Transport Modes Research Articles

Exploring the relationship between built environment and bike-sharing demand: Does the trip length matter?

ABSTRACT Over the past decade, bike-sharing has garnered significant attention in both research and practice. As a manpower-driven transportation mode, the usage of bikes seems more sensitive to trip length, since one could take a shared bike to a destination where is too far to walk, or choose it for simply replacing walking when going to a nearby place. This paper identifies a threshold of bike-sharing trip lengths from bike-sharing trace data, and employs the Semiparametric Geographically Weighted Poisson Regression model to investigate the relationship between built environment and bike-sharing demand with different lengths by considering the heterogeneity in the relationship. Results show that built environment has heterogeneous effects on the bike-sharing demand in urban areas, and the effects differ across groups with trip lengths. The findings contribute to understanding the relationships between built environment and bike-sharing demand, and providing support for the placements and dispatching of shared bikes.

‘Travelling for $1 a day!?’ Introducing rental vehicle relocation as a tourism practice

ABSTRACT The objective of this paper is to introduce and explore the concept of rental vehicle relocation (RVR) as a mode of transport and beyond for (drive) tourists. By situating RVR within the domain of drive tourism, we contextualise its emergence amidst current scholarly discourses. Methodologically, we adopt a dual-methods strategy that integrates autoethnographic and netnographic methods to collect material in the specific context of New Zealand. The findings shed light on the multifaceted role of RVR, highlighting its significance as a mode of transport, an attraction for travellers, and a unique tourist experience. Our research makes theoretical contributions towards establishing RVR in a distinct position within the tourism ecosystem. Furthermore, this research presents practical implications for RVR platforms and rental car providers at large to acknowledge, refine, and promote RVR services.

WALKING FOR HEALTH & WELLBEING: THE EFFECT OF STEP FREQUENCY

Walking as a mode of transport and recreation to get more active and stay fit. People of all ages can benefit from walking, evaluation of earlier research in this field shows that a study of walking specifically among those with different health challenges in Malaysia is lacking. This paper sets out to identified whether individual, environmental and social factors influence walking in neighbourhood, which is the objectives: to examine whether people in a neighbourhood walk during the week, to identify whether people who have health problems walk more or not at all, and to examine the relationship between people’s frequency of walk and their physical wellbeing. Data from 410 adults of between 18 and 60 years of age are analysed using a multiple linear regression analysis technique. Overall, the findings reveal that the respondents regularly walk despite their health status as none of them (0.00%) have assessed their health as being poor to begin with. Nevertheless, there is a significant correlation between self-reported health states and walking (X=22.519, p0.05), and their frequency of walk (X= 22.673, p0.05). The results indicate that 19.40% of the respondents walk regularly on a weekday compared to 21.20% on the weekend only, and 18.20% during both weekday and weekend. This study suggests that the neighbourhood must be safe, and the built environment is conducive and appropriate for proper walking. These factors are pertinent considerations for both housing developers and state policies to implement to initiate and promote more walking for physical wellbeing, recreation as well as for transportation purposes.

Laboratory and numerical investigations on land-sourced solute transport in coastal fractured aquifers

Land-based pollutants threaten coastal aquifers, highlighting the need to protect groundwater and nearshore marine ecosystems. While aquifer heterogeneity has been recognized as a significant factor affecting solute behavior, the impact of fractures on land-sourced solute transport in coastal aquifers remains unclear. This study attempted to address this issue through laboratory experiments and discrete fracture matrix (DFM) models. The impact of horizontal fractures on the temporal and spatial characteristics of solute transport, spreading, and discharge under seawater intrusion was analyzed based on variations in fracture position and length. The results show that fractures/low-velocity zones (LVZ) can accelerate/delay solutes, dividing them into different transport modes and enhancing/prolonging their spreading/discharge duration. Changes in fracture position and length also affect its transport acceleration and path deviation abilities, which ultimately determine when solute discharge occurs. The mixing zone and unsaturated zone, in addition to the LVZ, hinder solute transport, reducing the rate and delaying the end of solute discharge. Meanwhile, fractures facilitate solute transport into the saltwater wedge, expanding the solute discharge zone. However, if solutes are initially within the LVZ, their entry into the fracture rely on their distance from the fracture's near-land edge and the size of the fracture's convergence zone.

Last-train timetable synchronization for service compatibility maximization in urban rail transit networks with arrival uncertainties

The last train services of urban rail transit offer all passengers the final chance to reach their destinations. In the context of multimodal transport, considering the random delayed arrivals of late-night vehicles of other transport modes at urban hub stations and their adverse effects on multimodal passengers transferring to urban rail transit, a probabilistic scenario set is established to determine the arrival uncertainties of multimodal passengers. In each scenario, the timetable synchronization problem is formulated as a mixed-integer nonlinear programming model that requires a performance trade-off between destination reachability and the remaining path distance of both non-multimodal and multimodal passengers, integrally termed last train service compatibility. Through linearization techniques, the model is transformed into an equivalent mixed-integer linear programming form, which can be efficiently solved using the Gurobi solver to obtain the optimal last train timetable for each scenario and form an alternative scheme set. An improved probabilistic scenario set-based regret value theory is then developed, in which a novel regret value calculation method is proposed. The scheme with the minimum total weighted opportunity loss in all scenarios is selected as the optimal robust. Real case experiments based on the Chengdu–Chongqing high-speed railway line and Chengdu metro network are conducted to test the performance of our model. The results show that compared with the original timetable, the optimized timetable reduces the number of unreachable passengers by 34.29% and the sum of the average remaining path distance of non-multimodal and multimodal passengers by 57.43% with the help of path planning for all passengers. The proposed approach is proven not only to balance the demands of both reachable and unreachable passengers, but also to significantly improve the robustness of the last train timetable to arrival uncertainties of multimodal passengers and reduce their service inequity.

Nutrient dynamics and biogeochemical processes at the Polar front of Indian sector of the Southern Ocean: Influence of Circumpolar deep water intrusion

The Circumpolar Deep water (CDW) is one of the largest water masses in the Southern Ocean. The cross-frontal flow of the CDW is a vital mode of redistribution and transport of heat, salts, nutrients, etc. in the ocean. The present study aims to understand the role of the CDW intrusion on nutrient dynamics and biogeochemical processes at the Polar frontal region. The study noted a patch of high nutrients layer at ∼300-400 m in the Polar Front-II, underlying the dicothermal layer or the temperature minimum layer. This high nutrient patch was attributed to the intrusion of the CDW at the Polar front, influencing the physico-chemical processes in the water column. It was evident that, the density gradient, stratification and overall, the combined effect of physico-chemical and biological factors as a consequence of intrusion of the CDW contributed to the formation of the high nitrate patch at the Polar Front-II. Even though, the CDW intrusion did not significantly impact the photic layer dynamics during this study, the possible barrier effect during austral summer contribute to the formation of the high nitrate layer. This feature may be responsible for trapping nutrients in the subsurface due to stratification, made available to the photic region via upwelling processes, consequently triggering changes in the biological community structure and biogeochemical processes.

Bridge Detection in Autonomous Shipping: A YOLOv8 Approach with Autodistill and GroundedSAM

Abstract Inland waterway transportation remains a crucial and cost-effective mode of global transport despite challenges such as skilled personnel shortages, increasing competition, and ecological impacts. Autonomous technology enhances vessel navigation, scheduling, safety, and efficiency, making it a viable solution for smart ship and port operations. Reliable object detection is essential for autonomous ships to navigate safely and avoid collisions with static structures like bridges, piers, bollards, and locks. This paper presents an innovative approach for training a network using the Autodistill pipeline for bridge detection and segmentation. We generate a labeled bridge dataset using GroundedSAM, which integrates Grounding DINO and the Segment Anything Model (SAM) to detect and segment regions based on text input. The system focuses on identifying ‘bridge’ and ‘water’ classes, producing high-quality labeled data. Manual filtering improves label quality, enhancing the training of the YOLOv8 model, known for its superior object detection capabilities. Our approach demonstrates high performance in accurately detecting bridges, confirmed through evaluations with and without manual filtering. To validate our solution’s feasibility in real-world applications, we deployed the model on a NVIDIA Jetson AGX Orin for performance evaluation. Future work will extend this approach to additional static and mobile objects relevant to smart ship and port operations, such as ship locks and various ship types.

ENVIRONMENTAL PLANNING AND DESIGN: EXPLORING URBAN RESILIENCE THROUGH E-HAILING

Sustainable cities strive for balance in environmental health, economic vitality and social equity through efficient and resilient urban planning. Crucial to this balance, sustainable transportation systems reduce carbon emissions, ease traffic congestion and promote clean energy. Among various sustainable transportation options, e-hailing has gained global popularity, offering convenient rides through smartphone apps. This quantitative research focuses on exploring the usage patterns of e-hailing services among university students at the main campus of Universiti Sains Malaysia. A total of 392 university students were surveyed using stratified random sampling, and SPSS analysis revealed a moderate positive correlation between attitudes towards e-hailing and satisfaction with safety, price, convenience and availability. Furthermore, regression analysis confirmed significant relationships between attitude and satisfaction levels. By investigating how travel behaviour patterns and attitudes towards e-hailing influence student satisfaction, the study aims to understand e-hailing’s role as an alternative mode of transportation in enhancing overall transportation service satisfaction. These insights offer valuable guidance for transport planners, e-hailing companies and university administrations seeking to improve transportation options and student satisfaction.

Seasonal variations and temporal instability of motorcyclist injury severity in Cambodia: Analyses based on a random parameter logit model with heterogeneity in means and variances

Motorcycles are a prevalent mode of transportation in countries like Cambodia that experience distinct rainy and dry seasons. However, the safety concerns associated with motorcycling in this region have not been thoroughly investigated. This study addresses this research gap by examining the severity of motorcyclist injuries in Cambodia, considering the potential variations across seasons and the fluctuations in contributing factors over time. Utilizing a random parameter logit model with heterogeneity in means and variances, the research analyzes motorcycle crash data from 2015 to 2017 to identify heterogeneities in the determinants of injury severity. The study confirms seasonal variations and temporal instabilities in influential factors, highlighting the need for distinct modeling for dry and rainy seasons due to varying contributing factors. Key findings include the consistent increase in fatal injury risk associated with head-on collisions and elderly rider involvement across both seasons. During the rainy season, motorcycle-to-motorcycle crashes significantly heighten the likelihood of severe injuries, with weekend crashes more likely to result in fatalities. Furthermore, more than half of speeding incidents during the rainy season consistently led to fatal injuries across all three years. In contrast, during the dry season, riders faced a greater risk of severe injuries compared to pillion riders, with crashes on national roads more likely to lead to fatal outcomes. Temporal stability tests further reveal that the influence of external variables on motorcyclist injury severity varies across years, stressing the need for tailored, season-specific approaches to effectively mitigate and prevent crashes.

A novel method for evaluating ship concept performance in transport systems

Abstract Transport carries significant external costs such as climate change, accidents, pollution, and road congestion, driving national and international strategies for the development of new transport concepts. This includes shifting larger cargo volumes away from roads to more sustainable transport modes such as waterborne. The SEAMLESS project was launched in 2023 to answer to these needs by developing technology for autonomous waterborne zero-emission feeder-loop services. The realisation of such services depends on their modal competitiveness. Autonomous ships are expected to reduce transport costs and emissions, and ultimately improve logistical performance. There are, however, few published studies that quantify these impacts of autonomy. Furthermore, commercial waterborne autonomous transport services do not exist yet, limiting the possibilities for empirical analysis. Hence, research is needed to address exactly how and to what extent, autonomy improves competitiveness in different applications. This paper addresses the need for more empirical analyses of innovative waterborne transport performance, by presenting a novel method for evaluating ship concept performance in transport systems. The impacts of design choices are captured through hydrodynamic and logistical simulations. The method can be applied to transport systems consisting of both conventional and novel ship designs, operating on one or more routes including transhipments. It is implemented in the software SIMPACT and applied to a case study which establishes a shortsea feeder-loop service in the Bergen area in Norway. The Bergen municipality has decided that the container terminal is to be moved out of the city centre to reduce local traffic and emissions. However, in the absence of a competitive waterborne transport service in this region, the relocation will have the unfortunate consequence of an estimated annual net increase in regional truck traffic of 40,000 additional truck trips over 25km. By means of the proposed methodology, this paper investigates the feeder-loop concept and compare its quantified performance to truck transport and finds that competition is feasible.

Transportation Mode Recognition Based on Low-Rate Acceleration and Location Signals With an Attention-Based Multiple-Instance Learning Network

Transportation Mode Recognition Based on Low-Rate Acceleration and Location Signals With an Attention-Based Multiple-Instance Learning Network

Impacts of the sea-rail intermodal transport policy on carbon emission reduction: The China case study

China introduced the Intermodal Transport Demonstration Project Policy in 2015, spurring the rapid expansion of sea-rail intermodal transport (SRT) at coastal ports. This policy is of crucial importance as SRT is pivotal in supporting the transport sector's achievement of the “double carbon target”. Despite strong policy backing and notable growth of SRT, the impact of these SRT demonstration projects on curbing transport carbon emissions has not been comprehensively evaluated. This study addresses this gap by using panel data from hub seaports across nine provinces and applying a multi-period-continuous difference-in-differences model. The research quantifies SRT's contribution to reducing transport carbon emission reductions as a result of the policy. Key findings indicate: (1) Each 10,000 TEU increase in containerized SRT volume results in a 0.162% reduction in transport carbon emissions, with projected growth by 2025 leading to a 9.02% decrease in emissions. (2) SRT lowers transport energy consumption and carbon emission intensity, fostering a green transformation in energy use. (3) Geographical analysis indicates that policy implementation in the southern region requires further strengthening. This study enhances empirical understanding of low-carbon SRT development in hub ports, emphasizing its critical role in China's transition to decarbonized transport. It also highlights the importance of regional policy effectiveness, offering valuable insights for policymakers striving to promote sustainable transport modes.

Exploring intercity travel decision-making in a developing country: Insights from COVID-19 impacts in Iran

This study examines the impact of COVID-19 on intercity travel mode choices in Mazandaran province, Iran, addressing critical gaps in understanding how pandemics affect travel behavior in developing countries. The research focuses on how socio-economic factors, perceived health risks, and travel time influence individuals’ choice of transportation modes during the pandemic. Using a stated preference survey method with 669 participants, the study assessed how concerns about virus transmission and adherence to health protocols shape travel decisions. Discrete choice modeling was employed to predict travel mode shares between public transport and personal vehicles. The findings reveal that COVID-19 risk perception, socio-economic factors, and travel time significantly impact travel behavior. Specifically, heightened perceived risk of infection resulted in a 25% reduction in public transportation use, with individuals increasingly opting for personal vehicles. Additionally, strict adherence to health protocols, such as mask-wearing and vehicle cleaning, improved safety perception, leading to a 40% increase in confidence in public transport. The study also found that socio-economic factors like age, income, and education significantly shaped travel preferences. These insights provide valuable guidance for public health policymakers and transportation authorities to enhance the safety and management of intercity travel during ongoing and future pandemics.

A systematic literature review of factors influencing the regulation of autonomous inland shipping in Europe

The onset of autonomous inland shipping comes with regulatory challenges. First, regulations currently in place do not permit the operation of autonomous ships, mainly due to crewing requirements. Second, autonomous transport modes are deemed to be disruptive technologies that present a particular challenge to public regulators with regard to their potential for unforeseen risks and uncertainties inherent to their implementation. From a public regulator’s perspective, it is, therefore, of the utmost importance to identify and regulate the factors currently hindering the introduction of autonomous inland shipping, on the one hand, and the newly emerging issues, on the other hand, to minimise potential risk and uncertainty as much as possible. This paper presents a systematic literature review which was conducted to answer the research question: What factors influence the regulation of autonomous inland shipping? Following the outcome of the literature review, various factors could be identified, and four main categories of factors were established: technological, infrastructure, institutional and socio-economic readiness. The identified factors were subsequently discussed with regard to their significance for the regulatory agenda in light of factors found in the literature on autonomous maritime ships. The findings are of relevance for public regulators and policymakers working in the field of autonomous inland shipping. Moreover, the results presented in this paper might be also of interest for those seeking regulatory innovation regarding autonomous processes in other modes of transport.

Selected applications of satellite technologies in rail transport

Global Navigation Satellite Systems (GNSS) are increasingly being used in various modes of transport, including rail transport. When this technology is applied to railway traffic control, it is essential to ensure a high level of safety and reliability. The approval of a railway traffic control system requires a safety analysis, which includes hazard analysis and risk analysis. This also includes GNSS-based solutions in terms of their compliance with safety integrity requirements, i.e. THR (Tolerable Hazard Rate) and SIL (Safety Integrity Level) parameters, as defined in normative documents. In the case of railway traffic control systems, the level of dependability of the determined train position, referred to as position integrity, is very important in ensuring safety. Position integrity is affected by many factors, including: errors due to SIS (Signal-In-Space) propagation, multipath errors, signal interference or GNSS receiver errors. In order to improve position integrity, among other things, new data processing methods can be used to improve the accuracy and reliability of measurements. The paper presents the concept of satellite signal processing for precise determination of the position of objects in selected railway systems. The Kalman filter based model of satellite signal filtration and its selected applications, which were tested in the real condition, was presented. The application of Kalman filtration indicated in the paper is a universal method that improves the estimated measurement parameters and can be used in many applications of satellite systems for railway tasks. The applicability of satellite systems to automatic train operation, defect positioning in automatic and manual flaw detection tests, determining track spatial orientation and train integrity control have been considered. The conducted tests confirmed the correctness of the adopted concept and the model of satellite signals filtration developed for this purpose. According to the authors, the described methods can also be used in many other tasks related to rail transport.

ASSESSING THE POTENTIAL OF RAILWAYS TO REDUCE CO2 EMISSIONS: A COINTEGRATION STUDY OF ESTONIA'S TRANSPORT SECTOR

This The transportation sector is a major contributor to CO2 emissions, playing a significant role in exacerbating climate change. Railways, known for their greater energy efficiency compared to other modes of transport, present a viable solution for mitigating emissions, particularly in countries like Estonia. In 2014, transport-related CO2 emissions in Estonia were recorded at 12.7%, marking an overall upward trend since 1995, despite experiencing some fluctuations over the years. This study aims to explore the long-term equilibrium relationship between CO2 emissions from transport and railway passenger kilometers in Estonia through the application of cointegration analysis. Utilizing Engle-Granger and Phillips-Ouliaris cointegration tests, the analysis reveals a stable, long-term equilibrium relationship between the two variables. The findings suggest that an increase in railway passenger kilometers could potentially lead to a reduction in CO2 emissions from transport. These results underscore the importance of enhancing railway infrastructure and usage as a strategic measure to curb transportation-related emissions and contribute to environmental sustainability.

Does the type of transportation mode matter in the effect of freight transportation on the environmental pollution of OECD countries?

This study aims forward to explain the effect of freight transportation on environmental pollution and to investigate the role played by the usage rates of road and railway transportation modes in this relationship. As an auxiliary problem of the research, it was tried to determine whether there is a threshold value for these types of transportation and what consequences will arise in terms of environmental pollution below and above this threshold. For this purpose, the data of OECD countries for the period 2000-2018 were analyzed with the PSTR method. Analysis results showed a non-linear relationship between freight transportation and environmental pollution and that the road/rail transportation ratio plays an important role in this relationship. The determined threshold value is 68.18% for road freight transport and 29.55% for rail freight transport. Below the determined threshold values, as the amount of cargo transported increases, the rate of carbon emission increase gradually increases in both types of transport. Above the threshold values, road transport increases the rate of increase even more, while rail transport decreases this rate of increase. Within the scope of these results, countries should develop policies to increase the use of railways in freight transport while reducing the use of roads.

An Integrated Framework for Estimating Origins and Destinations of Multimodal Multi-Commodity Import and Export Flows Using Multisource Data

Estimating origin-destination (OD) demand is integral to urban, regional, and national freight transportation planning and modeling systems. However, in developing countries, existing studies reveal significant inconsistencies between OD estimates for domestic and import/export commodities derived from interregional input-output (IO) tables and those from regional IO tables. These discrepancies create a significant challenge for properly forecasting the freight demand of regional/interregional multimodal transportation networks. To this end, this study proposes a novel integrated framework for estimating regional and international (import/export) OD freight flows for a set of key commodities that dominate long-distance transportation. The framework leverages multisource data and follows a three-step process. First, a spatial economic model, PECAS activity allocation, is developed to estimate freight OD demand within a specific region. Second, the international (import and export) freight OD is estimated from different zones to foreign countries, including major import and export nodes such as international seaports, using a gravity model with the zone-pair friction obtained from a multimodal transportation model. Third, the OD matrices are converted from monetary value to tonnage and assigned to the multimodal transportation super network using the incremental freight assignment method. The model is calibrated using traffic counts of the highways, railways, and port throughput data. The proposed framework is tested through a case study of the Province of Jiangxi, which is crucial for forecasting freight demand before the planning, design, and operation of the Ganyue Canal. The predictive analytics of the proposed framework demonstrated high validity, where the goodness-of-fit (R2) between the observed and estimated freight flows on specific links for each of the three transport modes was higher than 0.9. This indirectly confirms the efficacy of the model in predicting freight OD demands. The proposed framework is adaptable to other regions and aids practitioners in providing a comprehensive tool for informed decision-making in freight demand modeling.

A COMPARATIVE STUDY OF HEADWAY-BASED AND TRANSPORT SYSTEM-BASED ASSIGNMENTS OF PUBLIC TRANSPORT IN VISUM: THE CITY OF KRYVYI RIH CASE

Many researchers have explored public transport assignment methodologies employing transport modelling software. Nevertheless, there remains a gap in evaluating real-world public transit networks utilizing diverse assignment procedures within Visum software. This paper introduces a thorough comparison of algorithms involved in public transport assignment processes, using the transport model of the city of Kryvyi Rih in Ukraine. The three scenarios of the model were developed depending on the public transport assignment procedure: headway-based, transport system-based utilized to all links, turns, and major turns in the network graph, and transport system-based applied only to the links, turns, and major turns traversed by the active public transport lines. The model of the network comprises 13 transport systems, 7 transport modes, 27598 links, 10097 nodes, 83270 turns, 238 zones, 1748 connections for private transport, 3013 connections for public transport, 534 stops, 1165 stop areas, 1190 stop points, 130 lines and 218 line routes. The transport demand model encompassed 14 demand segments. Compared to the outputs of the model calculation using the headway-based procedure, in the scenarios with transport system-based assignment, passenger flows on rail tram lines significantly decreased. Also, the results of scenarios with transport system-based assignment showed that the passengers extremely use parallel streets for travel from an origin to a destination. The modelling outputs for an actual urban network illustrate that the choice of the shortest route in the transport system-based assignment is closely linked to the main urban roads. This circumstance could potentially result in a rise in the number of transfers needed to sustain accessibility to districts residing far from the central highway.

МОДЕЛИРОВАНИЕ РЕЖИМОВ РАСПРЕДЕЛЕНИЯ ВОДНЫХ РЕСУРСОВ МЕЛИОРАТИВНЫХ СИСТЕМ ДВОЙНОГО РЕГУЛИРОВАНИЯ

The purpose of the research was to develop an algorithm for modeling water supply modes in the control loop "System section" in areas of reclamation systems of dual regulation for further development of software or information tools. The object of research was the pro-cess of water distribution on reclamation systems of dual regulation of the water regime. The information core of the work consisted of provisions on the rules of water distribution on rec-lamation systems and information technologies for automated systems, methods of processing and systematization of information. For modeling, a technique is applied with the allocation of control circuits for which standard modeling tools and algorithms are used. In the course of the research, a simulated water distribution control scheme was compiled on the reclamation systems of dual regulation with a regulating capacity in the control loop «System Section». Modeling functions are defined, such as calculation of water supply modes, modeling of wa-ter intake, transportation and supply modes, calculation of values of regulated levels and their modeling, as well as modeling of water discharge. Based on this and the determination of the initial information for modeling, an algorithm for modeling the distribution of water resources on the reclamation systems of dual regulation site was developed. The task of modeling in this "System section" circuit is to establish the modes of water supply from a regulating tank or a water source receiver by several groups of regulation «A group of water consumers». The de-veloped algorithm includes three stages: preparation for modeling, the modeling of water dis-tribution itself and the maintenance of the results of modeling results