Short-term Traffic Information Research Articles

Short-Term Traffic Prediction of the Urban Road Network based on the Intelligent Transportation System

As an important part of the intelligent transportation system research, traffic prediction is the premise of realizing traffic guidance and can provide decision-making basis for traveler service and traffic management. To realize the macromanagement of the entire road network, it must be based on the traffic information of all road sections in the road network. In fact, short-term traffic information has certain characteristics such as real-time, high-dimensional, nonlinear, and nonstationary characteristics, but the traffic information of the same road section has stability and regularity in different periods, and the short-term traffic state has a self-similarity. This makes short-term traffic information predictable. The prediction is made by using the information of the road sections with detectors related to it, and the dynamic dissimilarity matrix is introduced to deal with the three parameters of flow, speed, and time occupancy at the same time. The quantitative relationship between the traffic information of the nondetector road segment and the known traffic information of other road segments, so as to realize the prediction of the traffic information of the nondetector road segment and obtain the complete traffic information of the regional road network. In addition, we use the actual data of the local road network in a certain area to verify the feasibility of the method.

Game-Based Thermal-Delay-Aware Adaptive Routing (GTDAR) for Temperature-Aware 3D Network-on-Chip Systems

The thermal problem of three-dimensional Network-on-Chip (3D NoC) is proven to be severer than 2D NoC due to the stacking dies and heterogeneous thermal conduction between each silicon layers. To control the system temperature under a certain thermal limit, the current Dynamic Thermal Managements ( DTMs ) can be classified into temporal approaches and spatial approaches. The temporal DTM approaches reduce the processing speed of those overheated NoC components. However, for emerging cooling consideration, the full throttling scheme is usually applied as the system temperature reaches the alarming level, which results in significant system performance overhead. On the other hand, the spatial DTM approaches migrate the traffic load away from the overheated components. Although the spatial DTM approaches can mitigate the performance impact during the temperature control, the cooling period is longer than the temporal approaches because of the asynchronous phenomenon of traffic and temperature behavior among the NoC components. To consider the advantages of the temporal and spatial DTM approaches, it is necessary to synchronize the information of traffic and temperature behavior in the NoC systems. In this paper, we apply the Game Theory to propose a Game-based Thermal-Delay-aware Adaptive Routing ( GTDAR ) scheme. The GTDAR first adopts the Thermal-Delay principle to transfer the long-term temperature information to short-term traffic information by allocating the input buffer length of each NoC routers, which can reduce the thermal problem into the traffic problem. Afterward, the GTDAR involves the Nash Equilibrium property to distribute the packet routing to mitigate the thermal problem by considering the traffic and temperature simultaneously. In our experiments, the proposed Game-based Thermal-Delay-aware Adaptive Routing ( GTDAR ) scheme can help to improve 8.7 percent to 130 percent system performance with only 2.4 percent area overhead compared with the previous works.

A short-term traffic prediction model in the vehicular cyber–physical systems

The advances in Cyber–Physical Systems (CPS), vehicular networks and Intelligent Transportation System (ITS) boost a growing interest in the design, development and deployment of Vehicular Cyber–Physical Systems (VCPS) for some emerging applications. As one of the key application for realizing traffic guidance, the traffic prediction could provide better route planning for people and accuracy decision basis for traffic managements. In practice, short-term traffic information has the characteristics of real-time, incompleteness, non-linearity and non-stationary, and few proposed methods could successfully implement this forecasting. In this paper, we proposed a fuzzy Markov prediction model which can estimate the short-term traffic conditions in VCPS in urban environment. First, we selected a real-time GPS dataset in the Shanghai Transport Grid Project as our data source for traffic prediction and pre-process this raw dataset to make it consistent with the practical case. Next, we combine the fuzzy theory with Markov progress in the prediction model, and use the continuous three-step average method to reduce the errors caused by the one-step transition. Finally, we choose the speed and traffic flow to express the metrics of traffic state and use the fuzzy reasoning rules to give out the determined traffic state. The simulation results show that our proposed model can be precisely used for the short-term traffic prediction in urban environment.

Study on Route Assignment Strategy based on Anticipatory Stigmergy

In this paper, we evaluate how to provide or control traffic information based on an anticipatory stigmergy. Managing traffic congestion is one of the main issues for smart cities, and many works have been trying to address it from the IT and transportation research perspectives. Recently, dynamic coordination methods are becoming possible using the more short-term traffic information that can be gathered by probe-vehicles or smart phones. Some approaches have been trying to handle short-term traffic information in which a stigmergy-based approach is employed as an indirect communication method for cooperation among distributed agents and for managing traffic congestion. One drawback of these approaches is that handling near-future congestion remains problematic because stigmergies are basically past information. Therefore we propose anticipatory stigmergy for sharing information on near-future location and allocating drivers adequately. All vehicles submit their near-future intention as anticipatory stigmergy to search their routes. Our preliminary results demonstrate that anticipatory stigmergy with assignment strategy works well.

Understanding Drivers’ Route Choice Under Long-Term Pretrip and Short-Term En-Route Traffic Information Using Generalized Estimating Equations

This paper addresses two drivers' route choice paradigms by modeling the factors that affect drivers' compliance with a long-term pretrip advised route and modeling drivers' usage of en-route short-term traffic information. A travel simulator with a real network and real historical congestion levels was used as a data collection tool. A generalized estimating equations (GEEs) model with repeated observations and a binomial probit link function was used to ensure the validity of the statistical analysis. Four different correlation structures were used and compared. The results showed that familiarity with the device that provides the information and severe weather conditions increases the likelihood of complying with the pretrip advised route and following the en-route short-term information. Network familiarity and the number of traffic signals on the pretrip advised route have a negative effect on drivers' compliance. Providing qualitative information and proximity to the destination increase the usage of en-route traffic information.