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Wai Fai Tsang

Bio: Wai Fai Tsang is an academic researcher from VU University Amsterdam. The author has contributed to research in topics: Logit & Discrete choice. The author has an hindex of 4, co-authored 5 publications receiving 122 citations.

Papers
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TL;DR: In this article, the authors compare the predictive power of logit and neural network models for multimodal network flows and show that the predictive potential of neural networks is higher than logit analysis, and the results are also used to investigate the implications of various road tax systems on various segments of the European road network.
Abstract: This paper aims to compare the descriptive and predictive power of two classes of statistical estimation models for multimodal network flows, viz. the logit model and the neural network model. The application concerns a large data set on inter-regional European freight flows for two commodity categories (food and chemicals). After an exposition of policy issues, methodological and modelling questions and the database, a variety of experiments is carried out. The results show that in general the predictive potential of neural network models is higher than that of logit analysis. The statistical results are also used to investigate the implications of various road tax systems (e.g., e&axes) on various segments of the European road network

59 citations

Journal ArticleDOI
TL;DR: The results show that in general the predictive potential of neural network models is higher than that of discrete choice analysis in the context of multimodal network flows.

44 citations

Book Chapter
01 Jan 1997
TL;DR: The present paper aims to analyse interregional freight transport movements in Europe in order to forecast spatio-temporal patterns of new transport economic scenarios and two different approaches are compared, viz. the logit model and the neural network model.
Abstract: The present paper aims to analyse interregional freight transport movements in Europe in order to forecast spatio-temporal patterns of new transport economic scenarios. In view of the high dimension of our data-base on transport flows, two different approaches are compared, viz. the logit model and the neural network model. Logit models are well-known in the literature; however, applications of logit analysis to large samples are more rare. Neural networks are nowadays receiving a considerable attention as a new approach that is able to capture major patterns of flows, on the basis of fuzzy and incomplete information. In this context an assessment of this method on the basis of a large amount of data is an interesting research endeavour. The paper will essentially deal with a research experiment, oriented towards both calibration/learning procedures and spatial forecasting, in order to compare the two above methodologies as well as to investigate the potential/limitations of the two above mentioned different, but related assessment methods. The first results in this framework highlight the fact that the two models adopted, although methodologically different, are both able to provide a reasonable spatial mapping of the interregional transport flows under consideration.

17 citations

Book ChapterDOI
TL;DR: The results in this framework highlight the fact that the two models adopted, although methodologically different, are both able to provide a reasonable spatial representation of the interregional transport flows in Europe.
Abstract: In this paper, we analyse interregional freight transport movements in Europe with a view on new spatial patterns based on transport economic scenarios for environmental sustainability. Two different approaches are compared, viz. the logit model and the neural network model.

4 citations

Posted Content
TL;DR: In this article, the authors compare the predictive power of logit and neural network models for multimodal network flows and show that the predictive potential of neural networks is higher than logit analysis, and the results are also used to investigate the implications of various road tax systems on various segments of the European road network.
Abstract: This paper aims to compare the descriptive and predictive power of two classes of statistical estimation models for multimodal network flows, viz. the logit model and the neural network model. The application concerns a large data set on inter-regional European freight flows for two commodity categories (food and chemicals). After an exposition of policy issues, methodological and modelling questions and the database, a variety of experiments is carried out. The results show that in general the predictive potential of neural network models is higher than that of logit analysis. The statistical results are also used to investigate the implications of various road tax systems (e.g., e&axes) on various segments of the European road network

1 citations


Cited by
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Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors selected 27 cities as pre-candidate consolidation centers considering government policy and CRexpress operation experience, then evaluated the significance each node in Chinese railway, highway and national road networks using complex network theory.
Abstract: To solve problems of China railway express such as low load factor and profit margin, high pressure upon the government to subsidize the trains, this paper selects 27 cities as pre-candidate consolidation centers considering government policy and CRexpress operation experience, then evaluates the significance each node in Chinese railway, highway and national road networks using complex network theory. With the TOPSIS model and cargo rates to comprehensively evaluate the networks, 10 cities are identified. Of them, Taiyuan, Xi’an, Zhengzhou, Wuhan, Suzhou are selected as the optimal consolidation centers by a mixed integer programming.

84 citations

Journal ArticleDOI
TL;DR: In this paper, the authors quantified the contribution of the informal sector to e-waste transportation at the national level and discussed the policy implications for optimizing regional allocation of the ewaste recycling capacity as well as for improving the transparency of the reverse logistic system.
Abstract: China has built a territory-based formal e-waste recycling system as a response to the global e-waste challenge. This system created a division of labor between the informal sector and formal recycling plants by providing a subsidy to the latter to buy waste products collected by the former. Using provincial data of formal e-waste recycling plants in China in 2014, this paper quantifies the contribution of the informal sector to e-waste transportation at the national level. Despite the intention to plan a regional self-sufficient system for e-waste recycling at the provincial level, we find that significant interprovincial flows exist due to the complex market transactions within the informal collection network, which reveals the deep conflicts between market mechanism and public intervention in the evolvement of e-waste governance structure. We built a spatial interaction model to depict the interregional flows of e-waste that can quantitatively illustrate the change of spatial pattern of this network due to the introduction of the formal WEEE regulation in China. In conclusion, we discuss the policy implications for optimizing regional allocation of the e-waste recycling capacity as well as for improving the transparency of the reverse logistic system to include the informal sector in the future.

77 citations

Journal ArticleDOI
TL;DR: This paper showed that national trade tends to be more intense than international trade and that intra-and inter-regional trade tend to be less intense than inter-region trade, owing to the dearth of border effects.
Abstract: Recent literature on border effect has demonstrated that national trade (intra- as well as interregional trade) tends to be more intense than international trade. Unfortunately, owing to the dearth...

66 citations

DOI
15 Nov 2017
TL;DR: In this article, the authors examined what the main drivers for tourism's CO2 emissions development are and indicated what the tourism sector should look like in terms of improved energy efficiencies and volumes of trips, guest-nights, transport distances and transport mode choice to fit a "climatically sustainable development".
Abstract: In 2015, the global community came together in Paris and agreed on a CO2 emissions pathway to avoid a temperature anomaly of more than 2 °C above pre-industrial levels. A significant source of CO2 emissions, the main greenhouse gas causing climate change, is the tourism sector. From research published by the UNWTO (World Tourism Organisation) in 2008, this contribution to anthropogenic CO2 emissions was found to be significant at 4.9% in 2005 and to increase. These growing emissions contrast with the Paris Agreement goal to obtain a very substantial reduction of global CO2 emissions. This thesis examines what the main drivers for tourism’s CO2 emissions development are. It also indicates what the tourism sector should look like in terms of improved energy efficiencies and volumes of trips, guest-nights, transport distances and transport mode choice to fit a ‘climatically sustainable development’ and what policies may evoke changes toward such a tourism development. The main research question of my thesis is: ‘Which mechanisms drive the development of global tourism and its CO2 emissions, and what are potential effects and consequences of policy strategies to mitigate these emissions?

57 citations

Journal ArticleDOI
TL;DR: A new approach to mode choice of intercity freight transport modeling using artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) models is described, found to be highly adaptive and efficient in investigating non-linear relationships among different variables.
Abstract: Mode choice modeling is probably the most important element of transportation planning. It affects the general efficiency of travel and the allocation of resources. The development of mode choice models has recently witnessed significant advances in many fields, such as passenger and freight transport. A large number of mathematical models have been used to model the traveler's choice of mode and destination and the shipper's choice of mode, shipment size and supply market, among others. Such models are not only becoming almost intractable but also data intensive, difficult to calibrate and update, and intransferable. These models cover a wide range of mathematical complexity and accuracy. This paper describes a new approach to mode choice of intercity freight transport modeling using artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) models. The new approach combines the learning ability of artificial neural networks and the transparent nature of fuzzy logic. The approach is found to be highly adaptive and efficient in investigating non-linear relationships among different variables. The adaptive neuro-fuzzy inference system model is tested on the freight transport market in Turkey, Germany, France and Austria by using information on the freight flows and their attributes. The ANNs and ANFIS models are more successful in the representation of the non-linear behavior of mode choice of intercity freight transport compared to the classical models.

55 citations