As a crucial travel mode, taxi plays a significant role in residents' daily travel. Uncovering taxi traffic demand has become a hotspot in transport studies. Previous researchers pay more attention to the statistical characteristics of taxi trips, while few studies focus on the dynamic features in different periods of a day. In this paper, we study the taxi travel demand by constructing dynamic networks based on taxi trajectory data. In addition, relationship between travel intensity and point of interest (POI) in Xiamen, China is discussed. Firstly, the study area is divided by 1km×1km uniform cells. The pickup and drop-off activities of passengers are recorded for each cell. Secondly, the networks are constructed by regarding each cell as a node and regarding taxi trips from a cell to another cell as an edge. On this basis, we divide a day into 12 periods by two hours and construct the networks for different periods. Finally, correlation between travel intensity and POI intensity is detected with regression analysis. Results show that the taxi trip networks have large clustering coefficient and small shortest path length, which indicates they are 'small world' networks. Moreover, the taxi trip networks are disassortative networks that hotspot areas tend to connect with the common areas. Furthermore, the taxi trip length in a day follows a lognormal distribution and the peak hour of taxi trip appears around midnight. Finally, a cubic polynomial curve could fit the relationship between travel intensity and POI intensity. Our findings provide a new insight for understanding the traffic demand of taxi. INDEX TERMS Urban traffic demand, dynamic spatial-interaction network, spatiotemporal characteristics of taxi trips, taxi GPS trajectory data, point of interest This work is licensed under a Creative Commons Attribution 4.

Prominent regional differentiations of highway landslide disasters (HLDs) bring great difficulties in highway planning, designing and disaster mitigation, therefore, a comprehensive understanding of HLDs from the spatial perspective is a basis for reducing damages. Statistical prediction methods and machine learning methods have some defects in landslide susceptibility mapping (LSM), meanwhile, hybrid methods have been developed by combining the statistical prediction methods with machine learning methods in recent years, and some of them were reported to perform better than conventional methods. In view of this, the principal component analysis (PCA) method was used to extract the susceptibility evaluation indexes of HLDs; the particle swarm optimization-support vector machine (PSO-SVM) model and genetic algorithm-support vector machine (GA-SVM) model were implemented to the susceptibility mapping and zoning of HLDs in China. The research results show that the accumulative contribution rate of the four principal components is 92.050%; evaluation results of the PSO-SVM model are better than those of the GA-SVM model; micro dangerous areas, moderate dangerous areas, severe dangerous areas and extreme dangerous areas account for 24.24%, 19.49%, 36.53% and 19.74% of the total areas of China; among the 1543 disaster points in the HLDs inventory, there are 134, 182, 421 and 806 located in the above areas respectively.

The objective of this study is to evaluate comprehensive performance of high modulus asphalt concrete (HMAC) and propose common values for establishing evaluation system. Three gradations with different modifiers were conducted to study the high and low temperature performance, shearing behavior, and water stability. The laboratory tests for HMAC included static and dynamic modulus tests, rutting test, uniaxial penetration test, bending test, and immersion Marshall test. Dynamic modulus test results showed that modifier can improve the static modulus and the improvements were remarkable at higher temperature. Moreover, modulus of HMAC-20 was better than those of HMAC-16 and HMAC-25. The results of performance test indicated that HMAC has good performance to resist high temperature rutting, and the resistances of the HMAC-20 and HMAC-25 against rutting were better than that of HMAC-16. Then, the common values of dynamic stability were recommended. Furthermore, common values of HMAC performance were established based on pavement performance tests.

This paper analyzes the influence of a surfactant warm mix additive on unmodified asphalt’s conventional performance, viscosity-temperature characteristics, surface energy, and spreading performance on aggregate surfaces. The effect of the additive on asphalt’s microstructure was explored by infrared (IR) spectral analysis. The results show the additive has little influence on the penetration, softening point, ductility, and viscosity-temperature characteristics of asphalt; this suggests that the additive does not work by lowering viscosity. The additive can reduce the zero-shear viscosity of asphalt, and adding too much can reduce antirutting performance. The additive also increases the asphalt’s surface energy and the asphalt-water contact angle, while the polar component of surface energy decreases. The additive improves the spreading performance of asphalt on aggregate surfaces and reduces the asphalt-aggregate contact angle; the lower the temperature, the greater the reduction. IR spectral analysis shows that the additive does not react with asphalt—only physical blending occurs. The addition of a surfactant warm mix additive to asphalt allows asphalt mixtures to be more easily mixed and compacted at lower temperatures, thereby saving energy.

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