Abstract
The route guidance system (RGS) has been considered an important technology to mitigate urban traffic congestion. However, existing RGSs provide only route guidance after congestion happens. This reactive strategy imposes a strong limitation on the potential contribution of current RGS to the performance improvement of a traffic network. Thus, a proactive RGS based on congestion prediction is considered essential to improve the effectiveness of RGS. The problem of congestion prediction is translated into traffic amount (i.e. the number of vehicles on the individual roads) prediction, as the latter is a straightforward indicator of the former. We thereby propose two urban traffic prediction models using different modeling approaches. Model-1 is based on the traffic flow propagation in the network, while Model-2 is based on the time-varied spare flow capacity on the concerned road links. These two models are then applied to construct a centralized proactive RGS. Evaluation results show that (1) both of the proposed models reduce the prediction error up to 52% and 30% in the best cases compared to the existing Shift Model, (2) providing proactive route guidance helps reduce average travel time by up to 70% compared to providing reactive one and (3) non-rerouted vehicles could benefit more from route guidance than rerouted vehicles do.
Highlights
Urban road traffic congestion has been a global issue for many years due to rapid urbanization
6 Conclusions In this paper, we proposed two urban traffic amount prediction models based on the propagation of traffic flow and the spare road capacity, respectively, for applying the proposed models to a route guidance system (RGS) to reduce average travel time
We evaluated the prediction accuracy of the proposed models by comparing their performance with the Shift Model under varied prediction interval using the real data collected in the traffic simulator SUMO
Summary
Urban road traffic congestion has been a global issue for many years due to rapid urbanization. Liang and Wakahara EURASIP Journal on Wireless Communications and Networking 2014, 2014:85 http://jwcn.eurasipjournals.com/content/2014/1/85 happens instead of proactively guiding drivers to prevent congestion from happening Due to this strategic limitation of current RGS, the traffic prediction module in existing RGS has been mainly focusing on travel time prediction and the consistency of predicted travel time. The rest of the paper is organized as follows: In section 2, we discuss related works on RGS and traffic prediction; the proposed urban traffic amount prediction models are presented in section 3; in section 4, we construct a centralized proactive RGS based on the prediction models; in section 5, we evaluate the prediction accuracy of the proposed models, and investigate the impact of proactive route guidance on a traffic network; in the last section, we draw the conclusions
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