• Energy Research

This paper focuses on the estimation of energy consumption of Electric Vehicles (EVs) by means of models derived from traffic flow theory and vehicle locomotion laws. In particular, it proposes a bi-level procedure with the aim to calibrate (or update) the whole parameters of traffic flow models and energy consumption laws by means of Floating Car Data (FCD) and probe vehicle data. The reported models may be part of a procedure for designing and planning transport and energy systems. This aim is to verify if, and in what amount, the existing parameters of the resistances/energy consumptions model calibrated in the literature for Internal Combustion Engines Vehicles (ICEVs) change for EVs, considering the above circular dependency between supply, demand, and supply–demand interaction. The final results concern updated parameters to be used for eco-driving and eco-routing applications for design and a planning transport system adopting a multidisciplinary approach. The focus of this manuscript is on the transport area. Experimental data concern vehicular data extracted from traffic (floating car data and probe vehicle data) and energy consumption data measured for equipped EVs performing trips inside a sub-regional area, located in the Città Metropolitana of Reggio Calabria (Italy). The results of the calibration process are encouraging, as they allow for updating parameters related to energy consumption and energy recovered in terms of EVs obtained from data observed in real conditions. The latter term is relevant in EVs, particularly on urban routes where drivers experience unstable traffic conditions.

Abstract The mobility of people and goods represents an essential activity that influences sustainability owing to energy consumption. Generally, electrical mobility problems concern the selection of technology (e.g. electrical vehicles (EVs)), monitoring the ex-post effects of their implementation. However, an ex-ante quantitative assessment of transport services in relation to travel demand is necessary in decision-making processes. The objective of this study is to propose a framework that supports decision-making regarding the optimisation of transport services for people in an effort to minimise renewable energy resources. Transport system models (TSMs) are the core of the framework, adopted to design transport services operated with EVs. TSMs are examined in a practical scenario, represented by the touristic port “Porto delle Grazie”, located in the Citta Metropolitana of Reggio Calabria, south of Italy. Clean energy is produced from maritime waves. Transport services are designed considering travel demand by means of TSMs. The novelties of this study are a framework to support decision-making processes aimed at optimising renewable energy consumption, while ensuring the satisfaction of mobility requirements, and an application in a practical scenario where energy is produced by the sea waves to facilitate green transport services for people mobility. The application represents a prototypical experiment aimed at validating the proposed framework. A discussion section presents the main potentialities and limitations of this work, which can be extended to wider geographical scales and over different time periods.

This paper attempts to integrate data from models, traditional surveys and big data in a situation of limited information. The goal is to increase the capacity of transport planners to analyze, forecast, and plan passenger mobility. (Big) data are a precious source of information and substantial effort is necessary to filter, integrate, and convert big data into travel demand estimates. Moreover, data analytics approaches without demand models are limited because they allow: (a) the analysis of historical and/or real-time transport system configurations, and (b) the forecasting of transport system configurations in ordinary conditions. Without the support of travel demand models, the mere use of (big) data does not allow the forecasting of mobility patterns. The paper attempts to support traditional methods of transport systems engineering with new data sources from ICTs. By combining traditional data and floating car data (FCD), the proposed framework allows the estimation of travel demand models (e.g., trip generation and destination). The proposed method can be applied in a specific case of an area where FCD are available, and other sources of information are not available. The results of an application of the proposed framework in a sub-regional area (Calabria, southern Italy) are presented.

According to the literature, the path choice decision process of a user of a (road) transport network, named path choice problem (PCP), is composed of two levels/models: the definition of perceived alternative paths (choice set) and the choice of one path in the path choice set. The path choice probability can be estimated with two models: a choice model of the path choice set and a choice model of a path (Mansky paradigm). In this research, the paper’s contribution concerns two elements: extension of the PCP paradigm (two-level models) consolidated in the literature to the route choice decision process (vehicle routing problem (VRP)) and identification of common elements in the PCP and VRP concerning the criteria in the two decision levels and the procedure for route and path selection and choice. The experiment concerns the comparison of observed routes with simulated and optimized routes of commercial vehicles to analyse the level of similarity and coverage. The observed routes are extracted from floating car data (FCD) from commercial vehicles travelling inside a study area inside the Calabria Region (Southern Italy). The comparison is executed in terms of similarity of the sequences of nodes visited between observed routes and simulated/optimized routes.