With the rapid growth of instant delivery services in China, the number of couriers is rising due to low entry barriers such as minimal educational requirements, flexible hours, and competitive salaries. However, the industry faces challenges like excessive workloads and high accident rates, which could reduce couriers' job satisfaction. While the literature on couriers' job satisfaction is extensive, the application of holistic needs-based theories remains unexplored, particularly through advanced quantitative methods. This study operationalizes Maslow's Hierarchy of Needs Theory (MHNT) as a multi-dimensional construct and incorporates it into a Structural Equation Modeling (SEM) framework to examine its hierarchical impact on job satisfaction. Additionally, it explores the impact of physical health, occupational discrimination, and new technologies on couriers' job satisfaction. To test the framework and derive a nuanced understanding of factors influencing courier job satisfaction, data from 490 couriers in Shanghai, China, and nearby areas was collected. To account for differences in employment types, the survey data was split into full-time and part-time courier groups, with a multigroup analysis conducted using a structural equation model. The results show differing factors influencing job satisfaction. Part-time couriers are significantly affected by compensation and working environment, while full-time couriers are, besides compensation and working environment, also influenced by career development. These findings enhance the understanding of work conditions and motivators for couriers across different employment types within the instant delivery sector, offering key insights to enhance courier job satisfaction and promote sustainable development of this business.

The evolving field of electric moped sharing systems is shaped by various determinants influencing user preferences, including range anxiety, pricing strategies, and regulatory changes. Utilizing a stated preference approach with a hybrid choice model, this research explores how these factors, along with attitudinal constructs, impact user decisions. The findings reveal that remaining driving range plays a critical role, with significant individual variability in its sensitivity, while perceived range anxiety did not significantly influence choices. Recent changes in helmet regulations have shifted preferences towards faster vehicles. Furthermore, dynamic pricing strategies, such as adjusting ride or unlock fees, can incentivize the use of less desirable vehicles with lower battery range or aid in user-based relocation. Nevertheless, low-range vehicles are less likely to be chosen, even with incentives. These insights provide valuable guidance for operators of electric moped sharing system to improve fleet management and optimize user satisfaction through strategic pricing and battery management.

Congestion pricing has demonstrated significant effectiveness in managing traffic congestion in cities around the world. While numerous cities globally have implemented or explored various congestion pricing schemes, their potential remains largely untapped in Indian cities. This paper investigates the feasibility of congestion pricing as a tool to address traffic issues in India, focusing specifically on the city of Ahmedabad. By analyzing both successful and unsuccessful international cases, the study extracts critical lessons that can inform potential implementations in the Indian context. Through a theoretical assessment, this study identifies three distinct spatial characteristics in Indian urban environments that could facilitate effective congestion pricing: (1) well-defined charging zones, (2) advantageous geographical features, and (3) compact, densely built central business districts. These features, prevalent in Indian cities, could play a vital role in achieving successful outcomes similar to those observed internationally. In addition to spatial considerations, the paper addresses the unique challenges Indian policymakers may encounter, such as garnering public support, ensuring political feasibility, and developing the necessary technical infrastructure. To assist policymakers in overcoming these challenges, the study provides practical recommendations rooted in international best practices. Overall, the research extends the discussions on congestion pricing in the Indian context, resulting in the identification of valuable insights to guide decision-making for the implementation of congestion pricing schemes, ultimately promoting sustainable urban mobility and addressing traffic congestion issues in Indian cities.

The rapid growth of the online food delivery industry has led to a significant increase in the number of delivery riders navigating urban streets, predominantly using bikes and e-bikes. This growth has been accompanied by a concerning rise in crashes involving these riders, posing a critical challenge for city authorities and policymakers. Promoting safer riding behavior, such as choosing safer routes while delivering food, can potentially reduce crash risks. With this motivation, this paper aims to evaluate the effectiveness of strategies that encourage riders to choose safer routes and estimate the value riders place on reducing the risk of road crashes. The paper presents a stated preference experiment conducted with food delivery riders in Amsterdam and Copenhagen to assess two targeted strategies: ’safety information’ and ’monetary incentives’, designed to encourage riders toward selecting safer routes. The results from the route choice model show that presenting information about safety against crashes on different routes and offering monetary incentives can effectively motivate riders to choose safer routes, even if these are longer. The trade-offs riders make between safer and shorter routes were quantified by calculating the Value of Risk Reduction (VRR) and Willingness to Accept (WTA) indicators, which offer valuable insights into riders’ safety preferences. These indicators highlight how much riders value risk reduction and the compensation required to choose safer routes. Furthermore, the findings reveal that factors related to riders’ working arrangements and socio-demographic profiles significantly influence their route choice decisions. The paper concludes with a discussion about the practical challenges associated with implementing the strategies to enhance rider safety and proposing potential solutions that can be useful for food delivery platforms and policymakers.

Electric mobility hubs (eHUBS) represent an innovative approach to providing diverse shared electric transportation options, aimed at curbing private car use, and mitigating associated environmental impacts. Assessing the impact of eHUBS on travel choices across different cities requires significant resource and time investment due to the need for localized data collection and model development. This paper proposes a potential solution to this challenge by investigating the transferability of mode choice models originally developed for eHUBS in Amsterdam to predict behaviour towards eHUBS in Manchester. Multinomial Logit (MNL) and mixed logit models were transferred using four different procedures, and their effectiveness was evaluated using three assessment measures. The findings indicate that a scaled mixed logit model with an updated Alternative Specific Constant (ASC) outperforms other models in terms of its transfer effectiveness, both for disaggregate and aggregate assessment measures. The interplay between transfer procedures and assessment measures also was examined, with results indicating enhancements in disaggregate transferability measures with the 'scaling' transfer procedure, while 'updating the Alternative Specific Constants (ASCs)' improved predictions of aggregate mode shares. Following the analysis, the paper presents an in-depth discussion to provide a nuanced understanding of transferability and thus offers valuable insights for researchers planning future studies and practical considerations for policymakers.