Optimized and meta-optimized neural networks for short-term traffic flow prediction: A genetic approach

Vlahogianni, Eleni I.; Karlaftis, Matthew G.; Golias, John

doi:10.1016/j.trc.2005.04.007

Cited by 559 publications

(272 citation statements)

References 32 publications

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“…These methods can be broadly classified in two major categories; parametric methods (e.g. linear regression (Zhang and Rice, 2003), time series models (Yang, 2005;Min and Wynter, 2011), Kalman filtering (Okutani and Stephanedes, 1984;Van Lint, 2008)) and non-parametric methods (neural network models (Ledoux, 1997;Vlahogianni et al, 2005;Van Lint, 2006), support vector regression (Vanajakshi and Rilett, 2007), simulation models (Liu et al, 2006)). In the past years, neural network models have gained attention in transportation field and are frequently applied in traffic state prediction.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, instantaneous travel time, which does not consider congestion or speed evolution, is available at the departure time to predict instantaneous travel time in the following time interval. Data-driven approaches, which make use of instantaneous travel time, are consistent under some cases with the transitional physics of traffic flow and they are capable of constructing the underlying behavior of traffic without strong assumptions on its temporal evolution (see for example Jiang and Adeli, 2004;Vlahogianni et al, 2005). However, data-driven approaches cannot explicitly infer knowledge from point measurements for estimating link performance measures (Vlahogianni et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

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Experienced travel time prediction for congested freeways

Yildirimoḡlu

Geroliminis

2013

Transportation Research Part B: Methodological

141

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a b s t r a c tTravel time is an important performance measure for transportation systems, and dissemination of travel time information can help travelers make reliable travel decisions such as route choice or departure time. Since the traffic data collected in real time reflects the past or current conditions on the roadway, a predictive travel time methodology should be used to obtain the information to be disseminated. However, an important part of the literature either uses instantaneous travel time assumption, and sums the travel time of roadway segments at the starting time of the trip, or uses statistical forecasting algorithms to predict the future travel time. This study benefits from the available traffic flow fundamentals (e.g. shockwave analysis and bottleneck identification), and makes use of both historical and real time traffic information to provide travel time prediction. The methodological framework of this approach sequentially includes a bottleneck identification algorithm, clustering of traffic data in traffic regimes with similar characteristics, development of stochastic congestion maps for clustered data and an online congestion search algorithm, which combines historical data analysis and real-time data to predict experienced travel times at the starting time of the trip. The experimental results based on the loop detector data on Californian freeways indicate that the proposed method provides promising travel time predictions under varying traffic conditions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Experienced travel time prediction for congested freeways

Yildirimoḡlu

Geroliminis

2013

Transportation Research Part B: Methodological

141

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“…The most commonly used nonparametric method is the artificial neural network (ANN), which has been used widely in traffic forecasting (Van Lint et al, 2005), (Vlahogianni et al, 2005). Other commonly used methods include various forms of nonparametric regression (Smith et al, 2002); (Clark, 2003) and kernel methods (Chun-Hsin Wu et al, 2004); (Castro-Neto et al, 2009)).…”

Section: Space-time Forecastingmentioning

confidence: 99%

A Comparison of Neighbourhood Selection Techniques in Spatio-Temporal Forecasting Models

Haworth

Cheng

2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Spatio-temporal neighbourhood (STN) selection is an important part of the model building procedure in spatio-temporal forecasting. The STN can be defined as the set of observations at neighbouring locations and times that are relevant for forecasting the future values of a series at a particular location at a particular time. Correct specification of the STN can enable forecasting models to capture spatio-temporal dependence, greatly improving predictive performance. In recent years, deficiencies have been revealed in models with globally fixed STN structures, which arise from the problems of heterogeneity, nonstationarity and nonlinearity in spatio-temporal processes. Using the example of a large dataset of travel times collected on London's road network, this study examines the effect of various STN selection methods drawn from the variable selection literature, varying from simple forward/backward subset selection to simultaneous shrinkage and selection operators. The results indicate that STN selection methods based on penalisation are effective. In particular, the maximum concave penalty (MCP) method selects parsimonious models that produce good forecasting performance.* Corresponding author. This is useful to know for communication with the appropriate person in cases with more than one author.

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“…There are many methods for short-term passenger flow prediction: Time-series model [3] , neural network model [4] , nonparametric regression model [5] , linear regression model [6] , Kalman filter method [7] , prediction model based on chaos theory [8] . Li Yuancheng et al used wavelet principle to divide the traffic flow data into many smooth vectors.…”

Section: Introductionmentioning

confidence: 99%

Short-term Passenger Flow Prediction on Bus Stop Based on Hybrid Model

Wang¹,

Yang²,

Zang³

2018

Proceedings of the 2017 2nd International Conference on Electrical, Control and Automation Engineering (ECAE 2017)

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Abstract-Short-term passenger flow prediction on bus stop is an important base and technical support for bus dispatch strategy. In this paper, a new hybrid prediction model including two single models of BP neural network and time series model was proposed according to the periodicity and randomness properties of short-term passenger flow. By using the IC card data from buses, a three-layer BP neural network model was established to reflect the characteristics of the stability of shortterm passenger flow cycle. The time series model of cash ticket passenger flow data was established. Finally, the results of the two models were fitted to get the final prediction results. The results show that the proposed hybrid prediction model can effectively predict short-term passenger flow on bus stop.

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Optimized and meta-optimized neural networks for short-term traffic flow prediction: A genetic approach

Cited by 559 publications

References 32 publications

Experienced travel time prediction for congested freeways

Experienced travel time prediction for congested freeways

A Comparison of Neighbourhood Selection Techniques in Spatio-Temporal Forecasting Models

Short-term Passenger Flow Prediction on Bus Stop Based on Hybrid Model

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