Kaliprasana MUDULI, Deorishabh SAHU, Indrajit GHOSH

This study introduces a novel, adaptable framework for identifying and prioritising road traffic accident hotspots using the Getis Ord Gi* spatial autocorrelation tool. The framework classifies regions as hotspots or coldspots based on accident severity and frequency. A unique weighting system is developed to compute the Crash Severity Index (CSI), considering the severity of crashes in terms of fatalities and injuries. The identified hotspots are prioritised using the CSI, providing policymakers with a structured approach to allocate resources for crash remedial measures. The main contribution of this work is the development of a flexible framework applicable to various cities, states or countries to improve road safety. The framework’s effectiveness is demonstrated through a case study in Punjab, India, revealing that Sangrur, Hoshiarpur and Police Commissionerate Ludhiana are the top three hotspots. The study also offers a detailed analysis of crash statistics in Punjab, emphasising the severity of pedestrian crashes. This approach addresses the current lack of structured hotspot identification and prioritization strategies, marking a significant advancement in road safety management.

2025 (Vol 37), Issue 2

Wei ZHANG, Chuang ZHU, Yunchao QU, Guanhua LIU, Der-Horng LEE

With the ongoing urbanisation, the subway has become a vital component of modern cities, catering to the escalating demands of a mobile population. However, the increasing complexity of passenger flows within subway stations poses challenges to operations management. To optimise subway operations and enhance safety, researchers focus on extracting and analysing pedestrian trajectories within subway stations. Traditional trajectory extraction methods face limitations due to manual feature design and multi-stage processing. Leveraging advancements in deep learning, this paper integrates M-DeepSORT with YOLOv5 and proposes a feature association matching approach that addresses trajectory drift issues through simultaneous consideration of motion and appearance matching. The confidence-based (CB) Kalman filtering method is proposed to address the issue of random noise in pedestrian detection within subway scenes. The introduction of a momentum-based passenger trajectory centre update method reduces jitter, resulting in smoother trajectory extraction. Experimental results affirm the effectiveness of the proposed algorithm in detecting, tracking and statistically analysing subway station corridor passenger flow trajectories, demonstrating robust performance in diverse subway station scenarios.

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

Adisa MEDIĆ, Amel KOSOVAC, Ermin MUHAREMOVIĆ, Muhamed BEGOVIĆ

Machine learning (ML) is a crucial component of artificial intelligence that has recently attracted attention for its application in logistics. ML algorithms are used on large datasets. They create logic correlations among given data and provide predictions of specific values. This research paper aims to conduct a systematic literature review to showcase the potential applications of machine learning in urban logistics systems, specifically focusing on enhancing satisfaction for postal logistics operators and their customers. The authors used various research publication databases in this context (Web of Science, Scopus, Google Scholar etc). The analysis of different models provides insights into diverse aspects, such as predicting product prices and types of cargo, evaluating user satisfaction, forecasting user departures, assessing optimal geographical locations for implementing postal centres, predicting purchase times before online orders, estimating delivery times in the last phase of the logistics chain and more. The significance of this research is highlighted through the identification of shortcomings in existing literature, offering guidelines for future research in developing new machine learning model for optimal operator selection. This model aims to achieve improvements in both customer and operator satisfaction simultaneously.

2025 (Vol 37), Issue 2

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

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