Optimizing on-demand services – A complementary station-based and shared on-demand micro-transit service Glyfada wants to strengthen its public transport network with flexible, demand-driven mobility services. The idea is that the different services will work together and not compete. To this end, the city will lean on concepts and algorithms developed in DIT4TraM for passenger flow management, fleet management, and optimal vehicle allocation. The city is also deploying tradable mobility permits
Glyfada is a suburb at the south of Athens, Greece that is a very popular destination for tourists, as well as for locals, due to the beautiful sea view (Glyfada is part of the so-called Athens riviera) and the numerous restaurants and cafes. The municipality of Glyfada also includes an extensive residential area (population about 90000) which, however, is not served by a sufficient public transport network, forcing citizens to use their private vehicles, while the nearest metro station is at a neighboring municipality (Elliniko). As a result, the city center of Glyfada gets very crowded, especially during peak hours, which, in combination with the low public transport coverage, make traffic conditions unsustainable.
|Main Objective||Reduce congestion and private vehicle usage, connect residential areas with city center|
|Location of stations||Close to POIs (residential areas, business/commercial areas, public transport stations)|
|Fleet size||8-10 vehicles|
|Vehicle type||6-seat vans|
|Routing||Fixed location of station, optimal visiting order based on demand|
|Fee||Free of charge during pilot (exploitation strategy after pilot)|
So far, we have developed the routing algorithm that matches passenger requests with vehicles and conducted some simulation experiments that validate its efficiency. Also, we have developed two dedicated smart apps, for the interface between the system, the drivers and the users (screenshots provided below). In addition, we have defined an optimal set of locations for 11 stations (based on land use and demand data), which are close to residential areas, crowded business/commercial areas and public transport stations (map provided below) and, finally, we have collected and analyzed travel time data between the stations, which will be used for defining the optimal visiting order.
As of now, we haven’t produced any publications or other outputs. The only available document is D7.1, which outlines some initial specifications.