The proposition of utilizing unmanned aerial vehicles (UAVs), colloquially known as drones, for the purpose of delivery was ushered into the mainstream by Amazon in 2013. Since then, UAVs have quickly gained traction as a feasible option for last-mile operations in transport and logistics. In the context of this research, we construct a mathematical framework aiming to depict the collaborative interaction

Home health care service platforms (HHCSPs) serve older consumers by gathering informal caregivers who offer standardized care services and/or collaborative care services. These platforms aim to maximize both profit and consumer surplus by motivating participants to make proper price and quality decisions. Service prices can be determined by an HHCSP or an informal caregiver, leading to four scenarios:

Compared to static aircraft wake separation during landing/departure, reducing separation minima related to wake turbulence without compromising safety has initially demonstrated operational benefits, while dynamic pairwise separation remains under development. This paper proposes a two-stage probabilistic deep learning framework for wake vortex recognition and duration assessment, using wake images

The road traffic system is frequently disrupted by rainfall events, significantly impacting road system efficiency. It is challenging to capture the resilience of road traffic systems due to variations in road environments. We develop a method for measuring road segment and traffic system resilience using probabilistic modeling techniques. Hierarchical clustering is used to analyze the heterogeneity

This paper presents an optimization framework for adaptive scheduling of mixed bus services with flexible fleet size assignment under demand uncertainty. The service scheduling plans are driven by prevailing stochastic passenger demand subject to operational constraints. The optimization problem is formulated as a Markov decision process which aims to minimize passengers’ in-vehicle and waiting times

The integrated scheduling of quay cranes, internal vehicles, and yard cranes in container terminals aims to improve port operations and often requires scheduling robustness under uncertainty with cascade effects. In container terminal operations, uncertainty in equipment operating time poses challenges to effective scheduling, as even small fluctuations can create cascade effects throughout the operations

Walking plays a significant role in promoting public health, decreasing air pollution, and vitalizing cities. Walkability is an important notion that transportation planners and policymakers often use to quantify the suitability of and to promote travelers’ propensity for walking in an urban area. This paper adopts a data-driven approach using structural equation modeling for simultaneous estimation

Less attention has been paid to the potential ecological pressures brought about by high-speed rail (HSR) while promoting urban prosperity. Based on Normalized Difference Vegetation Index and multiple socio-economic and ecological data, this study examines the local effect and spatial spillover effect of HSR operation on green vegetation coverage (GVC) in Chinese cities from 2003 to 2014, using a

Designing effective built environment policies to reduce auto use is key to promoting sustainable transportation in suburban areas. However, most studies on the association between the built environment and auto use focus on the entire region rather than suburban areas. In addition, previous studies often ignore the possible nonlinear association between them. Applying Gradient Boosting Decision Trees

This study aims to explore the role of travel-related self-efficacy in sustained behavior change. Community-based electric ride-sharing as a pilot project has been introduced to reduce residents’ reliance on private cars in Bangkok, Thailand. This study integrated the Theory of Planned Behavior (TPB), Technology Acceptance Model (TAM), and goal-framing theory to explain the cognitive process from intending

The advent of electric bikes, or ebikes, has significantly enhanced competitiveness of bike-sharing systems, providing benefits to both riders (comfort during uphill and long-distance rides), platforms (more profit), and the environment. Operating such a hybrid bike-sharing system, i.e., with both bikes and ebikes, in a competitive multi-platform market, can be challenging due to the complex and unpredictable

It is critical to have accurate ship trajectory prediction for collision avoidance and intelligent traffic management of manned ships and emerging Maritime Autonomous Surface Ships (MASS). Deep learning methods for accurate prediction based on AIS data have emerged as a contemporary maritime transportation research focus. However, concerns about its accuracy and computational efficiency widely exist

Balancing efficiency and resilience in any supply chain commences from its ability to secure supplier networks. Blockchain technology applications in procurement can work as a major enabler in this process. Despite the unquestionable relevance of Blockchain Technology (BCT)-integrated Procurement, no comprehensive review exists. Considering this significant research gap, the present study aims to provide

Due to the recommendation of the European Commission, the railway companies have been replacing the conventional brake system of freight wagons. Since the retrofitting may lead to a change in noise characteristics, we aimed at examining its effectiveness to increase the pleasantness of the sounds. Realistic railway pass-by sounds were synthesized. We conducted a laboratory study with 44 participants

Shore power (SP) is a key technology to reduce emissions in a port. The deployment of SP has faced a significant challenge due to its high management costs. This study aims to compare various incentive policies that can promote SP from the perspectives of pollutant penalty, berth allocation, construction subsidy, electricity subsidy, and their combinations. To identify effective incentive policies

Railway traffic management is responsible for the detection and resolution of conflicts in case of disturbed operations. To minimise delay propagation, rescheduling decisions are taken by human dispatchers, possibly supported by mathematical models. Existing conflict detection and resolution (CDR) models mostly refer to conventional fixed-block multi-aspect signalling systems, in which minimum train

In this work, a family of finite volume discretization schemes for LWR-type first order traffic flow models (with possible on- and off-ramps) is proposed: the Traffic Reaction Model (TRM). These schemes yield systems of ODEs that are formally equivalent to the kinetic systems used to model chemical reaction networks. An in-depth numerical analysis of the TRM is performed. On the one hand, the analytical

Solving link-based route guidance problems for large-scale networks is computationally challenging and faces practical issues, such as spatial–temporal data coverage. Thus, regional route guidance has emerged as a promising strategy, which utilizes regional approaches (i.e., network-level macroscopic traffic models) to capture traffic dynamics. Existing regional route guidance models have mainly focused

To estimate network flow and travel behavior under recurrent traffic conditions, we leverage computational graphs and multi-source system-level data and solve a single-level optimization problem consistent with stochastic user equilibrium under logit assignment (SUELOGIT). Our model learns time-specific O-D matrices and utility functions and network flow parameters such as link flows, path flows, and

Connected and automated vehicles (CAVs) are projected to bring significant benefits to traffic efficiency and driving comfort. However, the realization of full CAV penetration rate will take a long time. In this paper, a framework is proposed for planning the trajectories of vehicles at a signalized road section in a mixed traffic environment consisting of CAVs, human-driven vehicles (HDVs) and connected

At many airports, space on the apron is scarce and has to be used efficiently. To that end, we optimize the layout of aircraft parking positions adjacent to the airport terminal, pursuing two lexicographically ordered objectives. First, we minimize the number of aircraft that have to be diverted to remote parking positions, because positions adjacent to the terminal are not available. Second, we minimize

The COVID-19 pandemic has allowed many workers to experience working from home. While in most cases, the transition to work from home was not voluntary, it forced many employees to experiment with new work schedules and new ways of communication. The current paper uses 3057 responses from a national survey to study the reported intended frequency of work from home after the pandemic. A mixed logit

The unprecedented container shipping freight rate levels post-Covid-19 are studied in a stochastic dynamic partial equilibrium framework. The proposed container marked model is calibrated on annual aggregate data from the two recent decades. The observed freight rate levels in 2021 and 2022 match a switch of competitive equilibria from Bertrand to Cournot competition. Whereas increased costs and reduced

Shipping alliances are regarded as a special group in antitrust immunity policies worldwide because of their abilities to coordinate shipping resources. In particular, the international shipping involves different countries and stakeholders, making it difficult to form a unified antitrust immunity and maritime policy. This study builds a tripartite dynamic evolutionary game model composed of stakeholders

As app-based ride-hailing (or e-hailing) services have achieved great success worldwide, the traditional taxi industry faces an unprecedented crisis. To gain a deeper understanding of these two distinct modes that both offer door-to-door mobility services, in this study, we use Chicago as a case to comparatively analyze the travel patterns of the app-based ride-hailing and traditional taxi services

The regulation of passenger congestion in mass transit is a persistent issue that requires ingenious and cost-effective solutions to ensure that related operations run at optimum capacity. Mass transit operators may implement Public Transport Demand Management (PTDM) strategies like cognitive levers to tackle this issue by targeting passengers’ behaviour during the route planning that precedes travel

For traffic safety reasons, Sweden has lowered speed limits on major roads that lack directional separation of traffic. For some of these roads, regional authorities, municipalities, and other local stakeholders have sent appeals to the government opposing the implemented speed limit reductions. The appeals have mainly referred to negative effects on regional development and have suggested that the

Spectrum scarcity is one of the main challenges of future wireless technologies. When looking at vehicle-to-everything (V2X), this is amplified as spectrum sharing could impact road safety and traffic efficiency. It is therefore of particular importance to study solutions that allow the coexistence, in the same geographical area and in the same channels, of what are today the main V2X access technologies

With the rapid development of unmanned aerial vehicle (UAV) and autonomous driving technology, wireless communication between UAV and vehicles has become one of the hotspots in the research of intelligent transportation systems (ITS). Particularly, link-level UAV-based communication requires correlation characteristics of propagation channel. In the current channel measurement, the transmitter or receiver

The Internet of Vehicles (IoV) systems improve road safety through coordination among vehicles, but they are vulnerable to impersonation attack due to their broadcast communication nature. Existing cryptographic authentication approaches are complex for resource-limited IoV devices, which motivated the development of less complex Physical Layer Authentication (PLA) approaches. However, the existing

Unmanned Aerial Vehicles (UAVs), i.e., drones, are expected to be widely used in various applications, such as parcel delivery and passenger transport, with the benefits of mitigating traffic congestion and reducing carbon emissions. In this paper, we study a UAV path planning problem under uncertain weather conditions, and design a data-driven dynamic decision support system for multiple types of

Dynamic wireless charging facilities deployed on the road network offer an effective charging method to alleviate the electric vehicle users’ range anxiety and thus facilitate the promotion of electric vehicles. The main benefit of wireless charging is that it extends EV’s driving range by enabling EV en-route recharging, which consequently reduces the required battery size for EVs. Basically, given

Port State Control (PSC) inspections are essential for port authorities to improve vessel quality and ensure maritime safety worldwide. However, the increasing frequency and duration of ship detentions indicate serious deficiencies of visiting vessels still largely exist, highlighting the urgent need for scientific solutions. This research aims to improve the efficiency of inspection policy and reduce

With the increasing number of connected vehicles on the road, vehicular communications have become an important research area. Federated learning (FL), a distributed machine learning technique that enables learning from distributed data sources while preserving data privacy and efficient learning, has the advantages of privacy preservation, low latency, and energy efficiency, making it a suitable technique

Inequitable distribution of public electric vehicle (EV) charging facilities may result in disparities in charging accessibility, potentially impeding the uptake of EVs. In response, this paper proposes an analytical framework to assess the accessibility-based equity of public EV charging infrastructure systematically, using Hong Kong (HK) as the study area. The results demonstrated severe spatial

We examine the air-quality effects of urban-rail development in Shenzhen, taking a difference-in-differences approach. This study is motivated by existing mixed evidence on the rail-pollution relationship, which we associate with the dynamic nature of the relationship itself. Our results demonstrate that the relationship varies by time, depending on network density and scale. New station openings had

Network-wide short-term inflow prediction is important in efficiently managing the urban transportation system. Nowadays, all kinds of traffic modes gradually become interconnected and form a complex multi-traffic modes system, while extensive studies focus on the single-traffic mode and ignore the correlations among different traffic modes. There exist some challenges for short-term inflow prediction

Bottleneck areas are prone to severe traffic congestion due to the sudden drop in capacity. To improve traffic efficiency in the bottleneck area, this paper proposes a multi-agent deep reinforcement learning framework integrating collision avoidance strategies to improve traffic efficiency in a mandatory lane change scenario. The proposed method considers distance-keeping and lane-changing coordination

Flexible transport systems such as Demand Responsive Transit (DRT) are becoming more and more popular over the last few years due to their convenience for customers. However, this convenience comes at a price. Transport authorities are currently looking for ways to improve service flexibility of Conventional Public Transport (CPT), which is undoubtedly cheaper than DRT. This justifies the need for

Knowledge of the core structure and inherent dynamics of urban freight transport systems is important for the development of policies, aimed at improving the livability and sustainability of cities. The past decade has witnessed a great deal of efforts into analyzing the geographic structure of urban freight transport systems. However, in-depth studies on the system core structure and underlying dynamics

Herein, a weibit-based model is proposed as an alternative to Recker's logit choice model with an “oddball” alternative, which explicitly focuses on a single alternative that has unique attributes to other conventional alternatives in the choice set [Recker, W.W. (1995). Discrete choice with an oddball alternative. Transportation Research, 29B, 201–212]. While retaining the closed-form probability

Connected and automated vehicles bring the capability to generate traffic data for intersection control applications. Unfortunately, for various reasons, such as signal blocking and interference, vehicles may experience communication failures leading to inefficiencies, e.g., unnecessarily long waiting queues, increased noise and greenhouse gas emissions or increased delays of emergency vehicles at

Empowered by the capabilities provided by fifth generation (5G) mobile communication systems, vehicle-to-everything (V2X) communication is heading from concept to reality. Given the nature of high-mobility and high-density for vehicle transportation, how to satisfy the stringent and divergent requirements for V2X communications such as ultra-low latency and ultra-high reliable connectivity appears

Bike-sharing systems (BSSs) have emerged in many cities worldwide. One key issue regarding the strategic design of BSSs is the deployment of bike stations. Innovations in technology have enabled new types of bikes, such as shared e-bikes, to work alongside conventional shared bikes. However, existing studies on bike station location design mainly focus on single bike type, and there is a lack of a

The transition to alternative energy sources and the adoption of on-demand operating modes in urban bus systems are crucial steps towards reducing carbon footprints and improving public transit services. This paper presents a two-phase approach for the collaborative optimization of charging schedules and passenger services, aimed at enhancing the operation of on-demand electric bus systems. First,

Platform retailers’ private labels have recently flourished, threatening the business of traditional national brand (NB) sellers. To fight against these platforms, some NB sellers introduce new products as low-quality substitutes through marketplace channels or exclusive channels. This raises a question: Considering the existence of a platform’s private label, how does an NB seller’s fight/no-fight

Traditional network models encapsulate travel behavior among all origin–destination pairs based on a simplified and generic travelers’ utility function. Typically, the utility function consists of travel time solely, and its coefficients are equated to estimates obtained from discrete choice models and stated preference data. While this modeling strategy is reasonable, the inherent sampling bias in

Physical-layer encryption has lately been highlighted as a promising security approach for visible light communications (VLC). Orthogonal frequency-division multiplexing (OFDM) is the main structural entity of multiple-carrier modulation in most modern communication systems, including vehicle ad hoc networks, 5G systems, and the Internet of Things. The integration of OFDM and spatial modulation (SM)

In 2022, transport accounted for around one third of UK territorial emissions. Many decarbonisation pathways include modal shift as a possible way of reducing passenger transport emissions. Yet, the mitigation potential of modal shift often depends on generic behavioural assumptions rather than the technical feasibility for using different transport modes. Here travel microdata from the UK is used

With work arrangements experiencing dramatic changes over the past three years due to the COVID-19 pandemic, and the possibility that altered work arrangements may persist well into the future, the implications of teleworking on activity-travel behavior are potentially profound. This paper aims to substantially add to the body of knowledge about the present and future of telework in the wake of the

This paper introduces a parallel computing framework based on the Spectral Partitioning (SP) method designed to enhance the computational efficiency of large-scale microscopic traffic simulation (LSMTS). The framework employs the SP method to partition road networks, taking into account vehicle information and road information as constitutive components for node weight determination. Micro-simulation

The optimization of airport taxi routes is crucial for increasing airport capacity and resource utilization. However, existing methods for preplanning schedules are often time-consuming. To address this, we propose a new approach that reduces the problem scale by predicting the intersection state in the airport taxiway network using a spatial–temporal graph neural network (STGNN) that incorporates

Due to variations in carrying capacity and visiting periods of vessels calling at terminals, certain transshipped containers may not be able to be transported immediately after arrival. Instead, they must be temporarily stored in the yard and scheduled for later transportation. Increased container storage time in the yard as a consequence of transit delays not only incurs additional costs for shipping

In the annual winter route maintenance, transportation agencies often deploy multiple fleets of trucks for snow control and removal activities over a vast maintenance area which creates an operational problem in determining the optimal maintenance routes and fleet size. The objective of this paper is to develop a snowplow routing optimization model to enhance the efficiency of snow removal route planning

This paper analyzes the European Commission’s (EC) climate policy package ‘Fit for 55′ and its potential impact on cargo ships calling at Swedish ports. Three scenarios are evaluated using the Swedish national freight transport model. The Main Scenario is based on the EC’s proposal to i) include ships weighing at least 5,000 gross tons (GT) in the European Emissions Trading System (EU ETS), and to