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Mitigating traffic congestion on urban roads, with paramount importance in urban development and reduction of energy consumption and air pollution, depends on our ability to foresee road usage and traffic conditions pertaining to the collective behavior of drivers, raising a significant question: to what degree is road traffic predictable in urban areas? Here we rely on the precise records of daily vehicle mobility based on GPS positioning device installed in taxis to uncover the potential daily predictability of urban traffic patterns. Using the mapping from the degree of congestion on roads into a time series of symbols and measuring its entropy, we find a relatively high daily predictability of traffic conditions despite the absence of any a priori knowledge of drivers origins and destinations and quite different travel patterns between weekdays and weekends. Moreover, we find a counterintuitive dependence of the predictability on travel speed: the road segment associated with intermediate average travel speed is most difficult to be predicted. We also explore the possibility of recovering the traffic condition of an inaccessible segment from its adjacent segments with respect to limited observability. The highly predictable traffic patterns in spite of the heterogeneity of drivers behaviors and the variability of their origins and destinations enables development of accurate predictive models for eventually devising practical strategies to mitigate urban road congestion.
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Understanding cities is central to addressing major global challenges from climate and health to economic resilience. Although increasingly perceived as fundamental socio-economic units, the detailed fabric of urban economic activities is only now accessible to comprehensive analyses with the availability of large datasets. Here, we study abundances of business categories across U.S. metropolitan statistical areas to investigate how diversity of economic activities depends on city size. A universal structure common to all cities is revealed, manifesting self-similarity in internal economic structure as well as aggregated metrics (GDP, patents, crime). A derivation is presented that explains universality and the observed empirical distribution. The model incorporates a generalized preferential attachment process with ceaseless introduction of new business types. Combined with scaling analyses for individual categories, the theory quantitatively predicts how individual business types systematically change rank with city size, thereby providing a quantitative means for estimating their expected abundances as a function of city size. These results shed light on processes of economic differentiation with scale, suggesting a general structure for the growth of national economies as integrated urban systems.
In this paper, urban traffic is modeled using dual graph representation of urban transportation network where roads are mapped to nodes and intersections are mapped to links. The proposed model considers both the navigation of vehicles on the network and the motion of vehicles along roads. The roads capacity and the vehicle-turning ability at intersections are naturally incorporated in the model. The overall capacity of the system can be quantified by a phase transition from free flow to congestion. Simulation results show that the systems capacity depends greatly on the topology of transportation networks. In general, a well-planned grid can hold more vehicles and its overall capacity is much larger than that of a growing scale-free network.
This letter propose a new model for characterizing traffic dynamics in scale-free networks. With a replotted road map of cities with roads mapped to vertices and intersections to edges, and introducing the road capacity L and its handling ability at intersections C, the model can be applied to urban traffic system. Simulations give the overall capacity of the traffic system which is quantified by a phase transition from free flow to congestion. Moreover, we report the fundamental diagram of flow against density, in which hysteresis is found, indicating that the system is bistable in a certain range of vehicle density. In addition, the fundamental diagram is significantly different from single-lane traffic model and 2-D BML model with four states: free flow, saturated flow, bistable and jammed.
A traffic incident analysis method based on extended spectral envelope (ESE) method is presented to detect the key incident time. Sensitivity analysis of parameters (the length of time window, the length of sliding window and the study period) are discussed on four real traffic incidents in Beijing. The results show that: (1) Moderate length of time window got the best accurate in detection. (2) The shorter the sliding window is, the more accurate the key incident time are detected. (3) If the study period is too short, the end time of an incident cannot be detected. Empirical studies show that the proposed method can effectively discover the key incident time, which can provide a theoretic basis for traffic incident management.
Moderate length of time window can get the best accurate result in detecting the key incident time using extended spectral envelope. This paper presents a method to calculate the moderate length of time window. Two factors are mainly considered: (1) The significant vertical lines consist of negative elements of eigenvectors; (2) the least amount of interruption. The elements of eigenvectors are transformed into binary variable to eliminate the interruption of positive elements. Sine transform is introduced to highlight the significant vertical lines of negative elements. A novel Quality Index (QI) is proposed to measure the effect of different lengths of time window. Empirical studies on four real traffic incidents in Beijing verify the validity of this method.
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