Search Results (30)

As electric vehicles (EVs) become increasingly integrated into urban mobility, the load on electrical grids increases, prompting innovative energy management strategies. This paper investigates the deployment of vehicle-to-grid (V2G) services at the University of Rome Tor Vergata, leveraging high-resolution floating car data (FCD) to forecast and schedule energy transfers from EVs to the grid. The methodology follows a four-step process: (1) vehicle trip detection, (2) the spatial identification of V2G in the campus, (3) a real-time scheduling algorithm for V2G services, which accommodates EV user mobility requirements and adheres to charging infrastructure constraints, and finally, (4) the predictive modelling of transferred energy using ARIMA and LSTM models. The results demonstrate that substantial energy can be fed back to the campus grid during peak hours, with predictive models, particularly LSTM, offering high accuracy in anticipating transfer volumes. The system aligns energy discharge with campus load profiles while preserving user mobility requirements. The proposed approach shows how campuses can function as microgrids, transforming idle EV capacity into dynamic, decentralised energy storage. This framework offers a scalable model for urban energy optimisation, supporting broader goals of grid resilience and sustainable development. Full article

The integration of connected autonomous vehicles (CAVs), advanced driver assistance systems (ADAS), and conventional vehicles necessitates the development of robust methodologies to enhance traffic efficiency and ensure safety across heterogeneous traffic streams. A comprehensive understanding of vehicle interactions and operating speed variability is essential to support informed decision-making in traffic management and infrastructure design. This study presents operating speed models aimed at estimating the 85th percentile speed (V₈₅) on straight road segments, utilizing floating car data (FCD) for both calibration and validation purposes. The dataset encompasses approximately 2000 km of the Italian road network, characterized by diverse geometric features. Speed observations were analyzed under three traffic conditions: general traffic, free-flow, and free-flow with dry pavement. Results indicate that free-flow conditions improve the model’s explanatory power, while dry pavement conditions introduce greater speed variability. Initial models based exclusively on geometric parameters exhibited limited predictive accuracy. However, the inclusion of posted speed limits significantly enhanced model performance. The most influential predictors identified were the V₈₅ on the preceding curve and the length of the straight segment. These findings provide empirical evidence to inform road safety evaluations and geometric design practices, offering insights into driver behavior in mixed-traffic environments. The proposed model supports the development of data-driven strategies for the seamless integration of automated and non-automated vehicles. Full article

Due to complex traffic conditions, freeway curves are associated with higher crash rates, particularly for trucks, which poses significant safety risks. Predicting truck crash rates on curves is essential for enhancing freeway safety. However, geometric design consistency indicators (GDCIs) are limited in terms of their ability to evaluate safety levels. To address this, this study identifies key factors influencing truck crash rates on curves and proposes a new safety evaluation indicator, the mean speed change rate (MSCR). A vague set, as an extension of the fuzzy set, was employed to integrate the MSCR and GDCI to identify high-risk curves. The factors contributing to differences in crash rates between the curves to the left and right are also analyzed. To assess the proposed approach, a case study was conducted using truck traffic data extracted from floating car data (FCD) collected on 32 freeway curves. The results demonstrate that the deflection angle, radius, and deflection direction are key contributions to truck crash risks. Importantly, the recognition accuracy of the MSCR indicator for crash risks on curves to the left and right is improved by 11.8% and 18.2% compared with GDCIs. Combining the proposed MSCR indicator with GDCIs can more comprehensively evaluate the safety of curves, with recognition accuracy rates of 88.2% and 27.3%, respectively. The indicator change value of the curves to the left are always larger, and the difference is more obvious as the geometric indicator changes. The MSCR indicator provides a more comprehensive curve safety assessment method than existing indicators, which is expected to promote the formulation of curve safety management strategies and further achieve sustainable development goals. Full article

Vehicle-to-grid (V2G) technology is emerging as an innovative paradigm for improving the electricity grid in terms of stabilization and demand response, through the integration of electric vehicles (EVs). A cornerstone in this field is the estimation of the aggregated available capacity (AAC) of EVs based on available data such as origin–destination mobility data, traffic and time of day. This paper considers a real case study, consisting of two aggregation points, identified in the city of Padua (Italy). As a result, this study presents a new method to identify potential applications of V2G by analyzing floating car data (FCD), which allows planners to infer the available AAC obtained from private vehicles. Specifically, the proposed method takes advantage of the opportunity provided by FCD to find private car users who may be interested in participating in V2G schemes, as telematics and location-based applications allow vehicles to be continuously tracked in time and space. Linear and nonlinear dynamic models with different input variables were developed to analyze their relevance for the estimation in one-step- and multiple-step-ahead prediction. The best results were obtained by using traffic data as exogenous input and nonlinear dynamic models implemented by multilayer perceptrons and long short-term memory (LSTM) networks. Both structures achieved an $R^2$ of 0.95 and 0.87 for the three-step-ahead AAC prediction in the two hubs considered, compared to the values of 0.88 and 0.72 obtained with the linear autoregressive model. In addition, the transferability of the obtained models from one aggregation point to another was analyzed to address the problem of data scarcity in these applications. In this case, the LSTM showed the best performance when the fine-tuning strategy was considered, achieving an $R^2$ of 0.80 and 0.89 for the two hubs considered. Full article

Rural roads, often characterized by winding paths and nearby settlements, feature frequent curvature changes, junctions, and closely spaced private accesses that lead to significant speed variations. These variations are typically represented by average speed or v₈₅ profiles. This paper examines complete speed distributions along rural two-lane roads using Floating Car Data (FCD). The Wasserstein distance, a non-parametric similarity measure, is employed to compare speed distributions recorded by a radar Control Unit (CU) and a selected FCD sample. Initially, FCD speeds were validated against CU speeds. Subsequently, differences in speed distributions between the CU location and specific sections identified by sharp curves, intersections, or accesses have been assessed. The Wasserstein Distance is proposed as the most effective synthetic indicator of speed distribution variability along roadways, attributed to its metric properties. This measure offers a more concise and immediate assessment compared to an extensive array of statistical metrics, such as mean, median, mode, variance, percentiles, v₈₅, interquartile range, kurtosis, and symmetry, as well as qualitative assessments derived from box plot trends. Full article

The reduction of urban congestion represents one of the main challenges for increasing sustainability. This implies the necessity to increase our knowledge of urban mobility and traffic. The fundamental diagram (FD) is a possible tool for analyzing the traffic conditions on an urban road link. FD is commonly associated with the links of a transport network, but it has recently been extended to the whole transport network and named the network macroscopic fundamental diagram (NMFD). When used at the link or network level, the FD is important for supporting the simulation, design, planning, and control of the transport system. Recently, floating car data (FCD), which are based on vehicles’ trajectories using GPS, are able to provide the trajectories of a number of vehicles circulating on the network. The objective of this paper is to integrate FCD with traffic data obtained from traditional loop-detector technology for building FDs. Its research contribution concerns the proposal of a methodology for the extraction of speed data from taxi FCD, corresponding to a specific link section, and the calibration of FDs from FCD and loop detector data. The methodology has been applied to a real case in the city of Santander. The first results presented are encouraging, supporting the paper’s thesis that FCD can be integrated with data obtained from loop detectors to build FD. Full article

This work aims to evaluate how a temporary school square implemented in the city of Bologna under the principles of the tactical urbanism approach impacted on vehicular patterns through exploiting TomTom Floating Car Data (FCD) from before and after the intervention. Such data, passively collected by vehicles acting as moving sensors on the network, have been used for the analyses instead of data collected through usual methods. After statistical validation of available datasets through two-tailed paired Student’s t-tests, trend analyses have been performed on sample sizes and speed-related values to detect global variations in the first place, and more thoroughly among clusters of road segments based on graph-calculated distance from the intervention site. Results suggest that traffic flows have been relocated from segments directly affected by the intervention, where a decrease has been registered (−23.87%), towards adjacent streets or segments in a buffer area, which have recorded an increase (+3.51% and +3.50%, respectively), so the phenomenon of traffic evaporation did not take place as opposed to more widespread tactical urbanism interventions described in the literature. OD matrices per 15-min time fractions over the three selected peak time slots have been extracted in order to obtain reliable input data for a future development of traffic microsimulation models. The extraction method is based on least squares optimization problems solving systems of linear equations representing OD flows assigned to the observed link, after selecting a set of $\overline{k}$ shortest paths through a Path Size Logit (PSL) model. Even though the availability of large amounts of data could not overcome typical underdetermination of the problem, due to the key issue of data dependence among traffic counts, the validation of retrieved matrices returned good results in terms of correlation between observed and estimated link flows. In the few cases where the quality of correlation fell, underlying causes have been investigated and the influence of outliers, amplified by the high fragmentation of the provided road graph, might represent the core problem. Full article

In this work, a new methodology to estimate the functional vulnerability of the road network of the city of Catania (Italy) is developed with the purpose to improve the resilience of urban transport during critical events. While the traditional approach for the estimation of vulnerability is based on topological data, the proposed methodology is based on spatial-temporal mobility profiles obtained with floating car data (FCD). The algorithm developed for the estimation of vulnerability combines topological properties of the road network with mobility patterns obtained from FCD to evaluate the consequences of failure events on trajectories and their associated travel times. The core operation of the algorithm is based on the computation of all possible travel paths within their assigned geographical zone every time a road link is disrupted. The procedure may prove useful to evaluate wide failure events and to facilitate emergency plans. Full article

Investigating the correlation between urban mobility patterns and the built environment is crucial to support an integrated approach to transportation and land-use planning in modern cities. In this study, we aim to conduct a data-driven analysis of these two interrelated parts of the urban environment through the estimation of a set of metrics to assist city planners in making well-informed strategic decisions. Metrics are computed by aggregating and correlating different types of data sources. Floating Car Data (FCD) are used to compute metrics on mobility demand and traffic patterns. The built environment metrics are mainly derived from population and housing census data, as well as by investigating the topology and the functional classification adopted in the OpenStreetMap Repository to describe the importance and the role of each street in the overall network. Thanks to this set of metrics, accessibility indexes are then estimated to capture and explain the interaction between traffic patterns and the built environment in three Italian cities: Brescia, Catania, and Salerno. The results confirm that the proposed data-driven approach can extract valuable information to support decisions leading to more sustainable urban mobility volumes and patterns. More specifically, the application results show how the physical shape of each city and the related street network characteristics affect the accessibility profiles of different city zones and, consequently, the associated traffic patterns and travel delays. In particular, the combined analysis of city layouts, street network distributions, and floating car profiles suggests that cities such as Brescia, which is characterized by a homogeneously distributed radial street system, exhibit a more balanced spread of activities and efficient mobility behaviors. Full article

Vehicle air pollution is a significant problem for health and climate change that can be solved by several approaches. The route is one of the many components to be considered. In this work, we propose a statistical analysis of a large FCD database in November 2017 in Lyon (France) in order to find alternative sustainable trips and evaluate potential emission reductions (CO₂, NOx, PM10). To this end, an innovative framework was built. First, we assessed vehicle speeds for each network section and the fifteen-minute period, when this information was reachable. Then, we used a regression random forest (RF) algorithm to fill in the missing data. This dynamical speed map allowed us to search for fewer pollutant trips, for the first ten days of November. By using COPERT emission factors (EFs) and the time-dependent Dijkstra algorithm, we successfully identified between 51% and 72% of alternative sustainable paths, depending on the engine technology and the pollutant. We investigated the influence of vehicle technology. In all cases, the number of alternative trips found tends to be the same as soon as the emission savings exceed 5%. Moreover, about 400 trips out of 11,000 have the potential to mitigate about 20% of emissions. Full article

The path choice models play a key role in transportation engineering, especially when coupled with an assignment procedure allowing link flows to be obtained. Their implementation could be complex and resource-consuming. In particular, such a task consists of several stages, including (1) the collection of a large set of data from surveys to infer users’ path choices and (2) the definition of a model able to reproduce users’ choice behaviors. Nowadays, stage (1) can be improved using floating car data (FCD), which allow one to obtain a reliable dataset of paths. In relation to stage (2), different structures of models are available; however, a compromise has to be found between the model’s ability to reproduce the observed paths (including the ability to forecast the future path choices) and its applicability in real contexts (in addition to guaranteeing the robustness of the assignment procedure). Therefore, the aim of this paper is to explore the opportunities offered by FCD to calibrate a path/route choice model to be included in a general procedure for scenario assessment. The proposed methodology is applied to passenger and freight transport case studies. Significant results are obtained showing the opportunities offered by FCD in supporting path choice simulation. Moreover, the characteristics of the model make it easily applicable and exportable to other contexts. Full article

The implementation of valid freight transport simulation models requires an extensive and detailed validation phase for understanding the feasibility of the outputs and the capacity of the structure of the proposed models in representing the real-world data. Traditional methods involve the use of surveys in order to describe the behaviour of stakeholders and to gather some aspects of the modal choices. Recent studies integrate this approach with Big Data as Floating Car Data to obtain better statistical information of the details at different levels. The current research involves the unexplored field of the validation of freight transport simulation models using a data-driven approach based on a large database of over 292 million Floating Car Data (FCD) signals generated by 29,298 commercial vehicles during the month of October 2019. The paper proposes an FCD processing methodology to identify freight vehicles using Ro-Ro/Ro-Pax services, and presents the results of an in-depth tracking analysis for combined transport and road transport. The validation phase permits the evaluation of the simulation tool results with real choices of heavy vehicles, referring also to the statistical information on travel times and the achievement of additional information through an in-depth analysis of tracking single vehicles. Full article

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. Full article

Data on the daily activity of private cars form the basis of many studies in the field of transportation engineering. In the past, in order to obtain such data, a large number of collection techniques based on travel diaries and driver interviews were used. Telematics applied to vehicles and to a broad range of economic activities has opened up new opportunities for transportation engineers, allowing a significant increase in the volume and detail level of data collected. One of the options for obtaining information on the daily activity of private cars now consists of processing data from automated vehicle monitoring (AVM). Therefore, in this context, and in order to explore the opportunity offered by telematics, this paper presents a methodology for obtaining origin–destination flows through basic info extracted from AVM/floating car data (FCD). Then, the benefits of such a procedure are evaluated through its implementation in a real test case, i.e., the Veneto region in northern Italy where full-day AVM/FCD data were available with about 30,000 vehicles surveyed and more than 388,000 trips identified. Then, the goodness of the proposed methodology for O-D flow estimation is validated through assignment to the road network and comparison with traffic count data. Taking into account aspects of vehicle-sampling observations, this paper also points out issues related to sample representativeness, both in terms of daily activities and spatial coverage. A preliminary descriptive analysis of the O-D flows was carried out, and the analysis of the revealed trip patterns is presented. Full article

This paper investigates the opportunities offered by floating car data (FCD) to infer delivering activities. A discrete trip-chain order model (within the random utility theory) for light goods vehicles (laden weight less than 3.5 tons) is hence proposed, which characterizes delivery tours in terms of the number of stops/deliveries performed. Thus, the main goal of the study is to calibrate a discrete choice model to estimate the number of stops/deliveries per tour by using FCD, which can be incorporated in a planning procedure for obtaining a preliminary assessment of parking demand. The data used refer to light goods vehicles operating in the Veneto region. The database contains more than 8000 tours undertaken in 60 working days. Satisfactory results have been obtained in terms of tour estimation and model transferability. Full article