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Traffic prediction is necessary not only for management departments to dispatch vehicles but also for drivers to avoid congested roads. Many traffic forecasting methods based on deep learning have been proposed in recent years, and their…
Accurate forecasting of electric vehicle (EV) charging demand is critical for grid management and infrastructure planning. Yet the field continues to rely on legacy benchmarks; such as the Palo Alto (2020) dataset; that fail to reflect the…
The burst of demand for TNCs has significantly changed the transportation landscape and dramatically disrupted the Vehicle for Hire (VFH) market that used to be dominated by taxicabs for many years. Since first being introduced by Uber in…
In this paper, we address two optimisation problems arising in the context of city logistics and two-level transportation systems. The two-echelon vehicle routing problem and the two-echelon location routing problem seek to produce vehicle…
A smart city improves operational efficiency and comfort of living by harnessing techniques such as the Internet of Things (IoT) to collect and process data for decision making. To better support smart cities, data collected by IoT should…
With the rapid explosion of data volume from mobile networks, edge caching has received significant attentions as an efficient approach to boost content delivery efficiency by bringing contents near users. In this article, cache-enabled…
The growing penetration of electric vehicles (EVs) significantly changes typical load curves in smart grids. With the development of fast charging technology, the volatility of EV charging demand is increasing, which requires additional…
In this study, we propose a novel heuristic two-step algorithm for shared ridehailing in which users can share their rides with only one more user. The algorithm, which is centrally formulated, starts with matching users and creating a set…
For traffic prediction in transportation services such as car-sharing and ride-hailing, mid-term road traffic prediction (within a few hours) is considered essential. However, the existing road-level traffic prediction has mainly studied…
As efficient traffic-management platforms, public vehicle (PV) systems are envisioned to be a promising approach to solving traffic congestions and pollutions for future smart cities. PV systems provide online/dynamic peer-to-peer…
Plug-in electric vehicles (PEVs) can contribute to energy and environmental challenges. Among different types of PEVs, plug-in hybrid electric taxis (PHETs) go in advance. In this study, we provide a spatial and temporal PHET charging…
To reduce passenger waiting time and driver search friction, ride-hailing companies need to accurately forecast spatio-temporal demand and supply-demand gap. However, due to spatio-temporal dependencies pertaining to demand and…
Hub location Problems seek to find hub facilities and assign non-hub nodes to them in such a way that the flow between origin and destination should be effectively established according to the desired goal. In general, in the literature of…
In rapidly sprawling urban areas and booming intercity express rail networks, efficiently designed feeder bus systems are more essential than ever to transport passengers to and from trunk-line rail terminals. When the feeder service region…
Electric vehicles can offer a low carbon emission solution to reverse rising emission trends. However, this requires that the energy used to meet the demand is green. To meet this requirement, accurate forecasting of the charging demand is…
Facility location decisions significantly impact customer behavior and consequently the resulting demand in a wide range of businesses. Furthermore, sequentially realized uncertain demand enforces strategically determining locations under…
Time-Triggered Ethernet (TTEthernet) has been widely applied in many scenarios such as industrial internet, automotive electronics, and aerospace, where offline routing and scheduling for TTEthernet has been largely investigated. However,…
The growing aging population has significantly increased demand for efficient home health care (HHC) services. This study introduces a Vehicle Routing and Appointment Scheduling Problem (VRASP) to simultaneously optimize caregiver routes…
Traffic flow forecasting is a fundamental research issue for transportation planning and management, which serves as a canonical and typical example of spatial-temporal predictions. In recent years, Graph Neural Networks (GNNs) and…
Spatio-temporal graphs are powerful tools for modeling complex dependencies in traffic time series. However, the distributed nature of real-world traffic data across multiple stakeholders poses significant challenges in modeling and…