Spatio‐Temporal Short‐Term Urban Traffic Volume Forecasting Using Genetically Optimized Modular Networks

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

doi:10.1111/j.1467-8667.2007.00488.x

Cited by 125 publications

(76 citation statements)

References 24 publications

(31 reference statements)

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“…Traffic analysts have utilized the spatial dependency of road segments to solve three typical problems in a traffic network: (1) short-term traffic forecasting [1, 2, 5, 6], (2) reliable path problem [7], and (3) missing data estimation [8]. …”

Section: Introductionmentioning

confidence: 99%

Using temporal detrending to observe the spatial correlation of traffic

2017

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This empirical study sheds light on the spatial correlation of traffic links under different traffic regimes. We mimic the behavior of real traffic by pinpointing the spatial correlation between 140 freeway traffic links in a major sub-network of the Minneapolis—St. Paul freeway system with a grid-like network topology. This topology enables us to juxtapose the positive and negative correlation between links, which has been overlooked in short-term traffic forecasting models. To accurately and reliably measure the correlation between traffic links, we develop an algorithm that eliminates temporal trends in three dimensions: (1) hourly dimension, (2) weekly dimension, and (3) system dimension for each link. The spatial correlation of traffic links exhibits a stronger negative correlation in rush hours, when congestion affects route choice. Although this correlation occurs mostly in parallel links, it is also observed upstream, where travelers receive information and are able to switch to substitute paths. Irrespective of the time-of-day and day-of-week, a strong positive correlation is witnessed between upstream and downstream links. This correlation is stronger in uncongested regimes, as traffic flow passes through consecutive links more quickly and there is no congestion effect to shift or stall traffic. The extracted spatial correlation structure can augment the accuracy of short-term traffic forecasting models.

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Section: Introductionmentioning

confidence: 99%

Using temporal detrending to observe the spatial correlation of traffic

2017

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“…Genetic algorithms have been used profusely to generate rules in many learning problems [2,9,24]. Also, genetic algorithms are used as a tool in many real-world problems, such as scheduling [14], forecasting [35], de-sign [26] or classification [10]. Finally, hybridization with fuzzy logic [31], neural networks [20] or simulation [11] are common strategies in evolutionary computation.…”

Section: State Of the Artmentioning

confidence: 99%

Mining quantitative association rules based on evolutionary computation and its application to atmospheric pollution

Martínez-Ballesteros

Troncoso

Martínez‐Álvarez

et al. 2010

ICA

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This research presents the mining of quantitative association rules based on evolutionary computation techniques. First, a real-coded genetic algorithm that extends the well-known binary-coded CHC algorithm has been projected to determine the intervals that define the rules without needing to discretize the attributes. The proposed algorithm is evaluated in synthetic datasets under different levels of noise in order to test its performance and the reported results are then compared to that of a multi-objective differential evolution algorithm, recently published. Furthermore, rules from real-world time series such as temperature, humidity, wind speed and direction of the wind, ozone, nitrogen monoxide and sulfur dioxide have been discovered with the objective of finding all existing relations between atmospheric pollution and climatological conditions.

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“…For instance, Vlahogianni et al [36] proposed a modular neural predictor in short-term traffic volume forecasting and incorporating temporal and spatial volume characteristics to improve the prediction accuracy from multiple locations of an urban signalized arterial roadway. Xie et al [38] investigated the application of a Kalman filter with discrete wavelet analysis in short-term traffic volume forecasting.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Approach for Short-Term Freeway Traffic Prediction During Peak Periods

Qi¹,

Ishak²

2013

IEEE Trans. Intell. Transport. Syst.

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Using a stochastic approach, this paper explores and models the basic stochastic characteristics of freeway traffic behavior under a wide range of traffic conditions during peak periods and then applies the models to short-term traffic speed prediction. The speed transition probabilities are estimated from real-world 30-s speed data over a six-year period at three different locations along the 38-mi corridor of Interstate 4 (I-4) in Orlando, FL. The cumulative negative/positive transition probabilities and expected values are derived from the transition probabilities and fitted using logistic and exponential models, respectively. The expected values associated with the most likely transition of speed are then derived from the fitted models and used for predicting speed. Each predicted speed is also associated with a probability value, indicating the chance of observing the occurrence of such transition. The prediction performance was compared for three methods using the root mean square errors (RMSEs). The weighted average method was very close to the higher probability method in most cases. For the two probabilistic methods, the performance was slightly better for the morning peak periods than the evening peak period or all data combined. While the prediction performance of the probabilistic models was comparable with those of other methods found in the literature, the probabilistic approach based on the higher probability provides estimates of the associated probability with each prediction. This provides a measure of confidence in the predicted values before such information is disseminated to the public by traffic agencies.

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Spatio‐Temporal Short‐Term Urban Traffic Volume Forecasting Using Genetically Optimized Modular Networks

Cited by 125 publications

References 24 publications

Using temporal detrending to observe the spatial correlation of traffic

Using temporal detrending to observe the spatial correlation of traffic

Mining quantitative association rules based on evolutionary computation and its application to atmospheric pollution

Stochastic Approach for Short-Term Freeway Traffic Prediction During Peak Periods

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