Abstract

The paper addresses the problem of the reconstruction and allocation of delivery positions in the urban area. The aim is to achieve the optimal reorganization of urban freight transport in old town core in the municipality of Celje. Optimal allocation relies on optimization based on the Monte Carlo simulation and represents a first stage of a two-stage optimization approach to re-design the existing urban freight transport. The number of optimal delivery positions is required to be as minimal as possible, which can still assure a maximal service area within the prescribed radius, while keeping the minimal walking distances of delivery personnel between the nearest delivery position and the customerʼs physical location. The main issues of the used heuristic allocation algorithm and the presentation of calculated results are provided. In the near future, the calculated delivery positions are going to be used for the purpose of physical implementation in order to improve the existing delivery transport. Der Artikel behandelt das Problem der Wiederaufbau und Allokation von den Lieferung Positionen in einem städtischen Gebiet. Der Zweck ist die optimale Reorganisation des städtischen Güterverkehrs im alten Stadtkern in der Gemeinde Celje zu Erreichen. Die optimale Allokation basiert auf der Optimierung auf Basis der Monte-Carlo-Simulationen und stellt die erste Stufe einer zweistufigen Optimierung Verfahren zur Re-Designs der bestehenden städtischen Güterverkehrs. Die Anzahl der optimalen Lieferpositionen sollte so wenig wie möglich sein, aber trotzdem einen maximalen Service-Bereich innerhalb des vorgeschriebenen Radius gewährleisten. Gleichzeitig sollte die Entfernung zwischen den nächstgelegenen Lieferpositionen und Kunden Positionen kleinstmöglich sein. Die wichtigsten Themen des verwendeten Heuristic Zuordnungsalgorithmus und die Darstellung der berechneten Ergebnisse sind gegeben. Die Standorte der berechneten Lieferung Positionen werden zum Zwecke der physikalischen Implementierung genutzt, um den bestehenden Güterverkehr zu verbessern. Document type: Article

Abstract

Standard cycles provide an easy way to evaluate the energy consumption of vehicles, but it is the energy consumption that occurs on real-world trips that really matters to the driver and, to a larger extent, society. This study shows how digital maps and vehicle simulation tools can be used to estimate energy consumption on a real-world trip. The user (1) selects a trip in the mapping service ADAS-RP (Advanced Driver Assistance Systems Research Platform), (2) defines a vehicle model in the vehicle powertrain simulation tool Autonomie, and (3) runs and analyzes the simulation in that same tool. For each section of the trip, ADAS-RP provides various information that can include speed limits, historic data on traffic pattern speeds, the slopes of the routes, and the positions of stop signs and traffic lights. The first stage of processing this information is to schedule the stops and to create an intermediate speed target that takes those stops into account. The final driver demand speed includes transitions – accelerations and decelerations – between sections with different intermediate speed targets, or around stops. The ADAS-RP/Autonomie process is then used to compute the energy consumption of a hybrid electric vehicle and a conventional vehicle on 10 trips defined across the United States and Germany. The hybrid vehicle is more fuel efficient, especially on congested routes and routes with downhill slopes, the effect of which is analyzed in further detail. Document type: Article

Abstract

The paper deals with the integration of data provided from traditional transport surveys (small data) with big data, provided from Information and Communication Technology (ICT), in building Transport System Models (TSMs). Big data are used to observe historical mobility patterns and transport facilities and services, but they are not able to assess ex-ante effects of planned interventions and policies. To overcome these limitations, TSMs can be specified, calibrated and validated with small data, but they are expensive to obtain. The paper proposes a procedure to increase the benefits of TSMs’ building in forecasting capabilities, on one side; and limiting the costs connected to traditional surveys thanks to the availability of big data, on the other side. Small data (e.g., census data) are enriched with Floating Car Data (FCD). At the current stage, the procedure focuses on two specific elements of TSMs: zoning and graph building. These processes are both executed considering the estimated values of an intensity function of FCDs, consistently with traditional methods based on small data. The data-fusion of small and big data, operated with a Geographic Information System (GIS) tool, in a real extra-urban context is presented in order to validate the proposed procedure. Document type: Article