Abstract
With the development of the advanced Intelligent Transportation System (ITS) in modern cities, it is of great significance to upgrade the forecasting methods for travel demand with the impact of ITS. The widespread use of ITS clearly changes the urban travelers’ behavior at present, in which case it is difficult for the conventional four-step travel demand forecasting model to have good performance. In this study, we apply the combined distribution and assignment (CDA) model to forecasting travel demand for modern urban transportation, in which travelers may choose the destination and path simultaneously. Furthermore, we present a new solution algorithm for solving the CDA model. With the network representation method that converts the CDA model into a standard traffic assignment problem (TAP), we develop a new path-based algorithm based on the gradient projection (GP) algorithm to solve the converted CDA model. The new solution algorithm is designed to find a more accurate solution compared with the widely used algorithm, the Evans’ two-stage algorithm. Two road networks, Sioux Falls and Chicago Sketch, are used to verify the performance of the new algorithm. Also, we conduct some experiments on the Sioux Falls network to illustrate several applications of the CDA model in consideration of the influences of ITS.
Highlights
The advanced Intelligent Transportation System (ITS) has been adopted increasingly in many cities
To improve the efficiency of the transportation network, it is significant to have a good knowledge of the effects that the strategies will have on travelers’ behavior and the network flow, which means that an accurate travel demand forecasting result is necessary
With the network representation method converting the combined distribution and assignment (CDA) model into a standard traffic assignment problem (TAP), we develop a new path-based algorithm inspired by the gradient projection (GP) algorithm to solve the converted CDA model
Summary
The advanced Intelligent Transportation System (ITS) has been adopted increasingly in many cities. We notice that with the widely used algorithm, Evans’ two-stage algorithm [6], it is difficult to achieve relatively high precision, which is required in the process of forecasting travel demand (for example, for consistent comparison between design scenarios [16]) This inferiority may Sustainability 2019, 11, 2167 result from the iterative equilibration between the origin and destination (O–D) flow and network flow. When there are plenty of paths existing between each O–D pair, the scheme could restrict the algorithm from obtaining high efficiency in solving the TAP To overcome this problem, we develop a new flow transferring strategy inspired by the GP method to improve the algorithm’s efficiency and accuracy.
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