This paper canvasses the port development of the Yangtze River Delta. Initially, we consider changes in container trade in the region since the 1990s. Competition between the ports of Ningbo and Shanghai are studied by measuring the overlapping hinterland of container distribution for Zhejiang province. We then analyse the strategies pursued by international carriers and terminal operators to secure success in this increasingly competitive environment.
The rapid urbanization has brought great challenges to the transportation network. However, travel flow at peak hours is not always the same. It is important to investigate how travel flow differs between peak hours to capture travel flow patterns and influential factors to facilitate traffic management and urban planning. This paper establishes a spatial model with endogenous weight matrix (SARBP-EWM) to investigate the travel flow differences between morning and evening peaks on both weekday and weekend based on automatic vehicle identification (AVI) data and point of interest (POI) data in Xuancheng, China. The results confirm strong spatial effects and endogeneity issue. Besides, facility variables such as number of offices and number of clinics reveal strong negative impacts on travel flow differences on both weekday and weekend, while the number of middle school shows significantly positive relation with travel flow differences. In addition, the endogenous weight matrix on both weekday and weekend is successfully estimated and compared. It is found that TAZ pairs tend to be clustered with lower spatial weights on weekday, while they are more randomly distributed with higher spatial weights at weekend. Based on the results above, the policies proposed from Xuancheng 14th Five-Year Plan are evaluated and discussed. The above empirical analysis quantifies impacts from key factors on urban travel flow differences between peak hours and provides important references for urban planning and policy making.
In high density urban areas, pedestrians have a great influence on the capacity of intersections. This paper studies the influence of pedestrians on road capacity and proposes an exclusive right-turn lane capacity model considering pedestrian-vehicle interaction (PV-RTC). Firstly, a pedestrian-vehicle interaction (PVI) model is proposed based on the logit model and static games theory of incomplete information. Through this model, the probability of 6 kinds of pedestrian-vehicle interaction situations (vehicles yield to pedestrians, pedestrians yield to vehicles, etc.) in the crosswalk can be obtained. Then, based on the basic idea of the stop line method and the probabilities of above situations, the PV-RTC model is established, and the sensitivity analysis of the important factors (pedestrian arrival rate, yielding rate, and green time ratio) affecting the model is carried out to clarify the mechanism of the proposed model. Finally, a pedestrian-vehicle interaction model of cellular automata for the exclusive right-turn lane is established and its simulation results are compared with the results of the PV-RTC model. The results show that the relative error between the microscopic simulation model and PV-RTC model is less than 15% overall, which verifies the validity of the PV-RTC model. This study provides references for a more precise estimation method of pedestrian impact on road capacity.
Under the targets of peaking CO2 emissions and carbon neutrality in China, it is a matter of urgency to reduce the CO2 emissions of road transport. To explore the CO2 emission reduction potential of road transport, this study proposes eight policy scenarios: the business-as-usual (BAU), clean electricity (CE), fuel economy improvement (FEI), shared autonomous vehicles (SAV), CO2 emission trading (CET) (with low, medium, and high carbon prices), and comprehensive (CS) scenarios. The road transport CO2 emissions from 2020 to 2060 in these scenarios are calculated based on the bottom-up method and are evaluated in the Low Emissions Analysis Platform (LEAP). The Log-Mean Divisia Index (LMDI) method is employed to analyze the contribution of each factor to road transport CO2 emission reduction in each scenario. The results show that CO2 emissions of road transport will peak at 1419.5 million tonnes in 2033 under the BAU scenario. In contrast, the peaks of road transport CO2 emissions in the CE, SAV, FEI, CET-LCP, CET-MCP, CET-HCP, and CS scenarios are decreasing and occur progressively earlier. Under the CS scenario with the greatest CO2 emission reduction potential, CO2 emissions of road transport will peak at 1200.37 million tonnes in 2023 and decrease to 217.73 million tonnes by 2060. Fuel structure and fuel economy contribute most to the emission reduction in all scenarios. This study provides possible pathways toward low-carbon road transport for the goal of carbon neutrality in China.
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