Search

Due to the rapid development of vehicular transportation and urbanization, traffic congestion has been increasing and becomes a serious problem in almost all major cities worldwide. Many instances of traffic congestion can be traced to their root causes, the so-called traffic bottlenecks, where relief of traffic congestion at bottlenecks can bring network-wide improvement. Therefore, it is important to identify the locations of bottlenecks and very often the most effective way to improve traffic flow and relieve traffic congestion is to improve traffic situations at bottlenecks. In this article, we first propose a novel definition of traffic bottleneck taking into account both the congestion level cost of a road segment itself and the contagion cost that the congestion may propagate to other road segments. Then, an algorithm is presented to identify congested road segments and construct congestion propagation graphs to model congestion propagation in urban road networks. Using the graphs, maximal spanning trees are constructed that allow an easy identification of the causal relationship between congestion at different road segments. Moreover, using Markov analysis to determine the probabilities of congestion propagation from one road segment to another road segment, we can calculate the aforementioned congestion cost and identify bottlenecks in the road network. Finally, simulation studies using SUMO confirm that traffic relief at the bottlenecks identified using the proposed technique can bring more effective network-wide improvement. Furthermore, when considering the impact of congestion propagation, the most congested road segments are not necessarily bottlenecks in the road network. The proposed approach can better capture the features of urban bottlenecks and lead to a more effective way to identify bottlenecks for traffic improvement. Experiments are further conducted using data collected from inductive loop detectors in Taipei road network and some road segments are identified as bottlenecks using the proposed method. [ABSTRACT FROM AUTHOR]

Taipei Metro adopted the diode-grounded scheme for stray current collection in construction of its cross rail network. During operation of the network, a high rail-to-earth potential (Vrail) has been observed at the east end of the Blue Line (i.e., stations BL13-BL16). To find effective countermeasures, a series of field tests in a step-by-step development nature was conducted from 1999-2000, which led to the decision of disconnecting the impedance bond at Gil of the tie line so that the negative return current of the Blue Line cannot flow to the rails of the Red-Green Line, and vice versa (detailed in Sections 111-A and V-C). This decision was implemented through contract-out work in 2003. Since then, the Vrail has been lowered by almost half before disconnection. To gain the insight characteristic before the contract-out, numerical simulations were also conducted by simulating the multi-train and multisection features of the cross transportation network. The simulation results (for Vrail and stray current, or Istray) were consistent with the field-test results. This paper presents the design of these field tests and their test results in comparison with the simulation results, based on which the countermeasures for reducing Vrail and the present status after Vrail reduction at Taipei Metro are presented. [ABSTRACT FROM AUTHOR]

As the population keeps growing, traffic congestion happens more and more often. Consequently, travel time has become an important indicator of driving experience. Accurate travel time information helps drivers plan their route more wisely and thus effectively alleviate traffic congestion. In this research, we propose a vehicle travel time prediction model for freeway traffic. The data used in this research are derived from the traffic dataset of the Taiwan Freeway Bureau, and the travel time prediction is made for the Sun Yat-sen Freeway between Taipei and Hsinchu. First, the missing value of the raw data is imputed by Autoencoder. The data are then segmented according to time series and are used to build the prediction model. To effectively capture the hidden features required to predict the travel time for the vehicle traveling on the freeway, a deep learning architecture is adopted in our system, which includes the GRU neural network model, the XGBoost model, and the Hybrid model that combines the GRU and XGBoost through linear regression. To increase computational efficiency, the travel time predictions for consecutive toll gates every 5 minutes apart are pre-computed offline, so that the online travel time prediction of the whole trip can be obtained by simply summing up a few numbers. Experimental results based on actual traffic data show that the proposed system can achieve good performance in terms of prediction accuracy and execution time. [ABSTRACT FROM AUTHOR]