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The economic development in China has brought about the urban traffic problems such as traffic congestion, long traffic waiting time, and inappropriate vehicle transfer. Therefore, under the technical background of China's Internet of Things platform, the fusion technology based on big data is studied and an algorithm for Chinese urban intelligent traffic safety scheduling is designed. In this paper, first of all, the urban traffic safety big data is clustered. Secondly, the gray distribution model of the big data is established by extracting the association rule features. Thirdly, the elements in Chinese urban traffic safety big data are fused, including the text, location, picture, audio, and video. On the condition of meeting highly time-sensitive needs of urban traffic intelligence, the video information after data fusion is applied to detect traffic flow parameters, so that an evaluation strategy for urban traffic safety state under the urban traffic speed dispersion is established. According to the fuzzy value of urban traffic drivers' satisfaction with the waiting time, the effect of traffic dispatching is measured, the convergence formula of urban traffic in the morning and evening peaks is constructed, and the optimal solution of the objective function of dispatching strategy is calculated by the particle swarm optimization algorithm. In this way, more efficient urban traffic safety scheduling in China is realized. As can be learned from the experimental results, the proposed algorithm can reasonably judge the urban traffic safety situation and reduce the time of waiting for urban vehicles with reasonable data fusion results, so it is proved to improve the urban traffic safety. [ABSTRACT FROM AUTHOR]

The superblock has become a typical land use in China and many growing Asian cities. Superblock access points generate traffic congestion and many conflicts among all road users. Driveway design is a critical process and has a major impact on traffic conditions around superblocks. There are various guidelines for the two key factors for driveway design, driveway width and curb radius, but they provide reference values corresponding to traffic volume and speed; these are not sufficient for managing the complex traffic environment around superblocks. To improve driveway design, we develop detailed access point design models that account for conflicts between turning and through motorized vehicles, conflicts between motorized and nonmotorized traffic, speed differential larger than 10 km/h, and lane encroachments of entering and exiting vehicles. The crash risk models evaluate and optimize the combination of driveway width and curb radius with respect to three traffic safety indexes: traffic conflict, lane encroachment, and speed differential. A case study evaluation shows that the updated driveway design models produce a lower crash risk; seven of the ten driveways improved by 16.14% or more. The updated driveway design for superblocks would be beneficial for analyzing, permitting, and managing traffic operations at superblocks and oversized development with many and complex driveways. [ABSTRACT FROM AUTHOR]

The nature of congested traffic flow has been not fully understood until now. An isolated bottleneck on the expressway generally influences a limited segment length, which means attenuation effects of the bottleneck on traffic stream by a distance within the influence area. However, such effects of bottleneck on speed-flow relationship have been inadequately emphasized. This paper investigates the location effects of a bottleneck on the speed-flow relationship. With consecutive 5 weekday median lane data collected by dual loop detectors on a congested expressway in Shanghai, integral speed-flow relationships including upper limb, lower limb, and capacity area are obtained from six sites. Further analyzing those sites on the same basic expressway segment unveils that it is the movement of the upper limb and lower limb that produces the integral curve. The right end of the upper limb gets an increase speed in with the site farther to its upstream bottleneck, whereas the lower limb gets a drop in speed with the site closer to its downstream bottleneck. Such a movement feature is in agreement with general driving behaviors of discharging or approaching a bottleneck. The factors intensifying or weakening the movement tendency are also identified. [ABSTRACT FROM AUTHOR]

• Firstly, from the image recognition perspective, we propose a method called FR based on the propagation characteristic of traffic congestion to determine the spatiotemporal impacted region (SIR). • Secondly we apply causal inference to quantify the impact on speed reductions induced by the incident. • Thirdly we provide a proof method to illustrate the rationality of the congestion threshold in FR using statistical hypothesis testing. The proposed method can avoid subjective judgment when we determine the SIR for a certain incident. This paper designs a systemic framework to quantify speed reduction induced by traffic incidents using a causal inference framework. The results can provide a reference to traffic managers for evaluating incident severities, thus take proper control measures after the incident in order not to underestimate or overestimate the negative impact. A two-phase scheme is proposed, including impacted region determination and speed reduction quantification. We first propose a Frame Region (FR) method, based on the shockwave propagation, to determine the spatiotemporal impacted region (SIR) using speed map. It is worth-noting that we design a statistical experiment to prove the rationality of congestion threshold selection. Secondly, we introduce a causal inference method for identifying the matched freeway segments. The traffic condition of finally matched freeway segments can be served as non-incident traffic condition of the incident occurred location, which contributes to quantifying the incident impact on speed reduction. We further demonstrate the proposed method in a case study by taking advantage of an incident record and related real freeway speed data in China. An interesting observation is that, along with the freeway segments away from the incident location, the congestion duration time of different freeway segments firstly rises and then decreases. The case study also illustrates the impact of incident on speed lasts almost 3 h and the congestion caused by the incident spreads 11 km, while the average causal effect of incident on all the impacted freeway segments is 42.3 km/h. [ABSTRACT FROM AUTHOR]