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The lack of some traffic flow data seriously affects the quality of data collection and analysis in the traffic system. Completing the missing data is one of the most important steps in achieving the functions of intelligent transportation systems. In this paper an approach based on fuzzy C-means (FCM) imputes missing traffic volume data in loop detectors. With spatial–temporal correlation between detectors, the conventional vector-based data structure is first transformed into a matrix-based data pattern. Then, the genetic algorithm is applied to optimize the parameters of cluster size and weighting factor in the FCM model. Finally, the actual traffic flow volume collected at different locations is designed as a testing data set, and two indicators including root mean square error and relative accuracy are used to evaluate the imputation performance of the proposed method by comparison with some conventional methods (multiple linear regression, autoregressive integrated moving average model, and average historical method) by missing ratio. The applications in four scenarios demonstrate that the FCM-based imputation method outperforms conventional methods.

Expedient incident detection and understanding are important in traffic management and control. Social media as important information venues have immense value for increasing an awareness of traffic incidents. In this paper, an attempt is made to assess the potential of using harvested social media for traffic incident detection. Twitter in Seattle, Washington, was chosen as a representative sample environment for this work. A hybrid mechanism based on latent Dirichlet allocation and document clustering was proposed to model incident-level semantic information, while spatial point pattern analysis was applied to explore the spatial patterns and to assess the spatial dependence between incident-topic tweets and traffic incidents. A global Monte Carlo K-test indicated that the incident-topic tweets were significantly clustered at different scales up to 600 m. The nearest neighbor clutter removal method was used to separate feature tweet points from clutter; then a density-based algorithm successfully detected the clusters of tweets posted spatially close to traffic incidents. In multivariate spatial point pattern analysis, K-cross functions were investigated with Monte Carlo simulation to characterize and model the spatial dependence, and a positive spatial correlation was inferred between incident-topic tweets and traffic incidents up to 800 m. Finally, the tweet intensity as a function of distance from the nearest traffic incident was estimated, and a log-linear model was summarized. The experiments supported the notion that social media feeds acted as sensors, which allowed enhancing awareness of traffic incidents and their potential disturbances.

Expedient incident detection and understanding are important in traffic management and control. Social media as important information venues have immense value for increasing an awareness of traffic incidents. In this paper, an attempt is made to assess the potential of using harvested social media for traffic incident detection. Twitter in Seattle, Washington, was chosen as a representative sample environment for this work. A hybrid mechanism based on latent Dirichlet allocation and document clustering was proposed to model incident-level semantic information, while spatial point pattern analysis was applied to explore the spatial patterns and to assess the spatial dependence between incident-topic tweets and traffic incidents. A global Monte Carlo K-test indicated that the incident-topic tweets were significantly clustered at different scales up to 600 m. The nearest neighbor clutter removal method was used to separate feature tweet points from clutter; then a density-based algorithm successfully detected the clusters of tweets posted spatially close to traffic incidents. In multivariate spatial point pattern analysis, K-cross functions were investigated with Monte Carlo simulation to characterize and model the spatial dependence, and a positive spatial correlation was inferred between incident-topic tweets and traffic incidents up to 800 m. Finally, the tweet intensity as a function of distance from the nearest traffic incident was estimated, and a log-linear model was summarized. The experiments supported the notion that social media feeds acted as sensors, which allowed enhancing awareness of traffic incidents and their potential disturbances.

The lack of some traffic flow data seriously affects the quality of data collection and analysis in the traffic system. Completing the missing data is one of the most important steps in achieving the functions of intelligent transportation systems. In this paper an approach based on fuzzy C-means (FCM) imputes missing traffic volume data in loop detectors. With spatial–temporal correlation between detectors, the conventional vector-based data structure is first transformed into a matrix-based data pattern. Then, the genetic algorithm is applied to optimize the parameters of cluster size and weighting factor in the FCM model. Finally, the actual traffic flow volume collected at different locations is designed as a testing data set, and two indicators including root mean square error and relative accuracy are used to evaluate the imputation performance of the proposed method by comparison with some conventional methods (multiple linear regression, autoregressive integrated moving average model, and average historical method) by missing ratio. The applications in four scenarios demonstrate that the FCM-based imputation method outperforms conventional methods.

Traffic flow prediction is considered a key technology of intelligent transportation systems. This paper presents a hybrid model that combines double exponential smoothing (DES) and a support vector machine (SVM) to predict traffic flow patterns on the basis of weekly similarities in traffic flow. First, in the hybrid model, DES is applied to predict the future data, and its smoothing parameters are determined by the Levenberg-Marquardt algorithm. Second, the SVM is employed to estimate the residual series between the prediction results by the DES model and actual measured data. In the SVM model, the cross-correlation rule is used to optimize its parameters. Third, a case study to test the proposed model with the data at different temporal scales is presented. Furthermore, data-smoothing strategies, including difference and ratio schemes based on weekly similarities, are applied as data processes before prediction. The proposed hybrid model along with the processing scheme demonstrates superiority in prediction accuracy compared with autoregressive integrated moving average, DES, and DES-SVM models.

Traffic flow prediction is considered a key technology of intelligent transportation systems. This paper presents a hybrid model that combines double exponential smoothing (DES) and a support vector machine (SVM) to predict traffic flow patterns on the basis of weekly similarities in traffic flow. First, in the hybrid model, DES is applied to predict the future data, and its smoothing parameters are determined by the Levenberg–Marquardt algorithm. Second, the SVM is employed to estimate the residual series between the prediction results by the DES model and actual measured data. In the SVM model, the cross-correlation rule is used to optimize its parameters. Third, a case study to test the proposed model with the data at different temporal scales is presented. Furthermore, data-smoothing strategies, including difference and ratio schemes based on weekly similarities, are applied as data processes before prediction. The proposed hybrid model along with the processing scheme demonstrates superiority in prediction accuracy compared with autoregressive integrated moving average, DES, and DES-SVM models.