Efficient Route Planning with Temporary Driving Bans, Road Closures, and Rated Parking Areas

We study the problem of planning routes in road networks when certain streets or areas are closed at certain times. For heavy vehicles, such areas may be very large since many European countries impose temporary driving bans during the night or on weekends. In this setting, feasible routes may require waiting at parking areas, and several feasible routes with different trade-offs between waiting and driving detours around closed areas may exist. We propose a novel model in which driving and waiting are assigned abstract costs, and waiting costs are location-dependent to reflect the different quality of the parking areas. Our goal is to find Pareto-optimal routes with regards to arrival time at the destination and total cost. We investigate the complexity of the model and determine a necessary constraint on the cost parameters such that the problem is solvable in polynomial time. We present a thoroughly engineered implementation and perform experiments on a production-grade real world data set. The experiments show that our implementation can answer realistic queries in around a second or less which makes it feasible for practical application

Cooperative transportation system for electric vehicles

Electric Vehicles (EVs) are being introduced in the market, but batteries reduced energy storage capacity and the lack of a high density charging infrastructure limit their autonomy range. In order to overcome this limitation, we propose developing a new solution enabling drivers to drive longer distances. This will be achieved by integrating some components of the cooperative transport infrastructure (charging system, public transport system and the vehicle), and by increasing driving autonomy through energy consumption reduction obtained with the improvement of driving efficiency. In this work we show how to create a cooperative system in a mobile device to integrate public transportation real time information in an EV. Integration of EVs with public transport system allows extension of driving autonomy beyond the storing capacity of vehicle’s batteries. Supplying information on availability, schedule and price of public transport allows the driver to plan the journey using EV and public transportation in a complementary way, using functions as car parking booking (and charging) and ticket buying. This information is integrated in a mobile device providing the driver with a collaborative holistic approach of different public transportation infrastructure sources, that can be combined with real traffic information, parking places and charging slots and current driver position, to support the driver decision making process.FEDER Funds - Operational Programme for Competitiveness Factors (COMPETE) PTDC/EEA-EEL/104569/2008 and the project MITPT/ EDAM-SMS/0030/2008.Fundação para a Ciência e a Tecnologia (FCT) - PTDC/EEA-EEL/104569/2008, MITPT/EDAM-SMS/0030/2008

Route planning methodology of an advanced traveller information system in Vilnius city

As a subsystem of an Intelligent Transportation System (ITS), an Advanced Traveller Information System (ATIS) disseminates real‐time traffic information to travellers. To help them with making better decisions on choosing their routes, a strong need to predict traffic congestion and to disseminate the predicted congestion information relating to travellers can be seen. This paper describes a methodology used by drivers for calculating an optimal driven route in Vilnius. The paper discusses how ATIS systems will likely evolve the experience of Information Service Providers (ISP) and optimal route planning calculations. A few methods of route planning have been taken into account. The paper presents the following types of route calculation: 1) the shortest route; 2) the quickest route; 3) the quickest forecasted route according to historical traffic information. Also, the paper deals with the architecture of the WEB based information system for drivers in Vilnius and analyzes data on traffic workflow. Furthermore, a comprehensive route planning procedure that forecasts data on driving time considering historical traffic is followed. First published online: 27 Oct 201