Freight Demand Forecast for a Proposed Railway in Canada With New Approach to Freight Rail Assignment

Author(s):  
Elham Boozarjomehri ◽  
Gordon R. Lovegrove

This research examined the freight demand forecast for a new short railway linking the Okanagan Valley in southern British Columbia to American railways in the South (Orville), and to Canadian railways in the North (Kamloops). An Origin-Destination (O-D) table including local, domestic and international demands for the Okanagan freight rail was developed based on available surveys and observed truck freight data. In the absence of data to derive utility functions, the current mode share for each commodity in the base year as well as current elasticities between truck and rail was used to forecast the mode share in the future year. Rail assignment techniques are among the forgotten problems of freight demand forecasting due to their complexities, including: 1) written and unwritten practices of the rail industry, and 2) cost functions that are classically employed in truck or auto assignments. In this study, a comprehensive review was conducted on the rail freight demand assignment techniques. A new assignment procedure was introduced by combining the available mathematical choice models and new initiatives of the Canadian government toward rail industry. Finally, the predicted share of freight rail was assigned to the rail network using three methods, which provided three independent freight demand forecasts. The mid-range forecast was selected as the freight demand for the Okanagan Valley while two others (low/high) were used for sensitivity analysis.

Author(s):  
Fábio C. Barbosa

Abstract Shortline industry plays a prominent role in the North American Freight Rail System (mainly United States and Canada), providing a customized freight rail service to the shippers, i.e. the first/last mile rail access for those low dense/light demand markets, outside the Class I’s business model (highly loaded corridors), as well as competition enhancers, through the connection of shippers facilities with more than one Class I railroad. The Short Line’s Rail industry role and its inherent freight rail business model have been strengthened in the years that followed the so called Staggers Act (1980), in the U.S., in which freight rail carriers have focused their efforts on the high density rail markets. Meanwhile, the Shortlines, also known as Class II and Class III freight rail companies, have lead the way in the light density branch lines, providing a customized freight rail service to those shippers located outside the boundary limits of the rail trunk corridors. The importance of Shortline for the U.S. freight rail industry is illustrated by the 603 U.S. shortlines currently operating on 76,000 km (47,500 miles), providing service for one in five (20%) cars moving each year, which accounts for 29% of freight rail production in the country. Furthermore, the recent launch of the controversial Class I Precision Schedule Railroading (PSR) concept, and its inherent asset maximization (mainly associated with disruptive service features — essentially lane and yards closures), has strengthened the strategic importance of Shortlines in the U.S. freight rail scenario, which ultimately requires an improved Class I – Shortline relationship, to guarantee/maintain a connection between shippers (farmers, manufacturers and other industries), and the customers market. Brazil, a continental country located in South America, has a sprawled and low density rail network (28,218 km – 17,636.25 mi). Besides sprawled/low density, the Brazilian rail network is not uniformly demanded, with just 40% of the network with used (demanded) capacities higher than 50%, basically associated with iron ore and agricultural commodities transport (which accounts for almost 80% of the country’s whole freight rail production), while almost 60% of the network remain with very light use (available capacity higher than 80%). This picture shows a great opportunity for the introduction of the Shortline Rail Concept in the Brazilian Freight Rail System, focused on smaller rail operators to provide a customized and accessible freight rail service for shippers located in the influence area of the rail network. To reach this target, Brazil has basically two alternative pathways: i) a structural approach, associated with a complete network restructuration (in a similar way the U.S. Class I railroads have marketed unproductive branches to short line operators) and ii) a regulatory approach, in which the current concession format would be maintained, with the imposition of rail stretches production targets to current rail concessionaires (incumbents), which ultimately could be encouraged to set operational partnerships with the so called Independent Rail Operators (IRO), to comply with those production rail targets. This work is supposed to present an overview, in a review format, of the North American Shortline Freight Rail experience, highlighting its operational regime/requirements, the business model, the tax incentives and the Shortline’s role in the class I PSR scenario. This analysis is, then, followed by an assessment of the perspectives and the inherent pathways for a Shortline Freight Rail Model implementation in Brazil.


2019 ◽  
Vol 19 (11) ◽  
pp. 2477-2495
Author(s):  
Ronda Strauch ◽  
Erkan Istanbulluoglu ◽  
Jon Riedel

Abstract. We developed a new approach for mapping landslide hazards by combining probabilities of landslide impacts derived from a data-driven statistical approach and a physically based model of shallow landsliding. Our statistical approach integrates the influence of seven site attributes (SAs) on observed landslides using a frequency ratio (FR) method. Influential attributes and resulting susceptibility maps depend on the observations of landslides considered: all types of landslides, debris avalanches only, or source areas of debris avalanches. These observational datasets reflect the detection of different landslide processes or components, which relate to different landslide-inducing factors. For each landslide dataset, a stability index (SI) is calculated as a multiplicative result of the frequency ratios for all attributes and is mapped across our study domain in the North Cascades National Park Complex (NOCA), Washington, USA. A continuous function is developed to relate local SI values to landslide probability based on a ratio of landslide and non-landslide grid cells. The empirical model probability derived from the debris avalanche source area dataset is combined probabilistically with a previously developed physically based probabilistic model. A two-dimensional binning method employs empirical and physically based probabilities as indices and calculates a joint probability of landsliding at the intersections of probability bins. A ratio of the joint probability and the physically based model bin probability is used as a weight to adjust the original physically based probability at each grid cell given empirical evidence. The resulting integrated probability of landslide initiation hazard includes mechanisms not captured by the infinite-slope stability model alone. Improvements in distinguishing potentially unstable areas with the proposed integrated model are statistically quantified. We provide multiple landslide hazard maps that land managers can use for planning and decision-making, as well as for educating the public about hazards from landslides in this remote high-relief terrain.


2021 ◽  
Vol 11 (18) ◽  
pp. 8612
Author(s):  
Santanu Kumar Dash ◽  
Michele Roccotelli ◽  
Rasmi Ranjan Khansama ◽  
Maria Pia Fanti ◽  
Agostino Marcello Mangini

The long-term electricity demand forecast of the consumer utilization is essential for the energy provider to analyze the future demand and for the accurate management of demand response. Forecasting the consumer electricity demand with efficient and accurate strategies will help the energy provider to optimally plan generation points, such as solar and wind, and produce energy accordingly to reduce the rate of depletion. Various demand forecasting models have been developed and implemented in the literature. However, an efficient and accurate forecasting model is required to study the daily consumption of the consumers from their historical data and forecast the necessary energy demand from the consumer’s side. The proposed recurrent neural network gradient boosting regression tree (RNN-GBRT) forecasting technique allows one to reduce the demand for electricity by studying the daily usage pattern of consumers, which would significantly help to cope with the accurate evaluation. The efficiency of the proposed forecasting model is compared with various conventional models. In addition, by the utilization of power consumption data, power theft detection in the distribution line is monitored to avoid financial losses by the utility provider. This paper also deals with the consumer’s energy analysis, useful in tracking the data consistency to detect any kind of abnormal and sudden change in the meter reading, thereby distinguishing the tampering of meters and power theft. Indeed, power theft is an important issue to be addressed particularly in developing and economically lagging countries, such as India. The results obtained by the proposed methodology have been analyzed and discussed to validate their efficacy.


2011 ◽  
Vol 11 (11) ◽  
pp. 31137-31158 ◽  
Author(s):  
W. Y. Xu ◽  
C. S. Zhao ◽  
P. F. Liu ◽  
L. Ran ◽  
N. Ma ◽  
...  

Abstract. Emission information is crucial for air quality modelling and air quality management. In this study, a new approach based on the understanding of the relationship between emissions and measured pollutant concentrations has been proposed to estimate pollutant emissions and source contributions. The retrieval can be made with single point in-situ measurements combined with backward trajectory analyses. The method takes into consideration the effect of meteorology on pollutant transport when evaluating contributions and is independent of energy statistics, therefore can provide frequent updates on emission information. The spatial coverage can be further improved by using measurements from several sites and combining the derived emission fields. The method was applied to yield the source distributions of black carbon (BC) and CO in the North China Plain (NCP) using in-situ measurements from the HaChi (Haze in China) Campaign and to evaluate contributions from specific areas to local concentrations at the measurement site. Results show that this method can yield a reasonable emission field for the NCP and can directly quantify areal source contributions. Major BC and CO emission source regions are Beijing, the western part of Tianjin and Langfang, Hebei, with Tangshan being an additional important CO emission source area. The source contribution assessment suggests that, aside from local emissions in Wuqing, Tianjin and Hebei S, SW (d < 100 km) are the greatest contributors to measured local concentrations, while emissions from Beijing contribute little during summertime.


2016 ◽  
Vol 11 (9) ◽  
pp. 1934578X1601100 ◽  
Author(s):  
Sabrina Adorisio ◽  
Alessandra Fierabracci ◽  
Ariele Rossetto ◽  
Isabella Muscari ◽  
Vincenza Nardicchi ◽  
...  

In Vietnam, two types of traditional medicine (TM) are practiced: thuoc nam, medicine of the South, and thuoc bac, medicine of the North, both of which are largely based on herbal drugs used by different Vietnamese ethnic groups. This review presents recently published information from various databases regarding TM, especially herbal drugs, and its integration with Western medical practices outside and inside Vietnam. We first discuss the integration of traditional and modern health concepts by Vietnamese immigrants living outside Vietnam. Next, we describe native and emigrated health education and practices of pharmacy students, health professionals, and citizens living in Vietnam. Finally, we report the recent biological validation of medicinal plants and non-herbal therapies emerging from Vietnamese TM and their current and potential medical uses as identified by Western approaches. The main example described here involves utilization of the tree Artocarpus tonkinensis by the ethnic minority of Black Hmong in northern Vietnam, who use a decoction of its leaves to treat arthritis and backache without apparent adverse effects. Our comprehensive review emphasizes that, although Vietnam has a very rich collection of TM practices (particularly the use of herbal drugs), these therapies should be biologically and clinically validated with modern Western methods for optimal integration of Western and traditional medicine in global populations.


2020 ◽  
Vol 12 (2) ◽  
pp. 713 ◽  
Author(s):  
Yiling Fang ◽  
Xinhui Wang ◽  
Jinjiang Yan

In this paper, we investigate price and order strategies for innovative green products using demand forecasting and sharing. We formulate the problem using a Stackelberg game and propose a dynamic contract that specifies an initial wholesale price, a minimum order quantity, a demand sharing agreement, and a decisions adjustment agreement. We arrived at the following main findings and implications. First, the manufacturer offers a higher or lower wholesale price than the initial one depending on the variation in the market status. Also, the retailer’s ordering decisions will increase with the wholesale price, which contradicts the common assumption that ordering decisions decrease with the wholesale price. Interestingly, if the market improves, the manufacturer obtains a higher profit margin than the retailer; if the market worsens, the manufacturer suffers more loss of profit margin than the retailer. Second, when the cost of information sharing is smaller than an upper bound, demand forecasting and sharing are always beneficial to the manufacturer. However, the value of demand forecasting and sharing for the retailer is significantly affected by the market status variation. Third, high information accuracy will not necessarily increase the profits of the manufacturer and the retailer, even if the market status is better than expected. Finally, numerical examples show the parameters’ effects. We have several main managerial insights. When the shared demand information is received from the retailer, the manufacturer can determine wholesale price strategies according to the retailer’s demand forecast. Moreover, if the manufacturer wants to ensure profitability, they should not choose retailers with a higher capability of demand forecasting.


Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4900 ◽  
Author(s):  
Hongze Li ◽  
Hongyu Liu ◽  
Hongyan Ji ◽  
Shiying Zhang ◽  
Pengfei Li

Ultra-short-term load demand forecasting is significant to the rapid response and real-time dispatching of the power demand side. Considering too many random factors that affect the load, this paper combines convolution, long short-term memory (LSTM), and gated recurrent unit (GRU) algorithms to propose an ultra-short-term load forecasting model based on deep learning. Firstly, more than 100,000 pieces of historical load and meteorological data from Beijing in the three years from 2016 to 2018 were collected, and the meteorological data were divided into 18 types considering the actual meteorological characteristics of Beijing. Secondly, after the standardized processing of the time-series samples, the convolution filter was used to extract the features of the high-order samples to reduce the number of training parameters. On this basis, the LSTM layer and GRU layer were used for modeling based on time series. A dropout layer was introduced after each layer to reduce the risk of overfitting. Finally, load prediction results were output as a dense layer. In the model training process, the mean square error (MSE) was used as the objective optimization function to train the deep learning model and find the optimal super parameter. In addition, based on the average training time, training error, and prediction error, this paper verifies the effectiveness and practicability of the load prediction model proposed under the deep learning structure in this paper by comparing it with four other models including GRU, LSTM, Conv-GRU, and Conv-LSTM.


2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiaoxi Zhou ◽  
Jianfei Meng ◽  
Guosheng Wang ◽  
Qin Xiaoxuan

PurposeThis paper examines the problem of lack of historical data and inadequate consideration of factors influencing demand in the forecasting of demand for fast fashion clothing and proposes an improved Bass model for the forecasting of such a demand and the demand for new clothing products.Design/methodology/approachFrom the perspective of how to solve the lack of data and improve the precision of the clothing demand forecast, this paper studies the measurement of clothing similarity and the addition of demand impact factors. Using the fuzzy clustering–rough set method, the degree of resemblance of clothing is determined, which provides a basis for the scientific utilisation of historical data of similar clothing to forecast the demand for new clothing. Besides, combining the influence of consumer preferences and seasonality on demand forecasting, an improved Bass model for a fast fashion clothing demand forecast is proposed. Finally, with a forecasting example of demand for clothing, this study also tests the validity of the method.FindingsThe objective measurement method of clothing similarity in this paper solves the problem of the difficult forecasting of demand for fast fashion clothing due to a lack of sales data at the preliminary stage of the clothing launch. The improved Bass model combines, comprehensively, consumer preferences and seasonality and enhances the forecast precision of demand for fast fashion clothing.Originality/valueThe paper puts forward a scientific, quantitative method for the forecasting of new clothing products using historical sales data of similar clothing, thus solving the problem of lack of sales data of the fashion.


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