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

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

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

Han RU, Ren LI, Chen ZHANG, Jian LUAN, Leyu WEI

The highway tunnel plays a critical role in highway traffic flow, yet its sections are particularly susceptible to traffic accidents. The research shows that the safety measures in the tunnel have a certain effect on improving the safety in the tunnel, but there is a lack of evaluation methods for the use effect of safety measures in the tunnel. To study the application effect of safety measures in tunnels (mainly strobe lights and information boards), this paper takes the driver’s subjective feelings and vehicle speed changes as indicators to evaluate the application effect of safety facilities. The Xingshuliang Tunnel in Shaanxi Province, which has been operated and meets the test standards, is used as the test site, and the driver between the Yaozhou and Huangling sections is randomly selected as the test object for data collection. Subjective feelings are mainly obtained by social survey methods to obtain data samples, and the driving speed is collected by NC2000, non-contact five-wheel instrument, video recorder and other equipment. The statistical analysis method is used to study the driving speed of each section inside and outside the tunnel and the driver’s response. According to the changing trend of speed, the weight of each test section is calculated by the combination of analytic hierarchy process and quantitative statistical method, and the comprehensive influence degree of safety measures is evaluated. The results show that both the strobe light and the information board induce the driver to reduce the driving speed by 3.1%, which can effectively reduce the driving speed. The strobe light mainly acts on the tunnel entrance and the inside of the tunnel, with a maximum influence range of 205 m. The information board has the greatest effect at the tunnel entrance, with a maximum influence range of 200 m. The above results provide a useful reference for the arrangement of safety measures and put forward the arrangement method of tunnel safety measures in combination with the conclusion, to help improve the safety of the driving environment in the tunnel.

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

Zhenbao WANG, Yanfang HE, Xueqiao ZHAO, Yuqi LIANG, Shihao LI

Previous studies have primarily focused on the effect of the built environment on ridership during weekdays and weekends. This paper aims to investigate the spatial heterogeneity of the effect of built environment factors on ridership at metro stations during National Day holidays. Beijing is divided into three zones from inner to outer areas. Taking metro station boarding and alighting ridership during National Dayholidays as the dependent variable, 13 built environment factors were selected as independent variables according to the “7D” dimension of the built environment. The recommended pedestrian catchments (PCA) combinations for the three zones in Beijing are 400 m_500 m_400 m by using the Multi-Scale Geographically Weighted Regression (MGWR) model. We investigated the effect of built environment factors on metro ridership and spatial heterogeneity. The influencing factors that have significant effects on both boarding and alighting ridership are building density, number of commercial facilities, bus lines density, number of entrance and exit, number of office facilities, mixed utilization of land and road density. The MGWR model results are helpful to propose targeted strategies for revitalising the built environment around metro stations.

2025 (Vol 37), Issue 2