Dear colleagues,

We are pleased to announce that our recent article entitled “Drivers’ Perception and Awareness of Delay at Traffic Signals with Countdown Timers” is now available online. This study investigates how drivers perceive and report stop times at signalised intersections, and assesses their sensitivity to variations in delay. It also examines the influence of different signal displays – conventional signal devices (CSD), hourglass displays (HD), and continuous countdown timers (CCT) – on drivers’ time perception. Data were collected through roadside interviews and field measurements at three intersections in Iran. The research provides practical insights for improving signal display design and enhancing traffic management by better aligning perceived and actual stop times.
https://doi.org/10.1680/jmuen.25.00001

We are thrilled to inform you that our paper entitled “The short-term prediction of daily traffic volume for rural roads using shallow and deep learning networks: ANN and LSTM” has been published in the Journal of Supercomputing. Predicting daily traffic volume in the short term is of great importance for rural roads since it assists in relieving congestion, trip planning, and improving the level of service (LOS). Benchmark parametric methods like seasonal autoregressive integrated moving average (SARIMA) is not sophisticated enough to properly employ big data. Shallow learning techniques like the artificial neural network (ANN) cannot capture short-term and long-term time dependencies of daily traffic volume. Therefore, long short-term memory (LSTM) has been suggested to estimate the daily traffic volume of rural roads. The daily traffic volume for three types of roads, i.e., high-volume roads, international roads for transit of goods, and recreational roads leading to the city of Mashhad, Iran, was estimated using LSTM. Interested readers can read the preprint of the paper via https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4168660 or access it via https://link.springer.com/article/10.1007/s11227-023-05333-w.

Dear Friedns
It is my pleasure to inform you that our paper entitled "Detecting crash hotspots using grid and density-based spatial clustering" was published in Proceedings of the Institution of Civil Engineers -Transport. Our paper employs a grid and density-based clustering algorithm called GriDBSCAN to analyse crash data and find their spatial patterns. Other clustering methods such as Nearest Neighbour Hierarchical (NNH) and Kernel Density Estimation (KDE) were also applied to validate the results of the GriDBSCAN algorithm. Crash points recorded for Gebze and Izmit (Turkey) were clustered through these methods. This paper can be accessed via the following link. Please feel free to contact me if you have any question.
https://www.icevirtuallibrary.com/doi/10.1680/jtran.20.00028

Dear friends
It is my pleasure to inform you that my paper entitled "Assessing Drivers’ Compliance with Restrictive Yellow Traffic Lights in a Developing Country" was published in Transportation Research Record (TRR). Driving rules adopt permissive or restrictive policies concerning yellow light running (YLR). In a restrictive policy, vehicles behind the stop line are not allowed to enter the intersection on yellow no matter how close they are to the stop line. This paper examines whether drivers are only non-compliant with red lights or whether non-conformity to any prohibitive yellow/red signal emerges as a wider behavioural issue. This article can be accessed through
https://journals.sagepub.com/eprint/MH3ZMMKWAQIANDXS6XEW/full
Plese note that the link provided here for downloadin the paper is intended for teaching purposes.

A. A. Rassafi, S. S. Ganji, H. Zaferanchi

Dear colleagues,
We are pleased to share that our recent paper entitled
“Evaluating the quality of metro services in terms of passenger satisfaction: a case study of Tehran” has been published in Transport Policy.
This study applies Q methodology to explore diverse passenger perspectives on metro service quality and policy priorities. By identifying seven distinct discourses, the research highlights how different user groups prioritise operational efficiency, accessibility, intermodal integration, and service improvements. The findings provide evidence-based insights for aligning metro policy and operational strategies with heterogeneous user expectations.
The paper is available at:
⬇️ Link

We would be grateful if you find it relevant to your research and practice.