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The primary objective of this paper is to develop models to predict bus arrival time at a target stop using actual multi-route bus arrival time data from previous stop as inputs. In order to mix and fully utilize the multiple routes bus arrival time data, the weighted average travel time and three Forgetting Factor Functions (FFFs) - F1, F2 and F3 - are introduced. Based on different combinations of input variables, five prediction models are proposed. Three widely used algorithms, i.e. Support Vector Machine (SVM), Artificial Neutral Network (ANN) and Linear Regression (LR), are tested to find the best for arrival time prediction. Bus location data of 11 road segments from Yichun (China), covering 12 bus stops and 16 routes, are collected to evaluate the performance of the proposed approaches. The results show that the newly introduced parameters, the weighted average travel time, can significantly improve the prediction accuracy: the prediction errors reduce by around 20%. The algorithm comparison demonstrates that the SVM and ANN outperform the LR. The FFFs can also affect the performance errors: F1 is more suitable for ANN algorithm, while F3 is better for SVM and LR algorithms. Besides, the virtual road concept in this paper can slightly improve the prediction accuracy and halve the time cost of predicted arrival time calculation. [ABSTRACT FROM AUTHOR]

For the last decades, fully erected container cranes have been delivered to a customer site by ships. On one hand, using this method of transportation is very attractive due to its cost and time savings. However, on the other hand, being exposed to cycling loads from the ship motions during the sea voyage, the crane structure accumulates fatigue damage. Using the accumulated fatigue damage parameter, the crane transportation could be associated with the amount of the working cycles the crane could have worked out during its normal operating at the customer site. In the presenting paper the research for the real case of a new crane voyage from China to Ukraine has been done. [ABSTRACT FROM AUTHOR]

At an isolated signalized intersection, short left-turn lanes may be provided to enhance intersection capacity and level of service. The capacity of an entire intersection depends not only on the effective green time per phase and the length of each short left-turn lane but also on the selected signal phase plan. This paper enumerates 16 typical signal phase plans for a four-leg intersection with protected left-turn phases. Given the existence of short left-turn lanes and the freedom to select signal phase plans, two optimization models are formulated. Numerical examples are carried out to illustrate the application of these models and show the sensitivity of the outcomes to impact factors. The orthogonal experiments show that the primary factor is whether a short left-turn lane is added on an approach and the demand distribution is the secondary factor in determining an optimal allocation of the time-space resources of an intersection. To analyse traffic flow operations under different signal phase sequences, the simulation tests are fulfilled using VISSIM under the assumption that the components of a signal phase plan and green splits are all identical. The simulated results indicate that the signal phase sequence for a specified approach may greatly affect traffic movements at an approach, and the leading green phasing is better than the lagging one for a specified approach when the left-turn bay length is short at the approach. Finally, two variations of the developed models are recommended for use and the procedure for the model application is provided in practice. [ABSTRACT FROM AUTHOR]

The paper begins with a brief review of evidence related to the different types of ‘green’ taxation measures that have been introduced by governments worldwide as a means to lessen the environmental consequences of private car ownership by incentivising the purchase of Low Emission Vehicles (LEV's). An overview is then provided of current understanding of individuals’ car purchasing decisions, highlighting gaps in knowledge that exist with current explanatory models. Based on these gaps in knowledge and to investigate the potential of various taxation measures in influencing individuals’ car purchasing decisions, an online survey was conducted with students from Dalian University of Technology, China. The questionnaire was designed to measure: (1) situational and psychological factors that would be important for their next car purchase; (2) the impacts of adapting current taxation measures to incentivise LEV purchases; (3) the potential role of a range of taxation measures on their decisions on type of future car purchases. K-Cluster means analysis was employed to identify population segments according to their psychological preparedness and importance attached to situational factors in their future decisions to purchase a LEV, or not. This resulted in three distinct segments – Pro-Greens (33.6% of sample), Maybe-Greens (20.2%) and No-Greens (46.2%). Pro-Greens were more psychologically prepared to purchase LEV's than the other two segments, whose future car purchasing decisions were primarily driven by situational factors (vehicle characteristics, performance, etc.). Pro-Greens were also more likely to be influenced by changes in current Chinese taxation measures (VAT and fuel duty), and also significantly more likely to be influenced by the introduction of future measures that would incentivise LEV purchases than the other two segments. In terms of future measures to incentivise future Chinese drivers LEV purchases and use, the introduction of a feebate system, a CO2emissions VAT system, a distance based user charging scheme and annual road tax based on CO2emissions would appear to have the greatest potential overall. [ABSTRACT FROM AUTHOR]

In recent years, China needs far more financing for transportation infrastructure than can be provided by the government alone. Meanwhile the capacity of the Chinese government to provide public services on its own in an effective and efficient way is being questioned and reassessed at various levels. Accordingly, the involvement of private investors in the development of Metropolitan Transportation Systems (MTS) has been promoted by the Chinese public sector by means of adopting the Public Private Partnership (PPP) model. However, China's dynamic and complex political, financial, legal and regulatory environment makes the private sector adjust to accommodate the existing regime by way of understanding the driving factors in PPP applications for improving the chances of project success. This paper presents two case studies in China's MTS. Based on a case study and comprehensive literature review, 15 driving factors associated with PPP projects from the perspective of the Chinese public sector have been identified. A structured questionnaire survey targeting the public sector has been conducted to seek, analyze and integrate its perception of the driving factors. Through statistical analysis of the relative significance of each of these driving factors, the writers determined five common major dimensions of the Chinese public sector's purpose in developing PPP schemes, and the key factors that quantify each purpose. The establishment of the measures for these objectives and the key factors that quantify each of these purposes will likely be useful in encouraging private participation in infrastructure management and directing/concentrating efforts of the private sector to deliver quality public services under mutually beneficial long-term contractual arrangements. [ABSTRACT FROM AUTHOR]