The proliferation of urban sensing, IoT, and big data in cities provides unprecedented
opportunities for a deeper understanding of occupant behaviour and energy usage patterns …

Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay
between space and time. Several available surveys capture STDM advances and report a …

Existing research on parking availability sensing mainly relies on extensive contextual and
historical information. In practice, the availability of such information is a challenge as it …

Airport arrival and departure movements are characterized by high dynamism, stochasticity,
and uncertainty. Therefore, it is of paramount importance to predict and analyze surface taxi …

Traveling Salesman Problem (TSP) is a classic NP-hard problem in Combinatorial
Optimization (CO), which has been widely studied. Traveling Officer Problem (TOP) derived …

The smart parking system is one of the most important problems in smart cities. Recently, an
increasing number of sensors installed in parking spaces have provided big spatio-temporal …

The proliferation of illegal parking has a major impact on traffic safety and efficiency. The
problem of studying how to capture as many parking infringements as possible in a limited …

Existing parking recommendation solutions mainly focus on finding and suggesting parking
spaces based on the unoccupied options only. However, there are other factors associated …

To increase the profit both of taxi drivers and operators, this paper proposes an approach
that efficiently collects the features of a customized-shape dispatch area to build the …

In recent years, with the increase in the frequency of residents' trips, the problem of taking
taxis in airports, train stations, and other transportation hubs has received wide attention …