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The purpose of this study is to explore how the personal traits of the informal entrepreneurs influence their formalization decisions.

This study adopted a qualitative approach using a multicase design in which 28 informal entrepreneurs situated in Kampala district, Uganda, were engaged. An interview guide, recorders and note books were used in data collection.

The results indicate that the traits of informal and semiformal entrepreneurs are distinct. Informal entrepreneurs have been noted to be more courageous and resilient, while their semiformal counterparts have greater passion for their businesses. It is thus observed that the formalization prospects are higher for the semiformal entrepreneurs than for their informal counterparts. Entrepreneurs that would be willing to formalize their businesses are discouraged by distance, technology and the cost of involving middlemen. Whereas the resilient entrepreneurs are noted to work through these challenges, the passive ones in both the informal and semiformal categories will not formalize their businesses by giving such excuses.

This study contributes to the extant literature on informal entrepreneurship by providing initial empirical evidence on how the personal traits of the entrepreneurs influence their formalization decisions specifically.

The purpose of this paper is to optimize the design of charging station deployed at the terminal station for electric transit, with explicit consideration of heterogenous charging modes.

The authors proposed a bi-level model to optimize the decision-making at both tactical and operational levels simultaneously. Specifically, at the operational level (i.e. lower level), the service schedule and recharging plan of electric buses are optimized under specific design of charging station. The objective of lower-level model is to minimize total daily operational cost. This model is solved by a tailored column generation-based heuristic algorithm. At the tactical level (i.e. upper level), the design of charging station is optimized based upon the results obtained at the lower level. A tabu search algorithm is proposed subsequently to solve the upper-level model.

This study conducted numerical cases to validate the applicability of the proposed model. Some managerial insights stemmed from numerical case studies are revealed and discussed, which can help transit agencies design charging station scientifically.

The joint consideration of heterogeneous charging modes in charging station would further lower the operational cost of electric transit and speed up the market penetration of battery electric buses.

This paper aims to optimize the charging schedule for battery electric buses (BEBs) to minimize the charging cost considering the time-of-use electricity price.

The BEBs charging schedule optimization problem is formulated as a mixed-integer linear programming model. The objective is to minimize the total charging cost of the BEB fleet. The charge decision of each BEB at the end of each trip is to be determined. Two types of constraints are adopted to ensure that the charging schedule meets the operational requirements of the BEB fleet and that the number of charging piles can meet the demand of the charging schedule.

This paper conducts numerical cases to validate the effect of the proposed model based on the actual timetable and charging data of a bus line. The results show that the total charge cost with the optimized charging schedule is 15.56% lower than the actual total charge cost under given conditions. The results also suggest that increasing the number of charging piles can reduce the charging cost to some extent, which can provide a reference for planning the number of charging piles.

Considering time-of-use electricity price in the BEBs charging schedule will not only reduce the operation cost of electric transit but also make the best use of electricity resources.

To support the standardized evaluation of bicyclist automatic emergency braking (AEB) systems, test scenarios, test procedures and test system hardware and software tools have been investigated and developed by the Transportation Active Safety Institute (TASI) at Indiana University-Purdue University Indianapolis. This paper aims to focus on the development of test scenarios and bicyclist surrogate for evaluating vehicle–bicyclist AEB systems.

The harmonized general estimates system (GES)/FARS 2010-2011 crash data and TASI 110-car naturalistic driving data (NDD) are used to determine the crash geometries and environmental factors of crash scenarios including lighting conditions, vehicle speeds, bicyclist speeds, etc. A surrogate bicyclist including a bicycle rider and a bicycle surrogate is designed to match the visual and radar characteristics of bicyclists in the USA. A bicycle target is designed with both leg pedaling and wheel rotation to produce proper micro-Doppler features and generate realistic motion for camera-based AEB systems.

Based on the analysis of the harmonized GES/FARS crash data, five crash scenarios are recommended for performance testing of bicyclist AEB systems. Combined with TASI 110-car naturalistic driving data, the crash environmental factors including lighting conditions, obscuring objects, vehicle speed and bicyclist speed are determined. The surrogate bicyclist was designed to represent the visual and radar characteristics of the real bicyclists in the USA. The height of the bicycle rider mannequin is 173 cm, representing the weighted height of 50th percentile US male and female adults. The size and shape of the surrogate bicycle were determined as 26-inch wheel and mountain/road bicycle frame, respectively. Both leg pedaling motion and wheel rotation are suggested to produce proper micro-Doppler features and support the camera-based AEB systems.

The results have demonstrated that the developed scenarios, test procedures and bicyclist surrogate will provide effective objective methods and necessary hardware and software tools for the evaluation and validation of bicyclist AEB systems. This is crucial for the development of advanced driver assistance systems.

The implementation of project-based learning (PBL) activities in the curricula of engineering students has become a consolidated method to improve their skills. The purpose of this paper is to share the experience acquired by the authors from a decennial case-study on a student team PBL activity focused on design and development of human-powered vehicles (HPVs). A review of the project evolution, boundary constraints and management choices could provide inspiration and suggestions to faculty staff that would like to set-up similar experiences for engineering students in their universities.

A student team was funded by the authors in 2008 to gather engineering students interested in design and construction of HPVs. In the past decade, the team has grown from 10 up to 60 students enrolled per year and stimulated to develop a range of HPV designs for sports and mobility. The project management evolved as a consequence to com ply with the growing ambitions of the group and complexity of the goals.

A thorough analysis of factors contributing to the success of the project led to identification of the key factors to increase student participation: persistence of the faculty staff is essential, attending competitions and challenges increases students’ recruitment and formal recognition of the activity through credits and the implementation of intermediate assessment steps increase the active participation rate. Bigger teams reduce the negative impact of recruits eventually abandoning the project in an early stage. Ambitious goals keep students motivated for longer periods and enable a virtuous circle by transferring enthusiasm and knowledge to new members.

The activity is analyzed starting from a subjective experience perspective and some of the findings/conclusions may be not applicable in a different context. However, such review can suggest strategies on the long-term period to create similar conditions elsewhere.

In the last part of the paper, it is pointed out how PBL projects can provide a fertile ground for innovation and lead to patents and development of new products aiming at the market.

This study contributes to provide an insight view of how a student team PBL activity can grow over a decade if guided by faculty staff.

This paper aims to analyze the effect of a recent disruption of Mexico's gasoline supply chain on the usage of public bike-sharing systems in Mexico City and Guadalajara.

The authors use a detailed data set to understand the usage patterns of Ecobici and Mibici. The authors assess both systems with a differences-in-differences econometric model using the least popular stations as a control group.

The authors find that the number of rides increased significantly shortly after the event because less popular stations became more utilized.

The authors show that when the effects of gasoline shortages were noticeable, usage rates increased in Guadalajara and Mexico City, but the rise primarily came from the users selecting more bikes from the less popular stations. Therefore, the authors show that citizens in both cities regarded bike-sharing as an adequate means of transportation, maximizing system usage during a disruptive time. This finding suggests that cities should invest in improving public bike-sharing systems to reduce carbon emissions and increase their population's well-being.

The authors use a publicly available data set to understand how citizens answered to a major disruption. Furthermore, this is one of the first papers that align supply chain risk management with sustainable transportation and analyzes its effects on citizen behavior in a Latin American setting.

El propósito de este artículo es analizar el efecto de una interrupción en la cadena de suministro de la gasolina en México sobre el uso del sistema de bicicletas públicas en Ciudad de México y Guadalajara.

Usamos una base de datos detallada para entender los patrones de uso de Ecobici y Mibici. Analizamos ambos sistemas a través de un modelo econométrico de diferencias en diferencias utilizando las estaciones menos afectadas como grupo de control.

Encontramos que la utilización del sistema aumentó en número de viajes de manera significativa luego del evento. Esto es debido a que las estaciones menos populares se empezaron a utilizar de manera más intensiva.

Demostramos que los habitantes de las dos ciudades decidieron buscar alternativas de transporte cuando los efectos de la escasez de gasolina se sintieron con fuerza. Esto significa que los ciudadanos consideran que las bicicletas públicas son un medio de transporte adecuado y que durante episodios inoportunos decidieron buscar opciones en las estaciones menos populares maximizando el uso del sistema. Por lo que las ciudades deberían invertir en la mejora de dichos sistemas para que sean más utilizados y así se reduzcan las emisiones de carbono y para que aumente el bienestar en la población.

Utilizamos una base de datos abierta para entender cómo los ciudadanos respondieron a una interrupción importante. Adicionalmente, este es uno de los primeros trabajos que alinea la gestión de riesgos en la cadena de suministros con transporte sostenible y analiza su efecto sobre el comportamiento de los usuarios en un contexto latinoamericano.

Recent advancements in Artificial Intelligence (AI) and, at its core, Machine Learning (ML) offer opportunities for organizations to develop new or enhance existing capabilities. Despite the endless possibilities, organizations face operational challenges in harvesting the value of ML-based capabilities (MLbC), and current research has yet to explicate these challenges and theorize their remedies. To bridge the gap, this study explored the current practices to propose a systematic way of orchestrating MLbC development, which is an extension of ongoing digitalization of organizations.

Data were collected from Finland's Artificial Intelligence Accelerator (FAIA) and complemented by follow-up interviews with experts outside FAIA in Europe, China and the United States over four years. Data were analyzed through open coding, thematic analysis and cross-comparison to develop a comprehensive understanding of the MLbC development process.

The analysis identified the main components of MLbC development, its three phases (development, release and operation) and two major MLbC development challenges: Temporal Complexity and Context Sensitivity. The study then introduced Fostering Temporal Congruence and Cultivating Organizational Meta-learning as strategic practices addressing these challenges.

This study offers a better theoretical explanation for the MLbC development process beyond MLOps (Machine Learning Operations) and its hindrances. It also proposes a practical way to align ML-based applications with business needs while accounting for their structural limitations. Beyond the MLbC context, this study offers a strategic framework that can be adapted for different cases of digital transformation that include automation and augmentation of work.

This paper discusses a case of truck sharing as an application of the sharing economy. This case study examines a real mixed feed company with multiple factories. In this company’s operation, bulk trucks located in a factory had not previously been shared for delivery with other factories to their pre-assigned customers of stock farms. Therefore, this paper suggests a new delivery system that facilitates truck-sharing and analyzes its effects on the transport cost and trucks’ CO₂ emissions. To this end, this paper develops vehicle routing models to represent the current delivery practice and the new truck-shared delivery (TSD). In addition, models are developed for a carbon control policy of an emission trading scheme (ETS) and the effects of the ETS on truck-sharing are investigated. Numerical analysis is conducted to identify the effects of the TSD and the carbon control policy and draw practical implications.

To assess the introduction and performance of light electric freight vehicles (LEFVs), more specifically cargo cycles in major 3PL organizations in at least two Nordic countries.

Case studies. Interviews. Company data on performance before as well as after the introduction. Study of differing business models as well as operational setups.

The results from the studied cases show that LEFVs can compete with conventional vans in last mile delivery operations of e-commerce parcels. We account for when this might be the case, during which circumstances and why.

Inherent limitations of the case study approach, specifically on generalization. Future research to include more public–private partnership and multi-actor approach for scalability.

Adding to knowledge on the public sector facilitation necessary to succeed with implementation and identifying cases in which LEFVs might offer efficiency gains over more traditional delivery vehicles.

One novelty is the access to detailed data from before the implementation of new vehicles and the data after the implementation. A fair comparison is made possible by the operational structure, area of delivery, number of customers, customer density, type of packages, and to some extent, the number of packages being quite similar. Additionally, we provide data showing how city hubs can allow cargo cycles to work synergistically with delivery vans. This is valuable information for organizations thinking of trying LEFVs in operations as well as municipalities/local authorities that are interested.