Spatiotemporal variable and parameter selection using sparse hybrid genetic algorithm for traffic flow forecasting

Abstract

Short-term traffic flow forecasting is a difficult yet important problem in intelligent transportation systems. Complex spatiotemporal interactions between the target road segment and other road segments can provide important information for the accurate forecasting. Meanwhile, spatiotemporal variable selection and traffic flow prediction should be solved in a unified framework such that they can benefit from each other. In this article, we propose a novel sparse hybrid genetic algorithm by introducing sparsity constraint and real encoding scheme into genetic algorithm in order to optimize shortterm traffic flow prediction model based on least squares support vector regression. This method can integrate spatiotemporal variable selection, parameter selection as well as traffic flow prediction in a unified framework, indicating that the ''goodness,'' that is, contribution, of selected spatiotemporal variables and optimized parameters directly depends on the final traffic flow prediction accuracy. The real-world traffic flow data are collected from 24 observation sites located around the intersection of Interstate 205 and Interstate 84 in Portland, OR, USA. The experimental results show that the proposed sparse hybrid genetic algorithm-least square support vector regression prediction model can produce better performance but with much fewer spatiotemporal variables in comparison with other related models.

Feature selection and extraction in spatiotemporal traffic forecasting: a systematic literature review

“…Abdulhai et al [42,43] suggested application of GA for selection of an optimal number of upstream and downstream spatial locations (as well as for other parameters of their neural network-based forecasting model). Recently GA were applied for spatial [44][45][46] and temporal [47] feature selection. The PSO approach was applied by Chan et al [48,49] and recently combined with GA by Zheng et al [50].…”

Short‐term traffic congestion prediction with Conv–BiLSTM considering spatio‐temporal features

“…In recent years, domestic and foreign traffic management departments and research institutes have carried out numerous studies on traffic congestion evaluation. Foreign traffic congestion evaluation indicators are roughly divided into the following categories: congestion definition indicators [40,41], travel time indicators [10,42], stream parameter indicators [43], service level indicators [44], and other indicators [45][46][47]. Traffic flow parameters mainly refer to parameters that reflect traffic flow characteristics such as traffic flow, average speed, traffic flow density, or occupancy rate.…”

ST-LSTM: A Deep Learning Approach Combined Spatio-Temporal Features for Short-Term Forecast in Rail Transit

“…However, it failed to imitate the structure of large urban scale. Xiaobo Chen et al proposed a new method to process spatial features by using sparse hybrid genetic algorithm [2]. Liu Qingchao et al proposed a model based on manifold similarity to capture the spatial regularity from freeway data [3].…”