Improve Demand Forecasting Research Articles

Insights to key operational questions in forecast-informed dam release operation: case of Hume Dam

ABSTRACT Dam operators decide on dam releases given downstream demands and other factors. Downstream tributaries complicate the problem due to significant uncertainties in their flows. Demands can also be highly uncertain. Incorporating probabilistic ensemble forecasts into the decision-making process can mitigate these uncertainties. We investigate how ensemble flow and demand forecasts can inform Hume Dam releases, in the Murray-Darling Basin, for delivery to Lake Mulwala, about four days downstream. We develop and simulate three forecast-based decision rules governing the releases. The first and second rules are based on the expectations and medians respectively of ensemble forecasts of water excess and shortage, forced by ensemble tributary inflow and demand forecasts. The third rule is based on the risks of an excess or shortage per the ensemble forecasts of water excess and shortage. We analyse the results to answer the following key operational questions: (1) Which decision rule is best for managing Hume Dam releases for water supply and under what conditions? (2) How far ahead of flow and demand forecasts should Hume Dam operators adopt? (3) How does limiting the day-to-day change in Hume Dam releases influence the value of forecasts? (4) What is the value of improved demand forecasts?

Enhancing business performance: The role of data-driven analytics in strategic decision-making

In today’s highly competitive business landscape, organizations are increasingly turning to data-driven analytics to enhance performance and inform strategic decision-making. This approach leverages vast amounts of data, transforming it into actionable insights that drive efficiency, innovation, and growth. The role of data-driven analytics is multifaceted, encompassing predictive, prescriptive, and descriptive analytics, each contributing uniquely to the decision-making process. Predictive analytics forecasts future trends and behaviors, enabling proactive strategies. Prescriptive analytics provides recommendations for optimal actions based on various scenarios, while descriptive analytics offers a clear view of past performance, helping to identify patterns and opportunities for improvement. The integration of advanced data analytics tools and techniques has revolutionized how businesses operate, particularly in areas such as marketing, finance, supply chain management, and customer relationship management. By utilizing data analytics, companies can identify market trends, understand customer preferences, optimize operations, and reduce costs. For example, in marketing, data-driven insights allow for targeted campaigns and personalized customer experiences, leading to higher conversion rates and customer loyalty. In supply chain management, analytics improve demand forecasting and inventory management, enhancing overall efficiency. Furthermore, the adoption of data-driven analytics fosters a culture of evidence-based decision-making within organizations. Leaders can make informed decisions quickly, reducing the risks associated with gut-feel approaches and ensuring that strategies are aligned with market realities and organizational goals. This shift towards analytics-based decision-making is supported by advancements in artificial intelligence and machine learning, which enhance the accuracy and depth of insights generated from data. However, the journey to becoming a data-driven organization is not without challenges. It requires significant investments in technology, talent, and change management. Organizations must also address data privacy and security concerns to maintain stakeholder trust. Despite these challenges, the benefits of data-driven analytics in strategic decision-making are undeniable. It empowers businesses to navigate complexities, seize opportunities, and achieve sustainable competitive advantages. In conclusion, data-driven analytics is a critical enabler of enhanced business performance. Its strategic application in decision-making processes not only drives operational excellence but also positions organizations for long-term success in a dynamic market environment. Keywords: Decision-Making, Data-Driven, Analytics, Business Performance, Strategic.

Role of Artificial Intelligence in Revenue Management and Pricing Strategies in Hotels

Purpose: The general objective of the study was to investigate the role of Artificial Intelligence in revenue management and pricing strategies in hotels. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the role of Artificial Intelligence in revenue management and pricing strategies in hotels. Preliminary empirical review revealed that the integration of artificial intelligence (AI) into revenue management and pricing strategies significantly enhanced the financial performance and operational efficiency of hotels. AI's ability to process large datasets in real-time improved demand forecasting and dynamic pricing, leading to increased revenue per available room (RevPAR) and average daily rate (ADR). Additionally, AI facilitated personalized guest experiences, boosting customer satisfaction and loyalty. Despite these benefits, the study identified challenges such as high implementation costs, data privacy concerns, and the need for robust data infrastructure. Addressing these issues through strategic planning and continuous staff training was deemed essential for maximizing AI's potential in the hotel industry. Unique Contribution to Theory, Practice and Policy: The Diffusion of Innovations theory, Technology Acceptance Model (TAM) and Resource Based View (RBV) may be used to anchor future studies on the role of AI in revenue management and pricing strategies in hotels. The study concluded that integrating AI into hotel revenue management and pricing strategies significantly enhances performance, contributing to both theoretical and practical advancements. It enriched the Diffusion of Innovations Theory by demonstrating factors influencing AI adoption in hospitality. Practically, it provided actionable insights for hotel managers on leveraging AI for improved key performance indicators and balancing dynamic pricing with customer satisfaction. Policy recommendations included establishing guidelines for AI implementation, enhancing data infrastructure, fostering a culture of innovation, and addressing skills gaps through training and development programs. The study emphasized the need for robust data management systems and regulatory support to facilitate AI adoption.

A Study to Know the Impact of AI on Sustainability of Products in the Carbonated Beverages

Carbonated beverage businesses are using artificial intelligence (AI) technology to sustainability in a variety of ways to streamline their processes, boost output, and improve the customer experience. The businesses in the carbonated beverage sector use AI in the following ways, Coca-Cola is enhancing its supply chain administration by utilizing AI. The business has created a platform powered by AI that applies machine learning algorithms to improve its distribution and manufacturing procedures. The platform aids the business in improved demand forecasting, inventory level optimization, cost-effective transportation, voice activated vending machines. PepsiCo: To create novel product formulations, PepsiCo is utilizing AI. The business has developed an AI-powered platform that examines customer preferences and spots market trends. The business can develop new products thanks to this platform. Dr. Pepper Snapple Group: Dr. Pepper Snapple Group is enhancing its marketing initiatives with artificial intelligence. The business has created an AI-driven platform that analyses customer data and enables it to develop more specialized and individualized marketing campaigns. The platform enables the business to deliver more pertinent and efficient marketing messages by assisting in the better understanding of customer preferences and behaviour. Keurig Dr Pepper: Keurig Dr Pepper is using AI to enhance its customer service. The business has created an AI-powered chatbot that can instantly respond to customer inquiries and help. The chatbot uses machine learning algorithms and natural language processing to comprehend customer queries and deliver pertinent and useful responses. Overall, the carbonated beverage industry is using AI technology in a variety of methods. Keywords- carbonated beverage, artificial intelligence (AI), supply chain administration, demand forecasting, inventory optimization, transportation optimization, voice activated vending machines, product formulation, customer preferences, market trends, marketing campaigns, customer service, chatbot, machine learning algorithms, natural language processing.

Improved Demand Forecasting of a Retail Store Using a Hybrid Machine Learning Model

Accurate demand forecasting is a competitive advantage for all supply chain components, including retailers. Approaches like naïve, moving average, weighted average, and exponential smoothing are commonly used to forecast demand. However, these simple approaches may lead to higher inventory and lost sales costs when the trend in demand is non-linear. Additionally, price strongly influences demand, and we can’t neglect the impact of price on demand. Similarly, the demand for a stock keeping unit (SKU) depends on the price of the competitor for the same SKU and the price of the competitive SKU. We thus propose a demand prediction model that considers historical demand data and the SKU price to forecast the demand. Our approach uses different machine-learning regressor algorithms and identifies the best machine-learning algorithm for the SKU with the lowest forecasting error. We further extend the forecasting model by training a hybrid model from the best two regression algorithms individually for each SKU. Forecasting error minimisation is the driving criterion for our literature. We evaluated the approach on 1000 SKUs, and the result showed that the Random Forest is the best-performing regressor algorithm with the lowest mean absolute percentage error (MAPE) of 8%. Furthermore, the hybrid model resulted in a lower inventory and lost sales cost with a MAPE of 7.74%. Overall, our proposed hybrid demand forecasting model can help retailers make informed decisions about inventory management, leading to improved operational efficiency and profitability.

Blood Demand Forecasting and Supply Management: An Analytical Assessment of Key Studies Utilizing Novel Computational Techniques

Use of data-driven methodologies in enhancing blood transfusion practices is rising, leveraging big data, machine learning, and optimization techniques to improve demand forecasting and supply chain management. This review used a narrative approach to identify, evaluate, and synthesize key studies that considered novel computational techniques for blood demand forecasting and inventory management through a search of PubMed and Web of Sciences databases for studies published from January 01, 2016, to March 30, 2023. The studies were analyzed for their utilization of various techniques, and their strengths, limitations, and areas for improvement. Seven key studies were identified. The studies focused on different blood components using various computational methods, such as regression, machine learning, hybrid models, and time series models, across different locations and time periods. Key variables used for demand forecasting were largely derived from electronic health record data, including clinical related predictors such as laboratory test results and hospital census by location. Each study offered unique strengths and valuable insights into the use of data-driven methods in blood bank management. Common limitations were unknown generalizability to other healthcare settings or blood components, need for field-specific performance measures, lack of ABO compatibility consideration, and ethical challenges in resource allocation. While data-driven research in blood demand forecasting and management has progressed, limitations persist and further exploration is needed. Understanding these innovative, interdisciplinary methods and their complexities can help refine inventory strategies and address healthcare challenges more effectively, leading to more robust, accurate models to enhance blood management across diverse healthcare scenarios.

New product demand forecasting and production capacity adjustment strategies: Within-product and cross-product word-of-mouth

Manufacturers attach increasing importance to consumer word-of-mouth (WOM) as it assists them to improve demand forecasting and determine production capacity adjustment strategies. Communication among potential consumers occurs as a result of within-product and cross-product word-of-mouth. Within-product WOM has received due academic attention touching upon production volume adjustment under infinite capacity, while the impact of cross-product WOM on demand is under-researched with limited number of studies investigating production capacity adjustment. To address this research gap, we employ Agent-based Modeling and Simulation (ABMS) approach and the Bass model, together with considering within-product WOM and cross-product WOM, to modify previous demand forecasting methods and propose production capacity adjustment strategies. Results show: (1) higher performances delivered by manufactures depends more on production capacity adjustment strategies rather than production volume adjustment strategies; (2) production capacity adjustment strategies are closely associated with factors including unit capacity fixed cost and cross-product WOM; (3) on condition of low unit capacity fixed cost, proactive strategy leads to maximal expected net present value (NPV) of profit and minimal lost customer number (LCN); (4) greater within-product WOM and cross-product WOM might not as beneficial as expected, in that expanding within-product WOM and cross-product WOM in most cases leads to growing lost customer number.

Leveraging Software Automation to Transform the Manufacturing Industry

The manufacturing industry is undergoing a paradigm shift driven by advancements in software automation technologies. This paper explores the transformative impact of leveraging software automation on various facets of the manufacturing sector. As industries strive for increased efficiency, cost-effectiveness, and agility, the integration of automation solutions has emerged as a key strategy.
 The paper begins by providing an overview of the current landscape of the manufacturing industry, highlighting the challenges faced in terms of manual processes, operational bottlenecks, and the need for enhanced productivity. It then delves into the role of software automation in addressing these challenges, showcasing how technologies such as robotic process automation (RPA), artificial intelligence (AI), and industrial IoT are revolutionizing traditional manufacturing workflows.
 Furthermore, the discussion covers the benefits of software automation in optimizing production processes, reducing errors, and enhancing overall quality. Real-world case studies and examples illustrate successful implementations of automation across diverse manufacturing sub-sectors, showcasing the tangible advantages experienced by companies that have embraced this transformative approach.
 The paper also explores the implications of software automation on the workforce, emphasizing the need for upskilling and reskilling initiatives to ensure a smooth transition to an automated manufacturing environment. It addresses concerns related to job displacement and emphasizes the potential for creating new roles that harness the strengths of human-machine collaboration.
 In addition, the paper discusses the impact of software automation on supply chain management, emphasizing how it contributes to improved demand forecasting, inventory management, and logistics optimization. The resulting supply chain efficiencies contribute to a more resilient and responsive manufacturing ecosystem.
 The conclusion of the paper summarizes key findings and outlines future prospects for the continued integration of software automation in the manufacturing industry. As technology continues to advance, embracing automation becomes not only a strategic imperative but also a means to foster innovation, sustainability, and competitiveness in an evolving global marketplace.

Seasonal Methods of Demand Forecasting in the Supply Chain as Support for the Company’s Sustainable Growth

Demand forecasting plays a key role in supply chain planning, management and its sustainable development, but it is a challenging process as demand depends on numerous, often unidentified or unknown factors that are seasonal in nature. Another problem is limited availability of information. Specifically, companies lacking modern IT systems are constrained to rely on historical sales observation as their sole source of information. This paper employs and contrasts a selection of mathematical models for short-term demand forecasting for products whose sales are characterized by high seasonal variations and a development trend. The aim of this publication is to demonstrate that even when only limited empirical data is available, while other factors influencing demand are unknown, it is possible to identify a time series that describes the sales of a product characterized by strong seasonal fluctuations and a trend, using selected mathematical methods. This study uses the seasonal ARIMA (autoregressive integrated moving average) model, ARIMA with Fourier terms model, ETS (exponential smoothing) model, and TBATS (Trigonometric Exponential Smoothing State Space Model with Box–Cox transformation, ARMA errors, Trend and Seasonal component). The models are presented as an alternative to popular machine learning models, which are more complicated to interpret, while their effectiveness is often similar. The selected methods were presented using a case study. The results obtained were compared and the best solution was identified, while emphasizing that each of the methods used could improve demand forecasting in the supply chain.

Coordination of a platform-based supply chain in the marketplace or reselling mode considering cross-channel effect and blockchain technology

We consider a supply chain consisting of a manufacturer and an online platform. The manufacturer produces products under the carbon cap-and-trade policy, and sells them through its own offline channel and the platform, which operates in either the marketplace or reselling mode. While enlarging the potential retail market size through the “platform power”, the platform provides logistics service for the online sales. We also consider the cross-channel effect concerning the impact of online sales on offline demand. The manufacturer can adopt blockchain technology to improve demand forecasting. We study the optimal production, pricing, delivery time decisions, and the coordination problem of the two firms under various scenarios. We compare the optimal decisions of the two firms and the manufacturer's profits with and without blockchain in each operation mode of the platform. We find that the cross-channel effect and blockchain have significant impacts on the optimal decisions and coordination results. In the marketplace mode, the optimal production quantity increases (decreases) with the commission rate if the cross-channel effect is high (low). The optimal delivery time increases (decreases) with the cross-channel effect and the cap in the marketplace (reselling) mode. The reselling mode can coordinate the manufacturer and platform regardless of whether delivery time or strategic inventory is considered, while the marketplace mode can coordinate the two firms only when delivery time is considered. If the cross-channel effect is low (high) and the platform power is high (low), adopting blockchain can generate more profit for the manufacturer. Furthermore, adopting blockchain promotes the coordination of the manufacturer and the platform.

Business analytics in the case of inventory management in Industry 4.0 conditions

Purpose: The purpose of this publication is to present the applications of usage of business analytics in inventory management. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: Industry 4.0 has ushered in a transformative era of technological innovation, particularly in inventory management. Business Analytics has emerged as a crucial tool for optimizing inventory control and supply chain management in this context. This integration of digital technologies, the Internet of Things, artificial intelligence, and big data has made inventory management more complex yet highly efficient. Business Analytics, driven by machine learning and predictive analytics, empowers organizations to make data-driven decisions, improve demand forecasting, and enhance supplier relationships, resulting in cost reduction, increased customer satisfaction, and sustainability efforts. Industry 4.0 has also introduced various software solutions that streamline inventory management, albeit with challenges. Overcoming these challenges, businesses leverage the advantages to adapt to a dynamic market, reduce costs, and enhance sustainability, solidifying the critical role of Business Analytics in inventory management in an evolving landscape. Keywords: business analytics, Industry 4.0, digitalization, artificial intelligence, real-time monitoring; inventory management. Category of the paper: literature review.

Evaluating human behaviour in response to AI recommendations for judgemental forecasting

Various advanced systems deploy artificial intelligence (AI) and machine learning (ML) to improve demand forecasting. Supply chain planners need to become familiar with these systems and trust them, considering real-world complexities and challenges the systems are exposed to. However, planners have the opportunity to intervene based on their experience or information that the systems may not capture. In this context, we study planners’ adjustments to AI-generated demand forecasts. We collect a large amount of data from a leading AI provider and a large European retailer. Our dataset contains 30 million forecasts at the SKU-store-day level for 2019, plus variables related to products, weather, and holidays. In our two-phase analysis, we aim to understand the adjustments made by planners and the quality of these adjustments. Within each phase, we first identify the drivers of adjustments and their quality using random forest, a well-known ML algorithm. Next, we investigate the collective effects of the different drivers on the occurrence and the quality of the adjustments using a decision tree approach. We find that product characteristics such as price, freshness, and discounts are important factors when making adjustments. Large positive adjustments occur more frequently but are often inaccurate, while large negative adjustments are generally more accurate but fewer in number. Thus, planners do not contribute to accuracy on average. Our findings provide insights for the better use of human knowledge in judgmental forecasting.

A hybrid demand forecasting model for greater forecasting accuracy: the case of the pharmaceutical industry

ABSTRACT In the era of modern technology, the competitive paradigm among organisations is changing at an unprecedented rate. New success measures are applied to the organisation’s supply chain performance to outperform the competition. However, this lead can only be obtained and sustained if the organisation has an effective and efficient supply chain and an appropriate forecasting technique. Thus, this study presents the demand-forecasting model, i.e., a good fit for the pharmaceutical sector, and shows promising results. Through this study, it is observed that combining forecasting algorithms can result in greater forecasting accuracies. Therefore, a combined forecasting technique ARIMA-HW hybrid1 i.e. (ARHOW) combines the Autoregressive Integrated Moving Average and Holt’ s-Winter model. The empirical findings confirm that ARHOW performs better than widely used forecasting techniques ARIMA, Holts Winter, ETS and Theta. The results of the study indicate that pharmaceutical companies can adopt this model for improved demand forecasting.

Internet based elastic logistics platforms for fashion quick response systems in the digital era

Quick response (QR) systems, which aim to respond promptly to market changes by postponing inventory decisions with a reduced lead time, are critical to support fast fashion operations. Under QR systems, in the digital data analytics era, the fashion brand can acquire market information using digital technologies to improve demand forecasting. This enhances the respective ordering and transportation mode selection decisions. It is known that to implement QR systems requires the availability of the right logistics option (e.g., transportation mode) and the needed logistics capacity in the future. An Internet based elastic logistics platform (ELP) hence emerges to help. In this paper, in the main model, we analytically derive via stochastic dynamic programming the optimal transportation option selection and inventory ordering policy with the ELP. We further examine the value of ELP and identify the situations in which it is especially beneficial to adopt it. Robustness checking is conducted which proves that the theoretical findings derived in the main model are solid. Specific managerial action plans and managerial implications are developed.

지방자치단체 문화시설 수요추정 개선방안 연구: 문화예술회관을 중심으로

본 연구의 목적은 문화시설 수요추정 방법론으로 가장 많이 활용되고 있는 중력모형의 방법론적 개선방안을 마련하는데 있다. 분석대상으로 현재 운영 중인 문화예술회관 가운데 4개의 사례집단을 설정하였으며 다양한 가정(준거시설 선정, 영향권 설정, 거리 및 규모의 민감도 설정)을 적용한 중력모형을 통해 수요를 추정하고 이를 실제 수요와 비교하여 시사점을 도출하였다. 주요 분석결과는 다음과 같다. 첫째, 준거집단 설정 시 특별･광역시와의 연접 여부, 인접 문화예술회관의 공연장 규모를 고려할 필요가 있다. 둘째, 특･광역시와 연접하지 않고 인접지역에 대규모 공연장이 없는 경우, 영향권 설정은 지자체 내로 한정할 수도 있지만 지자체 범위를 벗어나서도 검토가 가능한 것으로 나타났다. 셋째, 규모변수 설정 시 현재 일반적으로 사용되는 이용수요와 정비례하는 관계식이 성립하지 않은 것으로 나타났다.

A data-driven approach to adaptive synchronization of demand and supply in omni-channel retail supply chains

The integration of selling and fulfillment processes triggered by omni-channels is transforming the retailer’s operations management. In this context, there is a lack of research regarding the connection between digital and physical worlds in retail supply chains. This paper aims to propose a data-driven approach that combines machine-learning demand forecasting and operational planning simulation-based optimization to adaptively synchronize demand and supply in omni-channel retail supply chains. The findings are substantiated through the application of the approach in an omni-channel retail supply chain. The combination of clustering and neural networks improved demand forecast, supporting an assertive identification of demand volume and location. Simulation-based optimization allowed for the definition of which facility would serve identified demands most effectively. The approach reduced fulfillment lead time, mitigated backorders arising from incompatible product´s supply and demand, and lowered operational costs, which are key performance indicators in today’s competitive retail markets.

Impacts and costs of embodied and nutritional energy of food waste in the US food system: Distribution and consumption (Part B)

Abstract An efficient energy use in the food supply chain (FSC) is a major policy priority, considering the dual challenge of decreasing non-renewable resource availability and increasing world population. This article is one of two that analyzes the concept of the “dual energy waste” caused by food losses and waste (FLW): (i) nutritional energy and (ii) embodied energy used to produce food. Part A focused on the upstream segments (production, transport, and processing) of the United States FSC. In Part B the downstream segments (distribution, transport, home and out-of-home consumption) are analyzed. All direct and indirect energy inputs involved in food produced for domestic use in the USA were considered. From 2001 to 2015 the average energy use in the downstream part of FSC was 6,000 ± 550 PJ (about 5.8% of total energy use), while FLW were estimated at 57.8 Mt. This caused 370 PJ of nutritional energy waste, 2,250 PJ of embodied energy waste, and a wasted energy cost of almost $28 billion. Animal products represented only 34% of the FLW mass but generated 60% of the embodied energy waste. Appropriate food waste reduction strategies such as improved demand forecasts, more efficient product handling, discounted price on nearly expired foods, clearer product-life labeling, and more careful planning by consumers, could achieve energy saving and reduce the United States fossil fuel dependence and greenhouse gas emissions.

An Improved Demand Forecasting Model Using Deep Learning Approach and Proposed Decision Integration Strategy for Supply Chain

Demand forecasting is one of the main issues of supply chains. It aimed to optimize stocks, reduce costs, and increase sales, profit, and customer loyalty. For this purpose, historical data can be analyzed to improve demand forecasting by using various methods like machine learning techniques, time series analysis, and deep learning models. In this work, an intelligent demand forecasting system is developed. This improved model is based on the analysis and interpretation of the historical data by using different forecasting methods which include time series analysis techniques, support vector regression algorithm, and deep learning models. To the best of our knowledge, this is the first study to blend the deep learning methodology, support vector regression algorithm, and different time series analysis models by a novel decision integration strategy for demand forecasting approach. The other novelty of this work is the adaptation of boosting ensemble strategy to demand forecasting system by implementing a novel decision integration model. The developed system is applied and tested on real life data obtained from SOK Market in Turkey which operates as a fast‐growing company with 6700 stores, 1500 products, and 23 distribution centers. A wide range of comparative and extensive experiments demonstrate that the proposed demand forecasting system exhibits noteworthy results compared to the state‐of‐art studies. Unlike the state‐of‐art studies, inclusion of support vector regression, deep learning model, and a novel integration strategy to the proposed forecasting system ensures significant accuracy improvement.

Geographic mobility of recent immigrants and urban transit demand in the U.S.: New evidence and planning implications

Residential mobility rates in the U.S. have been in steady decline. Most notably, between 2005 and 2013, one-year intercity migration rate for immigrants has decreased by 0.7 percentage-points, compared to a 0.2 percentage-point decline for the U.S.-born population. Literature on urban implications of geographic mobility suggests that consideration of migration trends, or population flows, can improve urban planning, including transportation. Our research focuses on recent immigrants, a group that significantly contributes to public transit ridership in the U.S. In this study, we analyze the influence of the annual average in-migration rate of recent immigrants to various urban areas from within the country on transit ridership changes across the urban areas between 2008 and 2013. We also compare this effect with the effect of annual average in-migration rate of new immigrants to various urban areas from foreign countries. While the average effect of inflow of new foreign migrants on transit demand is suggested in the literature, distinguishing the transit demand of immigrants that are not movers and those that are movers from various locations remains unexplored.We derive migration flows from the American Community Survey microdata, and transit ridership from the U.S. National Transit Database. We perform geospatial analysis to overcome several constraints that make exploration of the migration-transportation connection difficult, particularly the lack of uniformity in geographic boundaries used for data presentation across and within government agencies, and over time.Our results indicate that consideration of domestic in-migration rates of recent immigrants can improve transit demand forecasting. As past literature has found, recent immigrants are highly likely to use transit. Recent immigrant migrants that have arrived directly from another country are even more likely to use transit. Interestingly, recent immigrants that move to a metropolitan area from another location in the U.S. are relatively less likely to use transit. Among domestic migrants, however, those that move to cities undergoing large increase in transit service relative to population are more likely to use transit. High population and transit stop density of both previous and current cities seem to positively affect transit mode choice for commute trips of recent immigrant movers. Declining inter-urban mobility among recent immigrants can indeed alter future transit demand trends. Transit agencies should not treat recent immigrants as a monolithic group. Consideration of the migration patterns of various types of recent immigrants, and factors that determine those patterns, can improve demand forecasting and planning.

Google Trends data for analysing tourists’ online search behaviour and improving demand forecasting: the case of Åre, Sweden

Accurate forecasting of tourism demand is of utmost relevance for the success of tourism businesses. This paper presents a novel approach that extends autoregressive forecasting models by considering travellers’ web search behaviour as additional input for predicting tourist arrivals. More precisely, the study presents a method with the capacity to identify relevant search terms and time lags (i.e. time difference between web search activities and tourist arrivals), and to aggregate these time series into an overall web search index with maximal forecasting power on tourism arrivals. The proposed approach enables a thorough analysis of temporal relationships between search terms and tourist arrivals, thus, identifying patterns that reflect online planning behaviour of travellers before visiting a destination. The study is conducted at the leading Swedish mountain destination, Are, using arrival data and Google web search data for the period 2005–2012. Findings demonstrate the ability of the proposed approach to outperform traditional autoregressive approaches, by increasing the predictive power in forecasting tourism demand.