A Traffic Path Recommendation Using Time Series Based Parameter Forecasting across Origin-Destination Pair
Keywords:
Dynamic traffic path, Time series, Graph - structured traffic data, Shortest path, Feature aggregationAbstract
Over the last few years, vehicular traffic path recommendation has become one of the important problems in operating road traffic networks. The shortest path recommendation across origin destination (OD) pairs is the opportunity which requires researcher’s attention to extract traffic parameters like journey time, traffic speed, and traffic flow thereby improving the path recommendation for different time periods of the day. Determining conglomerate spatio-temporal correlation of traffic data to precisely predict traffic parameters is crucial for traffic path recommendation. For different time periods of a day, there is a demand for traffic situation aware spatio-temporal path recommendations. However, previous path studies focus on one-by-one traffic parameters capturing spatial dependencies ignoring temporal correlation with other traffic parameters for different time instances of the day. The paper suggests a time series-based traffic data extraction model. Selected traffic data is formulated as time series-based graph-structured (TSBG) traffic data to accommodate spatial correlations as well as temporal dependencies. The proposed model learns the edge weight predictions using average and mean square values. Simulation results demonstrate the ability to identify all possible paths and recommend optimal ones thereby affirming the effectiveness of TSBG algorithm. This paper introduces architecture for historical graph-based traffic data representation, selected traffic parameter-based path recommendation, aggregation of selected parameters that significantly improve the accuracy of extracting all possible paths and simultaneously recommending the shortest path for OD pair. The proposed method achieves better results compared to traditional forecasting methods when tested rigorously.
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