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An input vector composed of various features plays an important role in short-term traffic forecasting. However, there is limited research on the optimal feature selection of an input vector for a certain forecasting task. To fill the gap, this paper proposes a cohesion-based heuristic feature selection method by analyzing the nature of the forecasting methods. This method is able to determine which features should be contained in an input vector to make a forecasting algorithm perform better. The proposed method is demonstrated in two experiments based on the empirical traffic flow data. The results show that the method is able to improve the performances of the short-term traffic forecasting algorithms. It is then suggested to consider the proposed method as a preprocessing procedure in practical forecasting applications.

This paper proposes a new traffic assignment model, named an N-path logit-based stochastic user equilibrium (NPSUE), which considers not only road users' perceptual errors in path choices, but also users' heterogeneity that allows the numbers of the limited path sets for each user to be different. The property of the N-path user equilibrium (NPUE) proposed in Dung-Ying Lin (2014) is first reanalyzed, and then the optimization program for the NPSUE models as well as the property of the optimal solution are explicitly discussed. For each type of users, the NPSUE assignment results are consistent with the logit-assignment results in their limited path set. Both the NPUE and NPSUE models are tested in three road networks: an simple grid network, Nguyen and Dupuis' network and Sioux Falls network. The numerical results show that (1) the NPUE can be equivalent to the UE under certain conditions, although the users don't have full information on the available paths; (2) the proposed NPSUE may provide an ideal equilibrium state for both system managers and road users; (3) the provision of appropriate path guidance information is beneficial to both system managers and road users; otherwise, the provision of excessive information may decrease the performance of the road network, and (4) under the NPSUE conditions, the provision of excessive information may decrease users' satisfaction.