We show that the hybrid tessellation strategy performs consistently better than either of the two strategies across the data sets considered, at multiple time scales, and with different performance metrics. We achieve an average accuracy of above 80% per km 2 for both datasets considered at 60 minute aggregation levels.

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Section: A Related Workmentioning

confidence: 99%

Taxi Demand Forecasting: A HEDGE-Based Tessellation Strategy for Improved Accuracy

Davis

Raina

Jagannathan

2018

IEEE Trans. Intell. Transport. Syst.

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“…Similarly, other researchers also divided traffic flow data into two parts. But then, they adopted two of the ARIMA model, the support vector machine, the generalized autoregressive conditional heteroscedasticity (GARCH) model and the Markov model to predict the two parts of traffic flow data [25][26][27][28][29][30]. To accurately capture the change rules of short-term traffic flow, Zhang et al [31] and Yang et al [32] divided traffic flow data into three parts, including the periodic trend, the deterministic part and the volatility part, and they pointed out that the volatility part is extremely important for short-term traffic flow prediction.…”

Section: Table 1 Summarization Of Single Methods Applied For Short-tmentioning

confidence: 99%

Period Division-Based Markov Models for Short-Term Traffic Flow Prediction

Yao

Zhang

2020

IEEE Access

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Short-term traffic flow prediction is very important and provides the basic data for traffic management and route guidance. The rules of traffic flow data during different periods in a day are different. Thus, this paper proposes a membership degree-based Markov (MM) model and two period division-based Markov (PM and PW) models. The MM model introduces the membership degree to determine the state of traffic flow. The PM and PW models introduce the Fisher optimal division method to divide one day into several periods based on traffic flow data. Then, the period division-based Markov models integrate the Markov (CM) or weighted Markov (WM) model with the MM model to predict traffic volumes during different periods. The impacts of vehicle type on traffic flow prediction are also discussed. The proposed models are verified using the field data. The results show that: (1) the PM and PW models both perform better than the CM, WM, state membership degree-based Markov and weighted state membership degreebased Markov models; (2) the PW model sometimes performs better than the backward propagation (BP) neural network; (3) when traffic flow data are distinguished by vehicle type, the performance of the PM and PW models can be improved. It is suggested to adopt the proposed period division-based Markov models to predict traffic flow with the concern of vehicle type, so that more accurate traffic flow information can be provided for traffic management and route guidance. INDEX TERMS short-term traffic flow; prediction; Markov models; period division; ordered clustering; traffic flow pattern; vehicle type

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“…The model-driven methods mainly include the autoregressive integrated moving average (ARIMA) model [9]- [11], seasonal ARIMA (SARIMA) model [12], [13], Markov chain (MC) [14]- [17], Bayesian network (BN) [18]- [20], and Kalman filter (KF) [21]- [23]. These methods cannot perform normally without several preconditions, e.g.…”

Section: Literature Reviewmentioning

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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Using an ARIMA-GARCH Modeling Approach to Improve Subway Short-Term Ridership Forecasting Accounting for Dynamic Volatility

Cited by 109 publications

References 38 publications

Taxi Demand Forecasting: A HEDGE-Based Tessellation Strategy for Improved Accuracy

Taxi Demand Forecasting: A HEDGE-Based Tessellation Strategy for Improved Accuracy

Period Division-Based Markov Models for Short-Term Traffic Flow Prediction

Traffic Flow Forecast Through Time Series Analysis Based on Deep Learning

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