The eCOMPASS project addresses high-demand urban mobility aspects, primarily aiming at reducing the environmental footprint related with the mobility of people and goods in the urban space. In this context, eCOMPASS primarily investigates two mobility scenarios with significant contribution to urban CO2 emissions and energy consumption: mobility of humans using private vehicles and mobility of goods (i.e. delivery, distribution and collection) through fleets of vehicles carrying light or heavy freights. The former is addressed through intelligent onboard navigator systems that seamlessly provide ‘green’ route recommendations, i.e. those with minimal environmental footprint and fuel consumption. The latter is addressed through the development of a logistics and fleet management system used by human administrators in conjunction with on-board systems mounted on vehicles and used by drivers. In parallel, eCOMPASS aims at developing advanced web and mobile information services that will facilitate the use of complex contemporary urban public transportation networks, thereby making ‘green’ human transports more appealing and usable to urban resident populations and tourists. In addition to establishing solid theoretical foundations for the proposed route planning services, an important objective of eCOMPASS is to implement novel and efficient algorithmic solutions and deliver the respective services to familiar end-user devices. The main objectives of eCOMPASS project are detailed below:

  1. Optimization of private vehicles navigation with respect to environmental footprint

    Innovative algorithmic approaches will be developed to enable efficient and effective routing methods for private vehicles moving within urban environments. Recommended routes will take into account real-time traffic conditions aiming at avoiding hotspots (i.e. congested areas) so as to ensure fast, energy-efficient and environmentally-friendly travel. The environmental footprint of alternative route options will be calculated based on novel emission models that take into account the vehicle’s speed, distance, speed’s variance along the vehicle route, variance of individual route segments’ altitude (e.g. a route including many steep uphill and downhill segments is relatively energy-demanding), driver’s behaviour, etc. Unlike existing traffic-aware navigation systems which provide route recommendations based on the unreliable ‘snapshot’ of current traffic conditions, we plan to employ sophisticated methods to increase the reliability of road traffic input data and derive eco-efficient routes through traffic prediction and traffic load balancing methods. eCOMPASS navigators route planning will adapt to drivers behaviour (i.e. the emissions pattern associated with the driving style) and will consider transfers of car drivers to means of public transportation, provided that such transfers are feasible (suitable transportation network stations exist along the vehicle’s route and public transportation services are available at the given time) and result in positive environmental balance. The focus on the environmental footprint represents the key difference between the proposed service and the traditional navigation systems that either minimize the overall driving distance or time.

  2. Optimization of vehicle fleets route planning with respect to the environmental footprint

    A primary cause of traffic congestion and pollution in cities relates with the transportation of goods. Routing through congested areas, routing of heavy duty vehicles along narrow road segments, uneven distribution of freight load amongst available vehicles and the use of heavier -than required- vehicles contribute to increased levels of CO2 emissions and other pollutants. eCOMPASS addresses these issues by developing novel algorithmic approaches for automating the logistics management and route planning for fleets of vehicles, hence minimizing the overall environmental footprint. Our derived solutions will tailor the needs of companies running either heavy duty trucks (e.g. truckload carriers, freight transportation companies) or vehicles carrying smaller freight volumes (e.g. courier services, Internet shop products and food deliveries). Picking up and delivery planning for staff or school buses will also be looked at, among many similar urban mobility aspects. Our route planning algorithms will consider various parameters and constraints such as the number and capacity of vehicles, service point locations, time constraints, etc; most importantly, traffic prediction (hence, transit delays and CO2 emissions) along alternative itineraries (i.e. service points’ visiting order) will be taken into account.

  3. Optimization of route planning over urban multi-modal public transportation networks

    The key objective of eco-friendly urban human mobility is predicated on the increased use of the - inherently ‘green’- public means of transportation. In this context, this objective refers to route planning web and mobile applications, aiming at hiding the complexity and increasing the usability of multi-modal urban public transportation networks. This objective is broken down into two sub-objectives, each addressing an individual mobility scenario:

    1. 3.1 Optimized origin-destination multi-modal public transportation route planning

      This refers to a common mobility scenario, wherein an individual requests an optimal route recommendation among the many alternatives to travel from one point to another using any available public transportation modality (e.g. walking, bus, metro, train, etc). Unlike similar tools which typically derive shortest-path or shortest-time routes, eCOMPASS will take into account multi-objective optimization parameters such as use of the most eco-friendly means of transport, minimum number of transfers amongst different means of transportation or minimal use of a specific transportation mode (e.g. elderly people or individuals carrying heavy loads are likely to request routes with minimum walkingdistance route segments).It should be stressed that this service will be consumed by both mobile devices users moving solely through public means of transportation and car drivers (navigator systems’ users) that may be recommended to use public means of transportation in the final leg of their route (provided nearby parking space availability), if this is more cost-efficient (e.g. in time or CO2 emissions).

    2. 3.2 Optimized, personalized daily multi-modal public transportation routes for tourists

      Tourists represent a special, growing and highly-mobile target group, often unfamiliar with public transportation options as well as the major tourist attractions and points of interest on the destination city. The design of daily tourist itineraries (typically starting and ending at the tourist’s accommodation location) which take into account the tourists’ personal profile and preferences represents a significant challenge that will be addressed in the context of eCOMPASS. Hence, our objective will be to recommend public transportation routes (a separate route for each day of stay) that go through various sights aiming at maximizing the overall ‘satisfaction’ of the tourists (i.e. include those sights which the tourist would potentially be interested in visiting). Our algorithmic solutions will take into account various parameters and constraints: start/end point; maximum daily time budget allowed for sightseeing; preferred number and duration of breaks; user preferences upon visiting options (explicitly declared or implicitly specified); weather conditions (e.g. ‘open-air’ sites should not be recommended in rainy days); information about points of interest (type, location, opening times, ‘objective’ importance, average visiting duration, admission cost). This service is expected to assist the care-free movement of visitors and tourists and also promote the cultural ‘reserve’ of any urban destination, thereby attracting more tourists.