Prachiti SHINDE, Marin MARINOV

The rapid expansion of the metro network, driven by urbanisation and a heightened focus on environmental sustainability, underscores the need for efficient and sustainable public transportation systems. This study utilises the West Midlands Metro system as a case study to investigate operational efficiency and utilisation challenges that are common across metro networks globally. Employing advanced simulation modelling with SIMUL8, this research evaluates the existing timetables and utilisation rates of the West Midlands Metro to uncover inefficiencies and untapped potential. Various scenarios, including increased service frequencies and disruptions at high-traffic stations, were simulated to provide actionable insights for optimising metro operations. Findings revealed that increasing service frequency from every 10 minutes to every 5 minutes enhanced utilisation levels and boosted the total number of completed services. Meanwhile, disruptions at major stops resulted in a reduction in utilisation in a negligible range. These results demonstrate that improved service frequency significantly bolsters operational efficiency and showcases resilience to disruptions with minimal impact on overall performance. As to future research, the study suggests that implementing adaptive scheduling through AI-driven maintenance and infrastructure improvements can further elevate the efficiency and passenger experience of metro operations.

2025 (Vol 37), Issue 2

Yueying HUO, Chenhao WANG, Jianrong LIU

Walking prevents disease and keeps older adults healthy. Studying the walking decision factors of ageing people is an imperative step in understanding and promoting their behaviour. The theory of planned behaviour (TPB) and the prototype willingness model (PWM) are two well-known frameworks that deal with reasoned and social reaction processes in decision-making. This paper used these frameworks to examine the motivational mechanisms of walking among older Chinese adults living in small towns and used the structural equation model (SEM) for regression analysis. The analysis was based on 407 questionnaires and compared two models. Model 1 is TPB and model 2 combines TPB with PWM. The results show that behaviour willingness (BW) is significantly correlated with behaviour intention (BI), and model 2 explains a higher proportion of intention variance than model 1. Perceived behavioural control (PBC) is the most significant predictor in the two models, which implies that walking usefulness and walking feasibility are critical to older adults’ willingness to walk and walking program development. Finally, the utility of the integrative model is discussed, in terms of the theoretical contribution to walking among older adults and the applied implications for the promotion of walking.

2025 (Vol 37), Issue 2

Patricija BAJEC, Eva PODOVŠOVNIK

Crowdshipping has garnered increasing interest due to its potential benefits for various stakeholders. However, despite challenges in attracting crowdshippers, limited research explores their preferences, including socio-demographic factors and the practical challenges providers face when testing or implementing crowdshipping. This study aims to identify key factors influencing willingness-to-work (WTW) among potential crowdshippers, both in general and within business-to-business (B2B) and business-to-customer (B2C) contexts. Based on the literature review, this paper identifies 19 barriers influencing WTW and develops 22 corresponding enablers to address these barriers. Using a survey of 432 participants from Slovenia, the overall significance of these factors without differentiating business models was first assessed. Then, chi-squared automatic interaction detection analysis was applied to predict WTW in B2B and B2C contexts, identifying variations across these models. The disclosure of a mobile number emerged as the most influential predictor in both settings. Other notable differences in enablers and barriers were observed depending on the business model. These findings emphasise the need to consider business models in future preference analyses and provide a foundation for targeted recruitment strategies for crowdshippers.

2025 (Vol 37), Issue 2

Hong JIANG, Jiaxue WANG, Xinhui REN

With the potential for fast, contactless and environmentally friendly delivery, unmanned aerial vehicles (UAVs) have gained increasing attention and application due to their cost-effectiveness and convenient and rapid delivery operations. In future cities, a multi-level airport that supports vertical take-off and landing (VTOL) of UAVs and forming a delivery network is necessary to improve delivery efficiency and provide a competitive advantage. This paper proposes a multi-level airport location-routing problem for UAVs that considers UAV flight energy consumption and operational costs. The goal is to minimise the number of locations and minimise delivery path planning while meeting delivery demands within the service range. Based on the traditional distribution centre site-path problem, the UAV distribution network is constructed to solve the problem of airport location and flight path planning, and the two-layer genetic algorithm is used to solve it. Based on this, the validity of the model and algorithm is verified using the urban area of Tianjin as an example. The experimental results show that the constructed model can be used for UAV airport layout planning, which is applicable to large-scale, multi-aircraft-type and multi-level airport layout planning. Data analysis results indicate that when the location layout of the vertical hub airport is on the edge of the VTOL points, both the flight distance and the total cost of the delivery network relatively increase. Increasing the payload capacity will reduce the number of UAV operations, but the total cost shows a decreasing-then-increasing trend. This study can provide a theoretical basis for the selection of airport sites and UAV types in future UAV urban delivery networks.

2025 (Vol 37), Issue 2

Meiling HE, Guangrong MENG, Xiaohui WU, Xun HAN, Jiangyang FAN

With the acceleration of urbanisation and the rapid increase in road traffic volume, the scientific prediction of traffic accidents has become crucial for improving road safety and enhancing traffic efficiency. However, traffic accident prediction is a complex and multifaceted problem that requires the comprehensive consideration of multiple factors, including people, vehicles, roads and the environment. This paper provides a detailed analysis of traffic accident prediction based on multi-source data. By thoroughly considering data sources, data processing and prediction methods, this paper introduces the various aspects of traffic accident prediction from different perspectives. It helps readers understand the characteristics of different data and methods, the process of accident prediction and the key technologies involved. At the end of the paper, the main challenges and future directions in road crash prediction research are summarised. For example, the lack of efficient data sharing between different departments and fields poses significant challenges to the integration of multi-source data. In the future, combining deep learning models with time-sensitive data, such as social media and vehicle network data, could effectively improve the accuracy of real-time accident prediction.

2025 (Vol 37), Issue 2

Suxiao CHEN, Guangjie LIU, Shen GAO, Jiming LI, Juan WU

Establishing simulation models is a widely used and effective approach for analysing passenger flow distribution in urban rail transit systems. Recently, multi-agent and discrete event-based simulation models have shown exceptional performance in studying passenger flow information within urban rail transit systems. While simulations of passengers and trains often yield satisfactory results, few models capture the overall operational status of urban rail transit systems. The complex interactions among stations, trains and passengers make it challenging to integrate these elements into a unified system framework. In this paper, we introduce a triple simulation framework that integrates stations, trains and passengers as foundational elements to comprehensively simulate the entire urban rail transit system and observe overall passenger flow distribution. Experimental results demonstrate that our system surpasses existing advanced simulation models, achieving an accuracy rate of 88.44% with a tolerance for a 30% deviation. To further illustrate the effectiveness of our framework in analysing passenger flows, we conducted experiments using the Nanjing Metro AFC dataset, analysing passenger flow distributions at stations and on trains.

2025 (Vol 37), Issue 2