Utilitarian cycling speed is a crucial input for applications such as infrastructure design, mode and route choice models, traffic microsimulation, safety evaluations, and health impact assessments. However, current methods fail to distinguish between average speed and cruising speed, the latter of which is more behaviourally indicative. This study aims to identify cruising speed from GPS data and

We innovatively shift the research focus from traditional metro systems to the broader spectrum of urban rail transit systems to study the relationship between rail transit development and urban pollution. Previous studies have predominantly concentrated on metro systems, often overlooking the various forms of rail transit such as light rail, trams, and commuter trains, each with distinct environmental

Fare-free public transport (FFPT) programs are gaining increasing popularity worldwide as a policy tool to mitigate the negative externalities associated with automobile usage. However, evidence regarding their effectiveness in reducing air pollution, a major automobile externality, remains limited. In this study, we empirically examine the effect of FFPT programs on air quality in Fuzhou, a provincial

The basic corridor problem is significant for the study of traffic congestion and travel behavior, paving way to insights into the spatial dynamics of rush-hour traffic flow pattern. In light of the fact that previous studies have yielded only preliminary results, this paper considers both early and late commuters to provide more comprehensive results. By introducing the continuous schedule preference

This study explores the need for efficient resource allocation in fifth generation (5G) New Radio (NR) sidelink communication for cellular vehicle-to-everything (C-V2X) applications. With the advent of 5G networks, C-V2X can enable direct connection between neighboring vehicles and infrastructure without relying on the cellular network. However, direct communication between devices in 5G NR sidelink

Unmanned aerial vehicles (UAVs) are being developed as a promising technology to assist mobile edge computing (MEC) systems due to their reliable wireless communication, flexible computing service capabilities, and flexible deployment. However, in the face of huge information and demanding task delay, it is a challenging problem to reduce the system cost. This paper studies task offloading and cache

This paper addresses traffic merging at highway intersections (labeled as OMM intersections) where mainline traffic with multiple lanes and on-ramp traffic converges, which often represent traffic bottlenecks causing severe traffic congestion and safety issues. To do that, we developed a Compliance-constrained Resilient System Optimal Trajectory Planning (CR-SOTP), which is devised as an event-triggered

Transportation terminals play an important role in the functioning of the transportation system. Therefore, the efficient use of the capacity of transportation terminals is considered a major determinant of the performance of transportation networks. An important decision related to the efficient functioning of congested terminals relates to the optimum allocation of the available capacity to different

The continuous growth of international container trade calls for logistics networks that seamlessly connect cross-border, domestic, and local transport services. In the design of these networks with various hubs and modes of transport, the consideration of both economies of scale for multimodal transport and congestion is essential since they can significantly impact the location of the hubs and their

Mobile Edge Computing (MEC) effectively alleviates the computational burden faced by vehicles in processing compute-intensive tasks due to resource limitations. However, traditional approaches typically employ coarse-grained task offloading strategies that utilize sequential protocols and discrete action spaces, resulting in high latency and increased energy consumption. These limitations render such

In vehicular networks, the increasing demand for computational resources often exceeds the capabilities of in-vehicle devices. To address these challenges, we propose a cloud-edge-device collaborative framework integrated with a Multi-Agent Deep Reinforcement Learning (MADRL) algorithm for dynamic optimization of task offloading and resource allocation. Experimental evaluations demonstrate the proposed

Intercity multimodal transportation systems hold great promise for enhancing travel, boosting regional economies, and promoting sustainability. However, realizing their full potential hinges on effective cooperation among transport service providers (TSPs), which current practices often lack. This study proposes a novel framework to address this challenge by leveraging price competition through mobility

Continuously increasing carbon emissions pose new challenges to the civil aviation industry. Although new techniques to reduce emissions are being developed, the aviation industry still calls for research on advanced management practices. Airport slot allocation in air traffic flow management aims to reduce flight delays by rescheduling flights. This study extends previous research by proposing a low-carbon

Vehicular Ad Hoc Networks (VANETs) are high-speed mobile wireless communication networks that play a pivotal role in shaping the future of intelligent transportation systems. In many fields, certificateless aggregate signcryption (CLASC) serves as a reliable method that reduces computational and communication overheads while enhancing security. Due to these advantages, in recent years, an increasing

Digital trading plays a crucial role in construction waste resource utilization. However, supply–demand imbalance and inefficient pricing exist under a prevalent negotiation-based trading approach and a fixed price mechanism, resulting in high transaction costs and rigid clearing prices. Furthermore, high ask prices restrict the transaction scale of recycled remanufacturing products of construction

The growing demand for bulky goods has spurred logistics firms to explore how to effectively manage and optimize multidepot delivery networks that consider three-dimensional loading constraints. The ability to divide customer demands and adaptively adjust vehicle compartments has significantly enhanced delivery efficiency and vehicle resource utilization. This study develops a collaborative multidepot

This paper addresses the overnight e-scooter rebalancing and charging problem in which e-scooters can be charged at a set of capacitated charging hubs. Operators use capacitated vehicles to collect e-scooters that need charging, drop them off at the hubs for charging, and pick them up later for repositioning. First, a discrete mathematical formulation is presented to optimize the drop-offs and pickups

This paper contributes to the literature on the operations management of double stack trains by introducing a new, real-world research problem that arises when loading trains at marine container terminals with on-dock rail service. Specifically, in this research we model the reality that containers that need to be loaded on railcars can be unavailable at the time of loading, while optimizing the assignment

This study investigates an automotive maritime transportation planning problem, considering roll-on/roll-off (Ro-Ro) shipping and container shipping. Automobiles are distributed from a manufacturer to overseas dealers through maritime transportation. This transportation process involves three key decisions: the choice of maritime transportation mode for the automobiles, the shipping volume of the ships

Connected and Automated Vehicles (CAVs) promise to redefine the future of transportation. Infrastructure Owners and Operators (IOOs require advanced analytical, modeling, and simulation (AMS) tools) to grasp the impact of CAVs on their strategic goals, such as improving safety, enhancing mobility, and advancing equity, and assess policy modifications to steer the deployment of this technology effectively

Public transport is a vital component of modern urban mobility, playing a significant role in reducing congestion and promoting environmental sustainability. Feeder bus services are essential for connecting residents to major public transport hubs, such as metro or rail stations. In this paper, a novel framework that enhances service coverage of the feeder bus while maintaining network efficiency is

Improving redundancy is one way of enhancing transportation network resilience by providing travelers with more alternative travel options in case of disastrous events. This paper studies an alternative means of improving network redundancy via retrofitting critical components at the strategical level, which is less constrained by the land use limitation and is less costly compared to building new

Conventional methods of rail transit passenger flow forecasting usually use general rail transit data for analysis, such as the spatial structure of the network, the distribution of stations, historical passenger flow, etc. However, these methods tend to focus on forecasting regular passenger flow and are insufficient under special events. With the widespread of social media, special events are often

We analyse time-varying tolling in the stochastic bottleneck model with price-sensitive demand and uncertain capacity. We find that price sensitivity and its interplay with uncertainty have important implications for the effects of tolling on travel costs, welfare and consumers. We evaluate three fully time-variant tolls and a step toll proposed in previous studies. We also consider a uniform toll

Predicting the credit risk for enterprises in Supply Chain Finance (SCF) often presents substantial challenges in supply chain management community. Considering the huge information asymmetry, we introduce the Bidirectional Encoder Representations from Transformers (BERT) technology in the fields of Deep Learning and Natural Language Processing (NLP) to extract textual insights from legal judgments

Effective management of battery degradation is crucial for electric vehicles (EVs) due to the high costs associated with replacing EV batteries. In practice, uninformed charging behaviors of EV drivers can accelerate battery wear without proper guidance. To address this challenge, this paper introduces a battery degradation mitigation-oriented charging and order-serving problem for EVs operating on