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Preprint ARTICLE | doi:10.20944/preprints202404.1230.v1

Investigation of Car Following and Lane Changing Behavior in Diverging Areas of Tunnel-Interchange Connecting Sections Based on Driving Simulation

Zhenhua Sun, Jinliang Xu, Chenwei Gu, Tian Xin, Wei Zhang

Subject: Engineering, Transportation Science And Technology Keywords: Tunnel-interchange sections; Signage information volume; Car following; Lane changing; Driving stability; Road safety

Online: 18 April 2024 (11:24:25 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202404.1252.v1

Hybrid Power Source Assisted by Renewable Energies for Hybrid Electric Vehicles

Carlos Armenta-Déu

Subject: Engineering, Transportation Science And Technology Keywords: Hybrid Electric Vehicle; Dual Power Source; Renewable Energies; Lithium Battery; Fuel Cell; Photovoltaic System; Battery Management; Driving Range; Efficiency Improvement; Cost Saving

Online: 18 April 2024 (11:20:39 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202404.1140.v1

Nonlinear Lateral Stability of 4-Axle Freight Car with the Y25 Bogies and the Measures to Improve Its Faults

Miroslaw Dusza, Milena Golofit-Stawinska, Krzysztof Zboinski

Subject: Engineering, Transportation Science And Technology Keywords: Y25 bogie; numerical model; suspension parameters; stability of motion; vehicle lateral stability

Online: 17 April 2024 (10:12:27 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202404.1028.v1

Overcoming the Technological Barriers in the Blockchain Supply Chain for Small Carriers

Marian Gogola, Dominika Rovnanikova, Mikulas Cerny

Subject: Engineering, Transportation Science And Technology Keywords: blockchain; supply chain; barriers; small carriers

Online: 16 April 2024 (10:50:06 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202402.1075.v2

Towards Real-Time Railroad Inspection Using Directional Eddy Current Probe

Meirbek Mussatayev, Mohammed Alanesi

Subject: Engineering, Transportation Science And Technology Keywords: Eddy current testing; lift-off; real-time inspection; probe optimization; signal-to-noise ratio

Online: 15 April 2024 (14:40:59 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202404.0763.v1

The Nonlinear and Threshold Effect of Built Environment on Ride-Hailing Travel Demand

Jiexiang Yin, Feiyan Zhao, Wenyun Tang, Jianxiao Ma

Subject: Engineering, Transportation Science And Technology Keywords: ride-hailing; built environment; XGBoost; SHAP; nonlinearity

Online: 11 April 2024 (14:10:15 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202404.0530.v1

Study of Tunnel Vehicle GNSS/INS/OD Combination Position Based on Lateral Distance Measurement and Lane Constraint

Hongbin Zhang, Xu Zhang

Subject: Engineering, Transportation Science And Technology Keywords: GNSS/INS combination; GNSS/INS/OD combination; laser radar; lateral distance measurement; lane line constraint

Online: 8 April 2024 (11:29:44 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202404.0444.v1

CFD Investigation of Hydrodynamic Force and Moment Acting on Stern-Rudder Plane Configurations of a Submarine

Tien Thua Nguyen, Thanh Long Phan, Thi Loan Mai

Subject: Engineering, Transportation Science And Technology Keywords: submarine; stern rudder -plane configuration; rudder force and moment; RANS-based simulation

Online: 7 April 2024 (13:59:38 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202404.0282.v1

Green-Powered Electric Public Mobility: Integrating Urban and Interurban Routes – A Case Study Analysis

Alessandro Franco, Giovanni Lutzemberger, Marco Giorgio Bevilacqua, Francesco Giuseppe Quilici, Matilde Vezzani

Subject: Engineering, Transportation Science And Technology Keywords: Electrification; Renewable energy integration; Public transit; Photovoltaic systems; Sustainable urban development; Energy independence; Modular solution.

Online: 3 April 2024 (10:34:08 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202404.0153.v1

Trend Research of Maritime Autonomous Surface Ships (MASS) based on Shipboard Electronics: Focusing on Text Mining and Network Analysis

Jinsick Kim, Sungwon Han, Hyeyoung Lee, Byeongsoo Koo, Moonju Nam, Kukjin Jang, Jooyeoun Lee, Myoungsug Chung

Subject: Engineering, Transportation Science And Technology Keywords: Maritime Autonomous Surface Ships (MASS), Research trends, Text Mining, Latent Dirichlet allocation (LDA) topic modeling, Shipboard Electronics, Shipping industry

Online: 2 April 2024 (16:16:15 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202403.1878.v1

New Design Options for Container Barges with Improved Navigability on the Danube

Bianca Duldner-Borca, Laura Hoerandner, Bernhard Bieringer, Reza Khanbilverdi, Lisa-Maria Putz-Egger

Subject: Engineering, Transportation Science And Technology Keywords: barge design; navigability; low water level; container transport; naval architecture; CAD; Danube

Online: 1 April 2024 (07:26:49 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202402.1363.v2

Assessing Ride Motion Discomfort Measurement Formulas

Louis T Klauder, Jr

Subject: Engineering, Transportation Science And Technology Keywords: vehicle-ride-motion; discomfort-measurement; discomfort-formula; motion-simulator; cross-match

Online: 29 March 2024 (10:40:55 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.1703.v1

Smart Delivery Assignment Problem through Machine Learning and the Munkres Algorithm

Juan Pablo Vásconez, Elias Schotborgh, Ingrid Nicole Vásconez, Viviana Moya, Andrea Pilco, Oswaldo Menéndez, Robert Guamán-Rivera, Leonardo Guevara

Subject: Engineering, Transportation Science And Technology Keywords: Smart delivery; Machine learning; Regression model; Munkres optimization algorithm

Online: 28 March 2024 (08:17:23 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.1516.v1

Modelling CO2 Emissions from Vehicles Fuelled with CNG Based on On-Road and Chassis Dynamometer Tests

Maksymilian Mądziel

Subject: Engineering, Transportation Science And Technology Keywords: vehicles; CNG; emission; PEMS; chassis dynamometer; pollution

Online: 26 March 2024 (11:59:10 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.1527.v1

Visions and Paradigms of Transport and Cities

Francesco Filippi

Subject: Engineering, Transportation Science And Technology Keywords: behaviour; evolutionary psychology; public health; land use; mobility; transport; cities; sustainability; urban planning

Online: 26 March 2024 (07:30:25 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.1404.v1

Analysis of Particle Number Emissions in a Retrofitted Heavy Duty-Spark Ignition Engine Powered by LPG

Vicente Bermúdez, Pedro Piqueras, Enrique José Sanchis, Brayan Conde

Subject: Engineering, Transportation Science And Technology Keywords: liquefied petroleum gas; particle number emissions; particle size distribution; three-way catalyst; retrofitted engine

Online: 25 March 2024 (13:36:38 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.1164.v1

Planning Studies of Driver Behavior at Pedestrian Crossings Using Whole-Vehicle Simulators

Rafał Burdzik, Dawid Simiński, Mikołaj Kruszewski, Anna Niedzicka, Aliya Batyrbekovna Zabieva, Jarosław Mamala, Ewa Dębicka

Subject: Engineering, Transportation Science And Technology Keywords: pedestrians; drivers; driving simulators; road safety; pedestrian crossings

Online: 19 March 2024 (14:50:49 CET)

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Preprint REVIEW | doi:10.20944/preprints202403.1137.v1

Research Status and Development Trends of Active Control Technology for Pantograph in High-Speed Railways

Shengdong Yu, Quan Sun, Dongyuan Zhang, Zan Jiang

Subject: Engineering, Transportation Science And Technology Keywords: High-speed railway; pantograph; active control; overhead contact system

Online: 19 March 2024 (11:25:51 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.1028.v1

An Energy-Efficient Logistic Drone Routing Method considering Dynamic Drone Speed and Payload

Kunpeng Wu, Shaofeng Lu, Haoqin Chen, Minling Feng, Zenghao Lu

Subject: Engineering, Transportation Science And Technology Keywords: Drone routing problem; Logistics drone; Mixed-integer linear programming; Adaptive large neighborhood search

Online: 18 March 2024 (11:39:14 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0966.v1

Exploring the Relationship Between Dangerous Driving Practices and Traffic Accidents in Pakistan

Adnan Yousaf, Jianping Wu, Abdul Moiz, Laiba Sethi, Shahzeb Ali Khan

Subject: Engineering, Transportation Science And Technology Keywords: traffic accidents, dangerous driving, risky driving, seat belt, DDDI, Driver training

Online: 18 March 2024 (10:02:38 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0764.v1

Investigating and Improving Pedestrian Safety in an Urban Environment of a LMIC: A Case Study of Yaoundé, Cameroon

Steffel Ludivin Feudjio Tezong, Tchaheu i Tchaheu Dimitri, Stephen Kome Fondzenyuy, Isaac Ndumbe Jackai II, Davide Shingo Usami, Luca Persia

Subject: Engineering, Transportation Science And Technology Keywords: Road safety; Pedestrian Safety; Urban environment; LMICs; Pedestrian Safety Index; Global Walkability Index

Online: 13 March 2024 (09:47:08 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0680.v1

Crack Detection in Orthographic Road Images Based on EC-YOLOX Algorithm

Lishuang Sun, Zeyu Liu, Zhiwei Xie

Subject: Engineering, Transportation Science And Technology Keywords: crack detection; Shallow features; YOLOX; ECA attention model; IOU loss; distortion correction

Online: 12 March 2024 (08:18:47 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0659.v1

Directional Vibro-Tactile Hazard Warnings for Drivers with Vision Impairments

Jing Xu, Felix M. Kölsch, Georg N. Dyszak, Christian Lehsing, Alex R. Bowers

Subject: Engineering, Transportation Science And Technology Keywords: driving safety; vision impairment; advanced driver assistance systems; hemianopia; central vision loss

Online: 11 March 2024 (18:14:22 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0656.v1

Assessment of Sustainable Mobility Patterns of University Students and Staff: Case of a LMIC

Stephen Kome Fondzenyuy, Isaac Ndumbe Jackai II, Steffel Ludivin Feudjio Tezong, Davide Shingo Usami, Brayan Gonzalez-Hernandez, Jean François WOUNBA, George NKENG ELAMBO, Luca Persia

Subject: Engineering, Transportation Science And Technology Keywords: mobility; travel choices; active modes; public transport; low- and middle-income countries

Online: 11 March 2024 (17:56:59 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0532.v1

Analysis of Factors Influencing Driver Yielding Behavior at Midblock Crosswalks on Urban Arterial Roads

Paramet Luathep, Pongsatorn - Pechteep, Sittha Jaensirisak, Nopadon Kronprasert

Subject: Engineering, Transportation Science And Technology Keywords: Driver yielding behavior; Midblock crosswalk; Pedestrian safety; Multinomial logistic regression.

Online: 8 March 2024 (14:21:16 CET)

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Preprint REVIEW | doi:10.20944/preprints202403.0161.v1

4IR Applications in the Transport Industry: Systematic Review of the State of the Art with Respect to Data Collection and Processing Mechanisms

O.O. Ajayi, A.M. Kurien, K. Djouani, L. Dieng

Subject: Engineering, Transportation Science And Technology Keywords: transportation systems; systematic review; industrial revolution; data collection; data processing

Online: 6 March 2024 (04:30:45 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0284.v1

Pavement Crack Detection of UAV Autonomous Inspection System for Mountainous Roads

Chen Xinbao, Wang Chenxi, Liu Chang, Xiaodong Zhu, Zhang Yaohui, Luo Tianxiang, Zhang Junhao

Subject: Engineering, Transportation Science And Technology Keywords: Concrete crack detection; UAV inspection system; YOLOv8; mountainous road

Online: 5 March 2024 (16:07:06 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0141.v1

Evaluating Distraction Safety Performance Indicators in an Urban Area of a LMIC: A Case Study of Yaoundé, Cameroon

Steffel Ludivin Feudjio Tezong, Boris Feudjio Tchinda, Stephen Kome Fondzenyuy, Davide Shingo Usami, Luca Persia

Subject: Engineering, Transportation Science And Technology Keywords: distracted driving; distraction; handheld mobile device; road safety; safety performance indicator; prevalence; LMIC; roadside observation; urban area.

Online: 4 March 2024 (11:24:16 CET)

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Preprint ARTICLE | doi:10.20944/preprints202402.1764.v1

Risk Analysis and Visualization of Merchant and Fishing Vessels Collision In Coastal Waters

Chuanguang Zhu, Jinyu Lei, Zhiyuan Wang, Decai Zheng, Chengqiang Yu, Mingzhong Chen, Wei He

Subject: Engineering, Transportation Science And Technology Keywords: Merchant and Fishing vessels collision; Coastal waters; Ship domain AIS; Data visualization

Online: 29 February 2024 (16:59:56 CET)

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Preprint ARTICLE | doi:10.20944/preprints202402.1703.v1

Evaluating Speeding Safety Performance Indicators in an Urban Area of a LMIC: A Case Study of Yaoundé, Cameroon.

Stephen Kome Fondzenyuy, Mobogshikeh Divine Moh, Steffel Ludivin Feudjio Tezong, Davide Shingo Usami, Luca Persia

Subject: Engineering, Transportation Science And Technology Keywords: speeding; road safety; speed management; speed compliance; safety performance indicators; urban areas

Online: 29 February 2024 (08:36:41 CET)

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Preprint ARTICLE | doi:10.20944/preprints202402.1540.v1

Novel Calibration Approach for Freeway Traffic Models in Single and Multi-Regimes Fundamental Diagram

Parisa Raufi, mohammad maniat

Subject: Engineering, Transportation Science And Technology Keywords: Traffic volume; Speed; Density; Fundamental diagram; Traffic flow theory; neural network

Online: 27 February 2024 (12:33:19 CET)

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Preprint ARTICLE | doi:10.20944/preprints202402.1363.v1

Assessing Ride Motion Discomfort Measurement Formulas

Louis T Klauder, Jr

Subject: Engineering, Transportation Science And Technology Keywords: vehicle-ride-motion; discomfort-measurement; discomfort-formula; motion-simulator; cross-match

Online: 23 February 2024 (09:53:52 CET)

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Preprint ARTICLE | doi:10.20944/preprints202402.1346.v1

The Classification of Work and Offenses of Professional Drivers from Slovakia and the Czech Republic

Miloš Poliak, Katerina Vichova, Jaroslav Mazanec, Mikulas Cerny, Jan Benus

Subject: Engineering, Transportation Science And Technology Keywords: Road freight transportation; Truck drivers; Social Legislation Policymaking

Online: 23 February 2024 (04:18:25 CET)

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Preprint ARTICLE | doi:10.20944/preprints202402.1316.v1

An Optimization Model for Flight Rescheduling from an Airport’s Centralized Perspective for Better Management of Demand and Capacity Utilization

Abbas Seifi, Kumaraswamy Ponnambalam, Anna Kudiakova, Lisa Aultman-Hall

Subject: Engineering, Transportation Science And Technology Keywords: flight scheduling; airport congestion; flight delays; schedule optimization; simulation of arrival and departure processes; runway capacity

Online: 22 February 2024 (13:25:41 CET)

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Preprint ARTICLE | doi:10.20944/preprints202402.1264.v1

Exploration of Shared E-Scooter Adoption Determinants and Latent Factors ‎in Urban Settings Using Machine Learning

Mostafa Jafarzadehfadaki, Virginia P. Sisiopiku

Subject: Engineering, Transportation Science And Technology Keywords: E-scooter; micromobility; latent factors; attitudes; machine learning; SHAP analysis; mode choice; ‎survey; urban setting

Online: 22 February 2024 (07:52:31 CET)

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Preprint ARTICLE | doi:10.20944/preprints202402.1163.v1

Smart City Scenario Editor for General What-if Analysis

Lorenzo Adreani, Pierfrancesco Bellini, Stefano Bilotta, Daniele Bologna, Enrico Collini, Marco Fanfani, Paolo Nesi

Subject: Engineering, Transportation Science And Technology Keywords: Smart City; Dashboard; What-If analysis; Scenario editor

Online: 20 February 2024 (14:46:02 CET)

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Preprint ARTICLE | doi:10.20944/preprints202402.1097.v1

Situational Modernization: Dynamic and Strength Characteristics of a Passenger Wagon Universal Design

Zhaisan Issa, Almas Alizhan, Janat Musayev, Algazy Zhauyt, Seitbek Zhunisbekov, Gulbarshyn Smailova, Gulmira Bikhozhayeva, And Timur Shigambayev

Subject: Engineering, Transportation Science And Technology Keywords: passenger wagon; body; modernization; dynamics; strength; stress-strain state; modeling; analysis

Online: 20 February 2024 (08:50:25 CET)

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Preprint ARTICLE | doi:10.20944/preprints202402.1075.v1

Towards Real-Time Railroad Inspection Using Directional Eddy Current Probe

Meirbek Mussatayev, Mohammed Alanesi

Subject: Engineering, Transportation Science And Technology Keywords: Eddy current testing; lift-off; real-time inspection; probe optimization; signal-to-noise ratio

Online: 20 February 2024 (04:06:29 CET)

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Preprint ARTICLE | doi:10.20944/preprints202402.0547.v1

Application of Computational Contact tools of Finite Element Analysis to Predict Ground‐Borne Vibrations Generated by Trains in Ballasted Tracks

Andrés García Moreno, Antonio Alonso López, Ignacio J. Turias, Juan Jesús Ruiz Aguilar

Subject: Engineering, Transportation Science And Technology Keywords: HSR; railway; ground‐borne vibrations; vibrational‐emissions; prediction; FEM; numerical modelling; computational contacts

Online: 9 February 2024 (10:54:22 CET)

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Predictive numerical models in the study of ground-borne vibrations generated by railway sys-tems have traditionally relied on the segmented approach of subsystems. In this approach, loads are individually applied, and the cumulative effect of rolling stock is obtained through superposi-tion. While this method serves to mitigate computational costs, it may not fully capture the com-plex interactions involved in ground-borne vibrations. Recent advancements in computational and software tools have enabled the development of more sophisticated vibrational pollution models. These advanced models encompass the entire dynamic system, from the rolling stock to the terrain, allowing for continuous simulations with a defined time step. Furthermore, the incorporation of computational contact tools between various ele-ments not only ensures temporal accuracy but also extends the analysis to the frequency domain. This paper explores the evolution of predictive numerical models in the context of ground-borne vibrations generated by trains on ballasted tracks. It highlights the shift from segmented models to continuous simulations, demonstrating the progressive advancement towards more comprehen-sive and accurate representations of railway-induced and generated vibrations in the track sur-roundings as an additional way of evaluate sustainability of infrastructures. In particular, this development of a numerical model from its inception aimed to expand upon ex-isting emission - prediction models by enriching the data with frequency-domain information. This approach has been driven by the aspiration to bridge the gap between numerical predictions and measured signals, striving for a higher level of accuracy in our obtained results. This model can predict the impact of a high-speed rail (HSR) vehicle passing on its own track. It possesses sufficient information in both the time and frequency domains to precisely characterize vibrations. This initiative marks a pivotal advancement, enabling us to more effectively capture the intricacies of ground-borne vibrations and their impact on the surrounding environment due to a deeper comprehension of the occurrences in the frequency domain, there's a heightened ca-pability to accurately characterize vibrations.

Preprint ARTICLE | doi:10.20944/preprints202402.0476.v1

High Precision Positioning and Rotation Angle Estimation of Flatbed Truck Based on Beidou and Vision

Xinli Yu, Yufei Ren, Xiaoxv Yin, Deqiang Meng, Haikuan Zhang

Subject: Engineering, Transportation Science And Technology Keywords: unmanned forklift; automated loading; feature construction; Beidou and vision localization; rotation angle estimation

Online: 8 February 2024 (05:11:45 CET)

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Preprint ARTICLE | doi:10.20944/preprints202402.0006.v1

Method of Performance Assessment of Lubricant Compositions Containing Nano-Additives Used for Wheel–Rail Pair

Valerii Kosarchuk, Mykola Chausov, Volodymyr Tverdomed, Kostyantyn Lopatko, Vaidas Lukoševičius

Subject: Engineering, Transportation Science And Technology Keywords: dispersion; hardness; lubricant; nano-additives; wheel-rail pair

Online: 1 February 2024 (09:51:45 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.1565.v1

CMARL: A Multi-agent Deep Reinforcement Learning Model with Emphasis on Communication Content

Ande Chang, Yuting Ji, Chunguang Wang, Yiming Bie

Subject: Engineering, Transportation Science And Technology Keywords: adaptive traffic signal control; deep reinforcement learning; multi-agent reinforcement learning; communication; traffic congestion)

Online: 22 January 2024 (07:54:13 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.1435.v1

Partially Automated Vehicle Simulation for Studying Driver Distraction in Real Scenarios

Mohammed Bendahmane, Lluís Ribas-Xirgo

Subject: Engineering, Transportation Science And Technology Keywords: autonomous vehicles; drivers’ distraction; simulated driving

Online: 19 January 2024 (02:55:32 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.1308.v1

Dynamic Right-of-Way Allocation on Bus Priority Lane considering Traffic System Resilience

Jia Hu, Zhexi Lian, Xiaoxue Sun, Zhen Zhang, Jintao Lai

Subject: Engineering, Transportation Science And Technology Keywords: Bus priority lane; Dynamic right-of-way allocation; Traffic system resilience

Online: 18 January 2024 (03:22:25 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.1129.v1

Analyzing The Effects of Road Accidents on Carbon Dioxide Emissions in Freeway Traffic

Yushan Wang, Chenjie Lv, Qin Nie, Haobing Liu

Subject: Engineering, Transportation Science And Technology Keywords: Freeways; CO2 Emission; Accident Management Measures

Online: 15 January 2024 (13:09:16 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.0737.v1

Unraveling Spatial-Temporal Patterns and Heterogeneity of Merging Behavior: Evidence from the exiD Dataset

Yiqi Wang, Yang Li, Ruijie Li, Shubo Wu, Linbo Li

Subject: Engineering, Transportation Science And Technology Keywords: Merging patterns; Driving heterogeneity; HD map

Online: 10 January 2024 (04:28:40 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.0600.v1

Real-Time Batch Optimization for the Stochastic Container Relocation Problem

Sifang Zhou, Qingnian Zhang

Subject: Engineering, Transportation Science And Technology Keywords: Container Relocation Problem; Stochastic Container Relocation Problem; Real-Time Batch Optimization; Heuristics

Online: 8 January 2024 (10:29:19 CET)

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Preprint ARTICLE | doi:10.20944/preprints202312.2204.v1

Analyzing Motorway Operator Data for Spatiotemporal Insights into Speed Dynamics

Katerina Koliou, Ioanna Spyropoulou

Subject: Engineering, Transportation Science And Technology Keywords: motorway; speed; patrol vehicles; loop detectors; GNSS; heatmaps; clustering

Online: 29 December 2023 (05:16:16 CET)

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Preprint ARTICLE | doi:10.20944/preprints202312.2174.v1

Network Screening on Low-Volume Roads using Risk Factors

Kazi Tahsin Huda, Ahmed Al-Kaisy

Subject: Engineering, Transportation Science And Technology Keywords: low-volume roads; crash prediction; Empirical Bayes; network screening; classification and regression tree; risk factors

Online: 28 December 2023 (15:05:44 CET)

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Preprint ARTICLE | doi:10.20944/preprints202312.1497.v1

A New Approach for Identifying Safety Improvement Sites on Rural Highways: Validation Study

Bishal Dhakal, Ahmed Al-Kaisy

Subject: Engineering, Transportation Science And Technology Keywords: Rural Roads Safety; Network Screening; Highway Safety Improvement Programs; Ranking; Regression Analysis

Online: 20 December 2023 (10:54:44 CET)

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Preprint ARTICLE | doi:10.20944/preprints202312.1330.v1

A Study on Micro-Mobility Data Collection System Using Incremental Learning Techniques

Eunkyu Lee, Taeseong Kim, Junaid Khan, Umar Zaman, Jaebin Ku, Kyungsup Kim

Subject: Engineering, Transportation Science And Technology Keywords: incremental learning; micro-mobility; data collection system; AI-based services; Broad Learning System algorithm

Online: 18 December 2023 (12:21:08 CET)

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Preprint ARTICLE | doi:10.20944/preprints202312.1055.v1

Method and Algorithm of Spatial-Temporal Graph Formation for Dynamic Transportation System

Aleksandr Rakhmangulov, Pavel Mishkurov

Subject: Engineering, Transportation Science And Technology Keywords: spatial-temporal graph; rail yard; dynamic transportation system; shunting operations; shunting route; double vertex graph; simulation; rail yard

Online: 14 December 2023 (05:58:44 CET)

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Preprint ARTICLE | doi:10.20944/preprints202312.0993.v1

Simulation Study of The Dump Trucks' Speed Profile Impact on Open Pit Efficiency

Aleksandr Rakhmangulov, Andrey Artiushin

Subject: Engineering, Transportation Science And Technology Keywords: open pit; excavator-and-dump truck complex; speed profile; road grade; simulation; AnyLogic

Online: 14 December 2023 (05:45:35 CET)

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Preprint ARTICLE | doi:10.20944/preprints202312.0942.v1

Multi-Criteria System's Design Methodology for Selecting Open Pits Dump Trucks

Aleksandr Rakhmangulov, Konstantin Burmistrov, Nikita Osintsev

Subject: Engineering, Transportation Science And Technology Keywords: open pit; dump track; multi-criteria system; MCDM; FUCOM

Online: 13 December 2023 (10:53:46 CET)

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Preprint ARTICLE | doi:10.20944/preprints202312.0892.v1

Tourism Itinerary Recommendation using Vehicle Routing Problem Time Windows Based on Shortest Path Method and Analytics Hierarchy Process

Surya Michrandi Nasution, Reza Rendian Septiawan, Fairuz Azmi

Subject: Engineering, Transportation Science And Technology Keywords: tourism itinerary; vehicle routing problem time windows; route recommendation; shortest path algorithm; analytics hierarchy process

Online: 12 December 2023 (12:49:28 CET)

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Preprint ARTICLE | doi:10.20944/preprints202312.0771.v1

Predicting Autonomous Driving Behavior through Human Factor Considerations in Safety-Critical Events

Jamal Raiyn, Galia Weidl

Subject: Engineering, Transportation Science And Technology Keywords: car– following; non-monotonic logic; driving behavior; naturalistic driving studies;
safety-critical events; cognitive vehicles

Online: 12 December 2023 (04:16:31 CET)

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This research explores the predictive capabilities of autonomous driving systems by integrating human factor considerations within the context of safety-critical events. Recognizing the significance of human behaviors in influencing driving dynamics, the study employs advanced modeling techniques to enhance the accuracy of predictions in scenarios that demand heightened safety measures. Traditional rule-based systems and monotonic logic often fall short in addressing the complexities of safety-critical events. To overcome these limitations, the research proposes the application of non-monotonic logic, allowing for flexible and adaptive reasoning that accommodates exceptions and context-specific information.The study emphasizes the importance of incorporating individual differences among drivers, such as risk-taking tendencies, reaction times, decision-making processes, and driving styles. By considering these human factors, the research aims to develop realistic and accurate autonomous driving models that capture the nuances of real-world driving scenarios, especially in safety-critical situations. The predictive model takes into account both internal and external factors, enabling the autonomous system to anticipate and respond effectively to unforeseen events.The primary goal is to provide autonomous vehicles with the capability to make plausible inferences, handle conflicting data, and adapt their behavior in real-time during safety-critical events. The proposed model integrates personalized cognitive agents for each driver, incorporating their unique preferences, characteristics, and needs. This personalized approach aims to optimize the safety and efficiency of autonomous driving, contributing to the ongoing evolution of intelligent transportation systems.In conclusion, this research contributes to advancing the field of autonomous driving by introducing a predictive model that leverages human factor considerations to enhance safety in safety-critical events. The incorporation of non-monotonic logic and individualized cognitive agents signifies a comprehensive approach to address the challenges associated with predicting autonomous driving behavior, paving the way for safer and more reliable autonomous vehicles in dynamic and unpredictable environments.

Preprint ARTICLE | doi:10.20944/preprints202312.0490.v1

Investigation on Medium-Term Performances of Porous Asphalt and Their Impacts on Tire/Pavement Noise

Hao Wu, Ge Wang, Ming Liang Li, Yue Zhao, Jun Li, Ding Ding Han, Peng Fei Li

Subject: Engineering, Transportation Science And Technology Keywords: Porous asphalt pavement; OBSI; Road performance degradation; Correlation; Tire/pavement noise

Online: 7 December 2023 (07:23:42 CET)

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As the lifespan of porous asphalt pavement extends, it invariably experiences performance degradation and an increase in tire/pavement noise compared with the original surface when it is newly built. To understand the medium-term performance of porous asphalt pavement during service and reveal the influence of different pavement performance indicators on tire/pavement noise generation, a seven-year continuous observation and data analysis study was conducted. Porous asphalt with different designed material parameters was taken into account, including thin-layer porous asphalt overlay with a maximum nominal aggregate size of 10mm (PUC-10), porous asphalt with a maximum nominal aggregate size of 13mm (PAC-13), and a two-layer porous asphalt incorporating both PUC-10 and PAC-13 (PUC-10+PAC-13). Using automated pavement technology testing equipment, key performance indexes, including damage rate (DR), rut depth (RD), international roughness index (IRI), and sideway force coefficient (SFC), were measured and calculated. The on-board sound intensity (OBSI) method was used to test noise levels in three types of porous asphalt pavement structures and on a dense graded easy compact asphalt concrete (ECA) for comparison. The progressions of the medium-term performances were analyzed according to the measured results. The correlations between tire/pavement noise levels and testing speed, testing lane, pavement structure, and road condition indicators were investigated by regression. The research results revealed that the DR and SFC of the three types of porous asphalt pavement displayed more significant attenuation in the service period, while the IRI and RD showed small changes. Among them, the SFC demonstrated significant attenuation over the first 4 years, decreasing between 13.8% and 17.0%. From the 3-7 years of operation, the transverse crack disease in porous asphalt pavement progressed rapidly. The three porous asphalt pavement structures tested exhibited commendable noise reduction capabilities. Compared to ECA, the OBSI noise levels of these structures were lower by 2.09 dB, 1.53 dB, and 2.88 dB, respectively. Compared to other pavement condition indicators, RD had a more significant impact on the OBSI value of the PUC-10 porous asphalt pavement. For the PUC-10+PAC-13 porous asphalt pavement, there was a significant linear relationship between the OBSI value and SFC, mainly due to polishing of the coarse aggregate surface. It leads to a decrease of the micro-texture, which lies in increasing tire/pavement noise at high-frequency ranges and decreasing SFC. This study will have guiding significance for the research of the attenuated law and durability improvement of porous asphalt tire/pavement noise.

Preprint ARTICLE | doi:10.20944/preprints202312.0292.v1

Vulnerable Road User Safety Using Mobile Phones with Vehicle-to-VRU Communication

Sukru Yaren Gelbal, Bilin Aksun-Guvenc, Levent Guvenc

Subject: Engineering, Transportation Science And Technology Keywords: vulnerable road user safety; vehicle-to-VRU communication; pedestrian collision warning

Online: 6 December 2023 (02:44:11 CET)

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Preprint ARTICLE | doi:10.20944/preprints202312.0008.v1

Analysis of Transverse Forces for Assessment of Safety against Derailment of the Specialized Train Composition for the Transportation of Long Rails

Valeri Stoilov, Petko Sinapov, Svetoslav Slavchev, Vladislav Maznichki, Sanel Purgic

Subject: Engineering, Transportation Science And Technology Keywords: railway; derailment; safety; assessment; long rails transportation; FEM; beam element

Online: 1 December 2023 (05:17:05 CET)

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Preprint ARTICLE | doi:10.20944/preprints202311.1916.v1

Lab Scale Investigation of Gaseous Emissions, Performance and Stability of An Aviation Turbo-Engine while Running on Biodiesel Based SAF

Radu Mirea, Grigore Cican

Subject: Engineering, Transportation Science And Technology Keywords: sustainable aviation fuel; engine efficiency; gaseous pollutants; kerosene blends; aviation turbo-engine

Online: 30 November 2023 (07:34:53 CET)

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Preprint ARTICLE | doi:10.20944/preprints202311.1764.v1

Analysis of the Effectiveness of a Freight Transport Vehicle at High Speed in a Vacuum Tube (Hyperloop Transport System)

David S. Pellicer, Emilio Larrodé

Subject: Engineering, Transportation Science And Technology Keywords: Mathematical modeling; high-speed transport; freight transport; sustainable transport

Online: 28 November 2023 (10:26:05 CET)

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Preprint REVIEW | doi:10.20944/preprints202311.1706.v1

A Literature Review on Charging Behaviour of Private Electric Vehicles

Natascia Andrenacci, Maria Pia Valentini

Subject: Engineering, Transportation Science And Technology Keywords: electric vehicles; charging behavior; private electric mobility.

Online: 27 November 2023 (13:21:36 CET)

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Preprint ARTICLE | doi:10.20944/preprints202311.1693.v1

A Novel Virtual Arrival Optimization Method for Traffic Organization Scenarios

Tianhao Shao, Weijie Du, Yun Ye, Haoqing Li, Jingxin Dong, Guiyun Liu, Pengjun Zheng

Subject: Engineering, Transportation Science And Technology Keywords: Virtual Arrival, traffic organization, carbon emission reduction, waiting time, multi-objective optimization

Online: 27 November 2023 (09:29:26 CET)

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Preprint ARTICLE | doi:10.20944/preprints202311.1466.v1

MaaS-Motivated Synergetic Design of Intelligent Mobility Service Supply Chain Network towards Seamless Traveling and Integrated Multimodal Journey Planning

Jiamin Zhang

Subject: Engineering, Transportation Science And Technology Keywords: Mobility as a Service (MaaS); intelligent mobility service supply chain network; hybrid synergy mechanism; urban rail transit (URT); Mobility-On-Demand (MOD) transport service; integrated multimodal journey planning

Online: 23 November 2023 (09:38:44 CET)

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Smart, reliable, and connected multi-modal mobility has been a long-standing goal of transit services. This paper focuses on the smart, seamless, and multi-modal mobility service in the context of “Mobility as a Service” (MaaS). Intelligent mobility is the smarter, greener, and more efficient movement of passengers around the world. Increasingly, mobility is approached as a service. This study first conducts an extensive literature review on mobility behavior and demand pattern of MaaS end-users. It then extends the mechanism of supply chain, MaaS, synergy (i.e., vertical cooperation synergy, horizontal competition synergy), and coopetition to develop the multi-tier closed-loop intelligent mobility service supply chain network. This paper explains the intelligent mobility service supply chain network from following perspectives: (i) mobility service taxonomy of MaaS; (ii) aims of intelligent mobility service supply chain network; (iii) urban rail transit (URT)-centered alternatives for integrated multimodal journey planning, i.e. access + URT + egress, and both access and egress can be served by Mobility-On-Demand (MOD) transport; (iv) node member imperatives. From a synthesis of insights from the ‘during’ journey, this study puts forward the synergetic design of intelligent mobility service supply chain network, including: (i) multi-tier closed-loop structure; (ii) key nodes identification for the physical multimodal transport network in the supply chain; (iii) hybrid synergy mechanisms among the partners, i.e., synergy principle, temporal splitting approach for coopetition synergy; (iv) index systems and evaluation methods for synergy measurement. This study also contributes to the integrated multimodal journey planning. In concluding, the paper highlights the important implications of the proposed intelligent mobility service supply chain network for MaaS bundle design and adverse effects reduction, resulting from 1 + 1 > 2 synergy effects.

Preprint REVIEW | doi:10.20944/preprints202311.1417.v1

Advanced Driver Information System at Critical Points of the Multimodal Traffic Network

Maja Tonec Vrančić, Pero Škorput, Krešimir Vidović

Subject: Engineering, Transportation Science And Technology Keywords: urban mobility; traffic safety; cooperative intelligent transport systems; traffic control

Online: 22 November 2023 (11:31:27 CET)

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Preprint ARTICLE | doi:10.20944/preprints202311.1312.v1

Research on Vibration Comfort of Non-motorized Lane Riding Based on Three Axis Acceleration

Yuecheng LI, Liangjie XU, Xi Huang, Hao XIAO

Subject: Engineering, Transportation Science And Technology Keywords: Non-motorized lane; Riding vibration comfort; Triaxial acceleration; Random forest; Logistic regression

Online: 21 November 2023 (10:24:59 CET)

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Preprint ARTICLE | doi:10.20944/preprints202311.1308.v1

Influence of Blind Spot Assistance Systems in Heavy Commercial Vehicles on Accident Reconstruction

Thomas König, Daniel Paula, Hans-Georg Schweiger, Stefan Quaschner

Subject: Engineering, Transportation Science And Technology Keywords: accident analysis; accident reconstruction; road safety; vehicle active safety

Online: 21 November 2023 (09:59:12 CET)

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Preprint ARTICLE | doi:10.20944/preprints202311.1156.v1

Urban Logistics as a Service (ULaaS) Platform: Proposing a Sustainable Digital Business Model for Logistic Services

Reinaldo Fioravanti, Orlando Fontes Lima Jr, Giset Natalia Montoya Moreno, Janaina Antonino Pinto

Subject: Engineering, Transportation Science And Technology Keywords: Sharing Economy; Collaborative Digital Platform; Digital Business Model; Urban Logistics

Online: 17 November 2023 (12:36:06 CET)

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Preprint ARTICLE | doi:10.20944/preprints202311.1098.v1

The Impact of Unconditional Priority for Escorted Vehicles in Traffic Network on Sustainable Urban Mobility

Miroslav Vujić, Martin Gregurić, Luka Dedić, Daniela Koltovska Nečoska

Subject: Engineering, Transportation Science And Technology Keywords: signalized intersections; intelligent transport systems; adaptive traffic control; sustainable smart city concept

Online: 16 November 2023 (14:28:06 CET)

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Preprint ARTICLE | doi:10.20944/preprints202311.1038.v1

Automobiles’ Acceleration Analysis When Crossing Road Intersections

Federico Scattolin, Alfonso Micucci

Subject: Engineering, Transportation Science And Technology Keywords: Car Acceleration; Traffic Accidents; Road Design; CAV Software Development

Online: 16 November 2023 (11:12:33 CET)

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Preprint ARTICLE | doi:10.20944/preprints202311.0953.v1

Analyzing Vehicle-Vehicle and Vehicle-Pedestrian Conflicts at Signalized Intersections: Exploring the Influence of Vehicle Angle on Conflict Severity Through LiDAR Sensor During Traffic Signal Failures

Alireza Ansariyar

Subject: Engineering, Transportation Science And Technology Keywords: LiDAR Sensor; Signalized Intersections; Traffic Signal Failure; Safety; Conflict’ s Angle; Conflict’ s Frequency and Severity

Online: 14 November 2023 (16:00:09 CET)

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This study explores the intricate relationship between the angle of vehicle-to-vehicle (V2V) conflicts and the severity of conflicts at signalized intersections during traffic signal failures, leveraging the advanced capabilities of LiDAR sensor technology. The research delves into the impact of conflict angles on collision outcomes and introduces LiDAR as a novel and precise tool for capturing spatial dynamics during unregulated traffic movements. Findings reveal that as the angle of V2V conflicts increases, indicating a wider approach between vehicles, the severity of conflicts tends to decrease. This reduction in severity is attributed to the detailed spatial data provided by LiDAR, enabling the identification of glancing impacts rather than direct head-on collisions. The mitigation of forces involved in collisions at increased angles contributes to a decreased likelihood of severe consequences, such as extensive vehicle damage and serious injuries to occupants.Conversely, as the angle of V2V conflicts decreases, signifying a narrower approach between vehicles, the potential for severe collisions tends to increase. LiDAR technology facilitates precise measurements of spatial coordinates and temporal dynamics, revealing that collisions occurring at smaller angles involve more direct and concentrated impact forces. This heightened risk of extensive damage to vehicles and increased injury severity is crucially captured by LiDAR, providing valuable insights into the relationship between conflict angles and collision outcomes during traffic signal failures. This study underscores the efficacy of LiDAR sensor technology in understanding the nuanced dynamics of V2V conflicts and highlights its role in developing targeted safety interventions. The integration of LiDAR data into traffic safety analyses offers a sophisticated approach to mitigating the impact of conflicts and enhancing overall safety at signalized intersections during signal failures. The research also highlighted that as the angle of V2P conflicts widens, PET experiences a proportional increase. A larger conflict angle signifies a more complex interaction between vehicles and pedestrians, leading to prolonged PET values. Conversely, a decrease in the angle of V2P conflicts is associated with a reduction in PET during traffic signal failures.The study commences with an examination of traffic characteristics at the E Cold Spring Ln – Hillen Rd intersection in Baltimore City, MD, utilizing a deployed LiDAR sensor. When the signal at this intersection experienced a temporary malfunction on a regular working day in September 2023, the LiDAR captured data related to vehicle and pedestrian volumes, as well as V2V and V2P conflicts. The primary objective of the research is to evaluate the repercussions of traffic signal failures on both traffic flow and safety, specifically V2V and V2P conflicts, occurring during periods of improper signal functionality. Additionally, this investigation delves into various factors that impact traffic signal performance, encompassing traffic demand, geometric layout, pedestrian interactions, and the incorporation of emerging technologies.

Preprint ARTICLE | doi:10.20944/preprints202311.0907.v1

Assessing Knowledge Performance for the Fast Track Delivery of Sustainable Mobility Solutions

Maria Morfoulaki, Maria Chatziathanasiou

Subject: Engineering, Transportation Science And Technology Keywords: capacity building; evaluation framework; Key Performance Indicators

Online: 14 November 2023 (11:58:55 CET)

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Preprint ARTICLE | doi:10.20944/preprints202311.0906.v1

Prospective Evaluation of the Public Transport System in the Petrosani Basin

Sorin Mihailescu

Subject: Engineering, Transportation Science And Technology Keywords: mobility; traffic; sustainable transport

Online: 14 November 2023 (11:12:20 CET)

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Preprint REVIEW | doi:10.20944/preprints202311.0665.v1

Vehicle Relocation in One-Way Carsharing: A Review

Afnan Fayez Eliyan, Laoucine Kerbache

Subject: Engineering, Transportation Science And Technology Keywords: Carsharing; Vehicle balancing; Resource dimensioning; Fleet management; Trip pricing

Online: 9 November 2023 (16:44:20 CET)

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Preprint ARTICLE | doi:10.20944/preprints202311.0600.v1

A New Vehicle Specific Power Model for the Estimation of Hybrid Vehicle Emissions

Ante Kozina, Tino Vidović, Gojmir Radica, Ante Vučetić

Subject: Engineering, Transportation Science And Technology Keywords: Hybrid Electric Vehicles; Vehicles models; VSP analyses

Online: 9 November 2023 (07:17:49 CET)

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Preprint ARTICLE | doi:10.20944/preprints202310.1964.v1

Comparative Analysis of Delay Time Estimations: LiDAR Sensor Technology versus AIMSUN Microsimulation at Signalized Intersections

Alireza Ansariyar

Subject: Engineering, Transportation Science And Technology Keywords: LiDAR Sensor Technology; Signalized Intersections; Delay Time; Microsimulation; AIMSUN Software; LiDAR-Derived Data

Online: 30 October 2023 (16:32:49 CET)

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Efficient traffic management at signalized intersections is integral to urban infrastructure development, requiring accurate estimation of delay times to mitigate congestion and enhance overall transportation systems. Traditional methodologies, including empirical observations and microsimulation software, have been prevalent in assessing delay times; however, their limitations have prompted the exploration of novel technologies like LiDAR (Light Detection and Ranging) sensors. This research study investigates and compares the accuracy of delay time estimations obtained from LiDAR sensor technology with those derived from microsimulation in AIMSUN. LiDAR sensors, known for their high-resolution, real-time data collection capabilities, offer a promising avenue for precise measurement of delay times at signalized intersections. Nonetheless, challenges in sensor placement, environmental influences, and data processing complexities suggest the need for further development and validation. In parallel, microsimulation software, exemplified by AIMSUN, provides a virtual platform for scenario testing but relies on assumptions that may not always mirror real-world traffic dynamics accurately. The comparative analysis conducted in this study aims to critically examine the discrepancies and potential complementarity between delay times obtained from LiDAR sensor technology and those derived from microsimulation in AIMSUN. The research involves an in-depth evaluation of real-time, high-resolution data collected by LiDAR sensors, assessing their accuracy in capturing the intricate movements and behavior of vehicles at signalized intersections. Simultaneously, AIMSUN microsimulation delay time models are scrutinized for their ability to accurately replicate these observed delay times. The disparities identified serve as critical insights into the challenges of both methodologies, prompting the discussion on the prospects of integrating LiDAR-derived data and microsimulation calibration processes to enhance the precision and reliability of delay time estimations. Future traffic management strategies can significantly benefit from a more accurate understanding of delay times, and this study endeavors to contribute to the advancement of methodologies in traffic engineering for more effective urban transportation systems.The paper's findings illuminate the potential and limitations of both LiDAR sensor technology and microsimulation in estimating delay times at signalized intersections. The results highlighted that the LiDAR sensors could accurately calculate delay times at a signalized intersection. Furthermore, the calculated delay time differences by LiDAR and AIMSUN at three days with the highest vehicle volumes (counts) are always less than 6.5%.

Preprint ARTICLE | doi:10.20944/preprints202310.1768.v1

Efficiency Improvement at Signalized Intersections: Investigating Smart Green Time Allocation with Two LiDAR Sensors and Aimsun Microsimulation

Alireza Ansariyar

Subject: Engineering, Transportation Science And Technology Keywords: LiDAR Sensor, Signalized Intersections, Green Time Allocation, Delay Time, Microsimulation, AIMSUN Software

Online: 30 October 2023 (06:39:08 CET)

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As urban populations continue to grow, efficient traffic management becomes paramount in reducing congestion, enhancing air quality, and improving overall quality of life. This study addresses the critical issue of intersection efficiency through the implementation of smart green time allocation strategies at a signalized intersection equipped with two LiDAR sensors. This research aims to investigate optimal green time allocations provided by two LiDAR sensors and analyze the LiDAR results by microsimulation in AIMSUN. The research first introduces the concept of LiDAR-equipped signalized intersections and their potential to enhance traffic control precision. Two LiDAR sensors are strategically placed at the intersection of Marlboro Pike and Brooks Dr. in Coral Hills, MD, USA to capture real-time data on vehicle and pedestrian movements. The data are then processed to generate accurate and dynamic traffic profiles, ensuring the responsiveness of the green time allocation system to varying traffic conditions.The heart of this study lies in the integration of AIMSUN microsimulation with LiDAR data. Through meticulous modeling and simulation, the research explores the optimal green allocation at morning, mid-day, and afternoon peak hours’ scenarios to comprehensively assess the impact of LiDAR-enabled dynamic signal control. The findings demonstrate that smart green time allocation, informed by real-time LiDAR data and implemented through AIMSUN microsimulation, significantly enhances intersection efficiency. By adapting signal timings to real-time traffic demands, congestion, travel times, and emissions are reduced. Furthermore, this research highlighted that the optimal green time allocation in morning, mid-day, and afternoon peak hour intervals can improve the delay time by 55.3%, 59.7%, and 55.6%, respectively.In conclusion, this paper sheds light on the potential of LiDAR technology to transform intersection management. Through a case study involving two LiDAR sensors and AIMSUN microsimulation, it reveals the tangible benefits of dynamic signal control in enhancing intersection efficiency and creating more sustainable urban environments. These findings are pivotal in advancing the discourse on modern urban traffic management and promoting data-driven solutions for the challenges of today's cities.

Preprint ARTICLE | doi:10.20944/preprints202310.1785.v1

Research on Effective Thermal Conductivity in Porous Media with Randomly Distributed Damaged Tree-Like Bifurcation Networks

Yihao Shao, Xiuya Guo, Huili Wang, Limei Zhu, Qian Zheng

Subject: Engineering, Transportation Science And Technology Keywords: effective thermal conductivity; heat conduction; heat convection; damaged tree-like bifurcation network

Online: 27 October 2023 (11:00:32 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202310.1660.v1

The Significance of the Factors Increasing the Asphalt Pavement Recycling Rate in the Country, Determined by MCDM Methods

Henrikas Sivilevičius, Mindaugas Martišius

Subject: Engineering, Transportation Science And Technology Keywords: asphalt recycling; reclaimed asphalt pavement (RAP); recycling rate; MCDM; criteria weight; expert evaluation; opinion consistency

Online: 25 October 2023 (16:16:00 CEST)

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Transport infrastructure’s asphalt pavement deteriorates under the influence of destructive factors. Damages which have been occurred during its exploitation period are repaired, and when their further rehabilitation is economically and technically irrational, the asphalt pavement is recycled. The material from the asphalt pavement layer that has reached its limit state is milled out or broken and crushed and then is repeatedly used in the production of hot-mix asphalt (HMA) or warm-mix asphalt (WMA) mixtures. In this paper, the dynamics of the percentage recycling ratio (RR) of old asphalt pavement material was investigated. RR represents the quantity of reclaimed asphalt pavement (RAP) used in the production of HMA and WMA mixtures in Europe and the USA, divided by the total amount of RAP prepared in the country. Factors and goals affecting it are analyzed. An original system of 10 criteria that increase the RR country has been created. By applying different multiple criteria decision making (MCDM) methods and using the importance given to these criteria by 14 experts, the normalized subjective weights of the criteria were determined. Analytic Hierarchy Process (AHP), rank correlation, Average Rank Transformation into Weight - Linear (ARTIW-L) and Non-linear (ARTIW-N), Direct Percentage Weight (DPW) methods were used in the study. The results display that the RAP recycle rate is close to 100% in countries with a sustainable economic background. In the Baltic countries, it is mostly increased by the adequacy of regulatory documents, the strategy promoting asphalt recycling in the country, and the homogeneity and classifying of RAP. The number and capacity of RAP stocks, the number and productivity of asphalt milling equipment and the wear and tear of the asphalt pavement have the least influence on the increase of RR. The opinions of experts in assessing the significance of all criteria are consistent. The averages of the weights of criteria determined by four MCDM methods (AHP, ARTIW-L, ARTIW-N, DPW) made it possible to obtain more reliable results. These results can be used to make strategic decisions and to create a plan of practical actions to increase the RAP recycling rate in the developing countries.

Preprint ARTICLE | doi:10.20944/preprints202310.1601.v1

A Frontier Approach to Eco-Efficiency Assessment in the World’s Busiest Sea Ports

Muhammet Enis Bulak

Subject: Engineering, Transportation Science And Technology Keywords: ecoefficiency; maritime economy; sustainable development goals; frontier approach; maritime trasnportation

Online: 25 October 2023 (08:35:18 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202310.1512.v1

A Charging Planning Method for Shared Electric Vehicles with the Collaboration of Mobile and Fixed Facilities

Qingyu Luo, Zhihao Ye, Hongfei Jia

Subject: Engineering, Transportation Science And Technology Keywords: shared electric vehicle; mobile charging vehicle; fixed charging station; cooperative charging

Online: 24 October 2023 (10:29:08 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202310.1441.v1

Roundabout Trajectory Planning: Integrating Human Driving Models for Autonomous Vehicles

Salvatore Leonardi, Natalia Distefano

Subject: Engineering, Transportation Science And Technology Keywords: Autonomous vehicles; Human driving models; Roundabouts; Speed profiles; Traffic-free planning

Online: 24 October 2023 (07:55:00 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202310.1451.v1

Autonomous Vehicle Decision Making with Policy Prediction for Handling a Round Intersection

Xinchen Li, Levent Guvenc, Bilin Aksun-Guvenc

Subject: Engineering, Transportation Science And Technology Keywords: autonomous driving in round intersection; Partially Observable Markov Decision Process; Object Oriented Partially Observable Markov Decision Process; Partially Observable Monte-Carlo Planning

Online: 24 October 2023 (02:19:50 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202310.1401.v1

Providing a Comprehensive Traffic Safety Analysis Collected by Two LiDAR Sensors at a Signalized Intersection

Alireza Ansariyar

Subject: Engineering, Transportation Science And Technology Keywords: LiDAR sensor technology; signalized intersections; historical crash data; traffic safety; vulnerable road users

Online: 23 October 2023 (10:28:27 CEST)

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This study investigates the application of Light Detection and Ranging (LiDAR) sensor technology for data collection at signalized intersections characterized by a high rate of traffic crashes. The research aims to provide valuable insights into the potential of LiDAR-based data analysis to enhance road safety and traffic management at signalized intersections. The research methodology involved the deployment of LiDAR sensors at Marlboro Pike & Brooks Dr. signalized intersection in Coral Hills, Maryland. Two LiDAR sensors installed in this intersection to collect high-resolution, three-dimensional data of the intersection area from June, 1^st to July, 7^th 2023. The data included information on vehicle trajectories, speeds, and behaviors, as well as pedestrian and cyclist movement patterns. Concurrently, historical traffic crashes recorded and traffic flow data were obtained for the same intersection.The analysis of LiDAR data involved several key aspects including LiDAR data allowed for a precise evaluation of traffic flow patterns, including congestion points, traffic volume fluctuations, and peak-hour behavior. This information provided insights into potential factors contributing to crashes. By analyzing LiDAR data, the study identified near-miss incidents, which are often precursors to actual crashes. This proactive approach could assist in identifying crash-prone areas within the intersection. The LiDAR data analysis also focused on pedestrian and cyclist movements, including jaywalking and bike lane usage. The aim was to identify areas where infrastructure improvements could enhance safety for vulnerable road users. LiDAR data was compared with historical crash data to identify specific locations within the intersection that exhibited a high frequency of crashes. This information can guide targeted safety interventions. Last but not least, the study explored opportunities to optimize traffic signal timings based on real-time traffic data from LiDAR. Adaptive signal control could help mitigate congestion and reduce the risk of crashes.The results of this study demonstrated the potential of LiDAR sensor technology in collecting detailed data for traffic analysis in signalized intersections. By combining LiDAR data with historical crash records and traffic flow data, traffic engineers and urban planners can develop evidence-based strategies to reduce the frequency and severity of crashes in high-risk areas. Ultimately, this research contributes to a comprehensive understanding of how LiDAR technology can be employed to enhance road safety and traffic management, providing valuable insights for traffic engineers, urban planners, and policymakers seeking to improve the safety and efficiency of signalized intersections with a history of high traffic crashes.

Preprint ARTICLE | doi:10.20944/preprints202310.1408.v1

Enhancing Intersection Efficiency through Smart Green Time Allocation at a Signalized Intersection Equipped with Two LiDAR Sensors

Alireza Ansariyar

Subject: Engineering, Transportation Science And Technology Keywords: LiDAR sensor technology; signalized intersections; green time allocation; delay time; vehicle volume

Online: 23 October 2023 (10:21:44 CEST)

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Traffic signal control plays a key role in managing urban traffic flow, enhancing safety, and minimizing congestion and conflicts. Effective green time allocation is a critical element of this control process. This research explores the utilization of LiDAR sensor technology in the optimization of green time allocation for one phase of a traffic signal at a signalized intersection. LiDAR sensors provide precise and real-time data on vehicle presence and traffic patterns, enabling a data-driven approach to traffic signal control. The study begins with an analysis of the limitations of traditional traffic signal control strategies, which often rely on fixed-time plans or rudimentary vehicle detection systems. These approaches can lead to suboptimal green time allocation, resulting in inefficient traffic management and increased vehicle delays.The integration of LiDAR sensors provides detailed information on vehicle queues, arrival rates, and vehicle types. The research presents a practical framework for green time allocation optimization, considering factors such as intersection geometry, traffic volumes, and signal coordination. An intelligent control algorithm was developed that uses LiDAR data to determine the optimal green time for a specific phase, thereby reducing unnecessary waiting times and enhancing intersection efficiency. The effectiveness of the proposed LiDAR-based green time allocation strategy is demonstrated through extensive field tests. The results indicate significant improvements in intersection throughput, reduced delays, and enhanced traffic safety. In conclusion, this research highlights the transformative potential of LiDAR sensor technology in traffic signal control, specifically in the context of optimizing green time allocation. The findings support the notion that adaptive and data-driven strategies, when integrated with LiDAR sensors, can contribute to more efficient, sustainable, and safe urban traffic management. This research aims to provide valuable insights for transportation engineers, policymakers, and researchers seeking innovative solutions for modern urban traffic control.

Preprint ARTICLE | doi:10.20944/preprints202310.1431.v1

Real-Time Traffic Control and Safety Measures Analysis Using LiDAR Sensor during Traffic Signal Failures

Alireza Ansariyar

Subject: Engineering, Transportation Science And Technology Keywords: LiDAR sensor technology; signalized intersections; traffic signal failure; traffic safety; congestion; environmental pollution

Online: 23 October 2023 (10:20:28 CEST)

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Traffic congestion is a persistent and challenging problem in urban areas, leading to increased travel times, fuel consumption, and environmental pollution. Signalized intersections play a pivotal role in regulating traffic flow, and their efficiency has a direct impact on the overall traffic performance of a city. This study investigates the effect of traffic signal in managing traffic volume and reducing congestion and delays at signalized intersections through a comprehensive analysis of existing research, data collection, and simulations.The research begins by analyzing the traffic characteristics by an installed LiDAR sensor at E Cold Spring Ln – Hillen Rd intersection in Baltimore City, MD. When the signal at this intersection stopped working for some hours during a working day in September 2023, the LiDAR recorded vehicle and pedestrian counts, vehicle-vehicle and vehicle-pedestrian conflicts, and jaywalking events conflicts. The research aims to assess the impact of traffic signal failures on traffic flow, congestion, safety (V2V and V2P conflicts), and the frequency of jaywalking events before, during, and after improper performance of the traffic signal. Furthermore, this study explores the factors influencing traffic signal performance, including traffic demand, geometric layout, pedestrian interactions, and the integration of emerging technologies. The analysis results highlighted the importance of signal control systems existence at this intersection that can adjust signal timing in response to changing the real-time traffic conditions.Reduced congestion, minimized delays, and enhanced traffic flow are observed outcomes, contributing to a more sustainable and efficient urban transportation system. However, it is crucial to consider the trade-offs and challenges associated with traffic signal optimization, such as the potential for increased travel times for certain modes of transportation and the need for ongoing maintenance and updates. In conclusion, this study underscores the pivotal role of traffic signals in managing traffic volume and reducing congestion and delays at signalized intersections. Through evidence-based analysis and innovative signal control strategies, urban planners and transportation authorities can work towards creating more efficient, sustainable, and less congested transportation networks. The insights derived from this research can inform policy decisions and guide the development of future traffic management solutions, ultimately leading to improved quality of life in urban areas.

Preprint ARTICLE | doi:10.20944/preprints202310.1386.v1

Visualizing Numerical Features: A Novel Approach to Airport Delay Prediction

Ligang Yuan, Jiazhi Jin, Haiyan Chen, Linghui Zhang, Yang Zeng

Subject: Engineering, Transportation Science And Technology Keywords: airport delay prediction; convective weather; image recognition; deep convolutional neural network; taxonomic imbalance

Online: 23 October 2023 (09:57:22 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202310.1125.v1

Influence of Sensor Position for Measurement of Lateral Acceleration of Delivery Van

Juraj Jagelcak, Jaroslava Kubanova

Subject: Engineering, Transportation Science And Technology Keywords: acceleration; cargo securing; MEMS accelerometer; ground vehicle safety; road safety; sensors for vehicle movement; vehicle dynamics

Online: 18 October 2023 (09:44:43 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202310.0709.v1

A Study on the Feasibility of LiFi in an Intra-Vehicular Data Transmission Application

Hongyu Guan, Manolo Dulva Hina

Subject: Engineering, Transportation Science And Technology Keywords: LiFi; Light Fidelity; wireless communication; intra-vehicular data transmission; VLC; intelligent transportation system; LED; optics

Online: 11 October 2023 (09:20:55 CEST)

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Preprint REVIEW | doi:10.20944/preprints202310.0413.v1

Enhancing Urban Air Mobility Integration in Cargo Transportation through Tiltrotor Technology

Saheed Adebimpe

Subject: Engineering, Transportation Science And Technology Keywords: Urban Air Mobility (UAM); Tiltrotor Technology; Cargo Logistics; Metropolitan Areas Aerial Vehicles; Vertical Takeoff And Landing (VTOL); Safety, Delivery Times; Autonomous Aerial Cargo Delivery

Online: 8 October 2023 (02:17:14 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202310.0193.v1

Method for theoretical Assessment of Safety against Wagon Derailment

Valeri Stoilov, Sanel Purgic, Svetoslav Slavchev, Vladislav Maznichki

Subject: Engineering, Transportation Science And Technology Keywords: railway; freight wagon; derailment; safety; assessment

Online: 4 October 2023 (11:18:30 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202309.2090.v1

Investigation on the Performance of Fire and Smoke Suppressing Asphalt Materials for Tunnels

Jiaquan Li, Fei Liu, Mingjun Hu, Changjun Zhou, Liujingyuan Su, Peng Cao

Subject: Engineering, Transportation Science And Technology Keywords: asphalt mastic; asphalt mixture; flame retardant - smoke suppressant; performance

Online: 30 September 2023 (10:13:04 CEST)

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Variety of harmful gases are produced in asphalt mixture after mixing, paving and rolling process. Effective measures must be tak-en to suppress the asphalt pavement in the tunnel due to fire accidents and other toxic gases and fumes, reducing the human health during the construction process. In this study, a flame retardant and smoke suppressant (compound) with Mg(OH)2 as the main component was developed, the flame retardant asphalt mixture and asphalt mastics were prepared to evaluate the flame retard-ant-smoke suppressant properties and performance effects. Firstly, its low and high temperature performances were investigated with the bending beam rheometer (BBR) and dynamic shear rheological (DSR), respectively. Then, the indoor combustion test and the cone calorimeter test were used to evaluate the fire retardant smoke suppression effect of the asphalt mastic. Thirdly, the flame retardant effect of asphalt mastic mixed with the compound was further analyzed by thermogravimetric (TG) test and scanning electron microscopy (SEM). The pyrolysis temperature, mass loss and microscopic state of asphalt surface were used to verify and explain the flame retardant reaction effect and process of the compound. Finally, the asphalt mixture performance was evaluated, as well as the flame retardant smoke suppression effect was verified by asphalt mixture combustion tests. The results showed that the flame retardant smoke suppression time of the flame retardant asphalt mixture was reduced by 66% and the smoke emission area was reduced by 20%. The flame retardant smoke suppression effect of the asphalt mixture was improved by 44%. The flame-retardant and smoke-suppressing compound and the asphalt mixture with the compound prepared in this study meet the asphalt mixture performance and flame retardant smoke suppression function, providing an option for application of fire retardant and smoke-suppressing asphalt pavement materials in tunnels.

Preprint ARTICLE | doi:10.20944/preprints202309.2004.v1

Impact of Port Clearance on shipping Safety, Energy Consumption and Green Ports Sustainability

Vytautas Paulauskas, Viktoras Senčila, Donatas Paulauskas, Martynas Simutis

Subject: Engineering, Transportation Science And Technology Keywords: maritime safety; energy efficiency; green and sustainable port; ships safety; environmental impact; emissions

Online: 28 September 2023 (11:02:02 CEST)

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Preprint COMMUNICATION | doi:10.20944/preprints202309.1512.v1

Analysis of the Usefulness of Cheap Audio Recorders for Spectral Measurement of Environmental Noise

Jacek Dominik Skibicki, Roksana Licow, Natalia Karosińska-Brzozowska, Karol Daliga, Piotr Chrostowski, Andrzej Wilk, Krzysztof Karwowski, Marek Szafrański, Tadeusz Widerski, Leszek Jarzebowicz, Slawomir Judek, Michał Michna, Sławomir Grulkowski, Julia Omilianowicz

Subject: Engineering, Transportation Science And Technology Keywords: environmental noise measurement; spectral analysis; sound measurement devices; railway transport

Online: 22 September 2023 (11:16:32 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202309.1341.v3

Improving Autonomous Vehicle Reasoning with Non-Monotonic Logic: Advancing Safety and Performance in Complex Environments

Jamal Raiyn, Galia Weidl

Subject: Engineering, Transportation Science And Technology Keywords: car–following; non-monotonic logic; reasoning; naturalistic driving studies; safetycritical events; cognitive vehicles

Online: 22 September 2023 (05:30:08 CEST)

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The software design of autonomous vehicles (AVs) incorporates artificial intelligence (AI) characteristics to enhance their safety and overall driving performance. Central to vehicle’s operation is the ability to reason effectively in complex and uncertain environments. However, traditional logical systems, such as monotonic logic, often struggle to handle the inherent uncertainties and exceptions encountered in real-world scenarios. This paper proposes the utilization of non-monotonic logic in order to enhance the reasoning capabilities of autonomous vehicles. By incorporating non-monotonic reasoning, vehicles can navigate intricate traffic scenarios, make plausible inferences, and adapt their decisions when faced with conflicting information. This research aims to provide a comprehensive review of non-monotonic logic's application in autonomous vehicles, highlighting its advantages over traditional logical systems and its potential impact on safety and performance. Additionally, through this research, we seek to contribute to the advancement of autonomous driving technology by enhancing the reasoning capabilities of vehicles in various scenarios, such as car- following related to critical safety events. The personalized cognitive agent is proposed in driving behavior to consider particularly in their assumptions of homogeneous drivers. The personalized cognitive agent is incorporating heterogeneous driving behaviors, based on individual user preferences, characteristics, and needs. Driving behavior is a complex interplay of various factors, encompassing both human and external elements. Human factors, including age, experience, and gender, contribute significantly to how individuals navigate the roads. These factors influence decisions, reactions, and risk-taking tendencies on the part of drivers. Additionally, external factors such as weather conditions further compound this intricate dynamic, requiring drivers to adapt their behavior to the prevailing environment. The goal of a personalized cognitive agent is to provide tailored and customized experiences to cognitive vehicles, taking into account the unique requirements and individual preferences of occupants inside autonomous vehicles.

Preprint ARTICLE | doi:10.20944/preprints202309.1341.v2

Improving Autonomous Vehicle Reasoning with Non-Monotonic Logic: Advancing Safety and Performance in Complex Environments

Jamal Raiyn, Galia Weidl

Subject: Engineering, Transportation Science And Technology Keywords: car–following; non-monotonic logic; reasoning; naturalistic driving studies; safetycritical events; cognitive vehicles

Online: 21 September 2023 (08:26:57 CEST)

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The software design of autonomous vehicles (AVs) incorporates artificial intelligence (AI) characteristics to enhance their safety and overall driving performance. Central to vehicle’s operation is the ability to reason effectively in complex and uncertain environments. However, traditional logical systems, such as monotonic logic, often struggle to handle the inherent uncertainties and exceptions encountered in real-world scenarios. This paper proposes the utilization of non-monotonic logic in order to enhance the reasoning capabilities of autonomous vehicles. By incorporating non-monotonic reasoning, vehicles can navigate intricate traffic scenarios, make plausible inferences, and adapt their decisions when faced with conflicting information. This research aims to provide a comprehensive review of non-monotonic logic's application in autonomous vehicles, highlighting its advantages over traditional logical systems and its potential impact on safety and performance. Additionally, through this research, we seek to contribute to the advancement of autonomous driving technology by enhancing the reasoning capabilities of vehicles in various scenarios, such as car- following related to critical safety events. The personalized cognitive agent is proposed in driving behavior to consider particularly in their assumptions of homogeneous drivers. The personalized cognitive agent is incorporating heterogeneous driving behaviors, based on individual user preferences, characteristics, and needs. Driving behavior is a complex interplay of various factors, encompassing both human and external elements. Human factors, including age, experience, and gender, contribute significantly to how individuals navigate the roads. These factors influence decisions, reactions, and risk-taking tendencies on the part of drivers. Additionally, external factors such as weather conditions further compound this intricate dynamic, requiring drivers to adapt their behavior to the prevailing environment. The goal of a personalized cognitive agent is to provide tailored and customized experiences to cognitive vehicles, taking into account the unique requirements and individual preferences of occupants inside autonomous vehicles.

Preprint ARTICLE | doi:10.20944/preprints202309.1341.v1

Improving Autonomous Vehicle Reasoning with Non-Monotonic Logic: Advancing Safety and Performance in Complex Environments

Jamal Raiyn

Subject: Engineering, Transportation Science And Technology Keywords: car –following, non-monotonic logic, reasoning, naturalistic driving studies, safety-critical events, cognitive vehicles

Online: 20 September 2023 (08:46:34 CEST)

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The software design of autonomous vehicles (AVs) incorporates artificial intelligence (AI) characteristics to enhance their safety and overall driving performance. Central to vehicle’s operation is the ability to reason effectively in complex and uncertain environments. However, traditional logical systems, such as monotonic logic, often struggle to handle the inherent uncertainties and exceptions encountered in real-world scenarios. This paper proposes the utilization of non-monotonic logic in order to enhance the reasoning capabilities of autonomous vehicles. By incorporating non-monotonic reasoning, vehicles can navigate intricate traffic scenarios, make plausible inferences, and adapt their decisions when faced with conflicting information. This research aims to provide a comprehensive review of non-monotonic logic's application in autonomous vehicles, highlighting its advantages over traditional logical systems and its potential impact on safety and performance. Additionally, through this research, we seek to contribute to the advancement of autonomous driving technology by enhancing the reasoning capabilities of vehicles in various scenarios, such as car- following related to critical safety events. The personalized cognitive agent is proposed in driving behavior to consider particularly in their assumptions of homogeneous drivers. The personalized cognitive agent is incorporating heterogeneous driving behaviors, based on individual user preferences, characteristics, and needs. Driving behavior is a complex interplay of various factors, encompassing both human and external elements. Human factors, including age, experience, and gender, contribute significantly to how individuals navigate the roads. These factors influence decisions, reactions, and risk-taking tendencies on the part of drivers. Additionally, external factors such as weather conditions further compound this intricate dynamic, requiring drivers to adapt their behavior to the prevailing environment. The goal of a personalized cognitive agent is to provide tailored and customized experiences to cognitive vehicles, taking into account the unique requirements and individual preferences of occupants inside autonomous vehicles.

Preprint ARTICLE | doi:10.20944/preprints202309.1071.v1

Future Cities Carbon Emission Models: Hybrid Vehicle Emission Modelling for Low-Emission Zones

Maksymilian Mądziel

Subject: Engineering, Transportation Science And Technology Keywords: vehicles; emission modelling; traffic simulation; air pollution; future cities; neural network

Online: 18 September 2023 (02:34:42 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202309.0811.v1

Demographic Analysis of Active Travel Users in Urban Context

Ahmed Jaber, Jamil Hamadneh

Subject: Engineering, Transportation Science And Technology Keywords: Active travel; mode choice; micro-mobility; shared mobility; walking

Online: 13 September 2023 (16:09:05 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202309.0836.v1

Modeling the Interaction of the Rolling Stock and the Track in the Emergency Braking Mode of a Passenger Train

Vladimir Solonenko, Janat Musayev, Seitbek Zhunisbekov, Algazy Zhauyt, Batyrkhan Kyrykbayev, Gulbarshyn Smailova, Ulbala Murzakhmetova, Saltanat Yussupova

Subject: Engineering, Transportation Science And Technology Keywords: wagon; passenger train; emergency braking; modeling; analysis; recommendations

Online: 13 September 2023 (09:58:15 CEST)

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